diff --git a/CONTRIBUTING.html b/CONTRIBUTING.html index d8254c0bc1..d4d55179c1 100644 --- a/CONTRIBUTING.html +++ b/CONTRIBUTING.html @@ -85,7 +85,8 @@

Making Pull RequestsREADME.
  • The gt package follows the tidyverse style guide so please adopt those style guidelines in your submitted code as best as possible.
  • The internal documentation uses roxygen2; if your contribution requires new or revised documentation ensure that the roxygen comments are added/modified (do not modify any .Rd files in the man folder).
    • -
  • We use testthat for code coverage; those contributions with test cases included are helpful easier to accept.
    • +

    Many functions share parameters. For common parameters like rows, columns, data, we use @inheritParams to document them. Only document these parameters if there is a special case for the function you are documenting. In this case, go to R/format-data.R, copy the parameter documentation to the desired function and add additional details.

    + diff --git a/pkgdown.yml b/pkgdown.yml index 9239539312..684d8b716f 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -7,7 +7,7 @@ articles: creating-summary-lines: creating-summary-lines.html gt-datasets: gt-datasets.html gt: gt.html -last_built: 2024-05-22T21:58Z +last_built: 2024-05-23T19:38Z urls: reference: https://gt.rstudio.com/reference article: https://gt.rstudio.com/articles diff --git a/reference/extract_cells.html b/reference/extract_cells.html index 215cdfa07f..c4c9c5afb7 100644 --- a/reference/extract_cells.html +++ b/reference/extract_cells.html @@ -118,7 +118,7 @@

    Arguments

    -

    The output format of the resulting character vector. This can either be +

    The output style of the resulting character vector. This can either be "auto" (the default), "plain", "html", "latex", "rtf", or "word". In knitr rendering (i.e., Quarto or R Markdown), the "auto" option will choose the correct output value

    diff --git a/reference/fmt_engineering.html b/reference/fmt_engineering.html index 7e5df309ce..4dd1e97b16 100644 --- a/reference/fmt_engineering.html +++ b/reference/fmt_engineering.html @@ -176,9 +176,7 @@

    Arguments. With 4 decimal places, the formatted value -becomes "2.3400". The trailing zeros can be removed with -drop_trailing_zeros = TRUE. If you always need decimals = 0, the -fmt_integer() function should be considered.

    +becomes "2.3400".

    drop_trailing_zeros
    diff --git a/reference/fmt_number.html b/reference/fmt_number.html index c39d8b2d58..82ad437148 100644 --- a/reference/fmt_number.html +++ b/reference/fmt_number.html @@ -176,9 +176,7 @@

    Arguments. With 4 decimal places, the formatted value -becomes "2.3400". The trailing zeros can be removed with -drop_trailing_zeros = TRUE. If you always need decimals = 0, the -fmt_integer() function should be considered.

    +becomes "2.3400".

    n_sigfig
    diff --git a/reference/fmt_partsper.html b/reference/fmt_partsper.html index 1fd609a57b..7ba6efc961 100644 --- a/reference/fmt_partsper.html +++ b/reference/fmt_partsper.html @@ -229,9 +229,7 @@

    Arguments. With 4 decimal places, the formatted value -becomes "2.3400". The trailing zeros can be removed with -drop_trailing_zeros = TRUE. If you always need decimals = 0, the -fmt_integer() function should be considered.

    +becomes "2.3400".

    drop_trailing_zeros
    diff --git a/reference/fmt_percent.html b/reference/fmt_percent.html index e97fd37d5a..a40c9ec42e 100644 --- a/reference/fmt_percent.html +++ b/reference/fmt_percent.html @@ -191,9 +191,7 @@

    Arguments. With 4 decimal places, the formatted value -becomes "2.3400". The trailing zeros can be removed with -drop_trailing_zeros = TRUE. If you always need decimals = 0, the -fmt_integer() function should be considered.

    +becomes "2.3400".

    drop_trailing_zeros
    diff --git a/reference/fmt_scientific.html b/reference/fmt_scientific.html index 02c7bc85c9..9019d5af51 100644 --- a/reference/fmt_scientific.html +++ b/reference/fmt_scientific.html @@ -183,9 +183,7 @@

    Arguments. With 4 decimal places, the formatted value -becomes "2.3400". The trailing zeros can be removed with -drop_trailing_zeros = TRUE. If you always need decimals = 0, the -fmt_integer() function should be considered.

    +becomes "2.3400".

    n_sigfig
    diff --git a/reference/grp_add.html b/reference/grp_add.html index 8b7790158b..e2196fdf32 100644 --- a/reference/grp_add.html +++ b/reference/grp_add.html @@ -103,7 +103,7 @@

    Argumentsgt() function.

    diff --git a/reference/grp_options.html b/reference/grp_options.html index b72925c356..7eae6c8e34 100644 --- a/reference/grp_options.html +++ b/reference/grp_options.html @@ -272,9 +272,9 @@

    Argumentsgt_group() function along with one or more gt_tbl objects, or, -made by splitting a gt table with gt_split().

    +

    This is a gt_group container object. It is typically generated through +use of the gt_group() function along with one or more gt_tbl objects, +or, made by splitting a gt table with gt_split().

    table.width
    diff --git a/reference/grp_replace.html b/reference/grp_replace.html index 435e1a3c9d..4be0b96ea4 100644 --- a/reference/grp_replace.html +++ b/reference/grp_replace.html @@ -103,7 +103,7 @@

    Argumentsgt() function.

    diff --git a/reference/vec_fmt_currency.html b/reference/vec_fmt_currency.html index d5aa5eff98..6bee28356c 100644 --- a/reference/vec_fmt_currency.html +++ b/reference/vec_fmt_currency.html @@ -204,14 +204,7 @@

    Arguments would give us a suitable glyph for the Dutch guilder in an HTML output table, and it would simply be the letter "f" in all other output contexts). Please note that decimals -will default to 2 when using the currency() helper function.

    -

    If nothing is provided here but a locale value has been set (either in -this function call or as part of the initial gt() call), the currency -will be obtained from that locale. Virtually all locales are linked to a -territory that is a country (use info_locales() for details on all -locales used in this package), so, the in-use (or de facto) currency will -be obtained. As the default locale is "en", the "USD" currency will be -used if neither a locale nor a currency value is given.

    +will default to 2 when using the currency() helper function.

    use_subunits
    @@ -225,21 +218,23 @@

    Arguments. With 4 decimal places, the formatted value -becomes "2.3400". The trailing zeros can be removed with -drop_trailing_zeros = TRUE. If you always need decimals = 0, the -fmt_integer() function should be considered.

    +

    scalar<numeric|integer>(val>=0) // default: NULL (optional)

    +

    The decimals values corresponds to the exact number of decimal places to +use. This value is optional as a currency has an intrinsic number of +decimal places (i.e., the subunits). A value such as 2.34 can, for +example, be formatted with 0 decimal places and if the currency used is +"USD" it would result in "$2". With 4 decimal places, the formatted +value becomes "$2.3400".

    drop_trailing_dec_mark

    Drop the trailing decimal mark

    scalar<logical> // default: TRUE

    A logical value that determines whether decimal marks should always appear -even if there are no decimal digits to display after formatting (e.g., 23 -becomes 23. if FALSE). By default trailing decimal marks are not shown.

    +even if there are no decimal digits to display after formatting. For +example, when use_subunits = FALSE or decimals = 0 a formatted value +such as "$23" can be fashioned as "$23." by setting +drop_trailing_dec_mark = FALSE.

    use_seps
    @@ -288,7 +283,11 @@

    Arguments, all numbers in the range of millions and billions will be in terms of millions).

    Any use of suffixing (where it is not set expressly as FALSE) means -that any value provided to scale_by will be ignored.

    +that any value provided to scale_by will be ignored.

    +

    If using system = "ind" then the default suffix set provided by +suffixing = TRUE will be the equivalent of c(NA, "L", "Cr"). This +doesn't apply suffixes to the thousands range, but does express values in +lakhs and crores.

    pattern
    @@ -330,9 +329,9 @@

    Arguments

    -

    The placement of the currency symbol. This can be either be left (as -in "$450") or right (which yields "450$").

    +

    singl-kw:[left|right] // default: "left"

    +

    The placement of the currency symbol. This can be either be "left" (as +in "$450") or "right" (which yields "450$").

    incl_space
    diff --git a/reference/vec_fmt_engineering.html b/reference/vec_fmt_engineering.html index 6628a1e7ee..c0279fecc2 100644 --- a/reference/vec_fmt_engineering.html +++ b/reference/vec_fmt_engineering.html @@ -153,9 +153,7 @@

    Arguments. With 4 decimal places, the formatted value -becomes "2.3400". The trailing zeros can be removed with -drop_trailing_zeros = TRUE. If you always need decimals = 0, the -fmt_integer() function should be considered.

    +becomes "2.3400".

    drop_trailing_zeros
    diff --git a/reference/vec_fmt_integer.html b/reference/vec_fmt_integer.html index 1d0982146f..e81b03a42f 100644 --- a/reference/vec_fmt_integer.html +++ b/reference/vec_fmt_integer.html @@ -177,7 +177,11 @@

    Arguments, all numbers in the range of millions and billions will be in terms of millions).

    Any use of suffixing (where it is not set expressly as FALSE) means -that any value provided to scale_by will be ignored.

    +that any value provided to scale_by will be ignored.

    +

    If using system = "ind" then the default suffix set provided by +suffixing = TRUE will be the equivalent of c(NA, "L", "Cr"). This +doesn't apply suffixes to the thousands range, but does express values in +lakhs and crores.

    pattern
    diff --git a/reference/vec_fmt_number.html b/reference/vec_fmt_number.html index 9a77a76e0f..f9f361c138 100644 --- a/reference/vec_fmt_number.html +++ b/reference/vec_fmt_number.html @@ -149,9 +149,7 @@

    Arguments. With 4 decimal places, the formatted value -becomes "2.3400". The trailing zeros can be removed with -drop_trailing_zeros = TRUE. If you always need decimals = 0, the -fmt_integer() function should be considered.

    +becomes "2.3400".

    n_sigfig
    @@ -226,7 +224,11 @@

    Arguments, all numbers in the range of millions and billions will be in terms of millions).

    Any use of suffixing (where it is not set expressly as FALSE) means -that any value provided to scale_by will be ignored.

    +that any value provided to scale_by will be ignored.

    +

    If using system = "ind" then the default suffix set provided by +suffixing = TRUE will be the equivalent of c(NA, "L", "Cr"). This +doesn't apply suffixes to the thousands range, but does express values in +lakhs and crores.

    pattern
    diff --git a/reference/vec_fmt_partsper.html b/reference/vec_fmt_partsper.html index 3eb36d9cba..e770bef06a 100644 --- a/reference/vec_fmt_partsper.html +++ b/reference/vec_fmt_partsper.html @@ -205,9 +205,7 @@

    Arguments. With 4 decimal places, the formatted value -becomes "2.3400". The trailing zeros can be removed with -drop_trailing_zeros = TRUE. If you always need decimals = 0, the -fmt_integer() function should be considered.

    +becomes "2.3400".

    drop_trailing_zeros
    diff --git a/reference/vec_fmt_percent.html b/reference/vec_fmt_percent.html index 9ae786fbcd..d500f1fb19 100644 --- a/reference/vec_fmt_percent.html +++ b/reference/vec_fmt_percent.html @@ -158,9 +158,7 @@

    Arguments. With 4 decimal places, the formatted value -becomes "2.3400". The trailing zeros can be removed with -drop_trailing_zeros = TRUE. If you always need decimals = 0, the -fmt_integer() function should be considered.

    +becomes "2.3400".

    drop_trailing_zeros
    @@ -249,9 +247,9 @@

    Arguments

    +

    singl-kw:[right|left] // default: "right"

    This option governs the placement of the percent sign. This can be either -be right (the default) or left.

    +be "right" (the default) or "left".

    locale
    diff --git a/reference/vec_fmt_scientific.html b/reference/vec_fmt_scientific.html index 141b58f844..68c9d00cb8 100644 --- a/reference/vec_fmt_scientific.html +++ b/reference/vec_fmt_scientific.html @@ -160,9 +160,7 @@

    Arguments. With 4 decimal places, the formatted value -becomes "2.3400". The trailing zeros can be removed with -drop_trailing_zeros = TRUE. If you always need decimals = 0, the -fmt_integer() function should be considered.

