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Read variant data from GISAID
-current_date <- "2024_01_15"
+current_date <- "2024_01_18"
 fpath.tar <- paste0("~/data/epicov/metadata_tsv_", current_date, ".tar.xz")
 fpath.qs <- paste0("~/data/epicov/metadata_tsv_", current_date, ".qs")
 
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index d64bdd97..ec57c763 100644
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index e8682bca..237849cb 100644
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diff --git a/docs/articles/United_Kingdom_animated_2024.gif b/docs/articles/United_Kingdom_animated_2024.gif
index 31a3cbaa..332d8356 100644
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diff --git a/docs/articles/VariantAnimation-Brazil.html b/docs/articles/VariantAnimation-Brazil.html
index eeb30cff..f0a48aee 100644
--- a/docs/articles/VariantAnimation-Brazil.html
+++ b/docs/articles/VariantAnimation-Brazil.html
@@ -106,7 +106,7 @@
 
Get variants data for Brazil
 

 
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_brazil <- gisaid_metadata %>%
   filter(Country == "Brazil") %>%
   filter(Host == "Human")
diff --git a/docs/articles/VariantAnimation-Brazil_files/figure-html/unnamed-chunk-5-1.png b/docs/articles/VariantAnimation-Brazil_files/figure-html/unnamed-chunk-5-1.png
index ef2b301a..6e5bdf94 100644
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diff --git a/docs/articles/VariantAnimation-Germany.html b/docs/articles/VariantAnimation-Germany.html
index a6829610..4d21eeb5 100644
--- a/docs/articles/VariantAnimation-Germany.html
+++ b/docs/articles/VariantAnimation-Germany.html
@@ -106,7 +106,7 @@
 
Get variants data for Germany
 

 
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_germany <- gisaid_metadata %>%
   filter(Country == "Germany") %>%
   filter(Host == "Human")
diff --git a/docs/articles/VariantAnimation-Germany_files/figure-html/unnamed-chunk-5-1.png b/docs/articles/VariantAnimation-Germany_files/figure-html/unnamed-chunk-5-1.png
index 141ef413..4688d61b 100644
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diff --git a/docs/articles/VariantAnimation-India.html b/docs/articles/VariantAnimation-India.html
index 1ed5199b..278b7956 100644
--- a/docs/articles/VariantAnimation-India.html
+++ b/docs/articles/VariantAnimation-India.html
@@ -106,7 +106,7 @@
 
Get variants data for India
 

 
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_india <- FilterGISAIDIndia(gisaid_metadata_all = gisaid_metadata)
 
 vocs <- GetVOCs()
diff --git a/docs/articles/VariantAnimation-India_files/figure-html/unnamed-chunk-11-1.png b/docs/articles/VariantAnimation-India_files/figure-html/unnamed-chunk-11-1.png
index 17e4423f..2f87cc52 100644
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index fe19b41f..daf02a25 100644
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diff --git a/docs/articles/VariantAnimation-NYC.html b/docs/articles/VariantAnimation-NYC.html
index 52d01ee7..acd50f60 100644
--- a/docs/articles/VariantAnimation-NYC.html
+++ b/docs/articles/VariantAnimation-NYC.html
@@ -108,7 +108,7 @@ 
Get variants data for NY state
 counties <- c("New York", "New York City", "New York County", "Queens County", "Bronx County", "Bronx", "Brooklyn", "Manhattan", "Queens", "Richmond County")
 
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_usa <- gisaid_metadata %>%
   filter(Country == "USA") %>%
   filter(Host == "Human")
diff --git a/docs/articles/VariantAnimation-NYC_files/figure-html/unnamed-chunk-4-1.png b/docs/articles/VariantAnimation-NYC_files/figure-html/unnamed-chunk-4-1.png
index e292b812..43bb6e6c 100644
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diff --git a/docs/articles/VariantAnimation-NewYork.html b/docs/articles/VariantAnimation-NewYork.html
index 60c972f9..6f1b2a4f 100644
--- a/docs/articles/VariantAnimation-NewYork.html
+++ b/docs/articles/VariantAnimation-NewYork.html
@@ -106,7 +106,7 @@
 
Get variants data for NY state
 

 
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_usa <- gisaid_metadata %>%
   filter(Country == "USA") %>%
   filter(Host == "Human")
diff --git a/docs/articles/VariantAnimation-NewYork_files/figure-html/unnamed-chunk-5-1.png b/docs/articles/VariantAnimation-NewYork_files/figure-html/unnamed-chunk-5-1.png
index 97f2d432..a0e90a7c 100644
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diff --git a/docs/articles/VariantAnimation-Singapore.html b/docs/articles/VariantAnimation-Singapore.html
index 7be759d8..4a867534 100644
--- a/docs/articles/VariantAnimation-Singapore.html
+++ b/docs/articles/VariantAnimation-Singapore.html
@@ -106,7 +106,7 @@
 
Get variants data for Singapore
 

 
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_singapore <- gisaid_metadata %>%
   filter(Country == "Singapore") %>%
   filter(Host == "Human")
diff --git a/docs/articles/VariantAnimation-Singapore_files/figure-html/unnamed-chunk-5-1.png b/docs/articles/VariantAnimation-Singapore_files/figure-html/unnamed-chunk-5-1.png
index 9ca912da..7913b1ac 100644
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diff --git a/docs/articles/VariantAnimation-UK.html b/docs/articles/VariantAnimation-UK.html
index dfb3c4ce..049411dd 100644
--- a/docs/articles/VariantAnimation-UK.html
+++ b/docs/articles/VariantAnimation-UK.html
@@ -106,7 +106,7 @@
 
Get variants data for United Kingdom
 

 
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_United_Kingdom <- gisaid_metadata %>%
   filter(Country == "United Kingdom") %>%
   filter(Host == "Human")
diff --git a/docs/articles/VariantAnimation-UK_files/figure-html/unnamed-chunk-5-1.png b/docs/articles/VariantAnimation-UK_files/figure-html/unnamed-chunk-5-1.png
index 97d717b9..fb40248f 100644
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diff --git a/docs/articles/VariantAnimation-USA.html b/docs/articles/VariantAnimation-USA.html
index 06f42080..b08bea0e 100644
--- a/docs/articles/VariantAnimation-USA.html
+++ b/docs/articles/VariantAnimation-USA.html
@@ -106,7 +106,7 @@
 
