Tutorial: tbl_summary

Last Updated: September 13, 2020

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 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.

Setup

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

# install.packages("gtsummary")
library(gtsummary)

Example data set

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

Variable Class Label

trt

character Chemotherapy Treatment

age

numeric Age

marker

numeric Marker Level (ng/mL)

stage

factor T Stage

grade

factor Grade

response

integer Tumor Response

death

integer Patient Died

ttdeath

numeric Months to Death/Censor
Includes mix of continuous, dichotomous, and categorical variables
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 this tutorial we’ll use a subset of the variables from the trial data set.

trial2 <- trial %>% select(trt, age, grade)

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.

trial2 %>% tbl_summary()
Characteristic N = 2001
Chemotherapy Treatment
Drug A 98 (49%)
Drug B 102 (51%)
Age 47 (38, 57)
Unknown 11
Grade
I 68 (34%)
II 68 (34%)
III 64 (32%)

1 n (%); Median (IQR)

Note the sensible defaults with this basic usage; each of the defaults may be customized.

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 statistical test.

trial2 %>% tbl_summary(by = trt) %>% add_p()
Characteristic Drug A, N = 981 Drug B, N = 1021 p-value2
Age 46 (37, 59) 48 (39, 56) 0.7
Unknown 7 4
Grade 0.9
I 35 (36%) 33 (32%)
II 32 (33%) 36 (35%)
III 31 (32%) 33 (32%)

1 Median (IQR); n (%)

2 Wilcoxon rank sum test; Pearson's Chi-squared test

Customize Output

There are four primary ways to customize the output of the summary table.

  1. Use tbl_summary() function 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.

Argument Description

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

missing_text

text label for the missing number row

sort

change the sorting of categorical levels by frequency

percent

print column, row, or cell percentages

Example modifying tbl_summary() arguments.

trial2 %>%
  tbl_summary(
    by = trt,
    statistic = list(all_continuous() ~ "{mean} ({sd})",
                     all_categorical() ~ "{n} / {N} ({p}%)"),
    digits = all_continuous() ~ 2,
    label = grade ~ "Tumor Grade",
    missing_text = "(Missing)"
  )
Characteristic Drug A, N = 981 Drug B, N = 1021
Age 47.01 (14.71) 47.45 (14.01)
(Missing) 7 4
Tumor Grade
I 35 / 98 (36%) 33 / 102 (32%)
II 32 / 98 (33%) 36 / 102 (35%)
III 31 / 98 (32%) 33 / 102 (32%)

1 Mean (SD); n / N (%)

There are multiple ways to specify the statistic= argument using a single formula, a list of formulas, and a named list. The following table shows equivalent ways to specify the mean statistic for continuous variables age and marker. Any {gtsummary} function argument that accepts formulas will accept each of these variations.

Select with Helpers Select by Variable Name Select with Named List

all_continuous() ~ "{mean}"

c("age", "marker") ~ "{mean}"

list(age = "{mean}", marker = "{mean}")

list(all_continuous() ~ "{mean}")

c(age, marker) ~ "{mean}"

list(c(age, marker) ~ "{mean}")

{gtsummary} functions to add information

The {gtsummary} package has functions to adding information or statistics to tbl_summary() tables.

Function Description

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 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.

Function Description

modify_header()

update column headers

modify_footnote()

update column footnote

modify_spanning_header()

update spanning headers

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

Example adding tbl_summary()-family functions

trial2 %>%
  tbl_summary(by = trt) %>%
  add_p(pvalue_fun = ~style_pvalue(.x, digits = 2)) %>%
  add_overall() %>%
  add_n() %>%
  modify_header(label ~ "**Variable**") %>%
  modify_spanning_header(c("stat_1", "stat_2") ~ "**Treatment Received**") %>%
  modify_footnote(
    all_stat_cols() ~ "Median (IQR) or Frequency (%)"
  ) %>%
  bold_labels()
Variable N Overall, N = 2001 Treatment Received p-value2
Drug A, N = 981 Drug B, N = 1021
Age 189 47 (38, 57) 46 (37, 59) 48 (39, 56) 0.72
Unknown 11 7 4
Grade 200 0.87
I 68 (34%) 35 (36%) 33 (32%)
II 68 (34%) 32 (33%) 36 (35%)
III 64 (32%) 31 (32%) 33 (32%)

1 Median (IQR) or Frequency (%)

2 Wilcoxon rank sum test; Pearson's Chi-squared test

{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.

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.

trial2 %>%
  tbl_summary(by = trt, missing = "no") %>%
  add_n() %>%
  as_gt() %>%
  gt::tab_source_note(gt::md("*This data is simulated*"))
Characteristic N Drug A, N = 981 Drug B, N = 1021
Age 189 46 (37, 59) 48 (39, 56)
Grade 200
I 35 (36%) 33 (32%)
II 32 (33%) 36 (35%)
III 31 (32%) 33 (32%)
This data is simulated

1 Median (IQR); n (%)

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 data set, 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 two primary ways to select variables by their summary type. 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()

      Dichotomous variables are, by default, included with all_categorical().