    +becomes "2.3400".

    n_sigfig
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Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://gt.rstudio.com/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://gt.rstudio.com/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement codeofconduct@posit.co. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://gt.rstudio.com/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://gt.rstudio.com/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. 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Values grouped category, labels rownames. addition, can give table nice title subtitle. first step, let’s try format columns, formatting counts, min, max medians fmt_integer(), percentages fmt_percent(), mean sd values fmt_number() using 1 2 decimals, respectively. intentionally keeping NA values now, needed cols_merge() pattern next step. looks way better table still rather wide style. collapse columns appropriately, use cols_merge(), combining mean SD, min max, well n percentages, respectively. use pattern argument specify custom merging pattern. Now looks like demographic table. let’s take step back understand merging pattern. { } used arrange single column values row-wise fashion. number curly braces corresponds order specified columns = argument. use << >> surround spans text omitted values within contain missing values. first column call cols_merge() contains values n’s, means medians printed values missing (meaning cells numeric categorical n’s, means medians min max). SD percentages categorical grouping variables appended cells mean categorical n’s, rows contain non-missing values. previous aspects ignored min max row, n’s, percentages, SD’s medians missing. , min max arranged. can now start look style features. Let us indent values stub using tab_stub_indent() left-align title opt_align_table_header(). Let’s now change column width Placebo Drug 1 columns align values center, making use cols_width() cols_align(). final step can now take care column names assign something meaningful. column header name study intervention together respective subject count. make use cols_label()’s ability handle lists, summarize new column labels named list.","code":"custom_summary <- function(df, group_var, sum_var) { group_var <- rlang::ensym(group_var) sum_var <- rlang::ensym(sum_var) is_categorical <- is.character(eval(expr(`$`(df, !!sum_var)))) | is.factor(eval(expr(`$`(df, !!sum_var)))) if (is_categorical) { category_lbl <- sprintf(\"%s, n (%%)\", attr(eval(expr(`$`(df, !!sum_var))), \"label\")) df_out <- df |> dplyr::group_by(!!group_var) |> dplyr::mutate(N = dplyr::n()) |> dplyr::ungroup() |> dplyr::group_by(!!group_var, !!sum_var) |> dplyr::summarize( val = dplyr::n(), pct = dplyr::n()/mean(N), .groups = \"drop\" ) |> tidyr::pivot_wider( id_cols = !!sum_var, names_from = !!group_var, values_from = c(val, pct) ) |> dplyr::rename(label = !!sum_var) |> dplyr::mutate( across(where(is.numeric), ~ifelse(is.na(.), 0, .)), category = category_lbl ) } else { category_lbl <- sprintf( \"%s (%s)\", attr(eval(expr(`$`(df, !!sum_var))), \"label\"), attr(eval(expr(`$`(df, !!sum_var))), \"units\") ) df_out <- df |> dplyr::group_by(!!group_var) |> dplyr::summarize( n = sum(!is.na(!!sum_var)), mean = mean(!!sum_var, na.rm = TRUE), sd = sd(!!sum_var, na.rm = TRUE), median = median(!!sum_var, na.rm = TRUE), min = min(!!sum_var, na.rm = TRUE), max = max(!!sum_var, na.rm = TRUE), min_max = NA, .groups = \"drop\" ) |> tidyr::pivot_longer( cols = c(n, mean, median, min_max), names_to = \"label\", values_to = \"val\" ) |> dplyr::mutate( sd = ifelse(label == \"mean\", sd, NA), max = ifelse(label == \"min_max\", max, NA), min = ifelse(label == \"min_max\", min, NA), label = dplyr::recode( label, \"mean\" = \"Mean (SD)\", \"min_max\" = \"Min - Max\", \"median\" = \"Median\" ) ) |> tidyr::pivot_wider( id_cols = label, names_from = !!group_var, values_from = c(val, sd, min, max) ) |> dplyr::mutate(category = category_lbl) } return(df_out) } adsl_summary <- dplyr::filter(rx_adsl, ITTFL == \"Y\") |> (\\(data) purrr::map_df( .x = dplyr::vars(AGE, AAGEGR1, SEX, ETHNIC, BLBMI), .f = \\(x) custom_summary(df = data, group_var = TRTA, sum_var = !!x) ))() rx_adsl_tbl <- adsl_summary |> gt( rowname_col = \"label\", groupname_col = \"category\" ) |> tab_header( title = \"x.x: Demographic Characteristics\", subtitle = \"x.x.x: Demographic Characteristics - ITT Analysis Set\" ) rx_adsl_tbl rx_adsl_tbl <- rx_adsl_tbl |> fmt_integer( columns = starts_with(c(\"val_\", \"min_\", \"max_\")), rows = label %in% c(\"n\", \"Median\", \"Min - Max\") ) |> fmt_percent( columns = starts_with(\"pct_\"), decimals = 1 ) |> fmt_number( columns = starts_with(\"val_\"), rows = label == \"Mean (SD)\", decimals = 1 ) |> fmt_number( columns = starts_with(\"sd_\"), rows = label == \"Mean (SD)\", decimals = 2 ) rx_adsl_tbl rx_adsl_tbl <- rx_adsl_tbl |> cols_merge( columns = c(\"val_Placebo\", \"pct_Placebo\", \"sd_Placebo\", \"min_Placebo\", \"max_Placebo\"), pattern = \"<<{1}>><< ({2})>><< ({3})>><<{4} - {5}>>\" ) |> cols_merge( columns = c(\"val_Drug 1\", \"pct_Drug 1\", \"sd_Drug 1\", \"min_Drug 1\", \"max_Drug 1\"), pattern = \"<<{1}>><< ({2})>><< ({3})>><<{4} - {5}>>\" ) rx_adsl_tbl rx_adsl_tbl <- rx_adsl_tbl |> tab_stub_indent( rows = everything(), indent = 5 ) |> opt_align_table_header(align = \"left\") rx_adsl_tbl rx_adsl_tbl <- rx_adsl_tbl |> cols_width( starts_with(\"val_\") ~ px(200), 1 ~ px(250) ) |> cols_align( align = \"center\", columns = starts_with(\"val_\") ) rx_adsl_tbl ### Count subjects per arm and summarize values in a list arm_n <- rx_adsl |> dplyr::filter(ITTFL == \"Y\") |> dplyr::group_by(TRTA) |> dplyr::summarize( lbl = sprintf(\"%s N=%i (100%%)\", unique(TRTA), dplyr::n()), .groups = \"drop\" ) |> dplyr::arrange(TRTA) collbl_list <- as.list(arm_n$lbl) names(collbl_list) <- paste0(\"val_\", arm_n$TRTA) rx_adsl_tbl <- rx_adsl_tbl |> cols_label(.list = collbl_list) rx_adsl_tbl"},{"path":"https://gt.rstudio.com/articles/case-study-clinical-tables.html","id":"response-event-rate-analysis-tables","dir":"Articles","previous_headings":"","what":"Response / Event Rate Analysis Tables","title":"Case Study: Clinical Tables","text":"another table, can summarize number subjects event per intervention subgroup defined age groups. Within intervention group counting number percentage participants event (EVNTFL == \"Y\") well total number participants. number participants event divided number without event odds experiencing event per study intervention. odds ratio computed odds Drug 1 divided odds Placebo. code performs calculation outlined within subgroup defined AAGEGR1, confidence intervals around event rates computed using Clopper Pearson method. Let’s first create basic gt table left-aligned table title subtitle. using tab_header() opt_align_table_header() . Next, formatting columns counts integers fmt_integer(), percentages CI’s around percentages numbers one decimal odds ratio CI around odds ratio numbers two decimals, cases using fmt_number(). can now merge columns participants events, total number participants percentage participants events, well 95% CI’s around event rate using cols_merge(). indicate intervention group adding tab spanners tab_spanner(). table looking way better now. Let’s now group two categories highlight fact actually age subgroups. using tab_row_group() manually add row group label Age. Next, ’ll take care column labels. now tab_row_group() label place, longer need label first column can assign empty string. Also, two tab spanners, can assign equal column labels event rates 95% CI’s intervention groups. Using cols_width() cols_align() can apply convenient column width left-align first column. Finally, make use tab_footnote() can add footnote columns 95% CI’s around event rates, indicating derived Clopper-Pearson method. change default symbol choice tab_footnote() numbers letters, add tab_options(footnote.marks = letters).","code":"rx_responders <- rx_adsl |> dplyr::filter(ITTFL == \"Y\") |> dplyr::group_by(TRTA, AAGEGR1) |> dplyr::summarize( n_resp = sum(EVNTFL == \"Y\"), n_total = dplyr::n(), pct = 100 * sum(EVNTFL == \"Y\") / dplyr::n(), ci_up = 100 * ( 1 + (dplyr::n() - sum(EVNTFL == \"Y\")) / ( (sum(EVNTFL == \"Y\") + 1) * qf( 0.975, 2 * (sum(EVNTFL == \"Y\") + 1), 2 * (dplyr::n() - sum(EVNTFL == \"Y\")) ) ) )^(-1), ci_low = ifelse( sum(EVNTFL == \"Y\") == 0, 0, 100 * ( 1 + (dplyr::n() - sum(EVNTFL == \"Y\") + 1) / (sum(EVNTFL == \"Y\") * qf( 0.025, 2 * sum(EVNTFL == \"Y\"), 2 * (dplyr::n() - sum(EVNTFL == \"Y\") + 1) ) ) )^(-1) ), odds = sum(EVNTFL == \"Y\") / (dplyr::n() - sum(EVNTFL == \"Y\")), .groups = \"drop\" ) |> tidyr::pivot_wider( id_cols = AAGEGR1, names_from = TRTA, values_from = c(n_resp, n_total, pct, ci_up, ci_low, odds) ) |> dplyr::mutate( or = ifelse( odds_Placebo == 0, NA_real_, !! sym(\"odds_Drug 1\") / odds_Placebo ), or_ci_low = exp( log(or) - qnorm(0.975) * sqrt( 1 / n_resp_Placebo + 1 / !!sym(\"n_resp_Drug 1\") + 1 / (n_total_Placebo - n_resp_Placebo) + 1 / (!!sym(\"n_total_Drug 1\") - !!sym(\"n_resp_Drug 1\")) ) ), or_ci_up = exp( log(or) + qnorm(0.975) * sqrt( 1 / n_resp_Placebo + 1 / !!sym(\"n_resp_Drug 1\") + 1 / (n_total_Placebo - n_resp_Placebo) + 1 / (!!sym(\"n_total_Drug 1\") - !!sym(\"n_resp_Drug 1\")) ) ) ) |> dplyr::select(-tidyselect::starts_with(\"odds_\")) rx_resp_tbl <- rx_responders |> gt() |> tab_header( title = \"x.x: Efficacy Data\", subtitle = \"x.x.x: Occurence of Event per Subgroup - {gt} Analysis Set\" ) |> opt_align_table_header(align = \"left\") rx_resp_tbl rx_resp_tbl <- rx_resp_tbl |> fmt_integer(columns = starts_with(\"n_\")) |> fmt_number(columns = starts_with(c(\"pct_\", \"ci_\")), decimals = 1) |> fmt_number(columns = starts_with(\"or\"), decimals = 2) rx_resp_tbl rx_resp_tbl <- rx_resp_tbl |> cols_merge( columns = c(\"n_resp_Placebo\", \"n_total_Placebo\", \"pct_Placebo\"), pattern = \"{1}/{2} ({3})\" ) |> cols_merge( columns = c(\"n_resp_Drug 1\", \"n_total_Drug 1\", \"pct_Drug 1\"), pattern = \"{1}/{2} ({3})\" ) |> cols_merge( columns = c(\"ci_low_Placebo\", \"ci_up_Placebo\"), pattern = \"[{1}, {2}]\" ) |> cols_merge( columns = c(\"ci_low_Drug 1\", \"ci_up_Drug 1\"), pattern = \"[{1}, {2}]\" ) |> cols_merge( columns = c(\"or_ci_low\", \"or_ci_up\"), pattern = \"[{1}, {2}]\" ) |> tab_spanner( label = \"Drug 1\", columns = c(\"n_resp_Drug 1\", \"ci_low_Drug 1\") ) |> tab_spanner( label = \"Placebo\", columns = c(\"n_resp_Placebo\", \"ci_low_Placebo\") ) rx_resp_tbl rx_resp_tbl <- rx_resp_tbl |> tab_row_group( label = \"Age\", rows = everything() ) rx_resp_tbl rx_resp_tbl <- rx_resp_tbl |> cols_align( align = \"center\", columns = starts_with(c(\"n_\", \"ci\", \"or\")) ) |> cols_label( .list = c( \"AAGEGR1\" = \"\", \"n_resp_Placebo\" = \"Event Rate (%)\", \"ci_low_Placebo\" = \"[95% CI]\", \"n_resp_Drug 1\" = \"Event Rate (%)\", \"ci_low_Drug 1\" = \"[95% CI]\", \"or\" = \"Odds ratio\", \"or_ci_low\" = \"[95% CI]\" ) ) |> cols_width( 1 ~ px(80), everything() ~ px(120) ) |> cols_align(align = \"left\", columns = 1) rx_resp_tbl rx_resp_tbl <- rx_resp_tbl |> tab_footnote( footnote = \"Event rate 95% exact confidence interval uses the Clopper−Pearson method.\", locations = cells_column_labels( columns = c(\"ci_low_Placebo\", \"ci_low_Drug 1\") ), placement = \"right\" ) |> tab_options(footnotes.marks = letters) rx_resp_tbl"},{"path":"https://gt.rstudio.com/articles/case-study-clinical-tables.html","id":"protocol-deviation-table","dir":"Articles","previous_headings":"","what":"Protocol Deviation Table","title":"Case Study: Clinical Tables","text":"summary table protocol deviations (PDs) use second CDISC-flavored dataset, namely rx_addv. dataset contains summary row, indicating whether subject ITT population rx_adsl experienced least one major PD . addition subject level summary, individual PDs summarized. now like build table summarize overall counts individual PDs treatment arm furthermore indicate, whether PD related COVID-19 . order build table, first need apply data wrangling functions dplyr tidyr. dataset serves starting point gt. start exposing dataset gt add usual left-aligned headers. next step, like create summary row individual PDs get overall number individual PDs, well corresponding percentage. , first create row group individual PDs using tab_row_group() applied rows PARCAT1 equal PROTOCOL DEVIATIONS. , ’ll arrange order row groups list individual PDs overall summary. Finally, can create summary row, using summary_rows() gt sums columns n’s percentages us (summary functions possible, fn = 'sum' job us). kept column PARCAT1 facilitate generation row group. can hide column now using cols_hide(): Now table roughly right shape, can start format numeric columns merge columns n’s percentages intervention group COVID-19 relationship flag. looks like PD table! can now modify column names create cascade column spanners. can now add footnote, indicating subjects can one PD course study. footnote added tab_footnote() row least one major Protocol Deviation. Finally, can style table, indenting individual PDs Study Procedure Deviations, left-aligning first column centering columns. Note indentation, can still use hidden column PARCAT1 identify individual PDs.","code":"rx_addv |> str() #> tibble [291 × 20] (S3: tbl_df/tbl/data.frame) #> $ STUDYID : chr [1:291] \"GT01\" \"GT01\" \"GT01\" \"GT01\" ... #> ..