Get variants data for USA
 

 
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_usa <- gisaid_metadata %>%
   filter(Country == "USA") %>%
   filter(Host == "Human")
diff --git a/docs/articles/VariantAnimation-USA_files/figure-html/unnamed-chunk-5-1.png b/docs/articles/VariantAnimation-USA_files/figure-html/unnamed-chunk-5-1.png
index 4b852f5b..347dd473 100644
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diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 00dfdd55..ed37507d 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -11,7 +11,7 @@ articles:
   VariantAnimation-Singapore: VariantAnimation-Singapore.html
   VariantAnimation-UK: VariantAnimation-UK.html
   VariantAnimation-USA: VariantAnimation-USA.html
-last_built: 2024-01-16T23:41Z
+last_built: 2024-01-18T18:31Z
 urls:
   reference: http://saket-choudhary.me/covmuller/reference
   article: http://saket-choudhary.me/covmuller/articles
diff --git a/docs/search.json b/docs/search.json
index c9d77d12..a6583e11 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1 +1 @@
-[{"path":"http://saket-choudhary.me/covmuller/articles/Introduction.html","id":"get-cases-data","dir":"Articles","previous_headings":"","what":"Get cases data","title":"Introduction to covmuller","text":"use covid19bharat.org get tally daily confirmed cases summarize monthly level.","code":"indian_state_cases <- GetIndiaConfirmedCasesMonthlyLong() india_cases <- indian_state_cases %>%   filter(State == \"India\") %>%   filter(value > 1) head(india_cases) #>   MonthYear State   value      type #> 1  Mar 2020 India    1635 Confirmed #> 2  Apr 2020 India   33232 Confirmed #> 3  May 2020 India  155781 Confirmed #> 4  Jun 2020 India  395044 Confirmed #> 5  Jul 2020 India 1111273 Confirmed #> 6  Aug 2020 India 1990350 Confirmed"},{"path":"http://saket-choudhary.me/covmuller/articles/Introduction.html","id":"plot-cases-for-india","dir":"Articles","previous_headings":"Get cases data","what":"Plot cases for India","title":"Introduction to covmuller","text":"easy visualize monthly case counts bar plot:","code":"p1 <- BarPlot(india_cases, ylabel = \"Cases per month\", label_si = TRUE, title = \"Total cases per month - India\", caption = \"**Source: covid19bharat.org**
\") p1"},{"path":"http://saket-choudhary.me/covmuller/articles/Introduction.html","id":"read-variant-data-from-gisaid","dir":"Articles","previous_headings":"","what":"Read variant data from GISAID","title":"Introduction to covmuller","text":"utilize GISAID data look prevalence variants. access data, GISAID requires registration.","code":"current_date <- \"2024_01_15\" fpath.tar <- paste0(\"~/data/epicov/metadata_tsv_\", current_date, \".tar.xz\") fpath.qs <- paste0(\"~/data/epicov/metadata_tsv_\", current_date, \".qs\")  if (file.exists(fpath.qs)) {   gisaid_metadata <- qs::qread(file = fpath.qs) } else {   gisaid_metadata <- ReadGISAIDMetada(path = fpath.tar)   qs::qsave(gisaid_metadata, fpath.qs) }"},{"path":"http://saket-choudhary.me/covmuller/articles/Introduction.html","id":"plot-total-sequenced-cases","dir":"Articles","previous_headings":"Read variant data from GISAID","what":"Plot total sequenced cases","title":"Introduction to covmuller","text":"can look absolute number cases sequenced country filtering information metadata made available GISAID (includes countries). , visualize total sequenced cases coming India:","code":"gisaid_india <- FilterGISAIDIndia(gisaid_metadata_all = gisaid_metadata) country_seq_stats <- TotalSequencesPerMonthCountrywise(gisaid_india, rename_country_as_state = TRUE) p2 <- BarPlot(country_seq_stats, ylabel = \"Sequenced per month\", color = \"slateblue1\", label_si = TRUE, title = \"Total sequences deposited to GISAID from India\", caption = \"**Source: gisaid.org **
\") p2"},{"path":"http://saket-choudhary.me/covmuller/articles/Introduction.html","id":"overall-how-much-has-india-sequenced-over-months","dir":"Articles","previous_headings":"Read variant data from GISAID","what":"Overall, how much has India sequenced over months?","title":"Introduction to covmuller","text":"absolute numbers informative, useful metric proportion cases (cases sequenced total cases) getting sequenced. look proportion cases sequenced India course pandemic:","code":"# india_cases_long <- GetIndiaConfirmedCasesMonthlyLong() %>% filter(State == \"India\") GetIndiaCases <- function() {   data <- read.csv(\"https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv\")   confirmed <- data %>% select(date, India)   colnames(confirmed)[2] <- c(\"cases\")   confirmed$MonthYear <- GetMonthYear(confirmed$date)    confirmed_subset_weekwise <- confirmed %>%     group_by(MonthYear) %>%     summarise(value = sum(cases, na.rm = T)) %>%     arrange(MonthYear) } india_cases_long <- GetIndiaCases() india_cases_long$State <- \"India\" india_cases_long$type <- \"Confirmed\"  india_sequencing_proportion <- CombineSequencedCases(   cases_sequenced = country_seq_stats,   confirmed_long = india_cases_long ) p3 <- BarPlot(india_sequencing_proportion, yaxis = \"percent_sequenced_collected\", ylabel = \"%  deposited to GISAID\", color = \"yellowgreen\", title = \"Proportion of cases deposited to GISAID from India\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\") p3"},{"path":"http://saket-choudhary.me/covmuller/articles/Introduction.html","id":"plot-proportion-of-cases-that-been-deposited-from-each-state","dir":"Articles","previous_headings":"Read variant data from GISAID","what":"Plot proportion of cases that been deposited from each state","title":"Introduction to covmuller","text":"can break proportion sequenced cases state level:","code":"state_seq_stats <- TotalSequencesPerMonthStatewise(gisaid_india, drop_country = TRUE) seq_stats <- rbind(country_seq_stats, state_seq_stats) state_cases_long <- GetIndiaConfirmedCasesMonthlyLong() india_sequencing_proportion <- CombineSequencedCases(   cases_sequenced = seq_stats,   confirmed_long = state_cases_long,   month.min = \"Jan 2022\",   month.max = \"Feb 2023\",   max.percent = 5 )  india_sequencing_proportion$State <- factor(   x = india_sequencing_proportion$State,   levels = as.character(GetIndianStates()) ) p4 <- PlotSequencedPropHeatmap(india_sequencing_proportion) # p4"},{"path":"http://saket-choudhary.me/covmuller/articles/Introduction.html","id":"plot-prevalence","dir":"Articles","previous_headings":"","what":"Plot Prevalence","title":"Introduction to covmuller","text":"Finally, look prevalence variants variants concern (VOCs):  animated version prevalence plot, check VariantAnimation.","code":"india_month_counts <- SummarizeVariantsMonthwise(gisaid_india) india_month_counts$State <- \"India\" india_month_prevalence <- CountsToPrevalence(india_month_counts) vocs <- GetVOCs() custom_voc_mapping <- list(   `JN.1` = \"JN.1\",   `JN.1.*` = \"JN.1\",   `HV.1` = \"HV.1\",   `HV.1.*` = \"HV.1\",   \"B.1\" = \"B.1\",   \"B.1.1.306*\" = \"B.1\",   \"B.1.1.326\" = \"B.1\",   \"B.1.36.29\" =  \"B.1\",   \"B.1.560\" = \"B.1\",   \"B.1.1\" =  \"B.1\",   \"B.1.210\" = \"B.1\",   \"B.1.36.8\" = \"B.1\",   \"B.1.36.\" = \"B.1\" ) india_month_prevalence <- CollapseLineageToVOCs(   variant_df = india_month_prevalence,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping, summarize = FALSE )  p5 <- StackedBarPlotPrevalence(india_month_prevalence) p5"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-Brazil.html","id":"get-variants-data-for-brazil","dir":"Articles","previous_headings":"","what":"Get variants data for Brazil","title":"Animation of projected weekly cases - Brazil","text":"","code":"gisaid_metadata <- qs::qread(\"~/data/epicov/metadata_tsv_2024_01_15.qs\") gisaid_brazil <- gisaid_metadata %>%   filter(Country == \"Brazil\") %>%   filter(Host == \"Human\") # format metadata gisaid_brazil <- FormatGISAIDMetadata(gisaid_brazil) gisaid_brazil <- gisaid_brazil %>%   arrange(State, MonthYearCollected) %>%   filter(pangolin_lineage != \"Unknown\")  vocs <- GetVOCs() custom_voc_mapping <- list(   `B.1` = \"B.1\",   `JN.1` = \"JN.1\",   `JN.1.*` = \"JN.1\",   `HV.1` = \"HV.1\",   `HV.1.*` = \"HV.1\" ) gisaid_brazil <- gisaid_brazil %>% filter(pangolin_lineage != \"None\")  gisaid_brazil <- CollapseLineageToVOCs(   variant_df = gisaid_brazil,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping,   summarize = FALSE )"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-Brazil.html","id":"get-weekly-cases-for-brazil","dir":"Articles","previous_headings":"","what":"Get weekly cases for Brazil","title":"Animation of projected weekly cases - Brazil","text":"","code":"GetCases <- function() {   data <- read.csv(\"https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv\")   confirmed <- data %>% select(date, Brazil)   colnames(confirmed)[2] <- c(\"cases\")   confirmed$MonthYear <- GetMonthYear(confirmed$date)   confirmed$WeekYear <- tsibble::yearweek(confirmed$date)   return(confirmed) }   GetCasesLong <- function() {   data <- read.csv(\"https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv\")   confirmed <- data %>% select(date, Brazil)   colnames(confirmed)[2] <- c(\"cases\")   confirmed$MonthYear <- GetMonthYear(confirmed$date)   confirmed$WeekYear <- tsibble::yearweek(confirmed$date)   confirmed_subset_weekwise <- confirmed %>%     group_by(WeekYear) %>%     summarise(cases = mean(cases, na.rm = T)) %>%     arrange(WeekYear)   confirmed_subset_weekwise$cases <- ceiling(confirmed_subset_weekwise$cases)   confirmed_subset_dateweekwise_long_india <- confirmed_subset_weekwise %>%     rename(n = cases) %>%     rename(WeekYearCollected = WeekYear) }   confirmed <- GetCases() confirmed_subset_dateweekwise_long <- GetCasesLong() gisaid_brazil_weekwise <- SummarizeVariantsWeekwise(gisaid_brazil)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-Brazil.html","id":"distribution-of-variants","dir":"Articles","previous_headings":"","what":"Distribution of variants","title":"Animation of projected weekly cases - Brazil","text":"","code":"state_month_counts <- SummarizeVariantsMonthwise(gisaid_brazil) state_month_counts$State <- \"Brazil\" state_month_prevalence <- CountsToPrevalence(state_month_counts) vocs <- GetVOCs()  state_month_prevalence <- CollapseLineageToVOCs(   variant_df = state_month_prevalence,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping, summarize = FALSE )  p5 <- StackedBarPlotPrevalence(state_month_prevalence) p5"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-Brazil.html","id":"project-weekly-cases-to-variant-prevalence-data-from-gisaid","dir":"Articles","previous_headings":"","what":"Project weekly cases to variant prevalence data from GISAID","title":"Animation of projected weekly cases - Brazil","text":"Look cases 2022, Look cases 2023  Look cases past weeks","code":"voc_to_keep <- gisaid_brazil_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_brazil_weekwise <- gisaid_brazil_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  brazil_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_brazil_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(brazil_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in Brazil by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"100 days\") gganimate::anim_save(filename = here::here(\"docs/articles/Brazil_animated.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2021 W35\"))  gisaid_brazil_subset <- gisaid_brazil %>% filter(MonthYearCollected > \"Oct 2021\") gisaid_brazil_weekwise <- SummarizeVariantsWeekwise(gisaid_brazil_subset)  voc_to_keep <- gisaid_brazil_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_brazil_weekwise <- gisaid_brazil_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  brazil_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_brazil_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(brazil_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in Brazil by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"100 days\") gganimate::anim_save(filename = here::here(\"docs/articles/Brazil_animated_2021.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2022 W35\"))  gisaid_brazil_subset <- gisaid_brazil %>% filter(MonthYearCollected > \"October 2022\") gisaid_brazil_weekwise <- SummarizeVariantsWeekwise(gisaid_brazil_subset)  voc_to_keep <- gisaid_brazil_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_brazil_weekwise <- gisaid_brazil_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  brazil_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_brazil_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(brazil_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in Brazil by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\") gganimate::anim_save(filename = here::here(\"docs/articles/Brazil_animated_2023.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2023 W23\"))  gisaid_brazil_subset <- gisaid_brazil %>% filter(MonthYearCollected > \"June 2023\") gisaid_brazil_weekwise <- SummarizeVariantsWeekwise(gisaid_brazil_subset)  voc_to_keep <- gisaid_brazil_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_brazil_weekwise <- gisaid_brazil_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  brazil_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_brazil_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(brazil_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in Brazil by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\") gganimate::anim_save(filename = here::here(\"docs/articles/Brazil_animated_2024.gif\"), animation = the_anim)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-Germany.html","id":"get-variants-data-for-germany","dir":"Articles","previous_headings":"","what":"Get variants data for Germany","title":"Animation of projected weekly cases - Germany","text":"","code":"gisaid_metadata <- qs::qread(\"~/data/epicov/metadata_tsv_2024_01_15.qs\") gisaid_germany <- gisaid_metadata %>%   filter(Country == \"Germany\") %>%   filter(Host == \"Human\") # format metadata gisaid_germany <- FormatGISAIDMetadata(gisaid_germany) gisaid_germany <- gisaid_germany %>%   arrange(State, MonthYearCollected) %>%   filter(pangolin_lineage != \"Unknown\")  vocs <- GetVOCs() custom_voc_mapping <- list(   `B.1` = \"B.1\",   `JN.1` = \"JN.1\",   `JN.1.*` = \"JN.1\",   `HV.1` = \"HV.1\",   `HV.1.*` = \"HV.1\" ) gisaid_germany <- gisaid_germany %>% filter(pangolin_lineage != \"None\")  gisaid_germany <- CollapseLineageToVOCs(   variant_df = gisaid_germany,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping,   summarize = FALSE )"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-Germany.html","id":"get-weekly-cases-for-germany","dir":"Articles","previous_headings":"","what":"Get weekly cases for Germany","title":"Animation of projected weekly cases - Germany","text":"","code":"GetCases <- function() {   data <- read.csv(\"https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv\")   confirmed <- data %>% select(date, Germany)   colnames(confirmed)[2] <- c(\"cases\")   confirmed$MonthYear <- GetMonthYear(confirmed$date)   confirmed$WeekYear <- tsibble::yearweek(confirmed$date)   return(confirmed) }   GetCasesLong <- function() {   data <- read.csv(\"https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv\")   confirmed <- data %>% select(date, Germany)   colnames(confirmed)[2] <- c(\"cases\")   confirmed$MonthYear <- GetMonthYear(confirmed$date)   confirmed$WeekYear <- tsibble::yearweek(confirmed$date)   confirmed_subset_weekwise <- confirmed %>%     group_by(WeekYear) %>%     summarise(cases = mean(cases, na.rm = T)) %>%     arrange(WeekYear)   confirmed_subset_weekwise$cases <- ceiling(confirmed_subset_weekwise$cases)   confirmed_subset_dateweekwise_long_india <- confirmed_subset_weekwise %>%     rename(n = cases) %>%     rename(WeekYearCollected = WeekYear) }   confirmed <- GetCases() confirmed_subset_dateweekwise_long <- GetCasesLong() gisaid_germany_weekwise <- SummarizeVariantsWeekwise(gisaid_germany)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-Germany.html","id":"distribution-of-variants","dir":"Articles","previous_headings":"","what":"Distribution of variants","title":"Animation of projected weekly cases - Germany","text":"","code":"state_month_counts <- SummarizeVariantsMonthwise(gisaid_germany) state_month_counts$State <- \"Germany\" state_month_prevalence <- CountsToPrevalence(state_month_counts) vocs <- GetVOCs()  state_month_prevalence <- CollapseLineageToVOCs(   variant_df = state_month_prevalence,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping, summarize = FALSE )  p5 <- StackedBarPlotPrevalence(state_month_prevalence) p5"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-Germany.html","id":"project-weekly-cases-to-variant-prevalence-data-from-gisaid","dir":"Articles","previous_headings":"","what":"Project weekly cases to variant prevalence data from GISAID","title":"Animation of projected weekly cases - Germany","text":"Look cases 2022, Look cases 2023  Look cases past weeks","code":"voc_to_keep <- gisaid_germany_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_germany_weekwise <- gisaid_germany_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  germany_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_germany_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(germany_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in Germany by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"100 days\") gganimate::anim_save(filename = here::here(\"docs/articles/Germany_animated.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2021 W35\"))  gisaid_germany_subset <- gisaid_germany %>% filter(MonthYearCollected > \"Oct 2021\") gisaid_germany_weekwise <- SummarizeVariantsWeekwise(gisaid_germany_subset)  voc_to_keep <- gisaid_germany_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_germany_weekwise <- gisaid_germany_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  germany_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_germany_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(germany_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in Germany by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"100 days\") gganimate::anim_save(filename = here::here(\"docs/articles/Germany_animated_2021.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2022 W35\"))  gisaid_germany_subset <- gisaid_germany %>% filter(MonthYearCollected > \"October 2022\") gisaid_germany_weekwise <- SummarizeVariantsWeekwise(gisaid_germany_subset)  voc_to_keep <- gisaid_germany_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_germany_weekwise <- gisaid_germany_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  germany_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_germany_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(germany_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in Germany by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\") gganimate::anim_save(filename = here::here(\"docs/articles/Germany_animated_2023.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2023 W23\"))  gisaid_germany_subset <- gisaid_germany %>% filter(MonthYearCollected > \"June 2023\") gisaid_germany_weekwise <- SummarizeVariantsWeekwise(gisaid_germany_subset)  voc_to_keep <- gisaid_germany_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_germany_weekwise <- gisaid_germany_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  germany_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_germany_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(germany_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in Germany by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\") gganimate::anim_save(filename = here::here(\"docs/articles/Germany_animated_2024.gif\"), animation = the_anim)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-India.html","id":"get-variants-data-for-india","dir":"Articles","previous_headings":"","what":"Get variants data for India","title":"Animation of projected weekly cases - India","text":"","code":"gisaid_metadata <- qs::qread(\"~/data/epicov/metadata_tsv_2024_01_15.qs\") gisaid_india <- FilterGISAIDIndia(gisaid_metadata_all = gisaid_metadata)  vocs <- GetVOCs() custom_voc_mapping <- list(   `JN.1` = \"JN.1\",   `JN.1.*` = \"JN.1\",   `HV.1` = \"HV.1\",   `HV.1.*` = \"HV.1\",   \"B.1\" = \"B.1\",   \"B.1.1.306*\" = \"B.1\",   \"B.1.1.326\" = \"B.1\",   \"B.1.36.29\" =  \"B.1\",   \"B.1.560\" = \"B.1\",   \"B.1.1\" =  \"B.1\",   \"B.1.210\" = \"B.1\",   \"B.1.36.8\" = \"B.1\",   \"B.1.36.\" = \"B.1\" ) gisaid_india <- gisaid_india %>%   filter(pangolin_lineage != \"None\") %>%   filter(pangolin_lineage != \"Unassigned\")   gisaid_india$District <- stringr::str_to_title(gisaid_india$District) gisaid_india$City <- stringr::str_to_title(gisaid_india$City)  gisaid_india$custom_city <- gisaid_india$City gisaid_india$custom_city[gisaid_india$custom_city == \"\"] <- gisaid_india$District[gisaid_india$custom_city == \"\"]  gisaid_india$custom_city <- stringr::str_to_title(gisaid_india$custom_city)  gisaid_india <- CollapseLineageToVOCs(   variant_df = gisaid_india,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping,   summarize = FALSE ) gisaid_india_all <- gisaid_india"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-India.html","id":"distribution-of-variants","dir":"Articles","previous_headings":"","what":"Distribution of variants","title":"Animation of projected weekly cases - India","text":"","code":"state_month_counts <- SummarizeVariantsMonthwise(gisaid_india) state_month_counts$State <- \"India\" state_month_prevalence <- CountsToPrevalence(state_month_counts) state_month_prevalence <- CollapseLineageToVOCs(   variant_df = state_month_prevalence,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping, summarize = FALSE )  p5 <- StackedBarPlotPrevalence(state_month_prevalence) p5"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-India.html","id":"get-weekly-cases-for-india","dir":"Articles","previous_headings":"","what":"Get weekly cases for India","title":"Animation of projected weekly cases - India","text":"","code":"GetIndiaCases <- function() {   data <- read.csv(\"https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv\")   confirmed <- data %>% select(date, India)   colnames(confirmed)[2] <- c(\"cases\")   confirmed$MonthYear <- GetMonthYear(confirmed$date)   confirmed$WeekYear <- tsibble::yearweek(confirmed$date)   confirmed_subset_weekwise <- confirmed %>%     group_by(WeekYear) %>%     summarise(cases = mean(cases, na.rm = T)) %>%     arrange(WeekYear)   confirmed_subset_weekwise$cases <- ceiling(confirmed_subset_weekwise$cases)   confirmed_subset_dateweekwise_long_india <- confirmed_subset_weekwise %>%     rename(n = cases) %>%     rename(WeekYearCollected = WeekYear) }   confirmed_subset_dateweekwise_long_india <- GetIndiaCases() gisaid_india_weekwise <- SummarizeVariantsWeekwise(gisaid_india)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-India.html","id":"project-weekly-cases-to-variant-prevalence-data-from-gisaid","dir":"Articles","previous_headings":"","what":"Project weekly cases to variant prevalence data from GISAID","title":"Animation of projected weekly cases - India","text":"Look cases January, 2022 :  Look cases last months:  Look cases last months:","code":"voc_to_keep <- gisaid_india_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 10) %>%   pull(lineage_collapsed) %>%   unique() gisaid_india_weekwise <- gisaid_india_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  india_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_india_weekwise, confirmed_subset_dateweekwise_long_india) the_anim <- PlotVariantPrevalenceAnimated(india_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in India by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**\", date_breaks = \"120 days\") gganimate::anim_save(filename = here::here(\"docs/articles/IN_animated.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long_india <- GetIndiaCases() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2021 W35\"))  gisaid_india <- gisaid_india %>% filter(MonthYearCollected > \"Dec 2021\") gisaid_weekwise <- SummarizeVariantsWeekwise(gisaid_india)  voc_to_keep <- gisaid_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 10) %>%   pull(lineage_collapsed) %>%   unique() gisaid_weekwise <- gisaid_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_weekwise, confirmed_subset_dateweekwise_long_india) the_anim <- PlotVariantPrevalenceAnimated(cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in India by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"90 days\") # , trans_y=\"log10\") gganimate::anim_save(filename = here::here(\"docs/articles/IN_animated_2021.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long_india <- GetIndiaCases() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2022 W12\"))  gisaid_india <- gisaid_india %>% filter(MonthYearCollected >= \"Mar 2022\") gisaid_weekwise <- SummarizeVariantsWeekwise(gisaid_india)  voc_to_keep <- gisaid_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 10) %>%   pull(lineage_collapsed) %>%   unique() gisaid_weekwise <- gisaid_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_weekwise, confirmed_subset_dateweekwise_long_india) the_anim <- PlotVariantPrevalenceAnimated(cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in India by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"90 days\") gganimate::anim_save(filename = here::here(\"docs/articles/IN_animated_2022.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long_india <- GetIndiaCases() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2022 W35\"))  gisaid_india <- gisaid_india %>% filter(MonthYearCollected >= \"Dec 2022\") gisaid_weekwise <- SummarizeVariantsWeekwise(gisaid_india)  voc_to_keep <- gisaid_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 10) %>%   pull(lineage_collapsed) %>%   unique() gisaid_weekwise <- gisaid_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_weekwise, confirmed_subset_dateweekwise_long_india) the_anim <- PlotVariantPrevalenceAnimated(cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in India by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"30 days\") gganimate::anim_save(filename = here::here(\"docs/articles/IN_animated_latest.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long_india <- GetIndiaCases() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2023 W23\"))  gisaid_india <- gisaid_india %>% filter(MonthYearCollected >= \"June 2023\") gisaid_weekwise <- SummarizeVariantsWeekwise(gisaid_india)  voc_to_keep <- gisaid_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 10) %>%   pull(lineage_collapsed) %>%   unique() gisaid_weekwise <- gisaid_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_weekwise, confirmed_subset_dateweekwise_long_india) the_anim <- PlotVariantPrevalenceAnimated(cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in India by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"30 days\") gganimate::anim_save(filename = here::here(\"docs/articles/IN_animated_2023.gif\"), animation = the_anim)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-India.html","id":"jn-1-variant","dir":"Articles","previous_headings":"Project weekly cases to variant prevalence data from GISAID","what":"JN.1 variant","title":"Animation of projected weekly cases - India","text":"many cases JN.1 variant deposited GISAID across states?","code":"jn.1 <- gisaid_india_all %>% filter(lineage_collapsed %in% c(\"JN.1\")) jn.1.grouped <- jn.1 %>%   group_by(State) %>%   tally() jn.1.grouped <- jn.1.grouped %>%   filter(!State %in% c(\"Unknown\", \"Unassigned\")) %>%   arrange(desc(n)) jn.1.grouped$State <- factor(jn.1.grouped$State, levels = jn.1.grouped$State) ggplot(jn.1.grouped, aes(State, n, label = n)) + # , color= \"#4682b4\"   geom_col(position = \"identity\", fill = \"#4682b4\") +   geom_bar_text(stat = \"identity\") +   xlab(\"\") +   ylab(\"Number of JN.1 samples deposited\") +   scale_x_discrete(guide = guide_axis(angle = 90)) +   labs(     title = \"Number of JN.1 samples deposited to GISAID from India\",     caption = paste0(\"**Source: gisaid.org** 
\", Sys.Date())   )"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-NYC.html","id":"get-variants-data-for-ny-state","dir":"Articles","previous_headings":"","what":"Get variants data for NY state","title":"Animation of projected weekly cases - New York City","text":"","code":"counties <- c(\"New York\", \"New York City\", \"New York County\", \"Queens County\", \"Bronx County\", \"Bronx\", \"Brooklyn\", \"Manhattan\", \"Queens\", \"Richmond County\")  gisaid_metadata <- qs::qread(\"~/data/epicov/metadata_tsv_2024_01_15.qs\") gisaid_usa <- gisaid_metadata %>%   filter(Country == \"USA\") %>%   filter(Host == \"Human\") # format metadata gisaid_usa <- FormatGISAIDMetadata(gisaid_usa) gisaid_usa <- gisaid_usa %>%   arrange(State, MonthYearCollected) %>%   filter(pangolin_lineage != \"Unknown\") gisaid_NY <- gisaid_usa %>% filter(State == \"New York\")   gisaid_NYC <- gisaid_NY %>% filter(District %in% counties)  vocs <- GetVOCs() custom_voc_mapping <- list(   `JN.1` = \"JN.1\",   `JN.1.*` = \"JN.1\",   `HV.1` = \"HV.1\",   `HV.1.*` = \"HV.1\" )  gisaid_NYC <- gisaid_NYC %>% filter(pangolin_lineage != \"None\")  gisaid_NYC <- CollapseLineageToVOCs(   variant_df = gisaid_NYC,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping,   summarize = FALSE )"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-NYC.html","id":"get-weekly-cases-for-new-york","dir":"Articles","previous_headings":"","what":"Get weekly cases for New York","title":"Animation of projected weekly cases - New York City","text":"","code":"confirmed <- read_csv(\"https://raw.githubusercontent.com/nychealth/coronavirus-data/master/trends/data-by-day.csv\") %>% select(date_of_interest, CASE_COUNT) colnames(confirmed) <- c(\"date\", \"daily_cases\") confirmed$WeekYear <- tsibble::yearweek(confirmed$date) confirmed$MonthYear <- GetMonthYear(confirmed$date, datefmt = \"%m/%d/%Y\")  confirmed_subset_dateweekwise_long <- confirmed %>%   group_by(WeekYear) %>%   summarise(n = ceiling(mean(daily_cases, na.rm = T))) %>%   arrange(WeekYear) %>%   rename(WeekYearCollected = WeekYear)   gisaid_NYC_weekwise <- SummarizeVariantsWeekwise(gisaid_NYC)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-NYC.html","id":"distribution-of-variants","dir":"Articles","previous_headings":"","what":"Distribution of variants","title":"Animation of projected weekly cases - New York City","text":"","code":"state_month_counts <- SummarizeVariantsMonthwise(gisaid_NYC) state_month_counts$State <- \"NYC\" state_month_prevalence <- CountsToPrevalence(state_month_counts)   state_month_prevalence <- CollapseLineageToVOCs(   variant_df = state_month_prevalence,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping, summarize = FALSE )  p5 <- StackedBarPlotPrevalence(state_month_prevalence) p5"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-NYC.html","id":"project-weekly-cases-to-variant-prevalence-data-from-gisaid","dir":"Articles","previous_headings":"","what":"Project weekly cases to variant prevalence data from GISAID","title":"Animation of projected weekly cases - New York City","text":"Look cases 2023:","code":"voc_to_keep <- gisaid_NYC_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_NYC_weekwise <- gisaid_NYC_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  newyork_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_NYC_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(newyork_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in New York City by variant\", caption = \"**Source: gisaid.org and NYC Health**
\", date_breaks = \"120 days\") gganimate::anim_save(filename = here::here(\"docs/articles/NYC_animated.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- confirmed %>%   filter(MonthYear > \"April 2023\") %>%   group_by(WeekYear) %>%   summarise(n = ceiling(mean(daily_cases, na.rm = T))) %>%   arrange(WeekYear) %>%   rename(WeekYearCollected = WeekYear)  gisaid_NYC_subset <- gisaid_NYC %>% filter(MonthYearCollected > \"April 2023\") gisaid_weekwise <- SummarizeVariantsWeekwise(gisaid_NYC_subset)  voc_to_keep <- gisaid_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 1) %>%   pull(lineage_collapsed) %>%   unique() gisaid_weekwise <- gisaid_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in New York City by variant\", caption = \"**Source: gisaid.org and NYC Health**
\", date_breaks = \"30 days\") ## `geom_line()`: Each group consists of only one observation. ## ℹ Do you need to adjust the group aesthetic? ## `geom_line()`: Each group consists of only one observation. ## ℹ Do you need to adjust the group aesthetic? gganimate::anim_save(filename = here::here(\"docs/articles/NYC_animated_2023.gif\"), animation = the_anim)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-NewYork.html","id":"get-variants-data-for-ny-state","dir":"Articles","previous_headings":"","what":"Get variants data for NY state","title":"Animation of projected weekly cases - New York State","text":"","code":"gisaid_metadata <- qs::qread(\"~/data/epicov/metadata_tsv_2024_01_15.qs\") gisaid_usa <- gisaid_metadata %>%   filter(Country == \"USA\") %>%   filter(Host == \"Human\") # format metadata gisaid_usa <- FormatGISAIDMetadata(gisaid_usa) gisaid_usa <- gisaid_usa %>%   arrange(State, MonthYearCollected) %>%   filter(pangolin_lineage != \"Unknown\") gisaid_NY <- gisaid_usa %>% filter(State == \"New York\")  vocs <- GetVOCs() custom_voc_mapping <- list(   `JN.1` = \"JN.1\",   `JN.1.*` = \"JN.1\",   `HV.1` = \"HV.1\",   `HV.1.*` = \"HV.1\" ) gisaid_NY <- gisaid_NY %>% filter(pangolin_lineage != \"None\") gisaid_NY <- CollapseLineageToVOCs(   variant_df = gisaid_NY,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping,   summarize = FALSE )"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-NewYork.html","id":"get-weekly-cases-for-new-york","dir":"Articles","previous_headings":"","what":"Get weekly cases for New York","title":"Animation of projected weekly cases - New York State","text":"","code":"confirmed <- covid19(country = \"USA\", level = 2) %>%   filter(administrative_area_level_2 == \"New York\") %>%   select(date, confirmed) %>%   filter(!is.na(confirmed)) #> We have invested a lot of time and effort in creating COVID-19 Data #> Hub, please cite the following when using it: #>  #>   Guidotti, E., Ardia, D., (2020), \"COVID-19 Data Hub\", Journal of Open #>   Source Software 5(51):2376, doi: 10.21105/joss.02376 #>  #> The implementation details and the latest version of the data are #> described in: #>  #>   Guidotti, E., (2022), \"A worldwide epidemiological database for #>   COVID-19 at fine-grained spatial resolution\", Sci Data 9(1):112, doi: #>   10.1038/s41597-022-01245-1 #> To print citations in BibTeX format use: #>  > print(citation('COVID19'), bibtex=TRUE) #>  #> To hide this message use 'verbose = FALSE'.  confirmed$daily_cases <- c(confirmed$confirmed[1], diff(confirmed$confirmed)) confirmed$WeekYear <- tsibble::yearweek(confirmed$date) confirmed$MonthYear <- GetMonthYear(confirmed$date)  confirmed_subset_dateweekwise_long <- confirmed %>%   group_by(WeekYear) %>%   summarise(n = ceiling(mean(daily_cases, na.rm = T))) %>%   arrange(WeekYear) %>%   rename(WeekYearCollected = WeekYear)  gisaid_NY_weekwise <- SummarizeVariantsWeekwise(gisaid_NY)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-NewYork.html","id":"distribution-of-variants","dir":"Articles","previous_headings":"","what":"Distribution of variants","title":"Animation of projected weekly cases - New York State","text":"","code":"state_month_counts <- SummarizeVariantsMonthwise(gisaid_NY) state_month_counts$State <- \"New York\" state_month_prevalence <- CountsToPrevalence(state_month_counts) state_month_prevalence <- CollapseLineageToVOCs(   variant_df = state_month_prevalence,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping, summarize = FALSE )  p5 <- StackedBarPlotPrevalence(state_month_prevalence) p5"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-NewYork.html","id":"project-weekly-cases-to-variant-prevalence-data-from-gisaid","dir":"Articles","previous_headings":"","what":"Project weekly cases to variant prevalence data from GISAID","title":"Animation of projected weekly cases - New York State","text":"Look cases february, 2022 :","code":"voc_to_keep <- gisaid_NY_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_NY_weekwise <- gisaid_NY_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  newyork_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_NY_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(newyork_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in New York state by variant\", caption = \"**Source: gisaid.org and covid19nytimes**
\", date_breaks = \"28 days\") gganimate::anim_save(filename = here::here(\"docs/articles/NY_animated.gif\"), animation = the_anim) confirmed$MonthYear <- GetMonthYear(confirmed$date)  confirmed_subset_dateweekwise_long <- confirmed %>%   filter(MonthYear > \"December 2022\") %>%   group_by(WeekYear) %>%   summarise(n = sum(daily_cases)) %>%   arrange(WeekYear) %>%   rename(WeekYearCollected = WeekYear)  gisaid_NY <- gisaid_NY %>% filter(MonthYearCollected > \"December 2022\") gisaid_weekwise <- SummarizeVariantsWeekwise(gisaid_NY)  voc_to_keep <- gisaid_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_weekwise <- gisaid_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in New York state by variant\", caption = \"**Source: gisaid.org and covid19nytimes**
