# Tutorial: tbl_summary

## Introduction

The tbl_summary() function calculates descriptive statistics for continuous, categorical, and dichotomous variables in R, and presents the results in a beautiful, customizable summary table perfect for creating tables ready for publication (for example, Table 1 or demographic tables).

This vignette will walk a reader through the tbl_summary() function, and the various functions available to modify and make additions to an existing table summary object.

To start, a quick note on the {magrittr} package’s pipe function, %>%. By default the pipe operator puts whatever is on the left hand side of %>% into the first argument of the function on the right hand side. The pipe function can be used to make the code relating to tbl_summary() easier to use, but it is not required. Here are a few examples of how %>% translates into typical R notation.

x %>% f() is equivalent to f(x)
x %>% f(y) is equivalent to f(x, y)
y %>% f(x, .) is equivalent to f(x, y)
z %>% f(x, y, arg = .) is equivalent to f(x, y, arg = z)

Here’s how this translates into the use of tbl_summary().

mtcars %>% tbl_summary() is equivalent to tbl_summary(mtcars)
mtcars %>% tbl_summary(by = am) is equivalent to tbl_summary(mtcars, by = am)
tbl_summary(mtcars, by = am) %>% add_p() is equivalent to
tbl = tbl_summary(mtcars, by = am)
add_p(tbl)

## Setup

Before going through the tutorial, install {gtsummary} and {gt}.

library(gtsummary)
library(dplyr)

## Example data set

We’ll be using the trial data set throughout this example.

• This set contains data from 200 patients who received one of two types of chemotherapy (Drug A or Drug B). The outcomes are tumor response and death.