- attr(*, \"label\")= chr \"Unique Study Identifier\" #> $ STUDYIDN: chr [1:291] \"4001\" \"4001\" \"4001\" \"4001\" ... #> ..- attr(*, \"label\")= chr \"Unique Study Identifier (N)\" #> $ USUBJID : chr [1:291] \"GT1001\" \"GT1002\" \"GT1002\" \"GT1003\" ... #> ..- attr(*, \"label\")= chr \"Unique Subject Identifier\" #> $ TRTA : Factor w/ 2 levels \"Placebo\",\"Drug 1\": 1 1 1 1 1 1 1 1 1 1 ... #> ..- attr(*, \"label\")= chr \"Actual Treatment\" #> $ TRTAN : num [1:291] 1 1 1 1 1 1 1 1 1 1 ... #> ..- attr(*, \"label\")= chr \"Actual Treatment (N)\" #> $ ITTFL : chr [1:291] \"Y\" \"Y\" \"Y\" \"Y\" ... #> ..- attr(*, \"label\")= chr \"ITT Population Flag\" #> $ AGE : int [1:291] 41 39 39 38 38 38 45 45 35 35 ... #> ..- attr(*, \"label\")= chr \"Age\" #> ..- attr(*, \"units\")= chr \"Years\" #> $ AAGEGR1 : Factor w/ 2 levels \"<40\",\">=40\": 2 1 1 1 1 1 2 2 1 1 ... #> ..- attr(*, \"label\")= chr \"Age Group\" #> $ SEX : Factor w/ 3 levels \"Male\",\"Female\",..: 1 2 2 1 1 1 1 1 2 2 ... #> ..- attr(*, \"label\")= chr \"Sex\" #> $ ETHNIC : Factor w/ 3 levels \"Hispanic or Latino\",..: 2 2 2 2 2 2 2 2 1 1 ... #> ..- attr(*, \"label\")= chr \"Ethnicity\" #> $ BLBMI : num [1:291] 33.4 30.5 30.5 22.9 22.9 ... #> ..- attr(*, \"label\")= chr \"Body Mass Index\" #> ..- attr(*, \"units\")= chr \"kg/m2\" #> $ DVTERM : chr [1:291] \"\" \"\" \"Lab values not taken at month 3\" \"\" ... #> ..- attr(*, \"label\")= chr \"Protocol Deviation Term\" #> $ PARAMCD : Factor w/ 6 levels \"PDANYM\",\"PDEV01\",..: 1 1 3 1 2 3 1 2 1 1 ... #> ..- attr(*, \"label\")= chr \"Parameter Code\" #> $ PARAM : Factor w/ 6 levels \"At least one major Protocol Deviation\",..: 1 1 3 1 2 3 1 2 1 1 ... #> ..- attr(*, \"label\")= chr \"Parameter\" #> $ PARCAT1 : chr [1:291] \"OVERALL\" \"OVERALL\" \"PROTOCOL DEVIATION\" \"OVERALL\" ... #> ..- attr(*, \"label\")= chr \"Parameter Category 1\" #> $ DVCAT : chr [1:291] \"\" \"\" \"Major\" \"\" ... #> ..- attr(*, \"label\")= chr \"Protocol Deviation Category\" #> $ ACAT1 : chr [1:291] \"\" \"\" \"Study Procedures Criteria Deviations\" \"\" ... #> ..- attr(*, \"label\")= chr \"Analysis Category 1\" #> $ AVAL : num [1:291] 0 1 1 0 1 1 1 1 0 1 ... #> ..- attr(*, \"label\")= chr \"Analysis Value\" #> $ CRIT1 : chr [1:291] \"COVID-19 Related\" \"COVID-19 Related\" \"COVID-19 Related\" \"COVID-19 Related\" ... #> ..- attr(*, \"label\")= chr \"Analysis Criterion 1\" #> $ CRIT1FL : chr [1:291] \"N\" \"N\" \"N\" \"N\" ... #> ..- attr(*, \"label\")= chr \"Criterion 1 Evaluation Flag\" addv_sum <- rx_addv |> dplyr::group_by(TRTA) |> dplyr::mutate( NTOT = n_distinct(USUBJID), .groups = \"drop\" ) |> dplyr::group_by(TRTA, PARCAT1, PARAM, CRIT1FL) |> dplyr::summarize( n = sum(AVAL, na.rm = TRUE), pct = 100 * sum(AVAL, na.rm = TRUE) / mean(NTOT), .groups = \"drop\" ) |> tidyr::pivot_wider( id_cols = c(PARCAT1, PARAM), names_from = c(TRTA, CRIT1FL), values_from = c(n, pct) ) |> dplyr::mutate(across(where(is.numeric), ~ifelse(is.na(.), 0, .))) |> dplyr::add_row(PARAM = \"Subjects with at least:\", .before = 1) addv_sum #> # A tibble: 7 × 10 #> PARCAT1 PARAM n_Placebo_N n_Placebo_Y `n_Drug 1_N` `n_Drug 1_Y` pct_Placebo_N #> #> 1 NA Subj… NA NA NA NA NA #> 2 OVERALL At l… 24 15 23 5 26.7 #> 3 PROTOCO… GTQ … 7 0 12 0 7.78 #> 4 PROTOCO… Lab … 12 3 7 0 13.3 #> 5 PROTOCO… GTQ … 7 2 7 1 7.78 #> 6 PROTOCO… Mont… 9 4 3 1 10 #> 7 PROTOCO… Mont… 9 8 9 3 10 #> # ℹ 3 more variables: pct_Placebo_Y , `pct_Drug 1_N` , #> # `pct_Drug 1_Y` addv_tbl <- addv_sum |> gt(rowname_col = \"PARAM\") |> tab_header( title = \"xx.x: Demographic and Baseline Data\", subtitle = \"xx.x.x: Major Protocol Deviations and Relationship to COVID-19 - ITT Set\" ) |> opt_align_table_header(align = \"left\") addv_tbl addv_tbl <- addv_tbl |> tab_row_group( label = \" \", rows = PARCAT1 == \"PROTOCOL DEVIATION\" ) |> row_group_order(groups = c(NA, \" \")) |> summary_rows( groups = \" \", columns = where(is.numeric), fns = list(label = \"Study Procedure Deviations\", fn = \"sum\"), side = \"top\" ) addv_tbl addv_tbl <- addv_tbl |> cols_hide(columns = \"PARCAT1\") addv_tbl addv_tbl <- addv_tbl |> sub_missing( rows = 1, missing_text = \"\" ) |> fmt_number( columns = starts_with(\"pct\"), decimals = 1 ) |> cols_merge_n_pct( col_n = \"n_Placebo_Y\", col_pct = \"pct_Placebo_Y\" ) |> cols_merge_n_pct( col_n = \"n_Placebo_N\", col_pct = \"pct_Placebo_N\" ) |> cols_merge_n_pct( col_n = \"n_Drug 1_Y\", col_pct = \"pct_Drug 1_Y\" ) |> cols_merge_n_pct( col_n = \"n_Drug 1_N\", col_pct = \"pct_Drug 1_N\" ) addv_tbl addv_tbl <- addv_tbl |> tab_spanner( label = md(\"COVID-19 Related\"), columns = c(\"n_Placebo_Y\", \"n_Placebo_N\"), id = \"cov_pla\" ) |> tab_spanner( label = md(\"COVID-19 Related\"), columns = c(\"n_Drug 1_Y\", \"n_Drug 1_N\"), id = \"cov_dru\" ) |> tab_spanner( label = md(\"Placebo \\n N=90 (100%) \\n n (%)\"), columns = c(\"n_Placebo_Y\", \"n_Placebo_N\") ) |> tab_spanner( label = md(\"Drug 1 \\n N=90 (100%) \\n n (%)\"), columns = c(\"n_Drug 1_Y\", \"n_Drug 1_N\") ) |> cols_label( .list = list( \"n_Placebo_Y\" = \"Yes\", \"n_Placebo_N\" = \"No\", \"n_Drug 1_Y\" = \"Yes\", \"n_Drug 1_N\" = \"No\" ) ) |> tab_style( style = cell_text(align = \"center\"), locations = cells_column_spanners(spanners = everything()) ) addv_tbl addv_tbl <- addv_tbl |> tab_footnote( footnote = \"Subjects can have more than one Protocol Deviation throughout the study.\", locations = cells_stub(rows = c(\"At least one major Protocol Deviation\")), placement = \"right\" ) addv_tbl addv_tbl |> cols_align( align = \"center\", columns = 3:6 ) |> cols_align( align = \"left\", columns = 1:2 ) |> tab_stub_indent( rows = PARCAT1 == \"PROTOCOL DEVIATION\", indent = 5 )"},{"path":"https://gt.rstudio.com/articles/case-study-gtcars.html","id":"row-groups","dir":"Articles","previous_headings":"","what":"Row Groups","title":"Case Study: gtcars","text":"Let’s use dplyr help make groupings ctry_origin column, provides country origin vehicle manufacturer car. can simply use dplyr::group_by() gtcars dataset pass gt(). get display table arranges cars row groups, name group displayed prominently . Getting row groups preferred order can done easily dplyr’s arrange() function. example, can groups arranged alphabetically manufacturer (mfr) sorted highest sticker price (msrp) lowest. also use factor levels get particular ordering within arrange(). example, can first arrange groups (country origin–ctry_origin) preferred ordering arrange mfr descending msrp . , group_by(ctry_origin) can used sorted tibble passing gt(). last variation combine manufacturer name model name, using combined strings row labels table. just little dplyr can use dplyr::mutate() make new car column followed dplyr::select() remove mfr model columns. introducing tibble gt() function, can now use rowname_col argument specify column serve row labels (newly made car column).","code":"# Use `group_by()` on `gtcars` and pass that to `gt()` gtcars_8 |> group_by(ctry_origin) |> gt() gtcars_8 |> group_by(ctry_origin) |> arrange(mfr, desc(msrp)) |> gt() # Define our preferred order for `ctry_origin` order_countries <- c(\"Germany\", \"Italy\", \"United States\", \"Japan\") # Reorder the table rows by our specific ordering of groups gtcars_8 |> arrange( factor(ctry_origin, levels = order_countries), mfr, desc(msrp) ) |> group_by(ctry_origin) |> gt() # Reorder the table rows by our specific ordering of groups tab <- gtcars_8 |> arrange( factor(ctry_origin, levels = order_countries), mfr, desc(msrp) ) |> mutate(car = paste(mfr, model)) |> select(-mfr, -model) |> group_by(ctry_origin) |> gt(rowname_col = \"car\") # Show the table tab"},{"path":"https://gt.rstudio.com/articles/case-study-gtcars.html","id":"hiding-and-moving-some-columns","dir":"Articles","previous_headings":"","what":"Hiding and Moving Some Columns","title":"Case Study: gtcars","text":"Let’s hide two columns don’t need final table: drivetrain bdy_style. can use cols_hide() function hide columns. end result might also achieved using gtcars |> dplyr::select(-c(drivetrain, bdy_style)), introducing table gt(). function ? Sometimes ’ll need variables conditional statements within gt won’t want display end. Aside hiding columns, let’s move . , done dplyr::select() options gt via cols_move_to_start(), cols_move(), cols_move_to_end() functions.","code":"# Use a few `cols_*()` functions to hide and move columns tab <- tab |> cols_hide(columns = c(drivetrain, bdy_style)) |> cols_move( columns = c(trsmn, mpg_c, mpg_h), after = trim ) # Show the table tab"},{"path":"https://gt.rstudio.com/articles/case-study-gtcars.html","id":"putting-columns-into-groups","dir":"Articles","previous_headings":"","what":"Putting Columns Into Groups","title":"Case Study: gtcars","text":"’s sometimes useful arrange variables/columns groups using spanner column labels. can done gt using tab_spanner() function. takes label columns arguments; label spanner column label columns columns belong group. , ’ll put mpg_c, mpg_h, hp, hp_rpm, trq, trq_rpm columns Performance spanner column, remaining columns won’t grouped together. single spanner column label styled Markdown using md() helper.","code":"# Put the first three columns under a spanner # column with the label 'Performance' tab <- tab |> tab_spanner( label = \"Performance\", columns = c(mpg_c, mpg_h, hp, hp_rpm, trq, trq_rpm) ) # Show the table tab"},{"path":"https://gt.rstudio.com/articles/case-study-gtcars.html","id":"merging-columns-together-and-labeling-them","dir":"Articles","previous_headings":"","what":"Merging Columns Together and Labeling Them","title":"Case Study: gtcars","text":"Sometimes ’d like combine data two columns single column. cols_merge() function allows us , just need describe data combined. table, let’s merge together following pairs columns: mpg_c mpg_h (miles per gallon city highway driving modes) hp hp_rpm (horsepower associated RPM) trq trq_rpm (torque associated RPM) cols_merge() function uses col_1 column col_2 column. combined, col_1 column retained col_2 column dropped. pattern argument uses {1} {2} represent content col_1 col_2. , can use string literals add text like rpm @ sign. Furthermore, targeting HTML table, can use
    tag insert linebreak. going something little special pattern mpg_c mpg_h columns wrap <<\/>>. special pattern syntax tells gt remove anything inside double braces NA value. find single row NA columns (‘Tesla Model S’) rather seeing \"NA\" appear table, ’s better remove entry entirely specialized pattern syntax. Labeling columns essentially means choosing display-friendly labels longer simply column names (default label). cols_label() function makes relabeling possible. accepts series named arguments form = , ....","code":"# Perform three column merges to better present # MPG, HP, and torque; relabel all the remaining # columns for a nicer-looking presentation tab <- tab |> cols_merge( columns = c(mpg_c, mpg_h), pattern = \"<<{1}c
    {2}h>>\" ) |> cols_merge( columns = c(hp, hp_rpm), pattern = \"{1}
    @{2}rpm\" ) |> cols_merge( columns = c(trq, trq_rpm), pattern = \"{1}