\") gganimate::anim_save(filename = here::here(\"docs/articles/NY_animated_2023.gif\"), animation = the_anim)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-Singapore.html","id":"get-variants-data-for-singapore","dir":"Articles","previous_headings":"","what":"Get variants data for Singapore","title":"Animation of projected weekly cases - Singapore","text":"","code":"gisaid_metadata <- qs::qread(\"~/data/epicov/metadata_tsv_2024_01_15.qs\") gisaid_singapore <- gisaid_metadata %>%   filter(Country == \"Singapore\") %>%   filter(Host == \"Human\") # format metadata gisaid_singapore <- FormatGISAIDMetadata(gisaid_singapore) gisaid_singapore <- gisaid_singapore %>%   arrange(State, MonthYearCollected) %>%   filter(pangolin_lineage != \"Unknown\")  vocs <- GetVOCs() custom_voc_mapping <- list(   `B.1` = \"B.1\",   `JN.1` = \"JN.1\",   `JN.1.*` = \"JN.1\",   `HV.1` = \"HV.1\",   `HV.1.*` = \"HV.1\" ) gisaid_singapore <- gisaid_singapore %>% filter(pangolin_lineage != \"None\")  gisaid_singapore <- CollapseLineageToVOCs(   variant_df = gisaid_singapore,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping,   summarize = FALSE )"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-Singapore.html","id":"get-weekly-cases-for-singapore","dir":"Articles","previous_headings":"","what":"Get weekly cases for Singapore","title":"Animation of projected weekly cases - Singapore","text":"","code":"GetCases <- function() {   data <- read.csv(\"https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv\")   confirmed <- data %>% select(date, Singapore)   colnames(confirmed)[2] <- c(\"cases\")   confirmed$MonthYear <- GetMonthYear(confirmed$date)   confirmed$WeekYear <- tsibble::yearweek(confirmed$date)   return(confirmed) }   GetCasesLong <- function() {   data <- read.csv(\"https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv\")   confirmed <- data %>% select(date, Singapore)   colnames(confirmed)[2] <- c(\"cases\")   confirmed$MonthYear <- GetMonthYear(confirmed$date)   confirmed$WeekYear <- tsibble::yearweek(confirmed$date)   confirmed_subset_weekwise <- confirmed %>%     group_by(WeekYear) %>%     summarise(cases = mean(cases, na.rm = T)) %>%     arrange(WeekYear)   confirmed_subset_weekwise$cases <- ceiling(confirmed_subset_weekwise$cases)   confirmed_subset_dateweekwise_long_india <- confirmed_subset_weekwise %>%     rename(n = cases) %>%     rename(WeekYearCollected = WeekYear) }   confirmed <- GetCases() confirmed_subset_dateweekwise_long <- GetCasesLong() gisaid_singapore_weekwise <- SummarizeVariantsWeekwise(gisaid_singapore)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-Singapore.html","id":"distribution-of-variants","dir":"Articles","previous_headings":"","what":"Distribution of variants","title":"Animation of projected weekly cases - Singapore","text":"","code":"state_month_counts <- SummarizeVariantsMonthwise(gisaid_singapore) state_month_counts$State <- \"Singapore\" state_month_prevalence <- CountsToPrevalence(state_month_counts) vocs <- GetVOCs()  state_month_prevalence <- CollapseLineageToVOCs(   variant_df = state_month_prevalence,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping, summarize = FALSE )  p5 <- StackedBarPlotPrevalence(state_month_prevalence) p5"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-Singapore.html","id":"project-weekly-cases-to-variant-prevalence-data-from-gisaid","dir":"Articles","previous_headings":"","what":"Project weekly cases to variant prevalence data from GISAID","title":"Animation of projected weekly cases - Singapore","text":"Look cases 2022, Look cases 2023  Look cases past weeks","code":"voc_to_keep <- gisaid_singapore_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_singapore_weekwise <- gisaid_singapore_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  singapore_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_singapore_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(singapore_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in Singapore by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"100 days\") gganimate::anim_save(filename = here::here(\"docs/articles/Singapore_animated.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2021 W35\"))  gisaid_singapore_subset <- gisaid_singapore %>% filter(MonthYearCollected > \"Oct 2021\") gisaid_singapore_weekwise <- SummarizeVariantsWeekwise(gisaid_singapore_subset)  voc_to_keep <- gisaid_singapore_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_singapore_weekwise <- gisaid_singapore_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  singapore_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_singapore_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(singapore_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in Singapore by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"100 days\") gganimate::anim_save(filename = here::here(\"docs/articles/Singapore_animated_2021.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2022 W35\"))  gisaid_singapore_subset <- gisaid_singapore %>% filter(MonthYearCollected > \"October 2022\") gisaid_singapore_weekwise <- SummarizeVariantsWeekwise(gisaid_singapore_subset)  voc_to_keep <- gisaid_singapore_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_singapore_weekwise <- gisaid_singapore_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  singapore_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_singapore_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(singapore_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in Singapore by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\") gganimate::anim_save(filename = here::here(\"docs/articles/Singapore_animated_2023.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2023 W23\"))  gisaid_singapore_subset <- gisaid_singapore %>% filter(MonthYearCollected > \"June 2023\") gisaid_singapore_weekwise <- SummarizeVariantsWeekwise(gisaid_singapore_subset)  voc_to_keep <- gisaid_singapore_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_singapore_weekwise <- gisaid_singapore_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  singapore_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_singapore_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(singapore_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in Singapore by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\") gganimate::anim_save(filename = here::here(\"docs/articles/Singapore_animated_2024.gif\"), animation = the_anim)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-UK.html","id":"get-variants-data-for-united-kingdom","dir":"Articles","previous_headings":"","what":"Get variants data for United Kingdom","title":"Animation of projected weekly cases - United Kingdom","text":"","code":"gisaid_metadata <- qs::qread(\"~/data/epicov/metadata_tsv_2024_01_15.qs\") gisaid_United_Kingdom <- gisaid_metadata %>%   filter(Country == \"United Kingdom\") %>%   filter(Host == \"Human\") # format metadata gisaid_United_Kingdom <- FormatGISAIDMetadata(gisaid_United_Kingdom) gisaid_United_Kingdom <- gisaid_United_Kingdom %>%   arrange(State, MonthYearCollected) %>%   filter(pangolin_lineage != \"Unknown\")  vocs <- GetVOCs() custom_voc_mapping <- list(   `B.1` = \"B.1\",   `JN.1` = \"JN.1\",   `JN.1.*` = \"JN.1\",   `HV.1` = \"HV.1\",   `HV.1.*` = \"HV.1\" ) gisaid_United_Kingdom <- gisaid_United_Kingdom %>% filter(pangolin_lineage != \"None\")  gisaid_United_Kingdom <- CollapseLineageToVOCs(   variant_df = gisaid_United_Kingdom,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping,   summarize = FALSE )"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-UK.html","id":"get-weekly-cases-for-united-kingdom","dir":"Articles","previous_headings":"","what":"Get weekly cases for United Kingdom","title":"Animation of projected weekly cases - United Kingdom","text":"","code":"GetCases <- function() {   data <- read.csv(\"https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv\")   confirmed <- data %>% select(date, United.Kingdom)   colnames(confirmed)[2] <- c(\"cases\")   confirmed$MonthYear <- GetMonthYear(confirmed$date)   confirmed$WeekYear <- tsibble::yearweek(confirmed$date)   return(confirmed) }   GetCasesLong <- function() {   data <- read.csv(\"https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv\")   confirmed <- data %>% select(date, United.Kingdom)   colnames(confirmed)[2] <- c(\"cases\")   confirmed$cases[is.na(confirmed$cases)] <- 0   confirmed$MonthYear <- GetMonthYear(confirmed$date)   confirmed$WeekYear <- tsibble::yearweek(confirmed$date)   confirmed_subset_weekwise <- confirmed %>%     group_by(WeekYear) %>%     summarise(cases = mean(cases, na.rm = T)) %>%     arrange(WeekYear)   confirmed_subset_weekwise$cases <- ceiling(confirmed_subset_weekwise$cases)   confirmed_subset_dateweekwise_long_india <- confirmed_subset_weekwise %>%     rename(n = cases) %>%     rename(WeekYearCollected = WeekYear) }   confirmed <- GetCases() confirmed_subset_dateweekwise_long <- GetCasesLong() gisaid_United_Kingdom_weekwise <- SummarizeVariantsWeekwise(gisaid_United_Kingdom)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-UK.html","id":"distribution-of-variants","dir":"Articles","previous_headings":"","what":"Distribution of variants","title":"Animation of projected weekly cases - United Kingdom","text":"","code":"state_month_counts <- SummarizeVariantsMonthwise(gisaid_United_Kingdom) state_month_counts$State <- \"United Kingdom\" state_month_prevalence <- CountsToPrevalence(state_month_counts) vocs <- GetVOCs()  state_month_prevalence <- CollapseLineageToVOCs(   variant_df = state_month_prevalence,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping, summarize = FALSE )  p5 <- StackedBarPlotPrevalence(state_month_prevalence) p5"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-UK.html","id":"project-weekly-cases-to-variant-prevalence-data-from-gisaid","dir":"Articles","previous_headings":"","what":"Project weekly cases to variant prevalence data from GISAID","title":"Animation of projected weekly cases - United Kingdom","text":"Look cases 2022, Look cases 2023  Look cases past weeks","code":"voc_to_keep <- gisaid_United_Kingdom_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_United_Kingdom_weekwise <- gisaid_United_Kingdom_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  United_Kingdom_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_United_Kingdom_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(United_Kingdom_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in United Kingdom by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"120 days\") gganimate::anim_save(filename = here::here(\"docs/articles/United_Kingdom_animated.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2021 W35\"))  gisaid_United_Kingdom_subset <- gisaid_United_Kingdom %>% filter(MonthYearCollected > \"Oct 2021\") gisaid_United_Kingdom_weekwise <- SummarizeVariantsWeekwise(gisaid_United_Kingdom_subset)  voc_to_keep <- gisaid_United_Kingdom_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_United_Kingdom_weekwise <- gisaid_United_Kingdom_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  United_Kingdom_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_United_Kingdom_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(United_Kingdom_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in United Kingdom by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"90 days\") gganimate::anim_save(filename = here::here(\"docs/articles/United_Kingdom_animated_2021.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2022 W35\"))  gisaid_United_Kingdom_subset <- gisaid_United_Kingdom %>% filter(MonthYearCollected > \"October 2022\") gisaid_United_Kingdom_weekwise <- SummarizeVariantsWeekwise(gisaid_United_Kingdom_subset)  voc_to_keep <- gisaid_United_Kingdom_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_United_Kingdom_weekwise <- gisaid_United_Kingdom_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  United_Kingdom_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_United_Kingdom_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(United_Kingdom_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in United Kingdom by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"90 days\") gganimate::anim_save(filename = here::here(\"docs/articles/United_Kingdom_animated_2023.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2023 W23\"))  gisaid_United_Kingdom_subset <- gisaid_United_Kingdom %>% filter(MonthYearCollected > \"June 2023\") gisaid_United_Kingdom_weekwise <- SummarizeVariantsWeekwise(gisaid_United_Kingdom_subset)  voc_to_keep <- gisaid_United_Kingdom_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_United_Kingdom_weekwise <- gisaid_United_Kingdom_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  United_Kingdom_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_United_Kingdom_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(United_Kingdom_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in United Kingdom by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\") gganimate::anim_save(filename = here::here(\"docs/articles/United_Kingdom_animated_2024.gif\"), animation = the_anim)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-USA.html","id":"get-variants-data-for-usa","dir":"Articles","previous_headings":"","what":"Get variants data for USA","title":"Animation of projected weekly cases - USA","text":"","code":"gisaid_metadata <- qs::qread(\"~/data/epicov/metadata_tsv_2024_01_15.qs\") gisaid_usa <- gisaid_metadata %>%   filter(Country == \"USA\") %>%   filter(Host == \"Human\") # format metadata gisaid_usa <- FormatGISAIDMetadata(gisaid_usa) gisaid_usa <- gisaid_usa %>%   arrange(State, MonthYearCollected) %>%   filter(pangolin_lineage != \"Unknown\")  vocs <- GetVOCs() custom_voc_mapping <- list(   `B.1` = \"B.1\",   `JN.1` = \"JN.1\",   `JN.1.*` = \"JN.1\",   `HV.1` = \"HV.1\",   `HV.1.*` = \"HV.1\" ) gisaid_usa <- gisaid_usa %>% filter(pangolin_lineage != \"None\")  gisaid_usa <- CollapseLineageToVOCs(   variant_df = gisaid_usa,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping,   summarize = FALSE )"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-USA.html","id":"get-weekly-cases-for-usa","dir":"Articles","previous_headings":"","what":"Get weekly cases for USA","title":"Animation of projected weekly cases - USA","text":"","code":"GetCases <- function() {   data <- read.csv(\"https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv\")   confirmed <- data %>% select(date, United.States)   colnames(confirmed)[2] <- c(\"cases\")   confirmed$MonthYear <- GetMonthYear(confirmed$date)   confirmed$WeekYear <- tsibble::yearweek(confirmed$date)   return(confirmed) }   GetCasesLong <- function() {   data <- read.csv(\"https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv\")   confirmed <- data %>% select(date, United.States)   colnames(confirmed)[2] <- c(\"cases\")   confirmed$MonthYear <- GetMonthYear(confirmed$date)   confirmed$WeekYear <- tsibble::yearweek(confirmed$date)   confirmed_subset_weekwise <- confirmed %>%     group_by(WeekYear) %>%     summarise(cases = mean(cases, na.rm = T)) %>%     arrange(WeekYear)   confirmed_subset_weekwise$cases <- ceiling(confirmed_subset_weekwise$cases)   confirmed_subset_dateweekwise_long_india <- confirmed_subset_weekwise %>%     rename(n = cases) %>%     rename(WeekYearCollected = WeekYear) }   confirmed <- GetCases() confirmed_subset_dateweekwise_long <- GetCasesLong() gisaid_usa_weekwise <- SummarizeVariantsWeekwise(gisaid_usa)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-USA.html","id":"distribution-of-variants","dir":"Articles","previous_headings":"","what":"Distribution of variants","title":"Animation of projected weekly cases - USA","text":"","code":"state_month_counts <- SummarizeVariantsMonthwise(gisaid_usa) state_month_counts$State <- \"USA\" state_month_prevalence <- CountsToPrevalence(state_month_counts) vocs <- GetVOCs()  state_month_prevalence <- CollapseLineageToVOCs(   variant_df = state_month_prevalence,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping, summarize = FALSE )  p5 <- StackedBarPlotPrevalence(state_month_prevalence) p5"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-USA.html","id":"project-weekly-cases-to-variant-prevalence-data-from-gisaid","dir":"Articles","previous_headings":"","what":"Project weekly cases to variant prevalence data from GISAID","title":"Animation of projected weekly cases - USA","text":"Look cases 2022, Look cases 2023","code":"voc_to_keep <- gisaid_usa_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_usa_weekwise <- gisaid_usa_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  usa_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_usa_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(usa_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in USA by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"120 days\") gganimate::anim_save(filename = here::here(\"docs/articles/USA_animated.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2021 W35\"))  gisaid_usa_subset <- gisaid_usa %>% filter(MonthYearCollected > \"Oct 2021\") gisaid_usa_weekwise <- SummarizeVariantsWeekwise(gisaid_usa_subset)  voc_to_keep <- gisaid_usa_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_usa_weekwise <- gisaid_usa_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  usa_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_usa_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(usa_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in USA by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"120 days\") gganimate::anim_save(filename = here::here(\"docs/articles/USA_animated_2021.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2022 W35\"))  gisaid_usa_subset <- gisaid_usa %>% filter(MonthYearCollected > \"October 2022\") gisaid_usa_weekwise <- SummarizeVariantsWeekwise(gisaid_usa_subset)  voc_to_keep <- gisaid_usa_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_usa_weekwise <- gisaid_usa_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  usa_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_usa_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(usa_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in USA by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"90 days\") gganimate::anim_save(filename = here::here(\"docs/articles/USA_animated_2023.gif\"), animation = the_anim)"},{"path":"http://saket-choudhary.me/covmuller/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Saket Choudhary. Maintainer.","code":""},{"path":"http://saket-choudhary.me/covmuller/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Choudhary S (2024). covmuller: tool model COVID19 variant prevalence using data GISAID. R package version 0.1.2.0005, https://saketkc.github.io/covmuller.","code":"@Manual{,   title = {covmuller: A tool to model COVID19 variant prevalence using data from GISAID},   author = {Saket Choudhary},   year = {2024},   note = {R package version 0.1.2.0005},   url = {https://saketkc.github.io/covmuller}, }"},{"path":"http://saket-choudhary.me/covmuller/index.html","id":"covmuller","dir":"","previous_headings":"","what":"A tool to model COVID19 variant prevalence using data from GISAID","title":"A tool to model COVID19 variant prevalence using data from GISAID","text":"Covmuller R-package designed analysis sequencing metadata deposited GISAID.","code":""},{"path":"http://saket-choudhary.me/covmuller/index.html","id":"features","dir":"","previous_headings":"","what":"Features","title":"A tool to model COVID19 variant prevalence using data from GISAID","text":"Covmuller currently supports following features: Read preprocess metadata file downloaded GISAID: Vignette Plot variant prevalence: Vignette India USA Canada UK Australia SouthAfrica India New York state South Africa United Kingdom USA New York City (USA) Delhi (India) Mumbai (India) Maharashtra (India) Pune (India)","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CleanAmericanStates.html","id":null,"dir":"Reference","previous_headings":"","what":"Clean states from the USA — CleanAmericanStates","title":"Clean states from the USA — CleanAmericanStates","text":"Clean states USA","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CleanAmericanStates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clean states from the USA — CleanAmericanStates","text":"","code":"CleanAmericanStates(states)"},{"path":"http://saket-choudhary.me/covmuller/reference/CleanAmericanStates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clean states from the USA — CleanAmericanStates","text":"state vector state 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CleanCanadaStates","text":"state vector state names","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CleanCanadaStates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clean states from Canada — CleanCanadaStates","text":"vector cleaned state names","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CleanIndianStates.html","id":null,"dir":"Reference","previous_headings":"","what":"Clean Indian States — CleanIndianStates","title":"Clean Indian States — CleanIndianStates","text":"Clean Indian States","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CleanIndianStates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clean Indian States — CleanIndianStates","text":"","code":"CleanIndianStates(states)"},{"path":"http://saket-choudhary.me/covmuller/reference/CleanIndianStates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clean Indian States — CleanIndianStates","text":"state vector state names","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CleanIndianStates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clean Indian States — CleanIndianStates","text":"vector cleaned state names","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CleanSouthAfricanStates.html","id":null,"dir":"Reference","previous_headings":"","what":"Clean states from South Africa — CleanSouthAfricanStates","title":"Clean states from South Africa — CleanSouthAfricanStates","text":"Clean states South Africa","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CleanSouthAfricanStates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clean states from South Africa — CleanSouthAfricanStates","text":"","code":"CleanSouthAfricanStates(states)"},{"path":"http://saket-choudhary.me/covmuller/reference/CleanSouthAfricanStates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clean states from South Africa — CleanSouthAfricanStates","text":"state vector state names","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CleanSouthAfricanStates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clean states from South Africa — CleanSouthAfricanStates","text":"vector cleaned state names","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CollapseLineageToVOCs.html","id":null,"dir":"Reference","previous_headings":"","what":"Collpase pangolin lineage a list of VOCs — CollapseLineageToVOCs","title":"Collpase pangolin lineage a list of VOCs — CollapseLineageToVOCs","text":"Collpase pangolin lineage list VOCs","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CollapseLineageToVOCs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Collpase pangolin lineage a list of VOCs — CollapseLineageToVOCs","text":"","code":"CollapseLineageToVOCs(   variant_df,   vocs = GetVOCs(),   custom_voc_mapping = NULL,   summarize = TRUE )"},{"path":"http://saket-choudhary.me/covmuller/reference/CollapseLineageToVOCs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Collpase pangolin lineage a list of VOCs — CollapseLineageToVOCs","text":"variant_df dataframe \"pangolin_lineage\" column vocs named list VOC (variant concernt) name key list lineages VOC. method replace lineages VOC name. custom_voc_mapping named vector custom mapping naming lineages. See example.","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CollapseLineageToVOCs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Collpase pangolin lineage a list of VOCs — CollapseLineageToVOCs","text":"dataframe new column \"lineage_collpased\".","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CombineSequencedCases.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a combined dataframe of sequenced cases and confirmed cases — CombineSequencedCases","title":"Create a combined dataframe of sequenced cases and confirmed cases — CombineSequencedCases","text":"Create combined dataframe sequenced cases confirmed cases","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CombineSequencedCases.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a combined dataframe of sequenced cases and confirmed cases — CombineSequencedCases","text":"","code":"CombineSequencedCases(   cases_sequenced,   confirmed_long,   prune_oversequenced = TRUE,   month.min = NULL,   month.max = NULL,   max.percent = 100 )"},{"path":"http://saket-choudhary.me/covmuller/reference/CombineSequencedCases.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a combined dataframe of sequenced cases and confirmed cases — CombineSequencedCases","text":"cases_sequenced long dataframe per state sequenced cases prune_oversequenced Whether round percentages 100 100. Default TRUE cases_total long dataframe total monthly cases","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CombineSequencedCases.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a combined dataframe of sequenced cases and confirmed cases — CombineSequencedCases","text":"combined dataframe case load sequenced","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CountsToPrevalence.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert monthwise counts to prevalence — CountsToPrevalence","title":"Convert monthwise counts to prevalence — CountsToPrevalence","text":"Convert monthwise counts prevalence","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CountsToPrevalence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert monthwise counts to prevalence — CountsToPrevalence","text":"","code":"CountsToPrevalence(variant_df)"},{"path":"http://saket-choudhary.me/covmuller/reference/CountsToPrevalence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert monthwise counts to prevalence — CountsToPrevalence","text":"variant_df dataframe","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CountsToPrevalence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert monthwise counts to prevalence — CountsToPrevalence","text":"dataframe monthwise prevalence variants","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CovmullerTheme.html","id":null,"dir":"Reference","previous_headings":"","what":"Set them — CovmullerTheme","title":"Set them — CovmullerTheme","text":"Set ","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CovmullerTheme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set them — CovmullerTheme","text":"","code":"CovmullerTheme()"},{"path":"http://saket-choudhary.me/covmuller/reference/FilterGISAIDIndia.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter GISAID India Metadata for India — FilterGISAIDIndia","title":"Filter GISAID India Metadata for India — FilterGISAIDIndia","text":"Filter GISAID India Metadata India","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/FilterGISAIDIndia.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter GISAID India Metadata for India — FilterGISAIDIndia","text":"","code":"FilterGISAIDIndia(gisaid_metadata_all)"},{"path":"http://saket-choudhary.me/covmuller/reference/FilterGISAIDIndia.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter GISAID India Metadata for India — FilterGISAIDIndia","text":"gisaid_metadata_all dataframe GISAID metadata","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/FilterGISAIDIndia.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter GISAID India Metadata for India — FilterGISAIDIndia","text":"dataframe Indian entries Human date known","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/FixDate.html","id":null,"dir":"Reference","previous_headings":"","what":"Fix date format — FixDate","title":"Fix date format — FixDate","text":"Fix date format","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/FixDate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fix date format — FixDate","text":"","code":"FixDate(datecol, datefmt = \"%Y-%m-%d\")"},{"path":"http://saket-choudhary.me/covmuller/reference/FormatGISAIDMetadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Format metadata.tar.x file downloaded from GISAID with some missing columns — FormatGISAIDMetadata","title":"Format metadata.tar.x file downloaded from GISAID with some missing columns — FormatGISAIDMetadata","text":"Format metadata.tar.x file downloaded GISAID missing columns","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/FormatGISAIDMetadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format metadata.tar.x file downloaded from GISAID with some missing columns — FormatGISAIDMetadata","text":"","code":"FormatGISAIDMetadata(   df,   collection_col = \"Collection date\",   submission_col = \"Submission date\" )"},{"path":"http://saket-choudhary.me/covmuller/reference/FormatGISAIDMetadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format metadata.tar.x file downloaded from GISAID with some missing columns — FormatGISAIDMetadata","text":"df GISAID metadata dataframe collection_col Name column representing date collection (Collection date) submission_col Name column representing date submission (Submission date)","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/FormatGISAIDMetadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format metadata.tar.x file downloaded from GISAID with some missing columns — FormatGISAIDMetadata","text":"data frame following extra columns: DateCollected, MonthYearCollected, YearCollected, MonthCollected, WeekCollected DateCollectedNumeric, MonthYearCollectedNumeric DateSubmitted, MonthYearSubmitted, YearSubmitted, MonthSubmitted, WeekSubmitted DateSubmittedNumeric, MonthYearSubmittedNumeric","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GeocodeAddress.html","id":null,"dir":"Reference","previous_headings":"","what":"Geocode a given address to get its latitude, longitude from Gooogle — GeocodeAddress","title":"Geocode a given address to get its latitude, longitude from Gooogle — GeocodeAddress","text":"Geocode given address get latitude, longitude Gooogle","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GeocodeAddress.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Geocode a given address to get its latitude, longitude from Gooogle — GeocodeAddress","text":"","code":"GeocodeAddress(address, api.key = NULL, restrict.to = NULL)"},{"path":"http://saket-choudhary.me/covmuller/reference/GeocodeAddress.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Geocode a given address to get its latitude, longitude from Gooogle — GeocodeAddress","text":"address String address geocode api.key API key Google Maps restrict.Country name search restricted , Currently supports 'India'","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GeocodeAddress.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Geocode a given address to get its latitude, longitude from Gooogle — GeocodeAddress","text":"dataframe geocoded address","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GetFiletype.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine filetype — GetFiletype","title":"Determine filetype — GetFiletype","text":"Determine filetype","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GetFiletype.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine filetype — GetFiletype","text":"","code":"GetFiletype(path)"},{"path":"http://saket-choudhary.me/covmuller/reference/GetFiletype.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine filetype — GetFiletype","text":"path Path file","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GetFiletype.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine filetype — GetFiletype","text":"Extension file","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GetIndiaConfirmedCasesCumulative.html","id":null,"dir":"Reference","previous_headings":"","what":"Get daily confirmed cases for India — GetIndiaConfirmedCasesCumulative","title":"Get daily confirmed cases for India — GetIndiaConfirmedCasesCumulative","text":"Get daily confirmed cases India","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GetIndiaConfirmedCasesCumulative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get daily confirmed cases for India — GetIndiaConfirmedCasesCumulative","text":"","code":"GetIndiaConfirmedCasesCumulative(   url = \"https://saketkc.github.io/covid19bharat_data//csv/latest/state_wise_daily.csv\" )"},{"path":"http://saket-choudhary.me/covmuller/reference/GetIndiaConfirmedCasesCumulative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get daily confirmed cases for India — GetIndiaConfirmedCasesCumulative","text":"url URL fetch data ","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GetIndiaConfirmedCasesCumulative.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get daily confirmed cases for India — 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)"},{"path":"http://saket-choudhary.me/covmuller/reference/GetIndiaConfirmedCasesMonthlyLong.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get India monthwose cases long — GetIndiaConfirmedCasesMonthlyLong","text":"url URL fetch data ","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GetIndiaConfirmedCasesMonthlyLong.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get India monthwose cases long — GetIndiaConfirmedCasesMonthlyLong","text":"data frame containing monthly cases state long form","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GetIndiaConfirmedCasesWeeklyLong.html","id":null,"dir":"Reference","previous_headings":"","what":"Get India weekwise cases long — GetIndiaConfirmedCasesWeeklyLong","title":"Get India weekwise cases long — GetIndiaConfirmedCasesWeeklyLong","text":"Get India weekwise cases long","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GetIndiaConfirmedCasesWeeklyLong.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get India weekwise cases long — GetIndiaConfirmedCasesWeeklyLong","text":"","code":"GetIndiaConfirmedCasesWeeklyLong(   url = \"https://saketkc.github.io/covid19bharat_data//csv/latest/state_wise_daily.csv\",   level = \"State\" )"},{"path":"http://saket-choudhary.me/covmuller/reference/GetIndiaConfirmedCasesWeeklyLong.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get India weekwise cases long — GetIndiaConfirmedCasesWeeklyLong","text":"url URL fetch data ","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GetIndiaConfirmedCasesWeeklyLong.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get India weekwise cases long — GetIndiaConfirmedCasesWeeklyLong","text":"data frame containing monthly cases state long form","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GetIndiaDailyCasesCumulative.html","id":null,"dir":"Reference","previous_headings":"","what":"Get daily cases for India for a given status type — GetIndiaDailyCasesCumulative","title":"Get daily cases for India for a given status type — GetIndiaDailyCasesCumulative","text":"Get daily cases India given status type","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GetIndiaDailyCasesCumulative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get daily cases for India for a given status type — GetIndiaDailyCasesCumulative","text":"","code":"GetIndiaDailyCasesCumulative(   url = \"https://saketkc.github.io/covid19bharat_data//csv/latest/state_wise_daily.csv\",   status = \"Confirmed\" )"},{"path":"http://saket-choudhary.me/covmuller/reference/GetIndiaDailyCasesCumulative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get daily cases for India for a given status type — GetIndiaDailyCasesCumulative","text":"url URL fetch data status One \"Confirmed\", \"Recovered\", \"Deceased\"","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GetIndiaDailyCasesCumulative.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get daily cases for India for a given status type — GetIndiaDailyCasesCumulative","text":"data frame containing daily cases state","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GetIndiaDailyData.html","id":null,"dir":"Reference","previous_headings":"","what":"Get daily statewise data for India — GetIndiaDailyData","title":"Get daily statewise data for India — GetIndiaDailyData","text":"'Status' column one 'Confirmed', 'Deceased' 'Recovered'","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GetIndiaDailyData.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get daily statewise data for India — GetIndiaDailyData","text":"","code":"GetIndiaDailyData(   url = \"https://saketkc.github.io/covid19bharat_data//csv/latest/state_wise_daily.csv\" )"},{"path":"http://saket-choudhary.me/covmuller/reference/GetIndiaDailyData.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get daily statewise data for India — GetIndiaDailyData","text":"url URL fetch data ","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GetIndiaDailyData.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get daily statewise data for India — GetIndiaDailyData","text":"data frame containing daily cases state","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GetIndiaDeceasedCasesCumulative.html","id":null,"dir":"Reference","previous_headings":"","what":"Get daily deceased cases for India — GetIndiaDeceasedCasesCumulative","title":"Get daily deceased cases for India — GetIndiaDeceasedCasesCumulative","text":"Get daily deceased cases India","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GetIndiaDeceasedCasesCumulative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get daily deceased cases for India — GetIndiaDeceasedCasesCumulative","text":"","code":"GetIndiaDeceasedCasesCumulative(   url = \"https://saketkc.github.io/covid19bharat_data//csv/latest/state_wise_daily.csv\" )"},{"path":"http://saket-choudhary.me/covmuller/reference/GetIndiaDeceasedCasesCumulative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get daily deceased cases for India — 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\"\")"},{"path":"http://saket-choudhary.me/covmuller/reference/GetIndianStates.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a list of Indian name states — GetIndianStates","title":"Get a list of Indian name states — GetIndianStates","text":"Get list Indian name states","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GetIndianStates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a list of Indian name states — GetIndianStates","text":"","code":"GetIndianStates()"},{"path":"http://saket-choudhary.me/covmuller/reference/GetIndianStates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a list of Indian name states — GetIndianStates","text":"list state abbreviation key full name value","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GetMonthYear.html","id":null,"dir":"Reference","previous_headings":"","what":"Get MonthYear (zoo) from a dataframe string formatted data column — GetMonthYear","title":"Get MonthYear (zoo) from a dataframe string formatted data column — GetMonthYear","text":"Get MonthYear (zoo) dataframe string formatted data column","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GetMonthYear.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get MonthYear (zoo) from a dataframe string formatted data column — GetMonthYear","text":"","code":"GetMonthYear(datecol, datefmt = \"%Y-%m-%d\")"},{"path":"http://saket-choudhary.me/covmuller/reference/GetMonthYear.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get MonthYear (zoo) from a dataframe string formatted data column — GetMonthYear","text":"datecol vector string formatted dates datefmt Format date (default Y-m-d)","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GetMonthYear.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get MonthYear (zoo) from a dataframe string formatted data column — GetMonthYear","text":"vector dates converted MonthYear format (zoo::.yearmon)","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GetVOCs.html","id":null,"dir":"Reference","previous_headings":"","what":"Get variants of concern — GetVOCs","title":"Get variants of concern — GetVOCs","text":"Get variants concern","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/GetVOCs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get variants of concern — GetVOCs","text":"","code":"GetVOCs()"},{"path":"http://saket-choudhary.me/covmuller/reference/GetVOCs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get variants of concern — GetVOCs","text":"list variants concern pangolin lineage","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/PlotSequencedPropHeatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot heatmap of cases sequenced — PlotSequencedPropHeatmap","title":"Plot heatmap of cases sequenced — PlotSequencedPropHeatmap","text":"Plot heatmap cases sequenced","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/PlotSequencedPropHeatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot heatmap of cases sequenced — PlotSequencedPropHeatmap","text":"","code":"PlotSequencedPropHeatmap(df)"},{"path":"http://saket-choudhary.me/covmuller/reference/PlotSequencedPropHeatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot heatmap of cases sequenced — PlotSequencedPropHeatmap","text":"cases_and_shared dataframe containing counts confirmed cases sequenced cases","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/PlotSequencedPropHeatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot heatmap of cases sequenced — PlotSequencedPropHeatmap","text":"heatmap total percentage cases sequenced","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/PlotTotalHeatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot heatmap of total cases/sequences — PlotTotalHeatmap","title":"Plot heatmap of total cases/sequences — PlotTotalHeatmap","text":"Plot heatmap total cases/sequences","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/PlotTotalHeatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot heatmap of total cases/sequences — PlotTotalHeatmap","text":"","code":"PlotTotalHeatmap(df, color_legend = \"Total cases\")"},{"path":"http://saket-choudhary.me/covmuller/reference/PlotTotalHeatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot heatmap of total cases/sequences — PlotTotalHeatmap","text":"cases_and_shared dataframe containing counts confirmed cases sequenced cases (column named 'value')","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/PlotTotalHeatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot heatmap of total cases/sequences — PlotTotalHeatmap","text":"heatmap total cases/sequences","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/ReadAuspiceInstrument.html","id":null,"dir":"Reference","previous_headings":"","what":"Read instrument metadata from GISAID batch downloads — ReadAuspiceInstrument","title":"Read instrument metadata from GISAID batch downloads — ReadAuspiceInstrument","text":"Read instrument metadata GISAID batch downloads","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/ReadAuspiceInstrument.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read instrument metadata from GISAID batch downloads — ReadAuspiceInstrument","text":"","code":"ReadAuspiceInstrument(path)"},{"path":"http://saket-choudhary.me/covmuller/reference/ReadAuspiceInstrument.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read instrument metadata from GISAID batch downloads — ReadAuspiceInstrument","text":"path Path list directories \"sequence.tsv\" inside subdirector","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/ReadAuspiceInstrument.