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The Epidemiologist R Handbook

Last updated
-

Oct 30, 2024

+

Nov 25, 2024

@@ -825,7 +825,7 @@

Acknowledgemen

Contributors

Editor: Neale Batra

-

Authors: Neale Batra, Alex Spina, Paula Blomquist, Finlay Campbell, Henry Laurenson-Schafer, Isaac Florence, Natalie Fischer, Aminata Ndiaye, Liza Coyer, Jonathan Polonsky, Yurie Izawa, Chris Bailey, Daniel Molling, Isha Berry, Emma Buajitti, Mathilde Mousset, Sara Hollis, Wen Lin

+

Authors: Neale Batra, Alex Spina, Paula Blomquist, Finlay Campbell, Henry Laurenson-Schafer, Isaac Florence, Natalie Fischer, Aminata Ndiaye, Liza Coyer, Jonathan Polonsky, Yurie Izawa, Chris Bailey, Daniel Molling, Isha Berry, Emma Buajitti, Mathilde Mousset, Sara Hollis, Wen Lin, Arran Hamlet

Reviewers and supporters: Pat Keating, Amrish Baidjoe, Annick Lenglet, Margot Charette, Danielly Xavier, Marie-Amélie Degail Chabrat, Esther Kukielka, Michelle Sloan, Aybüke Koyuncu, Rachel Burke, Kate Kelsey, Berhe Etsay, John Rossow, Mackenzie Zendt, James Wright, Laura Haskins, Flavio Finger, Tim Taylor, Jae Hyoung Tim Lee, Brianna Bradley, Wayne Enanoria, Manual Albela Miranda, Molly Mantus, Pattama Ulrich, Joseph Timothy, Adam Vaughan, Olivia Varsaneux, Lionel Monteiro, Joao Muianga

Illustrations: Calder Fong

@@ -1463,7 +1463,7 @@

Contribution

- +
+

Case counts aggregated by hospital

@@ -902,8 +903,8 @@

Import data

The first 50 rows are displayed below:

-
- +
+
@@ -1624,8 +1625,8 @@

Aggregated data

Often instead of a linelist, you begin with aggregated counts from facilities, districts, etc. You can make an epicurve with ggplot() but the code will be slightly different. This section will utilize the count_data dataset that was imported earlier, in the data preparation section. This dataset is the linelist aggregated to day-hospital counts. The first 50 rows are displayed below.

-
- +
+
@@ -1663,8 +1664,8 @@

Plotting

The first 50 rows of count_data_weekly are displayed below. You can see that the counts have been aggregated into weeks. Each week is displayed by the first day of the week (Monday by default).

-
- +
+

Now plot so that x = the epiweek column. Remember to add y = the counts column to the aesthetic mapping, and add stat = "identity" as explained above.

@@ -2279,64 +2280,90 @@

32.5 Dual-axis

-

Although there are fierce discussions about the validity of dual axes within the data visualization community, many epi supervisors still want to see an epicurve or similar chart with a percent overlaid with a second axis. This is discussed more extensively in the ggplot tips page, but one example using the argument sec_axis = of the function scale_y_continuous().

-

This walkthrough can be found in more detail in ggplot tips.

+

Although there are fierce discussions about the validity of dual axes within the data visualization community, many epi supervisors still want to see an epicurve or similar chart with a percent overlaid with a second axis. This is discussed more extensively in the ggplot tips page, but one example using the cowplot method is shown below:

+
    +
  • Two distinct plots are made, and then combined with cowplot package.
    +
  • +
  • The plots must have the exact same x-axis (set limits) or else the data and labels will not align
    +
  • +
  • Each uses theme_cowplot() and one has the y-axis moved to the right side of the plot
  • +
+
+
#load package
+pacman::p_load(cowplot)
+
+# Make first plot of epicurve histogram
+#######################################
+plot_cases <- linelist %>% 
+  
+  # plot cases per week
+  ggplot()+
+  
+  # create histogram  
+  geom_histogram(
+    
+    mapping = aes(x = date_onset),
+    
+    # bin breaks every week beginning monday before first case, going to monday after last case
+    breaks = weekly_breaks_all)+  # pre-defined vector of weekly dates (see top of ggplot section)
+        
+  # specify beginning and end of date axis to align with other plot
+  scale_x_date(
+    limits = c(min(weekly_breaks_all), max(weekly_breaks_all)))+  # min/max of the pre-defined weekly breaks of histogram
+  
+  # labels
+  labs(
+      y = "Daily cases",
+      x = "Date of symptom onset"
+    )+
+  theme_cowplot()
+
+
+# make second plot of percent died per week
+###########################################
+plot_deaths <- linelist %>%                        # begin with linelist
+  group_by(week = floor_date(date_onset, "week")) %>%  # create week column
+  
+  # summarise to get weekly percent of cases who died
+  summarise(n_cases = n(),
+            died = sum(outcome == "Death", na.rm=T),
+            pct_died = 100*died/n_cases) %>% 
+  
+  # begin plot
+  ggplot()+
+  
+  # line of weekly percent who died
+  geom_line(                                # create line of percent died
+    mapping = aes(x = week, y = pct_died),  # specify y-height as pct_died column
+    stat = "identity",                      # set line height to the value in pct_death column, not the number of rows (which is default)
+    size = 2,
+    color = "black")+
+  
+  # Same date-axis limits as the other plot - perfect alignment
+  scale_x_date(
+    limits = c(min(weekly_breaks_all), max(weekly_breaks_all)))+  # min/max of the pre-defined weekly breaks of histogram
+  
+  
+  # y-axis adjustments
+  scale_y_continuous(                # adjust y-axis
+    breaks = seq(0,100, 10),         # set break intervals of percent axis
+    limits = c(0, 100),              # set extent of percent axis
+    position = "right")+             # move percent axis to the right
+  
+  # Y-axis label, no x-axis label
+  labs(x = "",
+       y = "Percent deceased")+      # percent axis label
+  
+  theme_cowplot()                   # add this to make the two plots merge together nicely
+
+

Now use cowplot to overlay the two plots. Attention has been paid to the x-axis alignment, side of the y-axis, and use of theme_cowplot().

-
#Set up linelist for primary axis - the weekly cases epicurve
-linelist_primary_axis <- linelist %>%                
-     count(epiweek = lubridate::floor_date(date_onset, "week"))
-
-#Set up linelist for secondary axis - the line graph of the weekly percent of deaths
-linelist_secondary_axis <- linelist %>%         
-     group_by(
-          epiweek = lubridate::floor_date(date_onset, "week")) %>% 
-     summarise(
-          n = n(),
-          pct_death = 100*sum(outcome == "Death", na.rm = T) / n)
-
-#Set up scaling factor to transform secondary axis
-linelist_primary_axis_max <- linelist_primary_axis %>%
-     pull(n) %>%
-     max()
-
-linelist_secondary_axis_max <- linelist_secondary_axis %>%
-     pull(pct_death) %>%
-     max()
-
-#Create our scaling factor, how much the secondary axis value must be divided by to create values on the same scale as the primary axis
-scaling_factor <- linelist_secondary_axis_max/linelist_primary_axis_max
-
-ggplot() +
-     #First create the epicurve
-     geom_area(data = linelist_primary_axis,
-               mapping = aes(x = epiweek, 
-                             y = n), 
-               fill = "grey"
-               ) +
-     #Now create the linegraph
-     geom_line(data = linelist_secondary_axis,
-               mapping = aes(x = epiweek, 
-                             y = pct_death / scaling_factor)
-               )  +
-     #Now we specify the second axis, and note that we are going to be multiplying the values of the second axis by the scaling factor in order to get the axis to display the correct values
-     scale_y_continuous(
-          sec.axis = sec_axis(~.*scaling_factor, 
-                              name = "Weekly percent of deaths")
-     ) +
-     scale_x_date(
-          date_breaks = "month",
-          date_labels = "%b"
-          ) +
-     labs(
-          x = "Epiweek of symptom onset",
-          y = "Weekly cases",
-          title = "Weekly case incidence and percent deaths"
-     ) +
-     theme_bw()
+
aligned_plots <- cowplot::align_plots(plot_cases, plot_deaths, align="hv", axis="tblr")
+ggdraw(aligned_plots[[1]]) + draw_plot(aligned_plots[[2]])
-