    @{2}rpm\" ) |> cols_label( mpg_c = \"MPG\", hp = \"HP\", trq = \"Torque\", year = \"Year\", trim = \"Trim\", trsmn = \"Transmission\", msrp = \"MSRP\" ) # Show the table tab"},{"path":"https://gt.rstudio.com/articles/case-study-gtcars.html","id":"using-formatter-functions","dir":"Articles","previous_headings":"","what":"Using Formatter Functions","title":"Case Study: gtcars","text":"number formatter functions, general naming convention fmt*(). various formatters convenient applying formats numeric character values table’s field. , simply use fmt_currency() msrp column (still refer columns original names) get USD currency decimal places. ’re supplying anything rows argument means want apply formatting entire column data.","code":"# Format the `msrp` column to USD currency # with no display of the currency subunits tab <- tab |> fmt_currency(columns = msrp, decimals = 0) # Show the table tab"},{"path":"https://gt.rstudio.com/articles/case-study-gtcars.html","id":"column-alignment-and-style-changes","dir":"Articles","previous_headings":"","what":"Column Alignment and Style Changes","title":"Case Study: gtcars","text":"can change alignment data columns cols_align(). table, let’s center-align mpg_c, hp, trq columns. columns maintain default alignments. ’s sometimes useful modify default styles table cells. can targeted way tab_style() function. function require two key pieces information: style definition, one locations (cells styles applied ?). style argument commonly uses cell_*() helper functions. three (cell_text(), cell_fill() cell_borders()) contains different arguments different styles supported. use cell_text() helper function specify text size \"12px\" targeted cells—px(12) \"12px\" work equally well . also use helper functions locations argument cells_*() functions. like target data cells columns except year msrp need use cells_body supply target columns columns argument.","code":"# Center-align three columns in the gt table and # modify the text size of a few columns of data tab <- tab |> cols_align( align = \"center\", columns = c(mpg_c, hp, trq) ) |> tab_style( style = cell_text(size = px(12)), locations = cells_body( columns = c(trim, trsmn, mpg_c, hp, trq) ) ) # Show the table tab"},{"path":"https://gt.rstudio.com/articles/case-study-gtcars.html","id":"text-transforms","dir":"Articles","previous_headings":"","what":"Text Transforms","title":"Case Study: gtcars","text":"text transform via text_transform() function great way manipulate text data cells (even ’ve formatted fmt*() function). targeting data cells cells_body() location helper function, supply function fn argument processes vector text (vector x formatted text targeted cells). intend render HTML table, can directly apply HTML tags transformation function. function provide bit complicated build strings trsmn column read better display table.","code":"# Transform the column of text in `trsmn` using # a custom function within `text_transform()`; # here `x` represents a character vector defined # in the `cells_body()` function tab <- tab |> text_transform( locations = cells_body(columns = trsmn), fn = function(x) { # The first character of `x` always # indicates the number of transmission speeds speed <- substr(x, 1, 1) # We can carefully determine which transmission # type we have in `x` with a `dplyr::case_when()` # statement type <- dplyr::case_when( substr(x, 2, 3) == \"am\" ~ \"Automatic/Manual\", substr(x, 2, 2) == \"m\" ~ \"Manual\", substr(x, 2, 2) == \"a\" ~ \"Automatic\", substr(x, 2, 3) == \"dd\" ~ \"Direct Drive\" ) # Let's paste together the `speed` and `type` # vectors to create HTML text replacing `x` paste(speed, \" Speed
    \", type, \"<\/em>\") } ) # Show the table tab"},{"path":"https://gt.rstudio.com/articles/case-study-gtcars.html","id":"table-header-title-and-subtitle","dir":"Articles","previous_headings":"","what":"Table Header: Title and Subtitle","title":"Case Study: gtcars","text":"tab_header() function allows us place table title , optionally, subtitle top display table. ’s generally good idea table, subtitle provides additional information (though isn’t quite case example ).","code":"# Add a table title and subtitle; we can use # markdown with the `md()` helper function tab <- tab |> tab_header( title = md(\"The Cars of **gtcars**\"), subtitle = \"These are some fine automobiles\" ) # Show the table tab"},{"path":"https://gt.rstudio.com/articles/case-study-gtcars.html","id":"adding-a-source-citation","dir":"Articles","previous_headings":"","what":"Adding a Source Citation","title":"Case Study: gtcars","text":"source note can added display table using tab_source_note() function. can even add multiple source notes multiple calls function. , supply web URL using Markdown (md()) ’s easy create link source data.","code":"# Add a source note to the bottom of the table; this # appears below the footnotes tab <- tab |> tab_source_note( source_note = md( \"Source: Various pages within the Edmonds website.\" ) ) # Show the table tab"},{"path":"https://gt.rstudio.com/articles/case-study-gtcars.html","id":"using-the-complete-gtcars-table-and-adding-footnotes","dir":"Articles","previous_headings":"","what":"Using the Complete gtcars table and Adding Footnotes","title":"Case Study: gtcars","text":"Let’s bring together putting together statements developed gtcars_8, applying complete gtcars dataset. time, ’ll add interesting footnotes specific requirements footnoting : tab_footnote() function expects note text footnote argument, locations footnote mark attached. handle placement footnote mark also place footnote footnotes area. , ’ll use cells_body() location helper function. several location helper functions targeting parts table (e.g,. cells_body(), cells_stub(), etc.). location helper interface targeting cells refer documentation examples work practice (e.g., use ?cells_body). cells_body() expects columns (column names, can conveniently provided c()) rows (can vector row names row indices). cells_stub() location helper expects vector rows. cells_column_labels(), provide column labels columns argument. , targeting footnote msrp column label use columns = msrp. terms structuring code, ’re taking previous statements putting first. noted order statements matter end result, also put tab_footnote() statements first (, order) expect output table. . final table looks pretty good conveys additional information planned . table can used lot different places like R Markdown, Shiny, email messages… wherever HTML accepted.","code":"a. identifying the car with the best gas mileage (city) b. identifying the car with the highest horsepower c. stating the currency of the MSRP # Use dplyr functions to get the car with the best city gas mileage; # this will be used to target the correct cell for a footnote best_gas_mileage_city <- gtcars |> arrange(desc(mpg_c)) |> slice(1) |> mutate(car = paste(mfr, model)) |> pull(car) # Use dplyr functions to get the car with the highest horsepower # this will be used to target the correct cell for a footnote highest_horsepower <- gtcars |> arrange(desc(hp)) |> slice(1) |> mutate(car = paste(mfr, model)) |> pull(car) # Define our preferred order for `ctry_origin` order_countries <- c(\"Germany\", \"Italy\", \"United States\", \"Japan\") # Create a display table with `gtcars`, using all of the previous # statements piped together + additional `tab_footnote()` stmts tab <- gtcars |> arrange( factor(ctry_origin, levels = order_countries), mfr, desc(msrp) ) |> mutate(car = paste(mfr, model)) |> select(-mfr, -model) |> group_by(ctry_origin) |> gt(rowname_col = \"car\") |> cols_hide(columns = c(drivetrain, bdy_style)) |> cols_move( columns = c(trsmn, mpg_c, mpg_h), after = trim ) |> tab_spanner( label = \"Performance\", columns = c(mpg_c, mpg_h, hp, hp_rpm, trq, trq_rpm) ) |> cols_merge( columns = c(mpg_c, mpg_h), pattern = \"<<{1}c
    {2}h>>\" ) |> cols_merge( columns = c(hp, hp_rpm), pattern = \"{1}
    @{2}rpm\" ) |> cols_merge( columns = c(trq, trq_rpm), pattern = \"{1}
    @{2}rpm\" ) |> cols_label( mpg_c = \"MPG\", hp = \"HP\", trq = \"Torque\", year = \"Year\", trim = \"Trim\", trsmn = \"Transmission\", msrp = \"MSRP\" ) |> fmt_currency(columns = msrp, decimals = 0) |> cols_align( align = \"center\", columns = c(mpg_c, hp, trq) ) |> tab_style( style = cell_text(size = px(12)), locations = cells_body( columns = c(trim, trsmn, mpg_c, hp, trq) ) ) |> text_transform( locations = cells_body(columns = trsmn), fn = function(x) { speed <- substr(x, 1, 1) type <- dplyr::case_when( substr(x, 2, 3) == \"am\" ~ \"Automatic/Manual\", substr(x, 2, 2) == \"m\" ~ \"Manual\", substr(x, 2, 2) == \"a\" ~ \"Automatic\", substr(x, 2, 3) == \"dd\" ~ \"Direct Drive\" ) paste(speed, \" Speed
    \", type, \"<\/em>\") } ) |> tab_header( title = md(\"The Cars of **gtcars**\"), subtitle = \"These are some fine automobiles\" ) |> tab_source_note( source_note = md( \"Source: Various pages within the Edmonds website.\" ) ) |> tab_footnote( footnote = md(\"Best gas mileage (city) of all the **gtcars**.\"), locations = cells_body( columns = mpg_c, rows = best_gas_mileage_city ) ) |> tab_footnote( footnote = md(\"The highest horsepower of all the **gtcars**.\"), locations = cells_body( columns = hp, rows = highest_horsepower ) ) |> tab_footnote( footnote = \"All prices in U.S. dollars (USD).\", locations = cells_column_labels(columns = msrp) ) # Show the table tab"},{"path":"https://gt.rstudio.com/articles/creating-summary-lines.html","id":"preparing-the-input-data-table","dir":"Articles","previous_headings":"","what":"Preparing the Input Data Table","title":"Creating Summary Lines","text":"Let’s use exibble dataset (included gt package) demonstrate summary rows can added. table contains categorical columns named row group, along several columns varying data types. preview exibble dataset using solely gt() function options: ’ll create table stub row labels (using row column) row groups (using group column). end result table organized labeled rows grouped together (row group labels identify row groups). make examples bit easier follow, columns exibble first dropped dplyr::select() statement. two groups data table: grp_a grp_b. gives us flexibility create grand summary group-wise summary rows.","code":"exibble |> gt() # Create a gt table using the `exibble` dataset exibble_a <- exibble |> select(-c(fctr, date, time, datetime)) |> gt(rowname_col = \"row\", groupname_col = \"group\") |> sub_missing() exibble_a"},{"path":"https://gt.rstudio.com/articles/creating-summary-lines.html","id":"generating-group-wise-summary-rows","dir":"Articles","previous_headings":"","what":"Generating Group-wise Summary Rows","title":"Creating Summary Lines","text":"Group-wise summary rows can generated using summary_rows() function , importantly, specifying groups receive summary rows. can provide vector group names, c(\"grp_a\", \"grp_b\"), , use everything() signify groups receive summary rows. Aside selection groups, control columns used summary. Since call summary_rows() performs one set aggregation functions, may want specific aggregations different subsets columns. make sort summary, need use functions perform aggregation. can provide base functions mean(), sum(), min(), max(), , within list(). function provided result summary row group. function yield row, need able label row. , summary row receive summary row label. can provide preferred names naming functions within list (e.g, list(average = \"mean\", total = \"sum\", SD = \"sd\")). can specify aggregation functions use function names quotes (e.g., \"sum\"), bare functions (e.g., sum), one-sided R formulas using leading ~. formula representation, . serves data summarized, can use sum(., na.rm = TRUE). use named arguments recommended names serve summary row labels (labels can derived function names providing bare function names). Now summary_rows() function somewhat explained, let’s look can create group-wise summary rows exibble_a table. ’ll create summaries available groups (groups = everything()) use mean(), sum(), sd() functions function-name--quotes method (pertain num column). previous example NA value num/row_6 cell, get NA outputs mean(), sum(), sd() grp_b’s summary rows (replaced em dashes, controllable missing_text option). avoid , let’s rewrite using names--formulae method. see summary rows created groups. However, output summary row data quite different cell data. formatter argument (extra ... arguments) allows use fmt_*() functions ’d normally use format cell data. example (another rewrite ), cell data num column formatted scientific notation resulting summary cell data also formatted way (including options decimals = 3). input fns permissive regard functions defined. entirely valid provide functions various forms shown earlier list(\"sum\", avg = ~mean(., na.rm = TRUE), SD = \"sd\") correctly interpreted. recommended use formula notation. default formatter set fmt_number sensible default many scenarios. setting argument values particular formatter can done ... area function call (done decimals argument).","code":"# Create group-wise summary rows for both # groups (using `groups = everything()`); use