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read instrument metadata from GISAID batch downloads — ReadAuspiceInstrument","text":"dataframe instrument related metadata","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/ReadAuspiceMetadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Read all metadata from GISAID batch downloads — ReadAuspiceMetadata","title":"Read all metadata from GISAID batch downloads — ReadAuspiceMetadata","text":"Read metadata GISAID batch downloads","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/ReadAuspiceMetadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read all metadata from GISAID batch downloads — ReadAuspiceMetadata","text":"","code":"ReadAuspiceMetadata(path)"},{"path":"http://saket-choudhary.me/covmuller/reference/ReadAuspiceMetadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read all metadata from GISAID batch downloads — ReadAuspiceMetadata","text":"path Path list directories \"sequence.tsv\" inside subdirector","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/ReadAuspiceMetadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read all metadata from GISAID batch downloads — ReadAuspiceMetadata","text":"dataframe collated metadata","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/ReadGISAIDMetada.html","id":null,"dir":"Reference","previous_headings":"","what":"Read metadata downloaded from GISAID — ReadGISAIDMetada","title":"Read metadata downloaded from GISAID — ReadGISAIDMetada","text":"Read metadata downloaded GISAID","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/ReadGISAIDMetada.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read metadata downloaded from GISAID — ReadGISAIDMetada","text":"","code":"ReadGISAIDMetada(path, showProgress = FALSE, ...)"},{"path":"http://saket-choudhary.me/covmuller/reference/ReadGISAIDMetada.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read metadata downloaded from GISAID — ReadGISAIDMetada","text":"path Path tar.xz metadata.txt downloaded GISAID showProgress Whether print progress bars fread ... parameters data.table::fread","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/ReadGISAIDMetada.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read metadata downloaded from GISAID — ReadGISAIDMetada","text":"data frame parsed metadata extra columns","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/SanitizeColumn.html","id":null,"dir":"Reference","previous_headings":"","what":"Sanitize a vector to remove special characters and convert it to tile — SanitizeColumn","title":"Sanitize a vector to remove special characters and convert it to tile — SanitizeColumn","text":"function removes: 1. Commas space 2. Hyphen space 3. Multiple dots single space 4. Multiple spacies single space 5. NA \"UNKNOWN\"","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/SanitizeColumn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sanitize a vector to remove special characters and convert it to tile — SanitizeColumn","text":"","code":"SanitizeColumn(col)"},{"path":"http://saket-choudhary.me/covmuller/reference/SanitizeColumn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sanitize a vector to remove special characters and convert it to tile — SanitizeColumn","text":"col String vector","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/SanitizeColumn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sanitize a vector to remove special characters and convert it to tile — SanitizeColumn","text":"string vector sanitized entries","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/SummarizeVariantsDatewise.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the total number of variants per week — SummarizeVariantsDatewise","title":"Summarize the total number of variants per week — SummarizeVariantsDatewise","text":"Summarize total number variants per week","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/SummarizeVariantsDatewise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the total number of variants per week — SummarizeVariantsDatewise","text":"","code":"SummarizeVariantsDatewise(variant_df, by_state = FALSE)"},{"path":"http://saket-choudhary.me/covmuller/reference/SummarizeVariantsDatewise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the total number of variants per week — SummarizeVariantsDatewise","text":"variant_df dataframe","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/SummarizeVariantsDatewise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the total number of variants per week — SummarizeVariantsDatewise","text":"dataframe monthwise counts variant sequenced","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/SummarizeVariantsMonthwise.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the total number of variants per month — SummarizeVariantsMonthwise","title":"Summarize the total number of variants per month — SummarizeVariantsMonthwise","text":"Summarize total number variants per month","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/SummarizeVariantsMonthwise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the total number of variants per month — SummarizeVariantsMonthwise","text":"","code":"SummarizeVariantsMonthwise(variant_df, by_state = FALSE)"},{"path":"http://saket-choudhary.me/covmuller/reference/SummarizeVariantsMonthwise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the total number of variants per month — SummarizeVariantsMonthwise","text":"variant_df dataframe","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/SummarizeVariantsMonthwise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the total number of variants per month — SummarizeVariantsMonthwise","text":"dataframe monthwise counts variant sequenced","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/SummarizeVariantsWeekwise.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the total number of variants per week — SummarizeVariantsWeekwise","title":"Summarize the total number of variants per week — SummarizeVariantsWeekwise","text":"Summarize total number variants per week","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/SummarizeVariantsWeekwise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the total number of variants per week — SummarizeVariantsWeekwise","text":"","code":"SummarizeVariantsWeekwise(variant_df, by_state = FALSE)"},{"path":"http://saket-choudhary.me/covmuller/reference/SummarizeVariantsWeekwise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the total number of variants per week — SummarizeVariantsWeekwise","text":"variant_df dataframe","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/SummarizeVariantsWeekwise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the total number of variants per week — SummarizeVariantsWeekwise","text":"dataframe monthwise counts variant sequenced","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/TotalSequencesPerMonthCountrywise.html","id":null,"dir":"Reference","previous_headings":"","what":"Get total sequenced samples per country — TotalSequencesPerMonthCountrywise","title":"Get total sequenced samples per country — TotalSequencesPerMonthCountrywise","text":"Get total sequenced samples per country","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/TotalSequencesPerMonthCountrywise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get total sequenced samples per country — TotalSequencesPerMonthCountrywise","text":"","code":"TotalSequencesPerMonthCountrywise(variant_df, rename_country_as_state = TRUE)"},{"path":"http://saket-choudhary.me/covmuller/reference/TotalSequencesPerMonthCountrywise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get total sequenced samples per country — TotalSequencesPerMonthCountrywise","text":"variant_df dataframe","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/TotalSequencesPerMonthCountrywise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get total sequenced samples per country — TotalSequencesPerMonthCountrywise","text":"dataframe sequencing statistics per country","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/TotalSequencesPerMonthStatewise.html","id":null,"dir":"Reference","previous_headings":"","what":"Get total number of sequenced samples per month in a Country — TotalSequencesPerMonthStatewise","title":"Get total number of sequenced samples per month in a Country — TotalSequencesPerMonthStatewise","text":"Get total number sequenced samples per month Country","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/TotalSequencesPerMonthStatewise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get total number of sequenced samples per month in a Country — TotalSequencesPerMonthStatewise","text":"","code":"TotalSequencesPerMonthStatewise(variant_df, drop_country = FALSE)"},{"path":"http://saket-choudhary.me/covmuller/reference/TotalSequencesPerMonthStatewise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get total number of sequenced samples per month in a Country — TotalSequencesPerMonthStatewise","text":"dataframe sequencing statistics per state per country","code":""}]
+[{"path":"http://saket-choudhary.me/covmuller/articles/Introduction.html","id":"get-cases-data","dir":"Articles","previous_headings":"","what":"Get cases data","title":"Introduction to covmuller","text":"use covid19bharat.org get tally daily confirmed cases summarize monthly level.","code":"indian_state_cases <- GetIndiaConfirmedCasesMonthlyLong() india_cases <- indian_state_cases %>%   filter(State == \"India\") %>%   filter(value > 1) head(india_cases) #>   MonthYear State   value      type #> 1  Mar 2020 India    1635 Confirmed #> 2  Apr 2020 India   33232 Confirmed #> 3  May 2020 India  155781 Confirmed #> 4  Jun 2020 India  395044 Confirmed #> 5  Jul 2020 India 1111273 Confirmed #> 6  Aug 2020 India 1990350 Confirmed"},{"path":"http://saket-choudhary.me/covmuller/articles/Introduction.html","id":"plot-cases-for-india","dir":"Articles","previous_headings":"Get cases data","what":"Plot cases for India","title":"Introduction to covmuller","text":"easy visualize monthly case counts bar plot:","code":"p1 <- BarPlot(india_cases, ylabel = \"Cases per month\", label_si = TRUE, title = \"Total cases per month - India\", caption = \"**Source: covid19bharat.org**
\") p1"},{"path":"http://saket-choudhary.me/covmuller/articles/Introduction.html","id":"read-variant-data-from-gisaid","dir":"Articles","previous_headings":"","what":"Read variant data from GISAID","title":"Introduction to covmuller","text":"utilize GISAID data look prevalence variants. access data, GISAID requires registration.","code":"current_date <- \"2024_01_18\" fpath.tar <- paste0(\"~/data/epicov/metadata_tsv_\", current_date, \".tar.xz\") fpath.qs <- paste0(\"~/data/epicov/metadata_tsv_\", current_date, \".qs\")  if (file.exists(fpath.qs)) {   gisaid_metadata <- qs::qread(file = fpath.qs) } else {   gisaid_metadata <- ReadGISAIDMetada(path = fpath.tar)   qs::qsave(gisaid_metadata, fpath.qs) }"},{"path":"http://saket-choudhary.me/covmuller/articles/Introduction.html","id":"plot-total-sequenced-cases","dir":"Articles","previous_headings":"Read variant data from GISAID","what":"Plot total sequenced cases","title":"Introduction to covmuller","text":"can look absolute number cases sequenced country filtering information metadata made available GISAID (includes countries). , visualize total sequenced cases coming India:","code":"gisaid_india <- FilterGISAIDIndia(gisaid_metadata_all = gisaid_metadata) country_seq_stats <- TotalSequencesPerMonthCountrywise(gisaid_india, rename_country_as_state = TRUE) p2 <- BarPlot(country_seq_stats, ylabel = \"Sequenced per month\", color = \"slateblue1\", label_si = TRUE, title = \"Total sequences deposited to GISAID from India\", caption = \"**Source: gisaid.org **
\") p2"},{"path":"http://saket-choudhary.me/covmuller/articles/Introduction.html","id":"overall-how-much-has-india-sequenced-over-months","dir":"Articles","previous_headings":"Read variant data from GISAID","what":"Overall, how much has India sequenced over months?","title":"Introduction to covmuller","text":"absolute numbers informative, useful metric proportion cases (cases sequenced total cases) getting sequenced. look proportion cases sequenced India course pandemic:","code":"# india_cases_long <- GetIndiaConfirmedCasesMonthlyLong() %>% filter(State == \"India\") GetIndiaCases <- function() {   data <- read.csv(\"https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv\")   confirmed <- data %>% select(date, India)   colnames(confirmed)[2] <- c(\"cases\")   confirmed$MonthYear <- GetMonthYear(confirmed$date)    confirmed_subset_weekwise <- confirmed %>%     group_by(MonthYear) %>%     summarise(value = sum(cases, na.rm = T)) %>%     arrange(MonthYear) } india_cases_long <- GetIndiaCases() india_cases_long$State <- \"India\" india_cases_long$type <- \"Confirmed\"  india_sequencing_proportion <- CombineSequencedCases(   cases_sequenced = country_seq_stats,   confirmed_long = india_cases_long ) p3 <- BarPlot(india_sequencing_proportion, yaxis = \"percent_sequenced_collected\", ylabel = \"%  deposited to GISAID\", color = \"yellowgreen\", title = \"Proportion of cases deposited to GISAID from India\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\") p3"},{"path":"http://saket-choudhary.me/covmuller/articles/Introduction.html","id":"plot-proportion-of-cases-that-been-deposited-from-each-state","dir":"Articles","previous_headings":"Read variant data from GISAID","what":"Plot proportion of cases that been deposited from each state","title":"Introduction to covmuller","text":"can break proportion sequenced cases state level:","code":"state_seq_stats <- TotalSequencesPerMonthStatewise(gisaid_india, drop_country = TRUE) seq_stats <- rbind(country_seq_stats, state_seq_stats) state_cases_long <- GetIndiaConfirmedCasesMonthlyLong() india_sequencing_proportion <- CombineSequencedCases(   cases_sequenced = seq_stats,   confirmed_long = state_cases_long,   month.min = \"Jan 2022\",   month.max = \"Feb 2023\",   max.percent = 5 )  india_sequencing_proportion$State <- factor(   x = india_sequencing_proportion$State,   levels = as.character(GetIndianStates()) ) p4 <- PlotSequencedPropHeatmap(india_sequencing_proportion) # p4"},{"path":"http://saket-choudhary.me/covmuller/articles/Introduction.html","id":"plot-prevalence","dir":"Articles","previous_headings":"","what":"Plot Prevalence","title":"Introduction to covmuller","text":"Finally, look prevalence variants variants concern (VOCs):  animated version prevalence plot, check VariantAnimation.","code":"india_month_counts <- SummarizeVariantsMonthwise(gisaid_india) india_month_counts$State <- \"India\" india_month_prevalence <- CountsToPrevalence(india_month_counts) vocs <- GetVOCs() custom_voc_mapping <- list(   `JN.1` = \"JN.1\",   `JN.1.*` = \"JN.1\",   `HV.1` = \"HV.1\",   `HV.1.*` = \"HV.1\",   \"B.1\" = \"B.1\",   \"B.1.1.306*\" = \"B.1\",   \"B.1.1.326\" = \"B.1\",   \"B.1.36.29\" =  \"B.1\",   \"B.1.560\" = \"B.1\",   \"B.1.1\" =  \"B.1\",   \"B.1.210\" = \"B.1\",   \"B.1.36.8\" = \"B.1\",   \"B.1.36.\" = \"B.1\" ) india_month_prevalence <- CollapseLineageToVOCs(   variant_df = india_month_prevalence,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping, summarize = FALSE )  p5 <- StackedBarPlotPrevalence(india_month_prevalence) p5"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-Brazil.html","id":"get-variants-data-for-brazil","dir":"Articles","previous_headings":"","what":"Get variants data for Brazil","title":"Animation of projected weekly cases - Brazil","text":"","code":"gisaid_metadata <- qs::qread(\"~/data/epicov/metadata_tsv_2024_01_18.qs\") gisaid_brazil <- gisaid_metadata %>%   filter(Country == \"Brazil\") %>%   filter(Host == \"Human\") # format metadata gisaid_brazil <- FormatGISAIDMetadata(gisaid_brazil) gisaid_brazil <- gisaid_brazil %>%   arrange(State, MonthYearCollected) %>%   filter(pangolin_lineage != \"Unknown\")  vocs <- GetVOCs() custom_voc_mapping <- list(   `B.1` = \"B.1\",   `JN.1` = \"JN.1\",   `JN.1.*` = \"JN.1\",   `HV.1` = \"HV.1\",   `HV.1.*` = \"HV.1\" ) gisaid_brazil <- gisaid_brazil %>% filter(pangolin_lineage != \"None\")  gisaid_brazil <- CollapseLineageToVOCs(   variant_df = gisaid_brazil,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping,   summarize = FALSE )"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-Brazil.html","id":"get-weekly-cases-for-brazil","dir":"Articles","previous_headings":"","what":"Get weekly cases for Brazil","title":"Animation of projected weekly cases - Brazil","text":"","code":"GetCases <- function() {   data <- read.csv(\"https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv\")   confirmed <- data %>% select(date, Brazil)   colnames(confirmed)[2] <- c(\"cases\")   confirmed$MonthYear <- GetMonthYear(confirmed$date)   confirmed$WeekYear <- tsibble::yearweek(confirmed$date)   return(confirmed) }   GetCasesLong <- function() {   data <- read.csv(\"https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv\")   confirmed <- data %>% select(date, Brazil)   colnames(confirmed)[2] <- c(\"cases\")   confirmed$MonthYear <- GetMonthYear(confirmed$date)   confirmed$WeekYear <- tsibble::yearweek(confirmed$date)   confirmed_subset_weekwise <- confirmed %>%     group_by(WeekYear) %>%     summarise(cases = mean(cases, na.rm = T)) %>%     arrange(WeekYear)   confirmed_subset_weekwise$cases <- ceiling(confirmed_subset_weekwise$cases)   confirmed_subset_dateweekwise_long_india <- confirmed_subset_weekwise %>%     rename(n = cases) %>%     rename(WeekYearCollected = WeekYear) }   confirmed <- GetCases() confirmed_subset_dateweekwise_long <- GetCasesLong() gisaid_brazil_weekwise <- SummarizeVariantsWeekwise(gisaid_brazil)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-Brazil.html","id":"distribution-of-variants","dir":"Articles","previous_headings":"","what":"Distribution of variants","title":"Animation of projected weekly cases - Brazil","text":"","code":"state_month_counts <- SummarizeVariantsMonthwise(gisaid_brazil) state_month_counts$State <- \"Brazil\" state_month_prevalence <- CountsToPrevalence(state_month_counts) vocs <- GetVOCs()  state_month_prevalence <- CollapseLineageToVOCs(   variant_df = state_month_prevalence,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping, summarize = FALSE )  p5 <- StackedBarPlotPrevalence(state_month_prevalence) p5"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-Brazil.html","id":"project-weekly-cases-to-variant-prevalence-data-from-gisaid","dir":"Articles","previous_headings":"","what":"Project weekly cases to variant prevalence data from GISAID","title":"Animation of projected weekly cases - Brazil","text":"Look cases 2022, Look cases 2023  Look cases past weeks","code":"voc_to_keep <- gisaid_brazil_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_brazil_weekwise <- gisaid_brazil_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  brazil_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_brazil_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(brazil_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in Brazil by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"100 days\") gganimate::anim_save(filename = here::here(\"docs/articles/Brazil_animated.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2021 W35\"))  gisaid_brazil_subset <- gisaid_brazil %>% filter(MonthYearCollected > \"Oct 2021\") gisaid_brazil_weekwise <- SummarizeVariantsWeekwise(gisaid_brazil_subset)  voc_to_keep <- gisaid_brazil_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_brazil_weekwise <- gisaid_brazil_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  brazil_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_brazil_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(brazil_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in Brazil by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"100 days\") gganimate::anim_save(filename = here::here(\"docs/articles/Brazil_animated_2021.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2022 W35\"))  gisaid_brazil_subset <- gisaid_brazil %>% filter(MonthYearCollected > \"October 2022\") gisaid_brazil_weekwise <- SummarizeVariantsWeekwise(gisaid_brazil_subset)  voc_to_keep <- gisaid_brazil_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_brazil_weekwise <- gisaid_brazil_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  brazil_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_brazil_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(brazil_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in Brazil by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\") gganimate::anim_save(filename = here::here(\"docs/articles/Brazil_animated_2023.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2023 W23\"))  gisaid_brazil_subset <- gisaid_brazil %>% filter(MonthYearCollected > \"June 2023\") gisaid_brazil_weekwise <- SummarizeVariantsWeekwise(gisaid_brazil_subset)  voc_to_keep <- gisaid_brazil_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_brazil_weekwise <- gisaid_brazil_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  brazil_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_brazil_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(brazil_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in Brazil by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\") gganimate::anim_save(filename = here::here(\"docs/articles/Brazil_animated_2024.gif\"), animation = the_anim)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-Germany.html","id":"get-variants-data-for-germany","dir":"Articles","previous_headings":"","what":"Get variants data for Germany","title":"Animation of projected weekly cases - Germany","text":"","code":"gisaid_metadata <- qs::qread(\"~/data/epicov/metadata_tsv_2024_01_18.qs\") gisaid_germany <- gisaid_metadata %>%   filter(Country == \"Germany\") %>%   filter(Host == \"Human\") # format metadata gisaid_germany <- FormatGISAIDMetadata(gisaid_germany) gisaid_germany <- gisaid_germany %>%   arrange(State, MonthYearCollected) %>%   filter(pangolin_lineage != \"Unknown\")  vocs <- GetVOCs() custom_voc_mapping <- list(   `B.1` = \"B.1\",   `JN.1` = \"JN.1\",   `JN.1.*` = \"JN.1\",   `HV.1` = \"HV.1\",   `HV.1.*` = \"HV.1\" ) gisaid_germany <- gisaid_germany %>% filter(pangolin_lineage != \"None\")  gisaid_germany <- CollapseLineageToVOCs(   variant_df = gisaid_germany,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping,   summarize = FALSE )"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-Germany.html","id":"get-weekly-cases-for-germany","dir":"Articles","previous_headings":"","what":"Get weekly cases for Germany","title":"Animation of projected weekly cases - Germany","text":"","code":"GetCases <- function() {   data <- read.csv(\"https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv\")   confirmed <- data %>% select(date, Germany)   colnames(confirmed)[2] <- c(\"cases\")   confirmed$MonthYear <- GetMonthYear(confirmed$date)   confirmed$WeekYear <- tsibble::yearweek(confirmed$date)   return(confirmed) }   GetCasesLong <- function() {   data <- read.csv(\"https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv\")   confirmed <- data %>% select(date, Germany)   colnames(confirmed)[2] <- c(\"cases\")   confirmed$MonthYear <- GetMonthYear(confirmed$date)   confirmed$WeekYear <- tsibble::yearweek(confirmed$date)   confirmed_subset_weekwise <- confirmed %>%     group_by(WeekYear) %>%     summarise(cases = mean(cases, na.rm = T)) %>%     arrange(WeekYear)   confirmed_subset_weekwise$cases <- ceiling(confirmed_subset_weekwise$cases)   confirmed_subset_dateweekwise_long_india <- confirmed_subset_weekwise %>%     rename(n = cases) %>%     rename(WeekYearCollected = WeekYear) }   confirmed <- GetCases() confirmed_subset_dateweekwise_long <- GetCasesLong() gisaid_germany_weekwise <- SummarizeVariantsWeekwise(gisaid_germany)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-Germany.html","id":"distribution-of-variants","dir":"Articles","previous_headings":"","what":"Distribution of variants","title":"Animation of projected weekly cases - Germany","text":"","code":"state_month_counts <- SummarizeVariantsMonthwise(gisaid_germany) state_month_counts$State <- \"Germany\" state_month_prevalence <- CountsToPrevalence(state_month_counts) vocs <- GetVOCs()  state_month_prevalence <- CollapseLineageToVOCs(   variant_df = state_month_prevalence,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping, summarize = FALSE )  p5 <- StackedBarPlotPrevalence(state_month_prevalence) p5"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-Germany.html","id":"project-weekly-cases-to-variant-prevalence-data-from-gisaid","dir":"Articles","previous_headings":"","what":"Project weekly cases to variant prevalence data from GISAID","title":"Animation of projected weekly cases - Germany","text":"Look cases 2022, Look cases 2023  Look cases past weeks","code":"voc_to_keep <- gisaid_germany_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_germany_weekwise <- gisaid_germany_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  germany_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_germany_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(germany_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in Germany by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"100 days\") gganimate::anim_save(filename = here::here(\"docs/articles/Germany_animated.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2021 W35\"))  gisaid_germany_subset <- gisaid_germany %>% filter(MonthYearCollected > \"Oct 2021\") gisaid_germany_weekwise <- SummarizeVariantsWeekwise(gisaid_germany_subset)  voc_to_keep <- gisaid_germany_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_germany_weekwise <- gisaid_germany_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  germany_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_germany_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(germany_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in Germany by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"100 days\") gganimate::anim_save(filename = here::here(\"docs/articles/Germany_animated_2021.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2022 W35\"))  gisaid_germany_subset <- gisaid_germany %>% filter(MonthYearCollected > \"October 2022\") gisaid_germany_weekwise <- SummarizeVariantsWeekwise(gisaid_germany_subset)  voc_to_keep <- gisaid_germany_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_germany_weekwise <- gisaid_germany_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  germany_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_germany_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(germany_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in Germany by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\") gganimate::anim_save(filename = here::here(\"docs/articles/Germany_animated_2023.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2023 W23\"))  gisaid_germany_subset <- gisaid_germany %>% filter(MonthYearCollected > \"June 2023\") gisaid_germany_weekwise <- SummarizeVariantsWeekwise(gisaid_germany_subset)  voc_to_keep <- gisaid_germany_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_germany_weekwise <- gisaid_germany_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  germany_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_germany_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(germany_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in Germany by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\") gganimate::anim_save(filename = here::here(\"docs/articles/Germany_animated_2024.gif\"), animation = the_anim)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-India.html","id":"get-variants-data-for-india","dir":"Articles","previous_headings":"","what":"Get variants data for India","title":"Animation of projected weekly cases - India","text":"","code":"gisaid_metadata <- qs::qread(\"~/data/epicov/metadata_tsv_2024_01_18.qs\") gisaid_india <- FilterGISAIDIndia(gisaid_metadata_all = gisaid_metadata)  vocs <- GetVOCs() custom_voc_mapping <- list(   `JN.1` = \"JN.1\",   `JN.1.*` = \"JN.1\",   `HV.1` = \"HV.1\",   `HV.1.*` = \"HV.1\",   \"B.1\" = \"B.1\",   \"B.1.1.306*\" = \"B.1\",   \"B.1.1.326\" = \"B.1\",   \"B.1.36.29\" =  \"B.1\",   \"B.1.560\" = \"B.1\",   \"B.1.1\" =  \"B.1\",   \"B.1.210\" = \"B.1\",   \"B.1.36.8\" = \"B.1\",   \"B.1.36.\" = \"B.1\" ) gisaid_india <- gisaid_india %>%   filter(pangolin_lineage != \"None\") %>%   filter(pangolin_lineage != \"Unassigned\")   gisaid_india$District <- stringr::str_to_title(gisaid_india$District) gisaid_india$City <- stringr::str_to_title(gisaid_india$City)  gisaid_india$custom_city <- gisaid_india$City gisaid_india$custom_city[gisaid_india$custom_city == \"\"] <- gisaid_india$District[gisaid_india$custom_city == \"\"]  gisaid_india$custom_city <- stringr::str_to_title(gisaid_india$custom_city)  gisaid_india <- CollapseLineageToVOCs(   variant_df = gisaid_india,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping,   summarize = FALSE ) gisaid_india_all <- gisaid_india"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-India.html","id":"distribution-of-variants","dir":"Articles","previous_headings":"","what":"Distribution of variants","title":"Animation of projected weekly cases - India","text":"","code":"state_month_counts <- SummarizeVariantsMonthwise(gisaid_india) state_month_counts$State <- \"India\" state_month_prevalence <- CountsToPrevalence(state_month_counts) state_month_prevalence <- CollapseLineageToVOCs(   variant_df = state_month_prevalence,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping, summarize = FALSE )  p5 <- StackedBarPlotPrevalence(state_month_prevalence) p5"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-India.html","id":"get-weekly-cases-for-india","dir":"Articles","previous_headings":"","what":"Get weekly cases for India","title":"Animation of projected weekly cases - India","text":"","code":"GetIndiaCases <- function() {   data <- read.csv(\"https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv\")   confirmed <- data %>% select(date, India)   colnames(confirmed)[2] <- c(\"cases\")   confirmed$MonthYear <- GetMonthYear(confirmed$date)   confirmed$WeekYear <- tsibble::yearweek(confirmed$date)   confirmed_subset_weekwise <- confirmed %>%     group_by(WeekYear) %>%     summarise(cases = mean(cases, na.rm = T)) %>%     arrange(WeekYear)   confirmed_subset_weekwise$cases <- ceiling(confirmed_subset_weekwise$cases)   confirmed_subset_dateweekwise_long_india <- confirmed_subset_weekwise %>%     rename(n = cases) %>%     rename(WeekYearCollected = WeekYear) }   confirmed_subset_dateweekwise_long_india <- GetIndiaCases() gisaid_india_weekwise <- SummarizeVariantsWeekwise(gisaid_india)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-India.html","id":"project-weekly-cases-to-variant-prevalence-data-from-gisaid","dir":"Articles","previous_headings":"","what":"Project weekly cases to variant prevalence data from GISAID","title":"Animation of projected weekly cases - India","text":"Look cases January, 2022 :  Look cases last months:  Look cases last months:","code":"voc_to_keep <- gisaid_india_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 10) %>%   pull(lineage_collapsed) %>%   unique() gisaid_india_weekwise <- gisaid_india_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  india_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_india_weekwise, confirmed_subset_dateweekwise_long_india) the_anim <- PlotVariantPrevalenceAnimated(india_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in India by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**\", date_breaks = \"120 days\") gganimate::anim_save(filename = here::here(\"docs/articles/IN_animated.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long_india <- GetIndiaCases() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2021 W35\"))  gisaid_india <- gisaid_india %>% filter(MonthYearCollected > \"Dec 2021\") gisaid_weekwise <- SummarizeVariantsWeekwise(gisaid_india)  voc_to_keep <- gisaid_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 10) %>%   pull(lineage_collapsed) %>%   unique() gisaid_weekwise <- gisaid_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_weekwise, confirmed_subset_dateweekwise_long_india) the_anim <- PlotVariantPrevalenceAnimated(cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in India by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"90 days\") # , trans_y=\"log10\") gganimate::anim_save(filename = here::here(\"docs/articles/IN_animated_2021.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long_india <- GetIndiaCases() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2022 W12\"))  gisaid_india <- gisaid_india %>% filter(MonthYearCollected >= \"Mar 2022\") gisaid_weekwise <- SummarizeVariantsWeekwise(gisaid_india)  voc_to_keep <- gisaid_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 10) %>%   pull(lineage_collapsed) %>%   unique() gisaid_weekwise <- gisaid_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_weekwise, confirmed_subset_dateweekwise_long_india) the_anim <- PlotVariantPrevalenceAnimated(cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in India by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"90 days\") gganimate::anim_save(filename = here::here(\"docs/articles/IN_animated_2022.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long_india <- GetIndiaCases() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2022 W35\"))  gisaid_india <- gisaid_india %>% filter(MonthYearCollected >= \"Dec 2022\") gisaid_weekwise <- SummarizeVariantsWeekwise(gisaid_india)  voc_to_keep <- gisaid_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 10) %>%   pull(lineage_collapsed) %>%   unique() gisaid_weekwise <- gisaid_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_weekwise, confirmed_subset_dateweekwise_long_india) the_anim <- PlotVariantPrevalenceAnimated(cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in India by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"30 days\") gganimate::anim_save(filename = here::here(\"docs/articles/IN_animated_latest.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long_india <- GetIndiaCases() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2023 W23\"))  gisaid_india <- gisaid_india %>% filter(MonthYearCollected >= \"June 2023\") gisaid_weekwise <- SummarizeVariantsWeekwise(gisaid_india)  voc_to_keep <- gisaid_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 10) %>%   pull(lineage_collapsed) %>%   unique() gisaid_weekwise <- gisaid_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_weekwise, confirmed_subset_dateweekwise_long_india) the_anim <- PlotVariantPrevalenceAnimated(cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in India by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"30 days\") gganimate::anim_save(filename = here::here(\"docs/articles/IN_animated_2023.gif\"), animation = the_anim)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-India.html","id":"jn-1-variant","dir":"Articles","previous_headings":"Project weekly cases to variant prevalence data from GISAID","what":"JN.1 variant","title":"Animation of projected weekly cases - India","text":"many cases JN.1 variant deposited GISAID across states?","code":"jn.1 <- gisaid_india_all %>% filter(lineage_collapsed %in% c(\"JN.1\")) jn.1.grouped <- jn.1 %>%   group_by(State) %>%   tally() jn.1.grouped <- jn.1.grouped %>%   filter(!State %in% c(\"Unknown\", \"Unassigned\")) %>%   arrange(desc(n)) jn.1.grouped$State <- factor(jn.1.grouped$State, levels = jn.1.grouped$State) ggplot(jn.1.grouped, aes(State, n, label = n)) + # , color= \"#4682b4\"   geom_col(position = \"identity\", fill = \"#4682b4\") +   geom_bar_text(stat = \"identity\") +   xlab(\"\") +   ylab(\"Number of JN.1 samples deposited\") +   scale_x_discrete(guide = guide_axis(angle = 90)) +   labs(     title = \"Number of JN.1 samples deposited to GISAID from India\",     caption = paste0(\"**Source: gisaid.org** 
\", Sys.Date())   )"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-NYC.html","id":"get-variants-data-for-ny-state","dir":"Articles","previous_headings":"","what":"Get variants data for NY state","title":"Animation of projected weekly cases - New York City","text":"","code":"counties <- c(\"New York\", \"New York City\", \"New York County\", \"Queens County\", \"Bronx County\", \"Bronx\", \"Brooklyn\", \"Manhattan\", \"Queens\", \"Richmond County\")  gisaid_metadata <- qs::qread(\"~/data/epicov/metadata_tsv_2024_01_18.qs\") gisaid_usa <- gisaid_metadata %>%   filter(Country == \"USA\") %>%   filter(Host == \"Human\") # format metadata gisaid_usa <- FormatGISAIDMetadata(gisaid_usa) gisaid_usa <- gisaid_usa %>%   arrange(State, MonthYearCollected) %>%   filter(pangolin_lineage != \"Unknown\") gisaid_NY <- gisaid_usa %>% filter(State == \"New York\")   gisaid_NYC <- gisaid_NY %>% filter(District %in% counties)  vocs <- GetVOCs() custom_voc_mapping <- list(   `JN.1` = \"JN.1\",   `JN.1.*` = \"JN.1\",   `HV.1` = \"HV.1\",   `HV.1.*` = \"HV.1\" )  gisaid_NYC <- gisaid_NYC %>% filter(pangolin_lineage != \"None\")  gisaid_NYC <- CollapseLineageToVOCs(   variant_df = gisaid_NYC,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping,   summarize = FALSE )"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-NYC.html","id":"get-weekly-cases-for-new-york","dir":"Articles","previous_headings":"","what":"Get weekly cases for New York","title":"Animation of projected weekly cases - New York City","text":"","code":"confirmed <- read_csv(\"https://raw.githubusercontent.com/nychealth/coronavirus-data/master/trends/data-by-day.csv\") %>% select(date_of_interest, CASE_COUNT) colnames(confirmed) <- c(\"date\", \"daily_cases\") confirmed$WeekYear <- tsibble::yearweek(confirmed$date) confirmed$MonthYear <- GetMonthYear(confirmed$date, datefmt = \"%m/%d/%Y\")  confirmed_subset_dateweekwise_long <- confirmed %>%   group_by(WeekYear) %>%   summarise(n = ceiling(mean(daily_cases, na.rm = T))) %>%   arrange(WeekYear) %>%   rename(WeekYearCollected = WeekYear)   gisaid_NYC_weekwise <- SummarizeVariantsWeekwise(gisaid_NYC)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-NYC.html","id":"distribution-of-variants","dir":"Articles","previous_headings":"","what":"Distribution of variants","title":"Animation of projected weekly cases - New York City","text":"","code":"state_month_counts <- SummarizeVariantsMonthwise(gisaid_NYC) state_month_counts$State <- \"NYC\" state_month_prevalence <- CountsToPrevalence(state_month_counts)   state_month_prevalence <- CollapseLineageToVOCs(   variant_df = state_month_prevalence,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping, summarize = FALSE )  p5 <- StackedBarPlotPrevalence(state_month_prevalence) p5"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-NYC.html","id":"project-weekly-cases-to-variant-prevalence-data-from-gisaid","dir":"Articles","previous_headings":"","what":"Project weekly cases to variant prevalence data from GISAID","title":"Animation of projected weekly cases - New York City","text":"Look cases 2023:","code":"voc_to_keep <- gisaid_NYC_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_NYC_weekwise <- gisaid_NYC_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  newyork_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_NYC_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(newyork_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in New York City by variant\", caption = \"**Source: gisaid.org and NYC Health**
\", date_breaks = \"120 days\") gganimate::anim_save(filename = here::here(\"docs/articles/NYC_animated.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- confirmed %>%   filter(MonthYear > \"April 2023\") %>%   group_by(WeekYear) %>%   summarise(n = ceiling(mean(daily_cases, na.rm = T))) %>%   arrange(WeekYear) %>%   rename(WeekYearCollected = WeekYear)  gisaid_NYC_subset <- gisaid_NYC %>% filter(MonthYearCollected > \"April 2023\") gisaid_weekwise <- SummarizeVariantsWeekwise(gisaid_NYC_subset)  voc_to_keep <- gisaid_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 1) %>%   pull(lineage_collapsed) %>%   unique() gisaid_weekwise <- gisaid_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in New York City by variant\", caption = \"**Source: gisaid.org and NYC Health**