Multi-line Continuous Summaries

Continuous variables may also be summarized on multiple lines—a common format in some journals. To update the continuous variables to summarize on multiple lines, update the summary type to "continuous2" (for summaries on two or more lines).

trial2 %>%
  select(age, trt) %>%
  tbl_summary(
    by = trt,
    type = all_continuous() ~ "continuous2",
    statistic = all_continuous() ~ c("{N_nonmiss}",
                                     "{median} ({p25}, {p75})", 
                                     "{min}, {max}"),
    missing = "no"
  ) %>%
  add_p(pvalue_fun = ~style_pvalue(.x, digits = 2))
Characteristic Drug A, N = 98 Drug B, N = 102 p-value1
Age 0.72
N 91 98
Median (IQR) 46 (37, 59) 48 (39, 56)
Range 6, 78 9, 83

1 Wilcoxon rank sum test

Advanced Customization

The information in this section applies to all {gtsummary} objects.

The {gtsummary} table has two important internal objects:

Internal Object Description

.$table_body

data frame that is printed as the gtsummary output table

.$table_header

contains instructions for formatting .$table_body when printed

When you print output from the tbl_summary() function into the R console or into an R markdown document, the .$table_body data frame is formatted using the instructions listed in .$table_header. The default printer converts the {gtsummary} object to a {gt} object with as_gt() via a sequence of {gt} commands executed on .$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, var_type, var_class, 
#>     var_label, 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, beginning with gt::gt().

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 alignment is restored.

After the as_gt() function is run, additional formatting may be added to the table using {gt} functions. In the example below, a source note is added to the table.

tbl_summary(trial2, by = trt) %>%
  as_gt(include = -cols_align) %>%
  gt::tab_source_note(gt::md("*This data is simulated*"))
Characteristic Drug A, N = 981 Drug B, N = 1021
Age 46 (37, 59) 48 (39, 56)
Unknown 7 4
Grade
I 35 (36%) 33 (32%)
II 32 (33%) 36 (35%)
III 31 (32%) 33 (32%)
This data is simulated

1 Median (IQR); n (%)

Set Default Options with Themes

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 prespecified 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, review the themes vignette.

Survey Data

The {gtsummary} package also supports survey data (objects created with the {survey} package) via the tbl_svysummary() function. The syntax for tbl_svysummary() and tbl_summary() are nearly identical, and the examples above apply to survey summaries as well.

To begin, install the {survey} package and load the apiclus1 data set.

install.packages("survey")
# loading the api data set
data(api, package = "survey")

Before we begin, we convert the data frame to a survey object, registering the ID and weighting columns, and setting the finite population correction column.

svy_apiclus1 <- 
  survey::svydesign(
    id = ~dnum, 
    weights = ~pw, 
    data = apiclus1, 
    fpc = ~fpc
  ) 

After creating the survey object, we can now summarize it similarly to a standard data frame using tbl_svysummary(). Like tbl_summary(), tbl_svysummary() accepts the by= argument and works with the add_p() and add_overall() functions.

It is not possible to pass custom functions to the statistic= argument of tbl_svysummary(). You must use one of the pre-defined summary statistic functions (e.g. {mean}, {median}) which leverage functions from the {survey} package to calculate weighted statistics.

svy_apiclus1 %>%
  tbl_svysummary(
    # stratify summary statistics by the "both" column
    by = both, 
    # summarize a subset of the columns
    include = c(api00, api99, both),
    # adding labels to table
    label = list(api00 ~ "API in 2000",
                 api99 ~ "API in 1999")
  ) %>%
  add_p() %>%   # comparing values by "both" column
  add_overall() %>%
  # adding spanning header
  modify_spanning_header(c("stat_1", "stat_2") ~ "**Met Both Targets**")
Characteristic Overall, N = 6,1941 Met Both Targets p-value2
No, N = 1,6921 Yes, N = 4,5021
API in 2000 652 (552, 718) 631 (556, 710) 654 (551, 722) 0.4
API in 1999 615 (512, 691) 632 (548, 698) 611 (497, 686) 0.2

1 Median (IQR)

2 Wilcoxon rank-sum test for complex survey samples

tbl_svysummary() can also handle weighted survey data where each row represents several individuals:

Titanic %>%
  as_tibble() %>%
  survey::svydesign(data = ., ids = ~ 1, weights = ~ n) %>%
  tbl_svysummary(include = c(Age, Survived))
Characteristic N = 2,2011
Age
Adult 2,092 (95%)
Child 109 (5.0%)
Survived 711 (32%)

1 n (%)

Cross Tables

Use tbl_cross() to compare two categorical variables in your data. tbl_cross() is a wrapper for tbl_summary() that:

trial %>%
  tbl_cross(
    row = stage,
    col = trt,
    percent = "cell"
  ) %>%
  add_p()
Characteristic Chemotherapy Treatment Total p-value1
Drug A Drug B
T Stage 0.9
T1 28 (14%) 25 (12%) 53 (26%)
T2 25 (12%) 29 (14%) 54 (27%)
T3 22 (11%) 21 (10%) 43 (22%)
T4 23 (12%) 27 (14%) 50 (25%)
Total 98 (49%) 102 (51%) 200 (100%)

1 Pearson's Chi-squared test