the # `mean()`, `sum()`, and `sd()` functions # (only for the `num` column) exibble_b <- exibble_a |> summary_rows( groups = everything(), columns = num, fns = list( average = \"mean\", total = \"sum\", SD = \"sd\" ) ) exibble_b # Create group-wise summary rows for both # groups (using `groups = everything()`); we will # use names and formulas this time in `fns` exibble_c <- exibble_a |> summary_rows( groups = everything(), columns = num, fns = list( avg = ~ mean(., na.rm = TRUE), total = ~ sum(., na.rm = TRUE), s.d. = ~ sd(., na.rm = TRUE) ) ) exibble_c # Define a named list of aggregation # functions and summary row labels fns_labels <- list( avg = ~mean(., na.rm = TRUE), total = ~sum(., na.rm = TRUE), s.d. = ~sd(., na.rm = TRUE) ) # Create group-wise summary rows as # before, supply `fns_labels` to `fns`, # and format the cell summary data exibble_d <- exibble_a |> fmt_scientific( columns = num, decimals = 3 ) |> summary_rows( groups = everything(), columns = num, fns = fns_labels, fmt = list(~ fmt_scientific(., decimals = 3)) ) exibble_d"},{"path":"https://gt.rstudio.com/articles/creating-summary-lines.html","id":"using-multiple-calls-of-summary_rows","dir":"Articles","previous_headings":"","what":"Using Multiple Calls of summary_rows()","title":"Creating Summary Lines","text":"can re-use summary row labels fill otherwise empty summary cells similar aggregations perhaps different formatting options. ’s example currency column contains aggregate values share summary rows num column, adds two rows, uses currency formatting: thing note example even though two independent calls summary_rows() made, summary data within common summary row names ‘squashed’ together, thus avoiding fragmentation summary rows. Put another way, don’t create additional summary rows across separate calls referencing summary row labels. summary row labels provided fns different across columns however, additional summary rows produced even types data aggregations functionally equivalent. can also store argument values local variables pass separate fmt_*number*() calls also arguments within summary_rows() calls. useful standardizing formatting parameters across different table cell types. ’s example , additional passes fr_BE locale functions take locale value. Passing parameters like useful, especially larger numbers columns. store formatting parameters outside gt() pipeline, separate concerns data structuring data formatting. Putting styles options objects becomes important intend centralize formatting options reuse.","code":"# Create group-wise summary rows as # before, supply `fns_labels` to `fns`, # and format the cell summary data exibble_e <- exibble_a |> fmt_scientific( columns = num, decimals = 3 ) |> fmt_currency( columns = currency, currency = \"EUR\" ) |> summary_rows( groups = everything(), columns = num, fns = fns_labels, fmt = list(~ fmt_scientific(., decimals = 3)) ) |> summary_rows( groups = \"grp_a\", columns = currency, fns = c( fns_labels, min = ~ min(.), max = ~ max(.) ), fmt = list(~ fmt_currency(., currency = \"EUR\")) ) exibble_e # Provide common formatting parameters to a list # object named `formats`; the number of decimal # places will be `2` and the locale is \"fr_BE\" formats <- list( decimals = 3, locale = \"fr_BE\", currency = \"EUR\" ) # Format the `num` and `currency` columns # (using the values stored in `formats`); # when generating summary rows we can also # supply formatter options from this list exibble_f <- exibble_a |> fmt_scientific( columns = num, decimals = formats$decimals, locale = formats$locale ) |> fmt_currency( columns = currency, currency = formats$currency, locale = formats$locale ) |> summary_rows( groups = everything(), columns = num, fns = fns_labels, fmt = list(~ fmt_scientific(., decimals = formats$decimals, locale = formats$locale )) ) |> summary_rows( groups = \"grp_a\", columns = currency, fns = c( fns_labels, min = ~min(.), max = ~max(.) ), fmt = list(~ fmt_currency(., currency = formats$currency, locale = formats$locale )) ) exibble_f"},{"path":"https://gt.rstudio.com/articles/creating-summary-lines.html","id":"creating-a-grand-summary","dir":"Articles","previous_headings":"","what":"Creating a Grand Summary","title":"Creating Summary Lines","text":"grand summary aggregates column data regardless groups within data. Grand summaries can also created gt tables don’t row groups, , don’t stub. Finally, can create table group-wise summaries grand summary. Let’s keep simple create grand summary rows table without stub. ’ll use exibble dataset . exibble columns select()ed, passed gt(), grand_summary_rows(). Notice , resulting table, stub created just summary row labels (go somewhere). grand summary can used conjunction group-wise summaries. ’s table types summaries present: Note grand summary double line separating group-wise summary ’s part grp_b. default styling appears subtle, can elect add styling group-wise summaries grand summary using tab_options().","code":"# Create a simple grand summary on a gt # table that contains no stub exibble_g <- exibble |> select(num, char, currency) |> gt() |> grand_summary_rows( columns = c(num, currency), fns = fns_labels ) exibble_g # Using the table in `exibble_f`, create # grand summary rows (using two separate # calls of `grand_summary_rows()` since # the formatting will be different) exibble_h <- exibble_f |> grand_summary_rows( columns = num, fns = fns_labels, fmt = list(~ fmt_number(., suffixing = TRUE, locale = formats$locale )) ) |> grand_summary_rows( columns = currency, fns = fns_labels, fmt = list(~ fmt_currency(., suffixing = TRUE, locale = formats$locale )) ) exibble_h"},{"path":"https://gt.rstudio.com/articles/creating-summary-lines.html","id":"adding-some-style-to-the-summary-cells","dir":"Articles","previous_headings":"","what":"Adding Some Style to the Summary Cells","title":"Creating Summary Lines","text":"summary cells (group-wise grand) distinct appearance sets apart data cells, ’s always option modify appearance. can use tab_options() function perform customizations. options specific summary cells (group-wise summaries) grand summary cells: summary_row.background.color & grand_summary_row.background.color summary_row.padding & grand_summary_row.padding summary_row.text_transform & grand_summary_row.text_transform can also target summary cells grand summary cells location helper functions cells_summary() cells_grand_summary(). important adding footnotes tab_footnote() setting styles tab_style() (locations argument). example uses multiple calls tab_options() tab_footnote(). cell background color types summary cells modified two footnotes added.","code":"# Using the gt table of `exibble_h` as a # starting point, style summary cells with # `tab_options()` and add two footnotes exibble_i <- exibble_h |> tab_options( summary_row.background.color = \"lightblue\", grand_summary_row.background.color = \"lightgreen\" ) |> tab_footnote( footnote = md(\"Mean of all *num* values.\"), locations = cells_grand_summary( columns = \"num\", rows = \"avg\" ) ) |> tab_footnote( footnote = md(\"Highest `currency` value in **grp_a**\"), locations = cells_summary( groups = \"grp_a\", columns = \"currency\", rows = \"max\" ) ) exibble_i"},{"path":"https://gt.rstudio.com/articles/creating-summary-lines.html","id":"extracting-the-summary-data-from-the-gt-table-object","dir":"Articles","previous_headings":"","what":"Extracting the Summary Data from the gt Table Object","title":"Creating Summary Lines","text":"reproducible workflow, want situations data created modified accessed. summarized values created gt pipeline presents advantages readability intent analysis, recognized output table essentially ‘read ’, input data undergoes processing movement entirely different format. However, object created still contains data can obtain summary data gt table object using extract_summary() function. Taking gt_summary object, get list tibbles containing summary data preserving correct data types: output tibbles within list always contain groupname rowname columns. groupname column filled name row group given summary_rows(). rowname column contains descriptive stub labels summary rows (recall values either supplied explicitly summary_rows(), , generated function names). remaining columns original dataset. output data extract_summary() can reintroduced reproducible workflow serve downstream inputs undergo validation. Perhaps interestingly, output tibbles structured way facilitates direct input back gt() (.e., magic groupname rowname columns). can produce new, standalone summary table summary rows now data rows:","code":"# Extract the summary data from `exibble_d` # to a list object summary_list <- exibble_d |> extract_summary() # Print out the summary for the `grp_a` group summary_list$summary_df_data_list$grp_a #> # A tibble: 3 × 8 #> group_id row_id rowname num char currency row group #> #> 1 grp_a avg avg 120. NA NA NA NA #> 2 grp_a total total 480. NA NA NA NA #> 3 grp_a s.d. s.d. 217. NA NA NA NA # Print out the summary for the `grp_b` group summary_list$summary_df_data_list$grp_b #> # A tibble: 3 × 8 #> group_id row_id rowname num char currency row group #> #> 1 grp_b avg avg 3220850 NA NA NA NA #> 2 grp_b total total 9662550 NA NA NA NA #> 3 grp_b s.d. s.d. 4916123. NA NA NA NA # Take `exibble_d`, which internally has a list # of summary data frames, extract the summaries, # and then combine them; input that into `gt()`, # and format the `num` column with `fmt_number()` exibble_d |> extract_summary() |> unlist(recursive = FALSE) |> bind_rows() |> gt() |> fmt_number( columns = num, decimals = 1 ) |> sub_missing(columns = c(char, currency, row, group))"},{"path":"https://gt.rstudio.com/articles/creating-summary-lines.html","id":"providing-our-own-aggregation-functions-to-generate-summary-rows","dir":"Articles","previous_headings":"","what":"Providing Our Own Aggregation Functions to Generate Summary Rows","title":"Creating Summary Lines","text":"many functions available base R within packages useful aggregate functions, may occasionally need create custom functions. taking approach main things keep mind vector values main input, , single value returned. return value can pretty much class (e.g., numeric, character, logical) ’s formatter function handle custom formatting also converting character. , ’ll define function takes vector numeric values outputs two highest values (sorted low high) threshold value. output function always formatted character string. character value ’s returned, don’t need formatting functions like fmt_number(), fmt_percent(), etc. However, useful formatter (need formatter) fmt_passthrough() function. Like name suggests, great extent passes values formats character (like fmt_*() function ) provides option decorate output pattern. Let’s look agg_highest_two_above_value() function can used fmt_passthrough() formatter function. can extract grand summary data exibble_j object. Note columns num currency classed character since character outputs generated agg_highest_two_above_value() function.","code":"# Define a function that gives the # highest two values above a threshold agg_highest_two_above_value <- function(x, threshold) { # Get sorted values above threshold value values <- sort(round(x[x >= threshold], 2)) # Return character string with 2 highest values above threshold if (length(values) == 0) { return(paste0(\"No values above \", threshold)) } else { return( paste( formatC( tail( sort(round(x[x > threshold], 2)), 2), format = \"f\", digits = 2), collapse = \", \")) } } # Let's test this function with some values agg_highest_two_above_value( x = c(0.73, 0.93, 0.75, 0.86, 0.23, 0.81), threshold = 0.8 ) #> [1] \"0.86, 0.93\" # Create a gt table with `exibble_a` and use # the custom function with a threshold of `20`; # the `fmt_passthrough` funtion allows for # minimal formatting of the aggregate values exibble_j <- exibble_a |> grand_summary_rows( columns = c(num, currency), fns = list(high = ~ agg_highest_two_above_value(., 20)), fmt = list(~ fmt_passthrough(., pattern = \"({x})\")) ) exibble_j # Extract the summary list from `exibble_j` # and inspect using `str()` exibble_j |> extract_summary() |> str() #> List of 1 #> $ summary_df_data_list:List of 1 #> ..$ ::GRAND_SUMMARY: tibble [1 × 8] (S3: tbl_df/tbl/data.frame) #> .. ..$ group_id: chr \"::GRAND_SUMMARY\" #> .. ..$ row_id : chr \"high\" #> .. ..$ rowname : chr \"high\" #> .. ..$ num : chr \"777000.00, 8880000.00\" #> .. ..$ char : num NA #> .. ..$ currency: chr \"1325.81, 65100.00\" #> .. ..