• Each variable in the data frame has been assigned an attribute label (i.e. attr(trial$trt, "label") == "Chemotherapy Treatment") with the labelled package, which we highly recommend using. These labels are displayed in the {gtsummary} output table by default. Using {gtsummary} on a data frame without labels will simply print variable names, or there is an option to add labels later.  trt Chemotherapy Treatment age Age, yrs marker Marker Level, ng/mL stage T Stage grade Grade response Tumor Response death Patient Died ttdeath Years from Treatment to Death/Censor  • Our example dataset has a mix of continuous, dichotomous (0/1), and categorical variables, some with missing data (NA). head(trial) #> # A tibble: 6 x 8 #> trt age marker stage grade response death ttdeath #> <chr> <dbl> <dbl> <fct> <fct> <int> <int> <dbl> #> 1 Drug A 23 0.16 T1 II 0 0 24 #> 2 Drug B 9 1.11 T2 I 1 0 24 #> 3 Drug A 31 0.277 T1 II 0 0 24 #> 4 Drug A NA 2.07 T3 III 1 1 17.6 #> 5 Drug A 51 2.77 T4 III 1 1 16.4 #> 6 Drug B 39 0.613 T4 I 0 1 15.6 For brevity in the tutorial, let’s keep a subset of the variables from the trial data set. trial2 = trial %>% select(trt, marker, stage) ## Basic Usage The default output from tbl_summary() is meant to be publication ready. Let’s start by creating a table of summary statistics from the trial data set. The tbl_summary() function can take, at minimum, a data frame as the only input, and returns descriptive statistics for each column in the data frame. tbl_summary(trial2) Characteristic N = 2001 Chemotherapy Treatment Drug A 98 (49%) Drug B 102 (51%) Marker Level, ng/mL 0.64 (0.21, 1.39) Unknown 10 T Stage T1 53 (26%) T2 54 (27%) T3 43 (22%) T4 50 (25%) 1 Statistics presented: n (%); median (IQR) Note the sensible defaults with this basic usage (that can be customized later): • Variable types are automatically detected so that appropriate descriptive statistics are calculated. • Label attributes from the dataset are automatically printed. • Missing values are listed as “Unknown” in the table. • Variable levels are indented and footnotes are added if printed using {gt}. (can alternatively be printed using knitr::kable(); see options here) This is a great basic table, but for this study data the summary statistics should be split by treatment group, which can be done by using the by = argument. To compare two or more groups, include add_p() with the function call, which detects variable type and uses an appropriate test. tbl_summary(trial2, by = trt) %>% add_p() Characteristic Drug A, N = 981 Drug B, N = 1021 p-value2 Marker Level, ng/mL 0.84 (0.24, 1.57) 0.52 (0.19, 1.20) 0.085 Unknown 6 4 T Stage 0.9 T1 28 (29%) 25 (25%) T2 25 (26%) 29 (28%) T3 22 (22%) 21 (21%) T4 23 (23%) 27 (26%) 1 Statistics presented: median (IQR); n (%) 2 Statistical tests performed: Wilcoxon rank-sum test; chi-square test of independence ## Customize Output There are four primary ways to customize the output of the summary table. 1. Modify tbl_summary() function input arguments 2. Add additional data/information to a summary table with add_*() functions 3. Modify summary table appearance with the {gtsummary} functions 4. Modify table appearance with {gt} package functions ### Modifying tbl_summary() function arguments The tbl_summary() function includes many input options for modifying the appearance. label specify the variable labels printed in table type specify the variable type (e.g. continuous, categorical, etc.) statistic change the summary statistics presented digits number of digits the summary statistics will be rounded to missing whether to display a row with the number of missing observations sort change the sorting of categorical levels by frequency percent print column, row, or cell percentages ### {gtsummary} functions to add information The {gtsummary} package has built-in functions for adding to results from tbl_summary(). The following functions add columns and/or information to the summary table. add_p() add p-values to the output comparing values across groups add_overall() add a column with overall summary statistics add_n() add a column with N (or N missing) for each variable add_stat_label() add a column showing a label for the summary statistics shown in each row add_q() add a column of q values to control for multiple comparisons  ### {gtsummary} functions to format table The {gtsummary} package comes with functions specifically made to modify and format summary tables. modify_header() relabel columns in summary table bold_labels() bold variable labels bold_levels() bold variable levels italicize_labels() italicize variable labels italicize_levels() italicize variable levels bold_p() bold significant p-values  ### {gt} functions to format table The {gt} package is packed with many great functions for modifying table output—too many to list here. Review the package’s website for a full listing. https://gt.rstudio.com/index.html To use the {gt} package functions with {gtsummary} tables, the summary table must first be converted into a gt object. To this end, use the as_gt() function after modifications have been completed with {gtsummary} functions. trial %>% tbl_summary(by = trt, missing = "no") %>% add_n() %>% as_gt() %>% <gt functions> ### Example The code below calculates the standard table with summary statistics split by treatment with the following modifications • Report ‘mean (SD)’ and ‘n / N (%)’ • Round the marker mean and SD to 1 and 2 places, respectively • Modify variable labels in the table • Use t-test instead of Wilcoxon rank-sum • Round large p-values to two decimal place • Add column with statistic labels • Modify header to include percentages in each group • Bold variable labels • Italicize variable levels trial2 %>% # build base summary table tbl_summary( # split table by treatment variable by = trt, # change variable labels label = list(marker ~ "Marker, ng/mL", stage ~ "Clinical T Stage"), # change statistics printed in table statistic = list(all_continuous() ~ "{mean} ({sd})", all_categorical() ~ "{n} / {N} ({p}%)"), digits = list("marker" ~ c(1, 2)) ) %>% # add p-values, report t-test, round large pvalues to two decimal place add_p(test = list(marker ~ "t.test"), pvalue_fun = function(x) style_pvalue(x, digits = 2)) %>% # add statistic labels add_stat_label() %>% # bold variable labels, italicize levels bold_labels() %>% italicize_levels() %>% # bold p-values under a given threshold (default is 0.05) bold_p(t = 0.2) %>% # include percent in headers modify_header(stat_by = "**{level}**, N = {n} ({style_percent(p, symbol = TRUE)})") Characteristic Drug A, N = 98 (49%) Drug B, N = 102 (51%) p-value1 Marker, ng/mL, mean (SD) 1.0 (0.89) 0.8 (0.83) 0.12 Unknown 6 4 Clinical T Stage, n / N (%) 0.87 T1 28 / 98 (29%) 25 / 102 (25%) T2 25 / 98 (26%) 29 / 102 (28%) T3 22 / 98 (22%) 21 / 102 (21%) T4 23 / 98 (23%) 27 / 102 (26%) 1 Statistical tests performed: t-test; chi-square test of independence Each of the modification functions have additional options outlined in their respective help files. ## Select Helpers There is flexibility in how you select variables for {gtsummary} arguments, which allows for many customization opportunities! For example, if you want to show age and the marker levels to one decimal place in tbl_summary(), you can pass digits = c(age, marker) ~ 1. The selecting input is flexible, and you may also pass quoted column names. Going beyond typing out specific variables in your dataset, you can use: 1. All {tidyselect} helpers available throughout the tidyverse, such as starts_with(), contains(), and everything() (i.e. anything you can use with the dplyr::select() function can be used with {gtsummary}). 2. Additional {gtsummary} selectors that are included in the package to supplement tidyselect functions. • Summary type There are three types of summary types in {gtsummary}, and you may use the type to select columns. This is useful, for example, when you wish to report the mean and standard deviation for all continuous variables: statistic = all_continuous() ~ "{mean} ({sd})". all_continuous() all_categorical() all_dichotomous() • Vector class or type Select columns based on their class or type. all_numeric() all_integer() all_logical() all_factor() all_character() all_double() ### Examples In the example below, we report the mean and standard deviation for continuous variables, and percent for all categorical. We’ll report t-tests rather than Wilcoxon rank-sum test for continuous variables, and report Fisher’s exact test for response. Note that dichotomous variables are, by default, included with all_categorical(). Use all_categorical(dichotomous = FALSE) to exclude dichotomous variables. trial %>% select(trt, response, age, stage, marker, grade) %>% tbl_summary( by = trt, type = list(c(response, grade) ~ "categorical"), # select by variables in c() statistic = list(all_continuous() ~ "{mean} ({sd})", all_categorical() ~ "{p}%") # select by summary type ) %>% add_p(test = list(contains("response") ~ "fisher.test", # select using functions in tidyselect all_continuous() ~ "t.test")) Characteristic Drug A, N = 981 Drug B, N = 1021 p-value2 Tumor Response 0.5 0 71% 66% 1 29% 34% Unknown 3 4 Age, yrs 47 (15) 47 (14) 0.8 Unknown 7 4 T Stage 0.9 T1 29% 25% T2 26% 28% T3 22% 21% T4 23% 26% Marker Level, ng/mL 1.02 (0.89) 0.82 (0.83) 0.12 Unknown 6 4 Grade 0.9 I 36% 32% II 33% 35% III 32% 32% 1 Statistics presented: %; mean (SD) 2 Statistical tests performed: Fisher's exact test; t-test; chi-square test of independence ## Advanced Customization When you print output from the tbl_summary() function into the R console or into an R markdown, there are default printing functions that are called in the background: print.tbl_summary() and knit_print.tbl_summary(). The true output from tbl_summary() is a named list, but when you print the object, a formatted version of .$table_body is displayed. All formatting and modifications are made using the {gt} package.