\", date_breaks = \"30 days\") ## `geom_line()`: Each group consists of only one observation. ## ℹ Do you need to adjust the group aesthetic? ## `geom_line()`: Each group consists of only one observation. ## ℹ Do you need to adjust the group aesthetic? gganimate::anim_save(filename = here::here(\"docs/articles/NYC_animated_2023.gif\"), animation = the_anim)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-NewYork.html","id":"get-variants-data-for-ny-state","dir":"Articles","previous_headings":"","what":"Get variants data for NY state","title":"Animation of projected weekly cases - New York State","text":"","code":"gisaid_metadata <- qs::qread(\"~/data/epicov/metadata_tsv_2024_01_18.qs\") gisaid_usa <- gisaid_metadata %>%   filter(Country == \"USA\") %>%   filter(Host == \"Human\") # format metadata gisaid_usa <- FormatGISAIDMetadata(gisaid_usa) gisaid_usa <- gisaid_usa %>%   arrange(State, MonthYearCollected) %>%   filter(pangolin_lineage != \"Unknown\") gisaid_NY <- gisaid_usa %>% filter(State == \"New York\")  vocs <- GetVOCs() custom_voc_mapping <- list(   `JN.1` = \"JN.1\",   `JN.1.*` = \"JN.1\",   `HV.1` = \"HV.1\",   `HV.1.*` = \"HV.1\" ) gisaid_NY <- gisaid_NY %>% filter(pangolin_lineage != \"None\") gisaid_NY <- CollapseLineageToVOCs(   variant_df = gisaid_NY,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping,   summarize = FALSE )"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-NewYork.html","id":"get-weekly-cases-for-new-york","dir":"Articles","previous_headings":"","what":"Get weekly cases for New York","title":"Animation of projected weekly cases - New York State","text":"","code":"confirmed <- covid19(country = \"USA\", level = 2) %>%   filter(administrative_area_level_2 == \"New York\") %>%   select(date, confirmed) %>%   filter(!is.na(confirmed)) #> We have invested a lot of time and effort in creating COVID-19 Data #> Hub, please cite the following when using it: #>  #>   Guidotti, E., Ardia, D., (2020), \"COVID-19 Data Hub\", Journal of Open #>   Source Software 5(51):2376, doi: 10.21105/joss.02376 #>  #> The implementation details and the latest version of the data are #> described in: #>  #>   Guidotti, E., (2022), \"A worldwide epidemiological database for #>   COVID-19 at fine-grained spatial resolution\", Sci Data 9(1):112, doi: #>   10.1038/s41597-022-01245-1 #> To print citations in BibTeX format use: #>  > print(citation('COVID19'), bibtex=TRUE) #>  #> To hide this message use 'verbose = FALSE'.  confirmed$daily_cases <- c(confirmed$confirmed[1], diff(confirmed$confirmed)) confirmed$WeekYear <- tsibble::yearweek(confirmed$date) confirmed$MonthYear <- GetMonthYear(confirmed$date)  confirmed_subset_dateweekwise_long <- confirmed %>%   group_by(WeekYear) %>%   summarise(n = ceiling(mean(daily_cases, na.rm = T))) %>%   arrange(WeekYear) %>%   rename(WeekYearCollected = WeekYear)  gisaid_NY_weekwise <- SummarizeVariantsWeekwise(gisaid_NY)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-NewYork.html","id":"distribution-of-variants","dir":"Articles","previous_headings":"","what":"Distribution of variants","title":"Animation of projected weekly cases - New York State","text":"","code":"state_month_counts <- SummarizeVariantsMonthwise(gisaid_NY) state_month_counts$State <- \"New York\" state_month_prevalence <- CountsToPrevalence(state_month_counts) state_month_prevalence <- CollapseLineageToVOCs(   variant_df = state_month_prevalence,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping, summarize = FALSE )  p5 <- StackedBarPlotPrevalence(state_month_prevalence) p5"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-NewYork.html","id":"project-weekly-cases-to-variant-prevalence-data-from-gisaid","dir":"Articles","previous_headings":"","what":"Project weekly cases to variant prevalence data from GISAID","title":"Animation of projected weekly cases - New York State","text":"Look cases february, 2022 :","code":"voc_to_keep <- gisaid_NY_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_NY_weekwise <- gisaid_NY_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  newyork_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_NY_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(newyork_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in New York state by variant\", caption = \"**Source: gisaid.org and covid19nytimes**
\", date_breaks = \"28 days\") gganimate::anim_save(filename = here::here(\"docs/articles/NY_animated.gif\"), animation = the_anim) confirmed$MonthYear <- GetMonthYear(confirmed$date)  confirmed_subset_dateweekwise_long <- confirmed %>%   filter(MonthYear > \"December 2022\") %>%   group_by(WeekYear) %>%   summarise(n = sum(daily_cases)) %>%   arrange(WeekYear) %>%   rename(WeekYearCollected = WeekYear)  gisaid_NY <- gisaid_NY %>% filter(MonthYearCollected > \"December 2022\") gisaid_weekwise <- SummarizeVariantsWeekwise(gisaid_NY)  voc_to_keep <- gisaid_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_weekwise <- gisaid_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in New York state by variant\", caption = \"**Source: gisaid.org and covid19nytimes**
\") gganimate::anim_save(filename = here::here(\"docs/articles/NY_animated_2023.gif\"), animation = the_anim)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-Singapore.html","id":"get-variants-data-for-singapore","dir":"Articles","previous_headings":"","what":"Get variants data for Singapore","title":"Animation of projected weekly cases - Singapore","text":"","code":"gisaid_metadata <- qs::qread(\"~/data/epicov/metadata_tsv_2024_01_18.qs\") gisaid_singapore <- gisaid_metadata %>%   filter(Country == \"Singapore\") %>%   filter(Host == \"Human\") # format metadata gisaid_singapore <- FormatGISAIDMetadata(gisaid_singapore) gisaid_singapore <- gisaid_singapore %>%   arrange(State, MonthYearCollected) %>%   filter(pangolin_lineage != \"Unknown\")  vocs <- GetVOCs() custom_voc_mapping <- list(   `B.1` = \"B.1\",   `JN.1` = \"JN.1\",   `JN.1.*` = \"JN.1\",   `HV.1` = \"HV.1\",   `HV.1.*` = \"HV.1\" ) gisaid_singapore <- gisaid_singapore %>% filter(pangolin_lineage != \"None\")  gisaid_singapore <- CollapseLineageToVOCs(   variant_df = gisaid_singapore,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping,   summarize = FALSE )"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-Singapore.html","id":"get-weekly-cases-for-singapore","dir":"Articles","previous_headings":"","what":"Get weekly cases for Singapore","title":"Animation of projected weekly cases - Singapore","text":"","code":"GetCases <- function() {   data <- read.csv(\"https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv\")   confirmed <- data %>% select(date, Singapore)   colnames(confirmed)[2] <- c(\"cases\")   confirmed$MonthYear <- GetMonthYear(confirmed$date)   confirmed$WeekYear <- tsibble::yearweek(confirmed$date)   return(confirmed) }   GetCasesLong <- function() {   data <- read.csv(\"https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv\")   confirmed <- data %>% select(date, Singapore)   colnames(confirmed)[2] <- c(\"cases\")   confirmed$MonthYear <- GetMonthYear(confirmed$date)   confirmed$WeekYear <- tsibble::yearweek(confirmed$date)   confirmed_subset_weekwise <- confirmed %>%     group_by(WeekYear) %>%     summarise(cases = mean(cases, na.rm = T)) %>%     arrange(WeekYear)   confirmed_subset_weekwise$cases <- ceiling(confirmed_subset_weekwise$cases)   confirmed_subset_dateweekwise_long_india <- confirmed_subset_weekwise %>%     rename(n = cases) %>%     rename(WeekYearCollected = WeekYear) }   confirmed <- GetCases() confirmed_subset_dateweekwise_long <- GetCasesLong() gisaid_singapore_weekwise <- SummarizeVariantsWeekwise(gisaid_singapore)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-Singapore.html","id":"distribution-of-variants","dir":"Articles","previous_headings":"","what":"Distribution of variants","title":"Animation of projected weekly cases - Singapore","text":"","code":"state_month_counts <- SummarizeVariantsMonthwise(gisaid_singapore) state_month_counts$State <- \"Singapore\" state_month_prevalence <- CountsToPrevalence(state_month_counts) vocs <- GetVOCs()  state_month_prevalence <- CollapseLineageToVOCs(   variant_df = state_month_prevalence,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping, summarize = FALSE )  p5 <- StackedBarPlotPrevalence(state_month_prevalence) p5"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-Singapore.html","id":"project-weekly-cases-to-variant-prevalence-data-from-gisaid","dir":"Articles","previous_headings":"","what":"Project weekly cases to variant prevalence data from GISAID","title":"Animation of projected weekly cases - Singapore","text":"Look cases 2022, Look cases 2023  Look cases past weeks","code":"voc_to_keep <- gisaid_singapore_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_singapore_weekwise <- gisaid_singapore_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  singapore_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_singapore_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(singapore_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in Singapore by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"100 days\") gganimate::anim_save(filename = here::here(\"docs/articles/Singapore_animated.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2021 W35\"))  gisaid_singapore_subset <- gisaid_singapore %>% filter(MonthYearCollected > \"Oct 2021\") gisaid_singapore_weekwise <- SummarizeVariantsWeekwise(gisaid_singapore_subset)  voc_to_keep <- gisaid_singapore_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_singapore_weekwise <- gisaid_singapore_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  singapore_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_singapore_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(singapore_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in Singapore by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"100 days\") gganimate::anim_save(filename = here::here(\"docs/articles/Singapore_animated_2021.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2022 W35\"))  gisaid_singapore_subset <- gisaid_singapore %>% filter(MonthYearCollected > \"October 2022\") gisaid_singapore_weekwise <- SummarizeVariantsWeekwise(gisaid_singapore_subset)  voc_to_keep <- gisaid_singapore_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_singapore_weekwise <- gisaid_singapore_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  singapore_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_singapore_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(singapore_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in Singapore by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\") gganimate::anim_save(filename = here::here(\"docs/articles/Singapore_animated_2023.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2023 W23\"))  gisaid_singapore_subset <- gisaid_singapore %>% filter(MonthYearCollected > \"June 2023\") gisaid_singapore_weekwise <- SummarizeVariantsWeekwise(gisaid_singapore_subset)  voc_to_keep <- gisaid_singapore_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_singapore_weekwise <- gisaid_singapore_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  singapore_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_singapore_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(singapore_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in Singapore by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\") gganimate::anim_save(filename = here::here(\"docs/articles/Singapore_animated_2024.gif\"), animation = the_anim)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-UK.html","id":"get-variants-data-for-united-kingdom","dir":"Articles","previous_headings":"","what":"Get variants data for United Kingdom","title":"Animation of projected weekly cases - United Kingdom","text":"","code":"gisaid_metadata <- qs::qread(\"~/data/epicov/metadata_tsv_2024_01_18.qs\") gisaid_United_Kingdom <- gisaid_metadata %>%   filter(Country == \"United Kingdom\") %>%   filter(Host == \"Human\") # format metadata gisaid_United_Kingdom <- FormatGISAIDMetadata(gisaid_United_Kingdom) gisaid_United_Kingdom <- gisaid_United_Kingdom %>%   arrange(State, MonthYearCollected) %>%   filter(pangolin_lineage != \"Unknown\")  vocs <- GetVOCs() custom_voc_mapping <- list(   `B.1` = \"B.1\",   `JN.1` = \"JN.1\",   `JN.1.*` = \"JN.1\",   `HV.1` = \"HV.1\",   `HV.1.*` = \"HV.1\" ) gisaid_United_Kingdom <- gisaid_United_Kingdom %>% filter(pangolin_lineage != \"None\")  gisaid_United_Kingdom <- CollapseLineageToVOCs(   variant_df = gisaid_United_Kingdom,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping,   summarize = FALSE )"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-UK.html","id":"get-weekly-cases-for-united-kingdom","dir":"Articles","previous_headings":"","what":"Get weekly cases for United Kingdom","title":"Animation of projected weekly cases - United Kingdom","text":"","code":"GetCases <- function() {   data <- read.csv(\"https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv\")   confirmed <- data %>% select(date, United.Kingdom)   colnames(confirmed)[2] <- c(\"cases\")   confirmed$MonthYear <- GetMonthYear(confirmed$date)   confirmed$WeekYear <- tsibble::yearweek(confirmed$date)   return(confirmed) }   GetCasesLong <- function() {   data <- read.csv(\"https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv\")   confirmed <- data %>% select(date, United.Kingdom)   colnames(confirmed)[2] <- c(\"cases\")   confirmed$cases[is.na(confirmed$cases)] <- 0   confirmed$MonthYear <- GetMonthYear(confirmed$date)   confirmed$WeekYear <- tsibble::yearweek(confirmed$date)   confirmed_subset_weekwise <- confirmed %>%     group_by(WeekYear) %>%     summarise(cases = mean(cases, na.rm = T)) %>%     arrange(WeekYear)   confirmed_subset_weekwise$cases <- ceiling(confirmed_subset_weekwise$cases)   confirmed_subset_dateweekwise_long_india <- confirmed_subset_weekwise %>%     rename(n = cases) %>%     rename(WeekYearCollected = WeekYear) }   confirmed <- GetCases() confirmed_subset_dateweekwise_long <- GetCasesLong() gisaid_United_Kingdom_weekwise <- SummarizeVariantsWeekwise(gisaid_United_Kingdom)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-UK.html","id":"distribution-of-variants","dir":"Articles","previous_headings":"","what":"Distribution of variants","title":"Animation of projected weekly cases - United Kingdom","text":"","code":"state_month_counts <- SummarizeVariantsMonthwise(gisaid_United_Kingdom) state_month_counts$State <- \"United Kingdom\" state_month_prevalence <- CountsToPrevalence(state_month_counts) vocs <- GetVOCs()  state_month_prevalence <- CollapseLineageToVOCs(   variant_df = state_month_prevalence,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping, summarize = FALSE )  p5 <- StackedBarPlotPrevalence(state_month_prevalence) p5"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-UK.html","id":"project-weekly-cases-to-variant-prevalence-data-from-gisaid","dir":"Articles","previous_headings":"","what":"Project weekly cases to variant prevalence data from GISAID","title":"Animation of projected weekly cases - United Kingdom","text":"Look cases 2022, Look cases 2023  Look cases past weeks","code":"voc_to_keep <- gisaid_United_Kingdom_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_United_Kingdom_weekwise <- gisaid_United_Kingdom_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  United_Kingdom_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_United_Kingdom_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(United_Kingdom_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in United Kingdom by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"120 days\") gganimate::anim_save(filename = here::here(\"docs/articles/United_Kingdom_animated.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2021 W35\"))  gisaid_United_Kingdom_subset <- gisaid_United_Kingdom %>% filter(MonthYearCollected > \"Oct 2021\") gisaid_United_Kingdom_weekwise <- SummarizeVariantsWeekwise(gisaid_United_Kingdom_subset)  voc_to_keep <- gisaid_United_Kingdom_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_United_Kingdom_weekwise <- gisaid_United_Kingdom_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  United_Kingdom_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_United_Kingdom_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(United_Kingdom_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in United Kingdom by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"90 days\") gganimate::anim_save(filename = here::here(\"docs/articles/United_Kingdom_animated_2021.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2022 W35\"))  gisaid_United_Kingdom_subset <- gisaid_United_Kingdom %>% filter(MonthYearCollected > \"October 2022\") gisaid_United_Kingdom_weekwise <- SummarizeVariantsWeekwise(gisaid_United_Kingdom_subset)  voc_to_keep <- gisaid_United_Kingdom_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_United_Kingdom_weekwise <- gisaid_United_Kingdom_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  United_Kingdom_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_United_Kingdom_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(United_Kingdom_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in United Kingdom by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"90 days\") gganimate::anim_save(filename = here::here(\"docs/articles/United_Kingdom_animated_2023.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2023 W23\"))  gisaid_United_Kingdom_subset <- gisaid_United_Kingdom %>% filter(MonthYearCollected > \"June 2023\") gisaid_United_Kingdom_weekwise <- SummarizeVariantsWeekwise(gisaid_United_Kingdom_subset)  voc_to_keep <- gisaid_United_Kingdom_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_United_Kingdom_weekwise <- gisaid_United_Kingdom_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  United_Kingdom_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_United_Kingdom_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(United_Kingdom_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in United Kingdom by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\") gganimate::anim_save(filename = here::here(\"docs/articles/United_Kingdom_animated_2024.gif\"), animation = the_anim)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-USA.html","id":"get-variants-data-for-usa","dir":"Articles","previous_headings":"","what":"Get variants data for USA","title":"Animation of projected weekly cases - USA","text":"","code":"gisaid_metadata <- qs::qread(\"~/data/epicov/metadata_tsv_2024_01_18.qs\") gisaid_usa <- gisaid_metadata %>%   filter(Country == \"USA\") %>%   filter(Host == \"Human\") # format metadata gisaid_usa <- FormatGISAIDMetadata(gisaid_usa) gisaid_usa <- gisaid_usa %>%   arrange(State, MonthYearCollected) %>%   filter(pangolin_lineage != \"Unknown\")  vocs <- GetVOCs() custom_voc_mapping <- list(   `B.1` = \"B.1\",   `JN.1` = \"JN.1\",   `JN.1.*` = \"JN.1\",   `HV.1` = \"HV.1\",   `HV.1.*` = \"HV.1\" ) gisaid_usa <- gisaid_usa %>% filter(pangolin_lineage != \"None\")  gisaid_usa <- CollapseLineageToVOCs(   variant_df = gisaid_usa,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping,   summarize = FALSE )"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-USA.html","id":"get-weekly-cases-for-usa","dir":"Articles","previous_headings":"","what":"Get weekly cases for USA","title":"Animation of projected weekly cases - USA","text":"","code":"GetCases <- function() {   data <- read.csv(\"https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv\")   confirmed <- data %>% select(date, United.States)   colnames(confirmed)[2] <- c(\"cases\")   confirmed$MonthYear <- GetMonthYear(confirmed$date)   confirmed$WeekYear <- tsibble::yearweek(confirmed$date)   return(confirmed) }   GetCasesLong <- function() {   data <- read.csv(\"https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv\")   confirmed <- data %>% select(date, United.States)   colnames(confirmed)[2] <- c(\"cases\")   confirmed$MonthYear <- GetMonthYear(confirmed$date)   confirmed$WeekYear <- tsibble::yearweek(confirmed$date)   confirmed_subset_weekwise <- confirmed %>%     group_by(WeekYear) %>%     summarise(cases = mean(cases, na.rm = T)) %>%     arrange(WeekYear)   confirmed_subset_weekwise$cases <- ceiling(confirmed_subset_weekwise$cases)   confirmed_subset_dateweekwise_long_india <- confirmed_subset_weekwise %>%     rename(n = cases) %>%     rename(WeekYearCollected = WeekYear) }   confirmed <- GetCases() confirmed_subset_dateweekwise_long <- GetCasesLong() gisaid_usa_weekwise <- SummarizeVariantsWeekwise(gisaid_usa)"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-USA.html","id":"distribution-of-variants","dir":"Articles","previous_headings":"","what":"Distribution of variants","title":"Animation of projected weekly cases - USA","text":"","code":"state_month_counts <- SummarizeVariantsMonthwise(gisaid_usa) state_month_counts$State <- \"USA\" state_month_prevalence <- CountsToPrevalence(state_month_counts) vocs <- GetVOCs()  state_month_prevalence <- CollapseLineageToVOCs(   variant_df = state_month_prevalence,   vocs = vocs,   custom_voc_mapping = custom_voc_mapping, summarize = FALSE )  p5 <- StackedBarPlotPrevalence(state_month_prevalence) p5"},{"path":"http://saket-choudhary.me/covmuller/articles/VariantAnimation-USA.html","id":"project-weekly-cases-to-variant-prevalence-data-from-gisaid","dir":"Articles","previous_headings":"","what":"Project weekly cases to variant prevalence data from GISAID","title":"Animation of projected weekly cases - USA","text":"Look cases 2022, Look cases 2023","code":"voc_to_keep <- gisaid_usa_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_usa_weekwise <- gisaid_usa_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  usa_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_usa_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(usa_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in USA by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"120 days\") gganimate::anim_save(filename = here::here(\"docs/articles/USA_animated.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2021 W35\"))  gisaid_usa_subset <- gisaid_usa %>% filter(MonthYearCollected > \"Oct 2021\") gisaid_usa_weekwise <- SummarizeVariantsWeekwise(gisaid_usa_subset)  voc_to_keep <- gisaid_usa_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_usa_weekwise <- gisaid_usa_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  usa_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_usa_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(usa_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in USA by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"120 days\") gganimate::anim_save(filename = here::here(\"docs/articles/USA_animated_2021.gif\"), animation = the_anim) confirmed_subset_dateweekwise_long <- GetCasesLong() %>%   filter(WeekYearCollected >= tsibble::yearweek(\"2022 W35\"))  gisaid_usa_subset <- gisaid_usa %>% filter(MonthYearCollected > \"October 2022\") gisaid_usa_weekwise <- SummarizeVariantsWeekwise(gisaid_usa_subset)  voc_to_keep <- gisaid_usa_weekwise %>%   group_by(lineage_collapsed) %>%   summarise(n_sum = sum(n)) %>%   filter(n_sum > 50) %>%   pull(lineage_collapsed) %>%   unique() gisaid_usa_weekwise <- gisaid_usa_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)  usa_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_usa_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(usa_cases_pred_prob_sel_long, title = \"Estimated cases (weekly average) in USA by variant\", caption = \"**Source: gisaid.org and ourworldindata.org/coronavirus**
\", date_breaks = \"90 days\") gganimate::anim_save(filename = here::here(\"docs/articles/USA_animated_2023.gif\"), animation = the_anim)"},{"path":"http://saket-choudhary.me/covmuller/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Saket Choudhary. Maintainer.","code":""},{"path":"http://saket-choudhary.me/covmuller/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Choudhary S (2024). covmuller: tool model COVID19 variant prevalence using data GISAID. R package version 0.1.2.0005, https://saketkc.github.io/covmuller.","code":"@Manual{,   title = {covmuller: A tool to model COVID19 variant prevalence using data from GISAID},   author = {Saket Choudhary},   year = {2024},   note = {R package version 0.1.2.0005},   url = {https://saketkc.github.io/covmuller}, }"},{"path":"http://saket-choudhary.me/covmuller/index.html","id":"covmuller","dir":"","previous_headings":"","what":"A tool to model COVID19 variant prevalence using data from GISAID","title":"A tool to model COVID19 variant prevalence using data from GISAID","text":"Covmuller R-package designed analysis sequencing metadata deposited GISAID.","code":""},{"path":"http://saket-choudhary.me/covmuller/index.html","id":"features","dir":"","previous_headings":"","what":"Features","title":"A tool to model COVID19 variant prevalence using data from GISAID","text":"Covmuller currently supports following features: Read preprocess metadata file downloaded GISAID: Vignette Plot variant prevalence: Vignette India USA Canada UK Australia SouthAfrica India New York state South Africa United Kingdom USA New York City (USA) Delhi (India) Mumbai (India) Maharashtra (India) Pune (India)","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CleanAmericanStates.html","id":null,"dir":"Reference","previous_headings":"","what":"Clean states from the USA — CleanAmericanStates","title":"Clean states from the USA — CleanAmericanStates","text":"Clean states USA","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CleanAmericanStates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clean states from the USA — CleanAmericanStates","text":"","code":"CleanAmericanStates(states)"},{"path":"http://saket-choudhary.me/covmuller/reference/CleanAmericanStates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clean states from the USA — CleanAmericanStates","text":"state vector state names","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CleanAmericanStates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clean states from the USA — CleanAmericanStates","text":"vector cleaned state names","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CleanCanadaStates.html","id":null,"dir":"Reference","previous_headings":"","what":"Clean states from Canada — CleanCanadaStates","title":"Clean states from Canada — CleanCanadaStates","text":"Clean states Canada","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CleanCanadaStates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clean states from Canada — CleanCanadaStates","text":"","code":"CleanCanadaStates(states)"},{"path":"http://saket-choudhary.me/covmuller/reference/CleanCanadaStates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clean states from Canada — CleanCanadaStates","text":"state vector state names","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CleanCanadaStates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clean states from Canada — CleanCanadaStates","text":"vector cleaned state names","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CleanIndianStates.html","id":null,"dir":"Reference","previous_headings":"","what":"Clean Indian States — CleanIndianStates","title":"Clean Indian States — CleanIndianStates","text":"Clean Indian States","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CleanIndianStates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clean Indian States — CleanIndianStates","text":"","code":"CleanIndianStates(states)"},{"path":"http://saket-choudhary.me/covmuller/reference/CleanIndianStates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clean Indian States — CleanIndianStates","text":"state vector state names","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CleanIndianStates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clean Indian States — CleanIndianStates","text":"vector cleaned state names","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CleanSouthAfricanStates.html","id":null,"dir":"Reference","previous_headings":"","what":"Clean states from South Africa — CleanSouthAfricanStates","title":"Clean states from South Africa — CleanSouthAfricanStates","text":"Clean states South Africa","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CleanSouthAfricanStates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clean states from South Africa — CleanSouthAfricanStates","text":"","code":"CleanSouthAfricanStates(states)"},{"path":"http://saket-choudhary.me/covmuller/reference/CleanSouthAfricanStates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clean states from South Africa — CleanSouthAfricanStates","text":"state vector state names","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CleanSouthAfricanStates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clean states from South Africa — CleanSouthAfricanStates","text":"vector cleaned state names","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CollapseLineageToVOCs.html","id":null,"dir":"Reference","previous_headings":"","what":"Collpase pangolin lineage a list of VOCs — CollapseLineageToVOCs","title":"Collpase pangolin lineage a list of VOCs — CollapseLineageToVOCs","text":"Collpase pangolin lineage list VOCs","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CollapseLineageToVOCs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Collpase pangolin lineage a list of VOCs — CollapseLineageToVOCs","text":"","code":"CollapseLineageToVOCs(   variant_df,   vocs = GetVOCs(),   custom_voc_mapping = NULL,   summarize = TRUE )"},{"path":"http://saket-choudhary.me/covmuller/reference/CollapseLineageToVOCs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Collpase pangolin lineage a list of VOCs — CollapseLineageToVOCs","text":"variant_df dataframe \"pangolin_lineage\" column vocs named list VOC (variant concernt) name key list lineages VOC. method replace lineages VOC name. custom_voc_mapping named vector custom mapping naming lineages. See example.","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CollapseLineageToVOCs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Collpase pangolin lineage a list of VOCs — CollapseLineageToVOCs","text":"dataframe new column \"lineage_collpased\".","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CombineSequencedCases.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a combined dataframe of sequenced cases and confirmed cases — CombineSequencedCases","title":"Create a combined dataframe of sequenced cases and confirmed cases — CombineSequencedCases","text":"Create combined dataframe sequenced cases confirmed cases","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CombineSequencedCases.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a combined dataframe of sequenced cases and confirmed cases — CombineSequencedCases","text":"","code":"CombineSequencedCases(   cases_sequenced,   confirmed_long,   prune_oversequenced = TRUE,   month.min = NULL,   month.max = NULL,   max.percent = 100 )"},{"path":"http://saket-choudhary.me/covmuller/reference/CombineSequencedCases.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a combined dataframe of sequenced cases and confirmed cases — CombineSequencedCases","text":"cases_sequenced long dataframe per state sequenced cases prune_oversequenced Whether round percentages 100 100. Default TRUE cases_total long dataframe total monthly cases","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CombineSequencedCases.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a combined dataframe of sequenced cases and confirmed cases — CombineSequencedCases","text":"combined dataframe case load sequenced","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CountsToPrevalence.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert monthwise counts to prevalence — CountsToPrevalence","title":"Convert monthwise counts to prevalence — CountsToPrevalence","text":"Convert monthwise counts prevalence","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CountsToPrevalence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert monthwise counts to prevalence — CountsToPrevalence","text":"","code":"CountsToPrevalence(variant_df)"},{"path":"http://saket-choudhary.me/covmuller/reference/CountsToPrevalence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert monthwise counts to prevalence — CountsToPrevalence","text":"variant_df dataframe","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CountsToPrevalence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert monthwise counts to prevalence — CountsToPrevalence","text":"dataframe monthwise prevalence variants","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/CovmullerTheme.html","id":null,"dir":"Reference","previous_headings":"","what":"Set them — CovmullerTheme","title":"Set them — CovmullerTheme","text":"Set 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NA \"UNKNOWN\"","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/SanitizeColumn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sanitize a vector to remove special characters and convert it to tile — SanitizeColumn","text":"","code":"SanitizeColumn(col)"},{"path":"http://saket-choudhary.me/covmuller/reference/SanitizeColumn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sanitize a vector to remove special characters and convert it to tile — SanitizeColumn","text":"col String vector","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/SanitizeColumn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sanitize a vector to remove special characters and convert it to tile — SanitizeColumn","text":"string vector sanitized entries","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/SummarizeVariantsDatewise.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the total number of variants per week — SummarizeVariantsDatewise","title":"Summarize the total number of variants per week — SummarizeVariantsDatewise","text":"Summarize total number variants per week","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/SummarizeVariantsDatewise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the total number of variants per week — SummarizeVariantsDatewise","text":"","code":"SummarizeVariantsDatewise(variant_df, by_state = FALSE)"},{"path":"http://saket-choudhary.me/covmuller/reference/SummarizeVariantsDatewise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the total number of variants per week — SummarizeVariantsDatewise","text":"variant_df dataframe","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/SummarizeVariantsDatewise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the total number of variants per week — SummarizeVariantsDatewise","text":"dataframe monthwise counts variant sequenced","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/SummarizeVariantsMonthwise.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the total number of variants per month — SummarizeVariantsMonthwise","title":"Summarize the total number of variants per month — SummarizeVariantsMonthwise","text":"Summarize total number variants per month","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/SummarizeVariantsMonthwise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the total number of variants per month — SummarizeVariantsMonthwise","text":"","code":"SummarizeVariantsMonthwise(variant_df, by_state = FALSE)"},{"path":"http://saket-choudhary.me/covmuller/reference/SummarizeVariantsMonthwise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the total number of variants per month — SummarizeVariantsMonthwise","text":"variant_df dataframe","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/SummarizeVariantsMonthwise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the total number of variants per month — SummarizeVariantsMonthwise","text":"dataframe monthwise counts variant sequenced","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/SummarizeVariantsWeekwise.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the total number of variants per week — SummarizeVariantsWeekwise","title":"Summarize the total number of variants per week — SummarizeVariantsWeekwise","text":"Summarize total number variants per week","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/SummarizeVariantsWeekwise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the total number of variants per week — SummarizeVariantsWeekwise","text":"","code":"SummarizeVariantsWeekwise(variant_df, by_state = FALSE)"},{"path":"http://saket-choudhary.me/covmuller/reference/SummarizeVariantsWeekwise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the total number of variants per week — SummarizeVariantsWeekwise","text":"variant_df dataframe","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/SummarizeVariantsWeekwise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the total number of variants per week — SummarizeVariantsWeekwise","text":"dataframe monthwise counts variant sequenced","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/TotalSequencesPerMonthCountrywise.html","id":null,"dir":"Reference","previous_headings":"","what":"Get total sequenced samples per country — TotalSequencesPerMonthCountrywise","title":"Get total sequenced samples per country — TotalSequencesPerMonthCountrywise","text":"Get total sequenced samples per country","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/TotalSequencesPerMonthCountrywise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get total sequenced samples per country — TotalSequencesPerMonthCountrywise","text":"","code":"TotalSequencesPerMonthCountrywise(variant_df, rename_country_as_state = TRUE)"},{"path":"http://saket-choudhary.me/covmuller/reference/TotalSequencesPerMonthCountrywise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get total sequenced samples per country — TotalSequencesPerMonthCountrywise","text":"variant_df dataframe","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/TotalSequencesPerMonthCountrywise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get total sequenced samples per country — TotalSequencesPerMonthCountrywise","text":"dataframe sequencing statistics per country","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/TotalSequencesPerMonthStatewise.html","id":null,"dir":"Reference","previous_headings":"","what":"Get total number of sequenced samples per month in a Country — TotalSequencesPerMonthStatewise","title":"Get total number of sequenced samples per month in a Country — TotalSequencesPerMonthStatewise","text":"Get total number sequenced samples per month Country","code":""},{"path":"http://saket-choudhary.me/covmuller/reference/TotalSequencesPerMonthStatewise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get total number of sequenced samples per month in a Country — TotalSequencesPerMonthStatewise","text":"","code":"TotalSequencesPerMonthStatewise(variant_df, drop_country = FALSE)"},{"path":"http://saket-choudhary.me/covmuller/reference/TotalSequencesPerMonthStatewise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get total number of sequenced samples per month in a Country — TotalSequencesPerMonthStatewise","text":"dataframe sequencing statistics per state per country","code":""}]
diff --git a/vignettes/Introduction.Rmd b/vignettes/Introduction.Rmd
index 64e7211a..908ba316 100644
--- a/vignettes/Introduction.Rmd
+++ b/vignettes/Introduction.Rmd
@@ -56,7 +56,7 @@ p1
 We utilize [GISAID](https://www.gisaid.org/) data to look at prevalence of variants. To access this data, GISAID requires registration.
 
 ```{r}
-current_date <- "2024_01_15"
+current_date <- "2024_01_18"
 fpath.tar <- paste0("~/data/epicov/metadata_tsv_", current_date, ".tar.xz")
 fpath.qs <- paste0("~/data/epicov/metadata_tsv_", current_date, ".qs")
 
diff --git a/vignettes/VariantAnimation-Brazil.Rmd b/vignettes/VariantAnimation-Brazil.Rmd
index 8364f7b4..8a5f94ef 100644
--- a/vignettes/VariantAnimation-Brazil.Rmd
+++ b/vignettes/VariantAnimation-Brazil.Rmd
@@ -32,7 +32,7 @@ theme_set(CovmullerTheme())
 # Get variants data for Brazil
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_brazil <- gisaid_metadata %>%
   filter(Country == "Brazil") %>%
   filter(Host == "Human")
diff --git a/vignettes/VariantAnimation-Germany.Rmd b/vignettes/VariantAnimation-Germany.Rmd
index a2dedcb7..68002927 100644
--- a/vignettes/VariantAnimation-Germany.Rmd
+++ b/vignettes/VariantAnimation-Germany.Rmd
@@ -32,7 +32,7 @@ theme_set(CovmullerTheme())
 # Get variants data for Germany
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_germany <- gisaid_metadata %>%
   filter(Country == "Germany") %>%
   filter(Host == "Human")
diff --git a/vignettes/VariantAnimation-India.Rmd b/vignettes/VariantAnimation-India.Rmd
index 44969a46..a476446c 100644
--- a/vignettes/VariantAnimation-India.Rmd
+++ b/vignettes/VariantAnimation-India.Rmd
@@ -30,7 +30,7 @@ theme_set(CovmullerTheme())
 # Get variants data for India
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_india <- FilterGISAIDIndia(gisaid_metadata_all = gisaid_metadata)
 
 vocs <- GetVOCs()
diff --git a/vignettes/VariantAnimation-NYC.Rmd b/vignettes/VariantAnimation-NYC.Rmd
index 34501f78..77f0eb1c 100644
--- a/vignettes/VariantAnimation-NYC.Rmd
+++ b/vignettes/VariantAnimation-NYC.Rmd
@@ -20,7 +20,7 @@ theme_set(CovmullerTheme())
 ```{r, warning=FALSE, message=FALSE}
 counties <- c("New York", "New York City", "New York County", "Queens County", "Bronx County", "Bronx", "Brooklyn", "Manhattan", "Queens", "Richmond County")
 