$ row : num NA #> .. ..$ group : num NA"},{"path":"https://gt.rstudio.com/articles/gt-datasets.html","id":"countrypops","dir":"Articles","previous_headings":"","what":"countrypops","title":"gt Datasets","text":"dataset provides total populations 215 countries yearly basis, 1960 2021. source data comes World Bank, cleaned tidied inclusion package. row contains population value country given year, NA values population indicate country exist particular year. countrypops dataset tibble 13,330 rows 5 variables. explanations variables:","code":""},{"path":"https://gt.rstudio.com/articles/gt-datasets.html","id":"a-countrypops-example","dir":"Articles","previous_headings":"countrypops","what":"A countrypops Example","title":"gt Datasets","text":"table ’ll create countrypops meet requirements: use countries Oceania countries different regions Oceania grouped together provide populations 2000, 2010, 2020 years ; appear separate columns spanner group column stating columns refer population values format population figures contain commas provide descriptive title","code":"# Get vectors of 2-letter country codes for each region of Oceania Australasia <- c(\"AU\", \"NZ\") Melanesia <- c(\"NC\", \"PG\", \"SB\", \"VU\") Micronesia <- c(\"FM\", \"GU\", \"KI\", \"MH\", \"MP\", \"NR\", \"PW\") Polynesia <- c(\"PF\", \"WS\", \"TO\", \"TV\") # Create a gt table based on a preprocessed `countrypops` countrypops |> filter(country_code_2 %in% c( Australasia, Melanesia, Micronesia, Polynesia) ) |> filter(year %in% c(2000, 2010, 2020)) |> mutate(region = case_when( country_code_2 %in% Australasia ~ \"Australasia\", country_code_2 %in% Melanesia ~ \"Melanesia\", country_code_2 %in% Micronesia ~ \"Micronesia\", country_code_2 %in% Polynesia ~ \"Polynesia\", )) |> pivot_wider(names_from = year, values_from = population) |> arrange(region, desc(`2020`)) |> select(-starts_with(\"country_code\")) |> gt( rowname_col = \"country_name\", groupname_col = \"region\" ) |> tab_header(title = \"Populations of Oceania's Countries in 2000, 2010, and 2020\") |> tab_spanner( label = \"Total Population\", columns = everything() ) |> fmt_integer()"},{"path":"https://gt.rstudio.com/articles/gt-datasets.html","id":"sza","dir":"Articles","previous_headings":"","what":"sza","title":"gt Datasets","text":"solar zenith angle one measure solar position. can thought ‘height’ sun relation observer. solar zenith angle 0° indicates sun directly overhead (different solar angle, solar altitude angle, 90° instance). sun horizontal (e.g., sunrise/sunset) observe solar zenith angle around 90° (matter atmospheric refraction). nighttime, solar zenith angles range 90–180 possible (, depending location observer). sza dataset calculated values solar zenith angles every 30 minutes 04:00 12:00 (true solar time). Temporally, calculations 1st every month year. Spatially, observer located somewhere along latitudes 20°N, 30°N, 40°N, 50°N (using true solar time, longitude unimportant). quite extensive topic, information can found using ?sza R console visiting page. sza dataset tibble 816 rows 4 variables. explanations variables:","code":""},{"path":"https://gt.rstudio.com/articles/gt-datasets.html","id":"an-sza-example","dir":"Articles","previous_headings":"sza","what":"An sza Example","title":"gt Datasets","text":"table ’ll create sza meet requirements: filter data just use 20°N data remove latitude column NA values sza column removed reshape table columns tst (true solar time) contain angles degrees (sza column) gt output table months row labels stub missing values replaced empty string (cells blank) stubhead label state ’s inside stubs (months, 20°N) table heading decorated HTML Black Sun Rays (☀) symbol fit large amount data small area, use table options reduce text size row padding","code":"# Create a gt table based on a preprocessed `sza` sza |> filter(latitude == 20) |> select(-latitude) |> filter(!is.na(sza)) |> pivot_wider(names_from = \"tst\", values_from = sza) |> gt(rowname_col = \"month\") |> sub_missing(missing_text = \"\") |> tab_stubhead(label = md(\"month
    (20°N)\")) |> tab_header(title = md(\"☀ Solar Zenith Angles ☀\")) |> tab_options( column_labels.font.size = \"smaller\", table.font.size = \"smaller\", data_row.padding = px(3) )"},{"path":"https://gt.rstudio.com/articles/gt-datasets.html","id":"gtcars","dir":"Articles","previous_headings":"","what":"gtcars","title":"gt Datasets","text":"gtcars dataset takes mtcars left . contains 47 cars 2014-2017 model years. Many gtcars vehicles grand tourers. Indeed, many provide ability cross entire continent speed comfort yet, ’s called , allow experience driving thrills. chassis suspension cases top-notch supply superb handling roadholding routes one conceivably encounter grand touring experience. two plus two (2 + 2) seating configuration smartly designed deliver comfort driver passenger, adequate space luggage, room spare. gtcars dataset tibble 47 rows 15 variables. explanations variables:","code":""},{"path":"https://gt.rstudio.com/articles/gt-datasets.html","id":"a-gtcars-example","dir":"Articles","previous_headings":"gtcars","what":"A gtcars Example","title":"gt Datasets","text":"table ’ll create gtcars meet requirements: include German cars limit dataset top two expensive offerings German manufacturer information included manufacturer (mfr), car model (model), drivetrain, price (msrp) add table title combine car make model single column capitalize drivetrain text format prices USD currency commas decimal places shown relabel column headings provide nicer labels add two footnotes explain drivetrain abbreviations specify currency msrp prices; ensure footnote marks lowercase letters","code":"# Create a gt table based on a preprocessed `gtcars` gtcars |> filter(ctry_origin == \"Germany\") |> group_by(mfr) |> top_n(n = 2, msrp) |> ungroup() |> select(mfr, model, drivetrain, msrp) |> gt() |> tab_header(title = \"Select German Automobiles\") |> cols_merge(columns = c(mfr, model)) |> text_transform( locations = cells_body(columns = drivetrain), fn = function(x) toupper(x) ) |> fmt_currency(decimals = 0) |> cols_label( mfr = \"Car\", drivetrain = \"Drivetrain\", msrp = \"MSRP\" ) |> tab_footnote( footnote = \"Prices in USD.\", locations = cells_column_labels(columns = msrp) ) |> tab_footnote( footnote = \"AWD = All Wheel Drive, RWD = Rear Wheel Drive.\", locations = cells_column_labels(columns = drivetrain) ) |> opt_footnote_marks(marks = \"letters\")"},{"path":"https://gt.rstudio.com/articles/gt-datasets.html","id":"sp500","dir":"Articles","previous_headings":"","what":"sp500","title":"gt Datasets","text":"S&P 500 capitalization-weighted index 500 leading companies (bigger companies influence within index) common stock listed either NYSE NASDAQ markets. companies chosen intended provide representation U.S. economy. index managed list (managed S&P Dow Jones Indices LLC) occasional changes constituent companies based performance changes economy. daily S&P 500 data available sp500 dataset, daily indicators (price statistics, volume, etc.) 1950 2015, inclusive. 16,607 rows dataset, 7 variables:","code":""},{"path":"https://gt.rstudio.com/articles/gt-datasets.html","id":"an-sp500-example","dir":"Articles","previous_headings":"sp500","what":"An sp500 Example","title":"gt Datasets","text":"table ’ll create sp500 meet requirements: use data period 2010-06-02 2010-06-15 adjusted close adj_close column won’t included title subtitle added describe contents table put column labels title case format date column appear ‘2 Jun 2010’ price columns (open, high, low, close) appear USD large numbers volume shown billions (B suffix) - -pointing triangles (green red) added alongside close price appropriate","code":"# Define the start and end dates for the data range start_date <- \"2010-06-02\" end_date <- \"2010-06-15\" # The HTML decimal references for the black # up- and down-pointing triangles are: #9650 and #9660; # use an in-line style to apply color up_arrow <- \"▲<\/span>\" down_arrow <- \"▼<\/span>\" # Create a gt table based on a preprocessed `sp500` sp500 |> filter(date >= start_date & date <= end_date) |> select(-adj_close) |> gt() |> tab_header( title = \"S&P 500\", subtitle = glue::glue(\"{start_date} to {end_date}\") ) |> fmt_date( columns = date, date_style = \"day_m_year\" ) |> fmt_currency(columns = c(open, high, low, close)) |> fmt_number(columns = volume, suffixing = TRUE) |> text_transform( locations = cells_body( columns = close, rows = close > open ), fn = function(x) paste(x, up_arrow) ) |> text_transform( locations = cells_body( columns = close, rows = close < open ), fn = function(x) paste(x, down_arrow) ) |> cols_label_with( columns = everything(), fn = ~ paste0(toupper(substr(., 1, 1)), substr(., 2, nchar(.))) )"},{"path":"https://gt.rstudio.com/articles/gt-datasets.html","id":"pizzaplace","dir":"Articles","previous_headings":"","what":"pizzaplace","title":"gt Datasets","text":"pizzaplace dataset unusual say least. brings questions answers. ‘Greek’ pizza (the_greek) comes XL XXL sizes whilst (almost) pizzas adhere S-M-L paradigm? ‘Brie Carre’ pizza (brie_carre) small? Also, real (?), , nature reality? (questions quite complicated… however, can doubt existence material world, doubt existence someone thinking delicious pizzas offer pizzaplace.) somehow obtained 2015 sales pizzaplace, row pizza sold. 32 different types pizza 4 different categories: classic, chicken, supreme, veggie. great year sales, personal problems notwithstanding. kitchen fire late September help morale situation. Nevertheless, $817,860 sales year! indeed something cheerful . Let’s learn fascinating dataset structured:","code":""},{"path":"https://gt.rstudio.com/articles/gt-datasets.html","id":"a-pizzaplace-example","dir":"Articles","previous_headings":"pizzaplace","what":"A pizzaplace Example","title":"gt Datasets","text":"Let’s make reporting table pizzaplace dataset requirements: obtain total sale numbers revenue size pizza category (type) create gt table row represents combination size-type (size provides row labels type forms row groups) add title explain contents table format numeric sold column use commas decimal places format currency values (income) USD currency add summary grouping provides total sell counts revenue amounts color row groups summary cells add little pizzazz","code":"# Create a gt table based on a preprocessed `pizzaplace` pizzaplace |> dplyr::group_by(type, size) |> dplyr::summarize( sold = n(), income = sum(price), .groups = \"drop_last\" ) |> gt(rowname_col = \"size\") |> tab_header(title = \"Pizzas Sold in 2015\") |> fmt_integer(columns = sold) |> fmt_currency(columns = income) |> summary_rows( columns = sold, fns = list(TOTAL = \"sum\"), fmt = list(~ fmt_integer(.)) ) |> summary_rows( columns = income, fns = list(TOTAL = \"sum\"), fmt = list(~ fmt_currency(.)) ) |> tab_options( summary_row.background.color = \"#ACEACE\", row_group.background.color = \"#FFEFDB\" )"},{"path":"https://gt.rstudio.com/articles/gt-datasets.html","id":"exibble","dir":"Articles","previous_headings":"","what":"exibble","title":"gt Datasets","text":"example tibble ’s useful gt called exibble. ’s 8 rows, clear ordering data, columns contain data can tested various gt formatter functions (fmt*()). table describing columns exibble:","code":""},{"path":"https://gt.rstudio.com/articles/gt-datasets.html","id":"an-exibble-example","dir":"Articles","previous_headings":"exibble","what":"An exibble Example","title":"gt Datasets","text":"Let’s test many formatter functions possible exibble also using row labels row groups (furnished row group columns). ’ll format num display numbers 2 decimal places. dates date formatted date_style 6 (m_day_year style, use info_date_style() learn ). 