tbl_summary(trial2) %>% names()
#> [1] "table_body"   "table_header" "meta_data"    "inputs"       "N"
#> [6] "call_list"

These are the additional data stored in the tbl_summary() output list.

table_body   data frame with summary statistics
meta_data    data frame that is one row per variable with data about each
by, df_by    the by variable name, and a  data frame with information about the by variable
call_list    named list of each function called on the tbl_summary object
inputs       inputs from the tbl_summary() function call  

When a {gtsummary} object is printed, it is first converted to a {gt} object with as_gt() via a sequence of {gt} commands executed on x$table_body. Here’s an example of the first few calls saved with tbl_summary(): tbl_summary(trial2) %>% as_gt(return_calls = TRUE) %>% head(n = 4) #>$gt
#> gt::gt(data = x$table_body) #> #>$fmt_missing
#> gt::fmt_missing(columns = gt::everything(), missing_text = "")
#>
#> $fmt_missing_emdash #> list() #> #>$cols_align
#> cols_align[[1]] #> gt::cols_align(columns = gt::vars(variable, row_type, stat_0), #> align = "center") #> #>cols_align[[2]]
#> gt::cols_align(columns = gt::vars(label), align = "left")

The {gt} functions are called in the order they appear, always beginning with the gt::gt() function.

If the user does not want a specific {gt} function to run (i.e. would like to change default printing), any {gt} call can be excluded in the as_gt() function. In the example below, the default footnote will be excluded from the output.

After the as_gt() function is run, additional formatting may be added to the table using {gt} formatting functions. In the example below, a spanning header for the by= variable is included with the {gt} function tab_spanner().

tbl_summary(trial2, by = trt) %>%
as_gt(include = -tab_footnote) %>%
gt::tab_spanner(label = gt::md("**Treatment Group**"),
columns = gt::starts_with("stat_"))
Characteristic Treatment Group
Drug A, N = 98 Drug B, N = 102
Marker Level, ng/mL 0.84 (0.24, 1.57) 0.52 (0.19, 1.20)
Unknown 6 4
T Stage
T1 28 (29%) 25 (25%)
T2 25 (26%) 29 (28%)
T3 22 (22%) 21 (21%)
T4 23 (23%) 27 (26%)

## Setting Default Options

The {gtsummary} tbl_summary() function and the related functions have sensible defaults for rounding and presenting results. If you, however, would like to change the defaults there are a few options. The default options can be changed using the {gtsummary} themes function set_gtsummary_theme(). The package includes pre-specified themes, and you can also create your own. Themes can control baseline behavior, for example, how p-values and percentages are rounded, which statistics are presented in tbl_summary(), default statistical tests in add_p(), etc. For details on creating a theme and setting personal defaults, visit the themes vignette.