+

@@ -2346,33 +2373,33 @@

32.6 Cumulative Incidence

If beginning with a case linelist, create a new column containing the cumulative number of cases per day in an outbreak using cumsum() from base R:

-
cumulative_case_counts <- linelist %>% 
-  count(date_onset) %>%                # count of rows per day (returned in column "n")   
-  mutate(                         
-    cumulative_cases = cumsum(n)       # new column of the cumulative number of rows at each date
-    )
+
cumulative_case_counts <- linelist %>% 
+  count(date_onset) %>%                # count of rows per day (returned in column "n")   
+  mutate(                         
+    cumulative_cases = cumsum(n)       # new column of the cumulative number of rows at each date
+    )

The first 10 rows are shown below:

-
- +
+

This cumulative column can then be plotted against date_onset, using geom_line():

-
plot_cumulative <- ggplot() +
-  geom_line(
-    data = cumulative_case_counts,
-    aes(x = date_onset, y = cumulative_cases),
-    size = 2,
-    color = "blue")
-
-plot_cumulative
+
plot_cumulative <- ggplot() +
+  geom_line(
+    data = cumulative_case_counts,
+    aes(x = date_onset, y = cumulative_cases),
+    size = 2,
+    color = "blue")
+
+plot_cumulative
-

+

@@ -2382,20 +2409,21 @@

32.7 incidence2

Below we demonstrate how to make epicurves using the incidence2 package. The authors of this package have tried to allow the user to create and modify epicurves without needing to know ggplot2 syntax. Much of this page is adapted from the package vignettes, which can be found at the incidence2 github page.

+

While incidence2 can be very useful for quickly generating figures, the package is much less flexible than approaches described using ggplot2. While this may not be an issue for some users, those who want to have a greater control in creating and adapting their figures, should use ggplot2.

To create an epicurve with incidence2 you need to have a column with a date value (it does not need to be of the class Date, but it should have a numeric or logical order to it (i.e. “Week1”, “Week2”, etc)) and a column with a count variable (what is being counted). It should also not have any duplicated rows.

To create this, we can use the function incidence() which will summarise our data in a format that can be used to create epicurves. There are a number of different arguments to incidence(), type ?incidence in your R console to learn more.

-
#Load package
-pacman::p_load(
-     incidence2
-)
-
-#Create the incidence object
-epi_day <- linelist %>%
-     incidence(date_index = "date_onset",
-               interval = "day")
-
-epi_day
+
#Load package
+pacman::p_load(
+     incidence2
+)
+
+#Create the incidence object
+epi_day <- linelist %>%
+     incidence(date_index = "date_onset",
+               interval = "day")
+
+epi_day
# incidence:  367 x 3
 # count vars: date_onset
@@ -2416,18 +2444,18 @@ 

The object created by the function incidence() looks like a data frame, but has its own class of incidence2.

-
class(epi_day)
+
class(epi_day)
[1] "incidence2" "tbl_df"     "tbl"        "data.frame"

To plot the epicurve, you use the function plot(). To read the incidence2 specific documentation on plotting, type ?plot.incidence2.

-
plot(epi_day)
+
plot(epi_day)
-

+

@@ -2439,18 +2467,18 @@

-
# Create the incidence objects (with different intervals)
-epi_epiweek <- incidence(linelist, "date_onset", interval = "epiweek")    # epiweek 
-epi_month <- incidence(linelist, "date_onset", interval = "month")        # Monthly
-epi_quarter <- incidence(linelist, "date_onset", interval = "quarter")    # Quarterly
-epi_year <- incidence(linelist, "date_onset", interval = "year")          # Years
-
-# Plot the incidence objects (+ titles for clarity)
-############################
-plot(epi_epiweek) + labs(title = "2 (Monday) weeks")
-plot(epi_month) + labs(title = "Months")
-plot(epi_quarter) + labs(title = "Quarters")
-plot(epi_year) + labs(title = "Years")
+
# Create the incidence objects (with different intervals)
+epi_epiweek <- incidence(linelist, "date_onset", interval = "epiweek")    # epiweek 
+epi_month <- incidence(linelist, "date_onset", interval = "month")        # Monthly
+epi_quarter <- incidence(linelist, "date_onset", interval = "quarter")    # Quarterly
+epi_year <- incidence(linelist, "date_onset", interval = "year")          # Years
+
+# Plot the incidence objects (+ titles for clarity)
+############################
+plot(epi_epiweek) + labs(title = "2 (Monday) weeks")
+plot(epi_month) + labs(title = "Months")
+plot(epi_quarter) + labs(title = "Quarters")
+plot(epi_year) + labs(title = "Years")
@@ -2494,23 +2522,23 @@