24-h time values time use time_style 4 (hm_p, info info_time_style()). Datetimes datetime column can formatted fmt_datetime() function (uses date_style time_style arguments). column currency formatted currency fmt_currency ’ll consider values euros (currency = \"EUR\").","code":"# Create a gt table based on `exibble` exibble |> gt( rowname_col = \"row\", groupname_col = \"group\" ) |> fmt_number( columns = num, decimals = 2 ) |> fmt_date( columns = date, date_style = \"m_day_year\" ) |> fmt_time( columns = time, time_style = \"h_m_p\" ) |> fmt_datetime( columns = datetime, date_style = \"m_day_year\", time_style = \"h_m_p\" ) |> fmt_currency( columns = currency, currency = \"EUR\" ) |> tab_options( column_labels.font.size = \"small\", table.font.size = \"small\", row_group.font.size = \"small\", data_row.padding = px(3) )"},{"path":"https://gt.rstudio.com/articles/gt.html","id":"a-walkthrough-of-the-gt-basics-with-a-simple-table","dir":"Articles","previous_headings":"","what":"A Walkthrough of the gt Basics with a Simple Table","title":"Introduction to Creating gt Tables","text":"Let’s use less common dataset available R datasets package: islands. ’s actually data frame named vector. ’s okay though, can use use dplyr prepare tibble : Given islands_tbl tibble, now suitable input gt. main entry point gt API gt() function. pass islands_tbl function gt(), ’ll get gt Table output. aside, easily used data frame instead valid Table Data gt. doesn’t look bad. Sure, ’s basic really didn’t really ask much. receive proper table column labels data. Also, default striping nice touch. Oftentimes however, ’ll want bit : Table header, Stub, sometimes footnotes source notes Table Footer part.","code":"# Take the `islands` dataset and use some # dplyr functionality to obtain the ten # biggest islands in the world islands_tbl <- tibble( name = names(islands), size = islands ) |> arrange(desc(size)) |> slice(1:10) # Display the table islands_tbl #> # A tibble: 10 × 2 #> name size #> #> 1 Asia 16988 #> 2 Africa 11506 #> 3 North America 9390 #> 4 South America 6795 #> 5 Antarctica 5500 #> 6 Europe 3745 #> 7 Australia 2968 #> 8 Greenland 840 #> 9 New Guinea 306 #> 10 Borneo 280 # Create a display table showing ten of # the largest islands in the world gt_tbl <- gt(islands_tbl) # Show the gt Table gt_tbl"},{"path":"https://gt.rstudio.com/articles/gt.html","id":"adding-parts-to-this-simple-table","dir":"Articles","previous_headings":"","what":"Adding Parts to this Simple Table","title":"Introduction to Creating gt Tables","text":"gt package makes relatively easy add parts resulting gt Table better conveys information want present. table parts work well together possible variations arrangement can handle tabular presentation needs. previous gt Table demonstrated two parts, Column Labels Table Body. next examples show table parts available. way main parts table (subparts) fit together: parts (roughly top bottom) : Table Header (optional; title possibly subtitle) Stub Stub Head (optional; contains row labels, optionally within row groups row group labels possibly summary labels summary present) Column Labels (contains column labels, optionally spanner column labels) Table Body (contains columns rows cells) Table Footer (optional; possibly footnotes source notes) way add parts like Table Header footnotes Table Footer use tab_*() family functions. Table Header easy add let’s see previous table looks title subtitle. can add part using tab_header() function: Header table part provides opportunity describe data ’s presented. subtitle, functions subtitle, optional part Header. may also style title subtitle using Markdown! wrapping values passed title subtitle md() function. example table data truncated brevity: source note can added table’s footer use tab_source_note() function. works way tab_header() (also allows Markdown inputs) except can called multiple times—invocation results addition source note. Footnotes live inside Footer part footnote marks attached cell data. Footnotes added tab_footnote() function. helper function cells_body() can used location argument specify data cells target footnote. cells_body() helper two arguments columns rows. , can supply (1) vector colnames rownames, (2) vector column/row indices, (3) bare column names wrapped c() row labels within c(), (4) select helper function (starts_with(), ends_with(), contains(), matches(), one_of(), everything()). rows specifically, can use conditional statement column names variables (e.g., size > 15000). simple example footnotes can added table cell. Let’s add footnote references North America South America cells name column: slightly complex example adding footnotes use expressions rows help target cells column underlying data islands_tbl. First, set dplyr statements obtains name ‘island’ largest landmass. assigned largest object used first tab_footnote() call targets cell size column next name value stored largest (‘Asia’). second tab_footnote() similar except supplying conditional statement gets lowest population. able supply reference locations table using cells_body() helper function supplying necessary targeting columns rows arguments. cells_*() functions similar interfaces allow us target cells different parts table.","code":"# Make a display table with the `islands_tbl` # table; put a heading just above the column labels gt_tbl <- gt_tbl |> tab_header( title = \"Large Landmasses of the World\", subtitle = \"The top ten largest are presented\" ) # Show the gt Table gt_tbl # Use markdown for the heading's `title` and `subtitle` to # add bold and italicized characters gt(islands_tbl[1:2,]) |> tab_header( title = md(\"**Large Landmasses of the World**\"), subtitle = md(\"The *top two* largest are presented\") ) # Display the `islands_tbl` data with a heading and # two source notes gt_tbl <- gt_tbl |> tab_source_note( source_note = \"Source: The World Almanac and Book of Facts, 1975, page 406.\" ) |> tab_source_note( source_note = md(\"Reference: McNeil, D. R. (1977) *Interactive Data Analysis*. Wiley.\") ) # Show the gt table gt_tbl # Add footnotes (the same text) to two different # cell; data cells are targeted with `data_cells()` gt_tbl <- gt_tbl |> tab_footnote( footnote = \"The Americas.\", locations = cells_body(columns = name, rows = 3:4) ) # Show the gt table gt_tbl # Determine the row that contains the # largest landmass ('Asia') largest <- islands_tbl |> arrange(desc(size)) |> slice(1) |> pull(name) # Create two additional footnotes, using the # `columns` and `where` arguments of `data_cells()` gt_tbl <- gt_tbl |> tab_footnote( footnote = md(\"The **largest** by area.\"), locations = cells_body( columns = size, rows = name == largest ) ) |> tab_footnote( footnote = \"The lowest by area.\", locations = cells_body( columns = size, rows = size == min(size) ) ) # Show the gt table gt_tbl"},{"path":"https://gt.rstudio.com/articles/gt.html","id":"the-stub","dir":"Articles","previous_headings":"","what":"The Stub","title":"Introduction to Creating gt Tables","text":"Stub area left table contains row labels, may contain row group labels, summary labels. subparts can grouped sequence row groups. Stub Head provides location label describes Stub. Stub optional since cases Stub wouldn’t useful (e.g., display tables presented just fine without Stub). easy way generate Stub part specifying stub column gt() function rowname_col argument. signal gt named column used stub, making row labels. Let’s add stub islands_tbl dataset modifying call gt(): Notice landmass names left unstriped area? ’s stub. can apply ’s known stubhead label. label can added tab_stubhead() function: important thing note table now one column. , stub, two columns present (column labels name size) now column number 1 (column) size. apply table parts (including footnotes) use following statements: Let’s incorporate row groups display table. divides rows groups, creating row groups, results display row group labels right group. can easily done table containing row labels. can make new row group call tab_row_group() function. inputs row group names label argument, row references rows argument. can use strategies reference rows footnotes (e.g., vectors names/indices, select helpers, etc.). create three row groups (row group labels continent, country, subregion) grouping rows. Three row groups made since three unique categories groupname. Across top row group row group label contained separate row (cut across field contain nothing row group label). rearrangement rows carried ensure rows collected within appropriate row groups. groups rows row groups great way present information. Including data summaries particular group natural extension idea. process adding summary rows summary labels covered separate article (Creating Summary Lines). Another way make row groups column group names present input data table. example islands_tbl, groupname column categories continent, country, subregion appropriate rows produce row groups using gt() function’s groupname_col argument (e.g., gt(islands_tbl, rowname_col = \"name\", groupname_col = \"groupname\") |> ...). , need use tab_row_group() statements. strategy supplying group names column can sometimes advantageous since can rely functions available dplyr generate categories (e.g., using case_when() if_else()).","code":"# Create a gt table showing ten of the # largest islands in the world; this # time with a stub gt_tbl <- islands_tbl |> gt(rowname_col = \"name\") # Show the gt table gt_tbl # Generate a simple table with a stub # and add a stubhead label gt_tbl <- gt_tbl |> tab_stubhead(label = \"landmass\") # Show the gt table gt_tbl # Display the `islands_tbl` data with a stub, # a heading, source notes, and footnotes gt_tbl <- gt_tbl |> tab_header( title = \"Large Landmasses of the World\", subtitle = \"The top ten largest are presented\" ) |> tab_source_note( source_note = \"Source: The World Almanac and Book of Facts, 1975, page 406.\" ) |> tab_source_note( source_note = md(\"Reference: McNeil, D. R. (1977) *Interactive Data Analysis*. Wiley.\") ) |> tab_footnote( footnote = md(\"The **largest** by area.\"), locations = cells_body( columns = size, rows = largest ) ) |> tab_footnote( footnote = \"The lowest by population.\", locations = cells_body( columns = size, rows = contains(\"arc\") ) ) # Show the gt table gt_tbl # Create three row groups with the # `tab_row_group()` function gt_tbl <- gt_tbl |> tab_row_group( label = \"continent\", rows = 1:6 ) |> tab_row_group( label = \"country\", rows = c(\"Australia\", \"Greenland\") ) |> tab_row_group( label = \"subregion\", rows = c(\"New Guinea\", \"Borneo\") ) # Show the gt table gt_tbl"},{"path":"https://gt.rstudio.com/articles/gt.html","id":"the-column-labels","dir":"Articles","previous_headings":"","what":"The Column Labels","title":"Introduction to Creating gt Tables","text":"table’s Column Labels part contains, minimum, columns column labels. last example single column: size. Just Stub, can create groupings called spanner columns encompass one columns. better demonstrate Column Labels work displayed, let’s use input data table columns. case, input table airquality. following columns: Ozone: mean ground-level ozone parts per billion volume (ppbV), measured 13:00 15:00 Solar.R: solar radiation Langley units (cal/m2), measured 08:00 noon Wind: mean wind speed miles per hour (mph) Temp: maximum daily air temperature degrees Fahrenheit (°F) Month, Day: numeric month day month record know measurements took place 1973, year column added dataset passed gt(). Let’s organize time information Time spanner column label, put columns Measurement spanner column label. can tab_spanner() function. can two things make presentable: move Time columns beginning series (using cols_move_to_start()) customize column labels descriptive (using cols_label()) Let’s things next example. Note even though columns moved using cols_move_to_start(), spanner column labels still spanned correct column labels. number functions gt provides move columns, including cols_move(), cols_move_to_end(); ’s even function hide columns: cols_hide(). Multiple columns can renamed single use cols_label(). , helper functions md() html() can used create column labels additional styling. example, provided column labels HTML can insert linebreaks
    , insert superscripted 2 (2<\/sup>), insert degree symbol HTML entity (°).","code":"# Modify the `airquality` dataset by adding the year # of the measurements (1973) and limiting to 10 rows airquality_m <- airquality |> mutate(Year = 1973L) |> slice(1:10) # Create a display table using the `airquality` # dataset; arrange columns into groups gt_tbl <- gt(airquality_m) |> tab_header( title = \"New York Air Quality Measurements\", subtitle = \"Daily measurements in New York City (May 1-10, 1973)\" ) |> tab_spanner( label = \"Time\", columns = c(Year, Month, Day) ) |> tab_spanner( label = \"Measurement\", columns = c(Ozone, Solar.R, Wind, Temp) ) # Show the gt table gt_tbl # Move the time-based columns to the start of # the column series; modify the column labels of # the measurement-based columns gt_tbl <- gt_tbl |> cols_move_to_start( columns = c(Year, Month, Day) ) |> cols_label( Ozone = html(\"Ozone,
    ppbV\"), Solar.R = html(\"Solar R.,
    cal/m2<\/sup>\"), Wind = html(\"Wind,
    mph\"), Temp = html(\"Temp,