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_usa <- gisaid_metadata %>%
   filter(Country == "USA") %>%
   filter(Host == "Human")
diff --git a/vignettes/VariantAnimation-NewYork.Rmd b/vignettes/VariantAnimation-NewYork.Rmd
index 35a728c2..4ba9d564 100644
--- a/vignettes/VariantAnimation-NewYork.Rmd
+++ b/vignettes/VariantAnimation-NewYork.Rmd
@@ -26,7 +26,7 @@ theme_set(CovmullerTheme())
 # Get variants data for NY state
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_usa <- gisaid_metadata %>%
   filter(Country == "USA") %>%
   filter(Host == "Human")
diff --git a/vignettes/VariantAnimation-Singapore.Rmd b/vignettes/VariantAnimation-Singapore.Rmd
index 126be6d1..25f354a8 100644
--- a/vignettes/VariantAnimation-Singapore.Rmd
+++ b/vignettes/VariantAnimation-Singapore.Rmd
@@ -32,7 +32,7 @@ theme_set(CovmullerTheme())
 # Get variants data for Singapore
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_singapore <- gisaid_metadata %>%
   filter(Country == "Singapore") %>%
   filter(Host == "Human")
diff --git a/vignettes/VariantAnimation-UK.Rmd b/vignettes/VariantAnimation-UK.Rmd
index bff6e388..357d711a 100644
--- a/vignettes/VariantAnimation-UK.Rmd
+++ b/vignettes/VariantAnimation-UK.Rmd
@@ -32,7 +32,7 @@ theme_set(CovmullerTheme())
 # Get variants data for United Kingdom
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_United_Kingdom <- gisaid_metadata %>%
   filter(Country == "United Kingdom") %>%
   filter(Host == "Human")
diff --git a/vignettes/VariantAnimation-USA.Rmd b/vignettes/VariantAnimation-USA.Rmd
index f509f871..ef734ca7 100644
--- a/vignettes/VariantAnimation-USA.Rmd
+++ b/vignettes/VariantAnimation-USA.Rmd
@@ -32,7 +32,7 @@ theme_set(CovmullerTheme())
 # Get variants data for USA
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_usa <- gisaid_metadata %>%
   filter(Country == "USA") %>%
   filter(Host == "Human")
diff --git a/vignettes/_MultinomialModeling-Germany.Rmd b/vignettes/_MultinomialModeling-Germany.Rmd
index 48d125a7..f820b5fc 100644
--- a/vignettes/_MultinomialModeling-Germany.Rmd
+++ b/vignettes/_MultinomialModeling-Germany.Rmd
@@ -15,7 +15,7 @@ theme_set(CovmullerTheme())
 ```
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread(file = "~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread(file = "~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_germany <- gisaid_metadata %>%
   filter(Country == "Germany") %>%
   filter(Host == "Human")
diff --git a/vignettes/_MultinomialModeling_Australia.Rmd b/vignettes/_MultinomialModeling_Australia.Rmd
index 5a126d0d..28b8a011 100644
--- a/vignettes/_MultinomialModeling_Australia.Rmd
+++ b/vignettes/_MultinomialModeling_Australia.Rmd
@@ -15,7 +15,7 @@ theme_set(CovmullerTheme())
 ```
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread(file = "~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread(file = "~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_australia <- gisaid_metadata %>%
   filter(Country == "Australia") %>%
   filter(Host == "Human")
diff --git a/vignettes/_MultinomialModeling_Canada.Rmd b/vignettes/_MultinomialModeling_Canada.Rmd
index 0861add1..1b00cb2a 100644
--- a/vignettes/_MultinomialModeling_Canada.Rmd
+++ b/vignettes/_MultinomialModeling_Canada.Rmd
@@ -15,7 +15,7 @@ theme_set(CovmullerTheme())
 ```
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread(file = "~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread(file = "~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_canada <- gisaid_metadata %>%
   filter(Country == "Canada") %>%
   filter(Host == "Human")
diff --git a/vignettes/_MultinomialModeling_India.Rmd b/vignettes/_MultinomialModeling_India.Rmd
index 7bfc8c96..edad6e3c 100644
--- a/vignettes/_MultinomialModeling_India.Rmd
+++ b/vignettes/_MultinomialModeling_India.Rmd
@@ -15,7 +15,7 @@ theme_set(CovmullerTheme())
 ```
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread(file = "~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread(file = "~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_india <- FilterGISAIDIndia(gisaid_metadata_all = gisaid_metadata)
 ```
 
diff --git a/vignettes/_MultinomialModeling_SouthAfrica.Rmd b/vignettes/_MultinomialModeling_SouthAfrica.Rmd
index 4a2c2cb9..45b20bf0 100644
--- a/vignettes/_MultinomialModeling_SouthAfrica.Rmd
+++ b/vignettes/_MultinomialModeling_SouthAfrica.Rmd
@@ -15,7 +15,7 @@ theme_set(CovmullerTheme())
 ```
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread(file = "~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread(file = "~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_southafrica <- gisaid_metadata %>%
   filter(Country == "South Africa") %>%
   filter(Host == "Human")
diff --git a/vignettes/_MultinomialModeling_UK.Rmd b/vignettes/_MultinomialModeling_UK.Rmd
index b5da7f35..419a4670 100644
--- a/vignettes/_MultinomialModeling_UK.Rmd
+++ b/vignettes/_MultinomialModeling_UK.Rmd
@@ -15,7 +15,7 @@ theme_set(CovmullerTheme())
 ```
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread(file = "~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread(file = "~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_uk <- gisaid_metadata %>%
   filter(Country == "United Kingdom") %>%
   filter(Host == "Human")
diff --git a/vignettes/_SouthAfrica_contrast_India.Rmd b/vignettes/_SouthAfrica_contrast_India.Rmd
index e7a4b792..74aeb7e6 100644
--- a/vignettes/_SouthAfrica_contrast_India.Rmd
+++ b/vignettes/_SouthAfrica_contrast_India.Rmd
@@ -16,7 +16,7 @@ theme_set(CovmullerTheme())
 # Get data
 
