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)
Before going through the tutorial, install {gtsummary} and {gt}.
install.packages("gtsummary")
remotes::install_github("rstudio/gt")
library(gtsummary)
library(dplyr)
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 outcome is a binary tumor response. Each variable in the data frame has been assigned an attribute label (i.e. attr(trial$trt, "label") == "Chemotherapy Treatment")
with the labelled package. These labels are displayed in the output table by default. A data frame without labels will print variable names.
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
trt  age  marker  stage  grade  response  death  ttdeath 

Drug A  23  0.160  T1  II  0  0  24.00 
Drug B  9  1.107  T2  I  1  0  24.00 
Drug A  31  0.277  T1  II  0  0  24.00 
Drug A  NA  2.067  T3  III  1  1  17.64 
Drug A  51  2.767  T4  III  1  1  16.43 
Drug B  39  0.613  T4  I  0  1  15.64 
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()
can take, at minimum, a data set as the only input, and returns descriptive statistics for each column in the data frame.
For brevity, keeping a subset of the variables in the trial data set.
Characteristic^{1}  N = 200 

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)

This is a great table, but for this study data the summary statistics should be split by treatment group. To compare two or more groups, include add_p()
with the function call.
Characteristic^{1}  Drug A, N = 98  Drug B, N = 102  pvalue^{2} 

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 ranksum test; chisquare test of independence

There are four primary ways to customize the output of the summary table.
tbl_summary()
function input argumentsadd_*()
functionsThe 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
The {gtsummary} package has builtin functions for adding to results from tbl_summary()
. The following functions add columns and/or information to the summary table.
add_p() add pvalues 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
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 pvalues
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>
The code below calculates the standard table with summary statistics split by treatment with the following modifications
trial2 %>%
# build base summary table
tbl_summary(
by = trt,
# change variable labels
label = list(vars(marker) ~ "Marker, ng/mL",
vars(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 pvalues, report ttest, round large pvalues to two decimal place
add_p(test = list(vars(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 pvalues 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  Statistic  Drug A, N = 98 (49%)  Drug B, N = 102 (51%)  pvalue^{1} 

Marker, ng/mL  mean (SD)  1.0 (0.89)  0.8 (0.83)  0.12 
Unknown  n  6  4  
Clinical T Stage  0.87  
T1  n / N (%)  28 / 98 (29%)  25 / 102 (25%)  
T2  n / N (%)  25 / 98 (26%)  29 / 102 (28%)  
T3  n / N (%)  22 / 98 (22%)  21 / 102 (21%)  
T4  n / N (%)  23 / 98 (23%)  27 / 102 (26%)  
^{
1
}
Statistical tests performed: ttest; chisquare test of independence

Each of the modification functions have additional options outlined in their respective help files.
The {gtsummary} package includes a set of functions meant to help the user specify function arguments for groups of variables. For example, if all continuous variables will be summarized in tbl_summary()
as minimum and maximum, the all_continuous()
function can be used: statistic = all_continuous() ~ "{min} to {max}"
The set of select helper function includes the functions in the {tidyselect} package (used throughout the tidyverse), and functions specific to {gtsummary}. There are four types of select helpers.
Functions in the {tidyselect} package used throughout the tidyverse, including vars()
from the {dplyr} package.
Functions to select groups of variables based on their attributes like class or type.
Functions to select groups of variables based on their display type in tbl_summary()
List variables in a vector, e.g. "age"
or c("age", "stage")
{tidyselect}  attribute  summary type  character vector 

starts_with() , ends_with() , contains() , matches() , one_of() , everything() , num_range() , last_col() , vars() 
all_numeric() , all_integer() , all_logical() , all_factor() , all_character() , all_double() 
all_continuous() , all_categorical() , all_dichotomous() 
"age" or c("age", "stage") 
The select helpers are primarily used in tbl_summary()
and its related functions, e.g. add_p()
. We’ll review a few examples illustrating their use.
In the example below, we report the mean and standard deviation for continuous variables, and percent for all categorical. We’ll report ttests rather than Wilcoxon ranksum 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 %>%
dplyr::select(trt, response, age, stage, marker, grade) %>%
tbl_summary(
by = trt,
type = list(c("response", "grade") ~ "categorical"), # select by variables in a vector
statistic = list(all_continuous() ~ "{mean} ({sd})", all_categorical() ~ "{p}%") # select by summary type/attribute
) %>%
add_p(test = list(contains("response") ~ "fisher.test", # select using functions in tidyselect
all_continuous() ~ "t.test"))
Characteristic^{1}  Drug A, N = 98  Drug B, N = 102  pvalue^{2} 