    °F\") ) # Show the gt table gt_tbl"},{"path":"https://gt.rstudio.com/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Richard Iannone. Author, maintainer. Joe Cheng. Author. Barret Schloerke. Author. Ellis Hughes. Author. Alexandra Lauer. Author. JooYoung Seo. Author. Ken Brevoort. Author. Olivier Roy. Author. Posit Software, PBC. Copyright holder, funder.","code":""},{"path":"https://gt.rstudio.com/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Iannone R, Cheng J, Schloerke B, Hughes E, Lauer , Seo J, Brevoort K, Roy O (2024). gt: Easily Create Presentation-Ready Display Tables. R package version 0.10.1.9000, https://github.com/rstudio/gt, https://gt.rstudio.com.","code":"@Manual{, title = {gt: Easily Create Presentation-Ready Display Tables}, author = {Richard Iannone and Joe Cheng and Barret Schloerke and Ellis Hughes and Alexandra Lauer and JooYoung Seo and Ken Brevoort and Olivier Roy}, year = {2024}, note = {R package version 0.10.1.9000, https://github.com/rstudio/gt}, url = {https://gt.rstudio.com}, }"},{"path":"https://gt.rstudio.com/index.html","id":"installation","dir":"","previous_headings":"","what":"INSTALLATION","title":"Easily Create Presentation-Ready Display Tables","text":"gt package can installed CRAN : can also choose install development version gt GitHub: encounter bug, usage questions, want share ideas make package better, please feel free file issue.","code":"install.packages(\"gt\") devtools::install_github(\"rstudio/gt\")"},{"path":"https://gt.rstudio.com/index.html","id":"packages-that-use-or-extend-gt","dir":"","previous_headings":"","what":"Packages that use or extend gt","title":"Easily Create Presentation-Ready Display Tables","text":"several R packages either use gt generate tabular outputs extend gt amazing ways. short list great packages: gtsummary (GITHUB, WEBSITE) gtExtras (GITHUB, WEBSITE) pointblank (GITHUB, WEBSITE) tfrmt (GITHUB, WEBSITE) gto (GITHUB)","code":""},{"path":"https://gt.rstudio.com/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Easily Create Presentation-Ready Display Tables","text":"Please note gt project released contributor code conduct.participating project agree abide terms.","code":""},{"path":"https://gt.rstudio.com/index.html","id":"id_-license","dir":"","previous_headings":"","what":"📄 License","title":"Easily Create Presentation-Ready Display Tables","text":"gt licensed MIT license. See LICENSE.md file details. © Posit Software, PBC.","code":""},{"path":"https://gt.rstudio.com/index.html","id":"id_️-governance","dir":"","previous_headings":"","what":"🏛️ Governance","title":"Easily Create Presentation-Ready Display Tables","text":"project primarily maintained Rich Iannone. authors may occasionally assist duties.","code":""},{"path":"https://gt.rstudio.com/reference/adjust_luminance.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust the luminance for a palette of colors — adjust_luminance","title":"Adjust the luminance for a palette of colors — adjust_luminance","text":"adjust_luminance() function can brighten darken palette colors arbitrary number steps, defined real number -2.0 2.0. transformation palette fixed step function tend apply greater darkening lightening colors midrange compared dark light colors input palette.","code":""},{"path":"https://gt.rstudio.com/reference/adjust_luminance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust the luminance for a palette of colors — adjust_luminance","text":"","code":"adjust_luminance(colors, steps)"},{"path":"https://gt.rstudio.com/reference/adjust_luminance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust the luminance for a palette of colors — adjust_luminance","text":"colors Color vector vector // required vector colors undergo adjustment luminance. color value provided must either color name (set colors provided grDevices::colors()) hexadecimal string form \"#RRGGBB\" \"#RRGGBBAA\". steps Adjustment level scalar(-2>=val>=2) // required positive negative factor luminance colors colors vector adjusted. Must number -2.0 2.0.","code":""},{"path":"https://gt.rstudio.com/reference/adjust_luminance.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust the luminance for a palette of colors — adjust_luminance","text":"vector color values.","code":""},{"path":"https://gt.rstudio.com/reference/adjust_luminance.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Adjust the luminance for a palette of colors — adjust_luminance","text":"function can useful combined data_color() function's palette argument, can use vector colors col_* functions scales package (palette argument).","code":""},{"path":"https://gt.rstudio.com/reference/adjust_luminance.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjust the luminance for a palette of colors — adjust_luminance","text":"Get palette 8 pastel colors RColorBrewer package. Create lighter darker variants base palette (one step lower, one step higher). Create tibble make gt table . Color column order increasingly darker palettes (data_color()).","code":"pal <- RColorBrewer::brewer.pal(8, \"Pastel2\") pal_darker <- pal |> adjust_luminance(-1.0) pal_lighter <- pal |> adjust_luminance(+1.0) dplyr::tibble(a = 1:8, b = 1:8, c = 1:8) |> gt() |> data_color( columns = a, colors = scales::col_numeric( palette = pal_lighter, domain = c(1, 8) ) ) |> data_color( columns = b, colors = scales::col_numeric( palette = pal, domain = c(1, 8) ) ) |> data_color( columns = c, colors = scales::col_numeric( palette = pal_darker, domain = c(1, 8) ) )"},{"path":"https://gt.rstudio.com/reference/adjust_luminance.html","id":"function-id","dir":"Reference","previous_headings":"","what":"Function ID","title":"Adjust the luminance for a palette of colors — adjust_luminance","text":"8-8","code":""},{"path":"https://gt.rstudio.com/reference/adjust_luminance.html","id":"function-introduced","dir":"Reference","previous_headings":"","what":"Function Introduced","title":"Adjust the luminance for a palette of colors — adjust_luminance","text":"v0.2.0.5 (March 31, 2020)","code":""},{"path":[]},{"path":"https://gt.rstudio.com/reference/as.data.frame.gt_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform a gt object to a data frame — as.data.frame.gt_tbl","title":"Transform a gt object to a data frame — as.data.frame.gt_tbl","text":"facilitates conversion gt_tbl object data frame.","code":""},{"path":"https://gt.rstudio.com/reference/as.data.frame.gt_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform a gt object to a data frame — as.data.frame.gt_tbl","text":"","code":"# S3 method for gt_tbl as.data.frame(x, ...)"},{"path":"https://gt.rstudio.com/reference/as.data.frame.gt_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform a gt object to a data frame — as.data.frame.gt_tbl","text":"x gt table data object obj: // required gt table object commonly created use gt() function. ... Optional parameters // (optional) Anything provided ... passed internal function calls can consume .","code":""},{"path":"https://gt.rstudio.com/reference/as_gtable.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform a gt table to a gtable object — as_gtable","title":"Transform a gt table to a gtable object — as_gtable","text":"as_gtable() function performs transformation gt_tbl object gtable object.","code":""},{"path":"https://gt.rstudio.com/reference/as_gtable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform a gt table to a gtable object — as_gtable","text":"","code":"as_gtable(data, plot = FALSE, text_grob = grid::textGrob)"},{"path":"https://gt.rstudio.com/reference/as_gtable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform a gt table to a gtable object — as_gtable","text":"data gt table data object obj: // required gt table object commonly created use gt() function. plot Render graphics device? scalar // default: FALSE plot option determines whether gtable object rendered graphics device. text_grob function // default: grid::textGrob function used draw text. Defaults grid::textGrob() can swapped gridtext::richtext_grob() better render HTML content.","code":""},{"path":"https://gt.rstudio.com/reference/as_gtable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform a gt table to a gtable object — as_gtable","text":"gtable object.","code":""},{"path":"https://gt.rstudio.com/reference/as_gtable.html","id":"function-id","dir":"Reference","previous_headings":"","what":"Function ID","title":"Transform a gt table to a gtable object — as_gtable","text":"13-6","code":""},{"path":"https://gt.rstudio.com/reference/as_gtable.html","id":"function-introduced","dir":"Reference","previous_headings":"","what":"Function Introduced","title":"Transform a gt table to a gtable object — as_gtable","text":"Development","code":""},{"path":[]},{"path":"https://gt.rstudio.com/reference/as_latex.html","id":null,"dir":"Reference","previous_headings":"","what":"Output a gt object as LaTeX — as_latex","title":"Output a gt object as LaTeX — as_latex","text":"Get LaTeX content gt_tbl object knit_asis object. object contains LaTeX code attributes serve LaTeX dependencies (.e., LaTeX packages required table). Using .character() created object result single-element vector containing LaTeX code.","code":""},{"path":"https://gt.rstudio.com/reference/as_latex.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Output a gt object as LaTeX — as_latex","text":"","code":"as_latex(data)"},{"path":"https://gt.rstudio.com/reference/as_latex.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Output a gt object as LaTeX — as_latex","text":"data gt table data object obj: // required gt table object commonly created use gt() function.","code":""},{"path":"https://gt.rstudio.com/reference/as_latex.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Output a gt object as LaTeX — as_latex","text":"LaTeX packages required generate tables : booktabs, caption, longtable, colortbl, array, anyfontsize. event packages automatically added render phase document, please create include style file load . Inside document's YAML metadata, please include: gt_packages.sty file contain listed dependencies :","code":"output: pdf_document: # Change to appropriate LaTeX template includes: in_header: 'gt_packages.sty' \\usepackage{booktabs, caption, longtable, colortbl, array}"},{"path":"https://gt.rstudio.com/reference/as_latex.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Output a gt object as LaTeX — as_latex","text":"Use subset gtcars dataset create gt table. Add header tab_header() export table LaTeX code using as_latex() function. returned knit_asis object, makes easy include R Markdown documents knit PDF. can use .character() get just LaTeX code single-element vector.","code":"tab_latex <- gtcars |> dplyr::select(mfr, model, msrp) |> dplyr::slice(1:5) |> gt() |> tab_header( title = md(\"Data listing from **gtcars**\"), subtitle = md(\"`gtcars` is an R dataset\") ) |> as_latex()"},{"path":"https://gt.rstudio.com/reference/as_latex.html","id":"function-id","dir":"Reference","previous_headings":"","what":"Function ID","title":"Output a gt object as LaTeX — as_latex","text":"13-3","code":""},{"path":"https://gt.rstudio.com/reference/as_latex.html","id":"function-introduced","dir":"Reference","previous_headings":"","what":"Function Introduced","title":"Output a gt object as LaTeX — as_latex","text":"v0.2.0.5 (March 31, 2020)","code":""},{"path":[]},{"path":"https://gt.rstudio.com/reference/as_raw_html.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the HTML content of a gt table — as_raw_html","title":"Get the HTML content of a gt table — as_raw_html","text":"Get HTML content gt_tbl object single-element character vector. default, generated HTML inlined styles, CSS styles (previously contained CSS rule sets external element) included style attributes HTML table's tags. option preferable using output HTML table emailing context.","code":""},{"path":"https://gt.rstudio.com/reference/as_raw_html.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the HTML content of a gt table — as_raw_html","text":"","code":"as_raw_html(data, inline_css = TRUE)"},{"path":"https://gt.rstudio.com/reference/as_raw_html.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the HTML content of a gt table — as_raw_html","text":"data gt table data object obj: // required gt table object commonly created use gt() function. inline_css Use inline CSS scalar // default: TRUE option supply styles table elements inlined CSS styles. useful including table HTML part HTML email message body, since inlined styles largely supported email clients using CSS