Groups

In the example below, the cases in the whole outbreak are grouped by their age category. Missing values are included as a group. The epicurve interval is weeks.

-
# Create incidence object, with data grouped by age category
-age_outbreak <- incidence(
-  linelist,                  # dataset
-  date_index = "date_onset", # date column
-  interval = "week",         # Monday weekly aggregation of cases
-  groups = "age_cat"         # age_cat is set as a group
-)
-
-# plot the grouped incidence object
-plot(
-  age_outbreak,                # incidence object with age_cat as group
-  fill = "age_cat") +          # age_cat is used for bar fill color (must have been set as a groups column above)
-labs(fill = "Age Category")    # change legend title from default "age_cat" (this is a ggplot2 modification)
+
# Create incidence object, with data grouped by age category
+age_outbreak <- incidence(
+  linelist,                  # dataset
+  date_index = "date_onset", # date column
+  interval = "week",         # Monday weekly aggregation of cases
+  groups = "age_cat"         # age_cat is set as a group
+)
+
+# plot the grouped incidence object
+plot(
+  age_outbreak,                # incidence object with age_cat as group
+  fill = "age_cat") +          # age_cat is used for bar fill color (must have been set as a groups column above)
+labs(fill = "Age Category")    # change legend title from default "age_cat" (this is a ggplot2 modification)
-

+

@@ -2518,22 +2546,22 @@

Groups

TIP: Change the title of the legend by adding + the ggplot2 command labs(fill = "your title") to your incidence2 plot.

You can also have the grouped bars display side-by-side by setting stack = FALSE in plot(), as shown below:

-
# Make incidence object of monthly counts. 
-monthly_gender <- incidence(
- linelist,
- date_index = "date_onset",
- interval = "month",
- groups = "gender"            # set gender as grouping column
-)
-
-plot(
-  monthly_gender,   # incidence object
-  fill = "gender",  # display bars colored by gender
-  stack = FALSE)    # side-by-side (not stacked)
+
# Make incidence object of monthly counts. 
+monthly_gender <- incidence(
+ linelist,
+ date_index = "date_onset",
+ interval = "month",
+ groups = "gender"            # set gender as grouping column
+)
+
+plot(
+  monthly_gender,   # incidence object
+  fill = "gender",  # display bars colored by gender
+  stack = FALSE)    # side-by-side (not stacked)
-

+

@@ -2550,19 +2578,19 @@

Filtered data

The example below uses data filtered to show only cases at Central Hospital.

-
# filter the linelist
-central_data <- linelist %>% 
-  filter(hospital == "Central Hospital")
-
-# create incidence object using filtered data
-central_outbreak <- incidence(central_data, date_index = "date_onset", interval = "week")
-
-# plot the incidence object
-plot(central_outbreak, title = "Weekly case incidence at Central Hospital")
+
# filter the linelist
+central_data <- linelist %>% 
+  filter(hospital == "Central Hospital")
+
+# create incidence object using filtered data
+central_outbreak <- incidence(central_data, date_index = "date_onset", interval = "week")
+
+# plot the incidence object
+plot(central_outbreak, title = "Weekly case incidence at Central Hospital")
-

+

@@ -2574,27 +2602,27 @@

Aggregated co

For example, this data frame count_data is the linelist aggregated into daily counts by hospital. The first 50 rows look like this:

-
- +
+

If you are beginning your analysis with daily count data like the dataset above, your incidence() command to convert this to a weekly epicurve by hospital would look like this:

-
epi_counts <- incidence(              # create weekly incidence object
-  count_data,                         # dataset with counts aggregated by day
-  date_index = "date_hospitalisation",  # column with dates
-  counts = "n_cases",                    # column with counts
-  interval = "week",                  # aggregate daily counts up to weeks
-  groups = "hospital"                   # group by hospital
-  )
-
-# plot the weekly incidence epi curve, with stacked bars by hospital
-plot(epi_counts,                      # incidence object
-     fill = "hospital")                 # color the bars by hospital
+
epi_counts <- incidence(              # create weekly incidence object
+  count_data,                         # dataset with counts aggregated by day
+  date_index = "date_hospitalisation",  # column with dates
+  counts = "n_cases",                    # column with counts
+  interval = "week",                  # aggregate daily counts up to weeks
+  groups = "hospital"                   # group by hospital
+  )
+
+# plot the weekly incidence epi curve, with stacked bars by hospital
+plot(epi_counts,                      # incidence object
+     fill = "hospital")                 # color the bars by hospital
-

+

@@ -2604,23 +2632,23 @@

Aggregated co

Facets/small multiples

Below, we set both columns hospital and outcome as grouping columns in the incidence() command. For grouped data, the plot method will create a faceted plot across groups unless a fill variable is specified. Note we are dropping NA values only for visualisation purposes.

-
epi_wks_hosp_out <- incidence(
-  linelist %>%
-       select(date_onset,
-              outcome,
-              gender) %>%
-       drop_na(),                   # dataset
-  date_index = "date_onset",       # date column
-  interval = "quarter",            # monthly bars  
-  groups = c("outcome", "gender")  # both outcome and hospital are given as grouping columns
-  )
-
-# plot
-plot(epi_wks_hosp_out)
+
epi_wks_hosp_out <- incidence(
+  linelist %>%
+       select(date_onset,
+              outcome,
+              gender) %>%
+       drop_na(),                   # dataset
+  date_index = "date_onset",       # date column
+  interval = "quarter",            # monthly bars  
+  groups = c("outcome", "gender")  # both outcome and hospital are given as grouping columns
+  )
+
+# plot
+plot(epi_wks_hosp_out)
-

+

@@ -2633,25 +2661,25 @@

Moving averages) of the first plot we created with the object epi_day.

-
plot(epi_day) +
-     # plot moving average
-     tidyquant::geom_ma(       
-          mapping = aes(x = date_index,
-                        y = count), 
-      n = 7,           
-      size = 1,
-      color = "red",
-      linetype = "solid") +
-     labs(x = "Onset date",
-          y = "Daily cases",
-          title = "Epicurve of Ebola cases",
-          fill = "") +
-     theme_dark() +
-     theme(legend.position = "none")
+
plot(epi_day) +
+     # plot moving average
+     tidyquant::geom_ma(       
+          mapping = aes(x = date_index,
+                        y = count), 
+      n = 7,           
+      size = 1,
+      color = "red",
+      linetype = "solid") +
+     labs(x = "Onset date",
+          y = "Daily cases",
+          title = "Epicurve of Ebola cases",
+          fill = "") +
+     theme_dark() +
+     theme(legend.position = "none")
-

+

@@ -3256,7 +3284,7 @@

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a/html_outputs/new_pages/errors.html b/html_outputs/new_pages/errors.html index 3a88f26d..4e4062d4 100644 --- a/html_outputs/new_pages/errors.html +++ b/html_outputs/new_pages/errors.html @@ -252,7 +252,7 @@ The Epidemiologist R Handbook

R Markdown errors

diff --git a/html_outputs/new_pages/phylogenetic_trees.html b/html_outputs/new_pages/phylogenetic_trees.html index b9b2aa76..6bdc6e8d 100644 --- a/html_outputs/new_pages/phylogenetic_trees.html +++ b/html_outputs/new_pages/phylogenetic_trees.html @@ -309,7 +309,7 @@ The Epidemiologist R Handbook
+ ggnewscale # to add additional layers of color schemes + )
@@ -871,8 +872,8 @@

Import data

Below are the first 50 rows of the data:

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Collapsing
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Subsetting a
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Subsetting a
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+

@@ -1223,7 +1224,7 @@

Rotati
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+

@@ -1238,7 +1239,7 @@

Rotati
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@@ -2156,7 +2157,7 @@

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