 ```{r}
-current_date <- "2024_01_15"
+current_date <- "2024_01_18"
 fpath.tar <- paste0("~/data/epicov/metadata_tsv_", current_date, ".tar.xz")
 fpath.qs <- paste0("~/data/epicov/metadata_tsv_", current_date, ".qs")
 gisaid_metadata <- qs::qread(file = fpath.qs)
diff --git a/vignettes/_VariantAnimation-Bengaluru.Rmd b/vignettes/_VariantAnimation-Bengaluru.Rmd
index 76af1173..0f3792a0 100644
--- a/vignettes/_VariantAnimation-Bengaluru.Rmd
+++ b/vignettes/_VariantAnimation-Bengaluru.Rmd
@@ -19,7 +19,7 @@ theme_set(CovmullerTheme())
 # Get variants data for India
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_india <- FilterGISAIDIndia(gisaid_metadata_all = gisaid_metadata)
 
 vocs <- GetVOCs()
diff --git a/vignettes/_VariantAnimation-Chennai.Rmd b/vignettes/_VariantAnimation-Chennai.Rmd
index 993ee2d3..2fad0e52 100644
--- a/vignettes/_VariantAnimation-Chennai.Rmd
+++ b/vignettes/_VariantAnimation-Chennai.Rmd
@@ -19,7 +19,7 @@ theme_set(CovmullerTheme())
 # Get variants data for India
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_india <- FilterGISAIDIndia(gisaid_metadata_all = gisaid_metadata)
 