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; ttest; chisquare test of independence

Reproducible reports are an import part of good practices. We often need to report the results from a table in the text of an R markdown report. Inline reporting has been made simple with inline_text()
.
First create a basic summary table.
Characteristic^{1}  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%) 
^{
1
}
Statistics presented: median (IQR); n (%)

To report the median (IQR) of the marker levels in each group, use the following commands inline.
The median (IQR) marker level in the Drug A and Drug B groups are
`r inline_text(tab1, variable = "marker", column = "Drug A")`
and`r inline_text(tab1, variable = "marker", column = "Drug B")`
, respectively.
Here’s how the line will appear in your report.
The median (IQR) marker level in the Drug A and Drug B groups are 0.84 (0.24, 1.57) and 0.52 (0.19, 1.20), respectively.
If you display a statistic from a categorical variable, include the level
argument.
`r inline_text(tab1, variable = "stage", level = "T1", column = "Drug B")`
resolves to “25 (25%)”
For more detail about inline code, review to the RStudio documentation page.
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] "gt_calls" "kable_calls" "table_body" "table_header" "meta_data"
#> [6] "inputs" "N" "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
gt_calls named list of {gt} function calls
kable_calls named list of function calls to be applied before knitr::kable()
gt_calls
is a named list of saved {gt} function calls. The {gt} calls are run when the object is printed to the console or in an R markdown document. Here’s an example of the first few calls saved with tbl_summary()
:
tbl_summary(trial2) %>% purrr::pluck("gt_calls") %>% head(n = 5)
#> $gt
#> gt::gt(data = x$table_body)
#>
#> $cols_align
#> gt::cols_align(align = 'center') %>% gt::cols_align(align = 'left', columns = gt::vars(label))
#>
#> $fmt_missing
#> gt::fmt_missing(columns = gt::everything(), missing_text = '')
#>
#> $tab_style_text_indent
#> gt::tab_style(style = gt::cell_text(indent = gt::px(10), align = 'left'),locations = gt::cells_data(columns = gt::vars(label),rows = row_type != 'label'))
#>
#> $cols_label
#> gt::cols_label(label = gt::md("**Characteristic**"), stat_0 = gt::md("**N = 200**"))
The {gt} functions are called in the order they appear, always beginning with the 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 by specifying the exclude
argument. For example, the tbl_summary()
call creates many named {gt} function calls: gt, cols_align, fmt_missing, tab_style_text_indent, cols_label, cols_hide, fmt, tab_footnote. Any of these can be excluded. 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(exclude = "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%) 
The tbl_summary()
function and it’s related functions have sensible defaults for rounding and formatting results. If you, however, would like to change the defaults there are a few options. The default options can be changed for a single script with addition an options()
command in the script. The defaults can also be set on the project or userlevel R profile, .Rprofile
.
The following parameters are available to be set:
Description  Example  Functions 

Formatting and rounding pvalues  options(gtsummary.pvalue_fun = function(x) gtsummary::style_pvalue(x, digits = 2)) 
add_p() , tbl_regression() , tbl_uvregression() 
Formatting and rounding percentages  options(gtsummary.tbl_summary.percent_fun = function(x) sprintf("%.2f", 100 * x)) 
tbl_summary() 
Print tables with gt or kable 
options(gtsummary.print_engine = "kable") options(gtsummary.print_engine = "gt") 
All tbl_*() functions 
When setting default rounding/formatting functions, set the default to a function object rather than an evaluated function. For example, if you want to round percentages to 3 significant figures use,