 vocs <- GetVOCs()
diff --git a/vignettes/_VariantAnimation-Delhi.Rmd b/vignettes/_VariantAnimation-Delhi.Rmd
index 5f038241..a37d785b 100644
--- a/vignettes/_VariantAnimation-Delhi.Rmd
+++ b/vignettes/_VariantAnimation-Delhi.Rmd
@@ -19,7 +19,7 @@ theme_set(CovmullerTheme())
 # Get variants data for India
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_india <- FilterGISAIDIndia(gisaid_metadata_all = gisaid_metadata)
 
 vocs <- GetVOCs()
diff --git a/vignettes/_VariantAnimation-Gujarat.Rmd b/vignettes/_VariantAnimation-Gujarat.Rmd
index de504123..37b4c9aa 100644
--- a/vignettes/_VariantAnimation-Gujarat.Rmd
+++ b/vignettes/_VariantAnimation-Gujarat.Rmd
@@ -20,7 +20,7 @@ theme_set(CovmullerTheme())
 # Get variants data for India
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_india <- FilterGISAIDIndia(gisaid_metadata_all = gisaid_metadata)
 
 vocs <- GetVOCs()
diff --git a/vignettes/_VariantAnimation-Hyderabad.Rmd b/vignettes/_VariantAnimation-Hyderabad.Rmd
index 1e482706..1a4c6ed9 100644
--- a/vignettes/_VariantAnimation-Hyderabad.Rmd
+++ b/vignettes/_VariantAnimation-Hyderabad.Rmd
@@ -19,7 +19,7 @@ theme_set(CovmullerTheme())
 # Get variants data for India
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_india <- FilterGISAIDIndia(gisaid_metadata_all = gisaid_metadata)
 
 vocs <- GetVOCs()
diff --git a/vignettes/_VariantAnimation-Jaipur.Rmd b/vignettes/_VariantAnimation-Jaipur.Rmd
index 314f2cdb..853bac1c 100644
--- a/vignettes/_VariantAnimation-Jaipur.Rmd
+++ b/vignettes/_VariantAnimation-Jaipur.Rmd
@@ -19,7 +19,7 @@ theme_set(CovmullerTheme())
 # Get variants data for India
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_india <- FilterGISAIDIndia(gisaid_metadata_all = gisaid_metadata)
 
 vocs <- GetVOCs()
diff --git a/vignettes/_VariantAnimation-Kerala.Rmd b/vignettes/_VariantAnimation-Kerala.Rmd
index 8457e34c..9d0a9bf1 100644
--- a/vignettes/_VariantAnimation-Kerala.Rmd
+++ b/vignettes/_VariantAnimation-Kerala.Rmd
@@ -20,7 +20,7 @@ theme_set(CovmullerTheme())
 # Get variants data for India
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_india <- FilterGISAIDIndia(gisaid_metadata_all = gisaid_metadata)
 
 vocs <- GetVOCs()
diff --git a/vignettes/_VariantAnimation-Maharashtra.Rmd b/vignettes/_VariantAnimation-Maharashtra.Rmd
index 9b50d4b6..7e8bb8ce 100644
--- a/vignettes/_VariantAnimation-Maharashtra.Rmd
+++ b/vignettes/_VariantAnimation-Maharashtra.Rmd
@@ -20,7 +20,7 @@ theme_set(CovmullerTheme())
 # Get variants data for India
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_india <- FilterGISAIDIndia(gisaid_metadata_all = gisaid_metadata)
 
 vocs <- GetVOCs()
diff --git a/vignettes/_VariantAnimation-Mumbai.Rmd b/vignettes/_VariantAnimation-Mumbai.Rmd
index a746997e..8075fe4c 100644
--- a/vignettes/_VariantAnimation-Mumbai.Rmd
+++ b/vignettes/_VariantAnimation-Mumbai.Rmd
@@ -20,7 +20,7 @@ theme_set(CovmullerTheme())
 # Get variants data for India
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_india <- FilterGISAIDIndia(gisaid_metadata_all = gisaid_metadata)
 
 vocs <- GetVOCs()
diff --git a/vignettes/_VariantAnimation-Pune.Rmd b/vignettes/_VariantAnimation-Pune.Rmd
index 9f6bef7d..dd7b7673 100644
--- a/vignettes/_VariantAnimation-Pune.Rmd
+++ b/vignettes/_VariantAnimation-Pune.Rmd
@@ -19,7 +19,7 @@ theme_set(CovmullerTheme())
 # Get variants data for India
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_india <- FilterGISAIDIndia(gisaid_metadata_all = gisaid_metadata)
 
 vocs <- GetVOCs()
diff --git a/vignettes/_VariantAnimation-SouthAfrica.Rmd b/vignettes/_VariantAnimation-SouthAfrica.Rmd
index 2f50a4d2..a5517951 100644
--- a/vignettes/_VariantAnimation-SouthAfrica.Rmd
+++ b/vignettes/_VariantAnimation-SouthAfrica.Rmd
@@ -32,7 +32,7 @@ theme_set(CovmullerTheme())
 # Get variants data for South Africa
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_South_Africa <- gisaid_metadata %>%
   filter(Country == "South Africa") %>%
   filter(Host == "Human")
diff --git a/vignettes/_VariantAnimation-Tamilnadu.Rmd b/vignettes/_VariantAnimation-Tamilnadu.Rmd
index fb48c93b..fa1bd63e 100644
--- a/vignettes/_VariantAnimation-Tamilnadu.Rmd
+++ b/vignettes/_VariantAnimation-Tamilnadu.Rmd
@@ -20,7 +20,7 @@ theme_set(CovmullerTheme())
 # Get variants data for India
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_india <- FilterGISAIDIndia(gisaid_metadata_all = gisaid_metadata)
 
 vocs <- GetVOCs()
diff --git a/vignettes/_VariantAnimation-Telangana.Rmd b/vignettes/_VariantAnimation-Telangana.Rmd
index 7f6eebc2..7d94c3da 100644
--- a/vignettes/_VariantAnimation-Telangana.Rmd
+++ b/vignettes/_VariantAnimation-Telangana.Rmd
@@ -20,7 +20,7 @@ theme_set(CovmullerTheme())
 # Get variants data for India
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_india <- FilterGISAIDIndia(gisaid_metadata_all = gisaid_metadata)
 
 vocs <- GetVOCs()
diff --git a/vignettes/_VarinatAnimation-Kolkata.Rmd b/vignettes/_VarinatAnimation-Kolkata.Rmd
index 360fe14a..16845ff7 100644
--- a/vignettes/_VarinatAnimation-Kolkata.Rmd
+++ b/vignettes/_VarinatAnimation-Kolkata.Rmd
@@ -19,7 +19,7 @@ theme_set(CovmullerTheme())
 # Get variants data for India
 
 ```{r, warning=FALSE, message=FALSE}
-gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_15.qs")
+gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_01_18.qs")
 gisaid_india <- FilterGISAIDIndia(gisaid_metadata_all = gisaid_metadata)
 
 vocs <- GetVOCs()
diff --git a/vignettes/replace_date.sh b/vignettes/replace_date.sh
deleted file mode 100644
index b672f2bf..00000000
--- a/vignettes/replace_date.sh
+++ /dev/null
@@ -1,3 +0,0 @@
-for file in *; do
-  sed -i "s/$1/$2/g" "$file"
-done