1 Introduction
Table summaries are a fundamental tool in an analyst’s toolbox that help us understand and communicate patterns in our data. The ability to easily create and export polished and reproducible tables is essential. The gtsummary (Sjoberg et al. 2020) package provides an elegant and flexible framework to create publication-ready analytical and summary tables in R. This package works to close the gap between a reproducible RMarkdown report and the final report. Specifically, gtsummary allows the user to fully customize and format summary tables with code, eliminating the need to modify any tables by hand after the table has been exported. Removing the need to modify tables after the table has been created eliminates an error-prone step in our workflow and increases the reproducibility of our analyses and reports.
Using gtsummary, analysts can easily summarize data frames, present and compare descriptive statistics between groups, summarize regression models, and report statistics inline in RMarkdown reports. After identifying these basic structures of most tables presented in the medical literature (and other fields), we wrote gtsummary to ease the creation of fully-formatted, ready-to-publish tables.
Additionally, gtsummary leverages other analysis and tidying R
packages to create a complete analysis and reporting framework. For
example, we take advantage of the existing
broom (Robinson et al. 2020) tidiers to
prepare regression results for tbl_regression() and use
gt (Iannone et al. 2020) to print gtsummary
tables to various output formats (e.g., HTML, PDF, Word, or RTF).
Furthermore, gtsummary functions are designed to work within a
"tidy" framework, utilizing the
magrittr (Bache and Wickham 2020)
pipe operator and
tidyselect
(Henry and Wickham 2020) functions used throughout the
tidyverse
(Wickham et al. 2019).
While other R packages are available to present data and regression model summary tables, such as skimr, stargazer, finalfit, and tableone, gtsummary is unique in that it is a one-stop-shop for most types of statistical tables and offers diverse features to customize the content of tables to a high degree. The default gtsummary table is suitable to be published in a scientific journal with little or no additional formatting. For example, gtsummary has specific internal algorithms to identify variable data types, so there is no need for users to specify whether a variable should be displayed with categorical or continuous summaries, which yields summary tables with minimal code.
Along with descriptive summaries, gtsummary summarizes statistical
models, survey data, survival data and builds cross-tabulations. After
data are summarized in a table, gtsummary allows users to combine
tables, either side-by-side (with tbl_merge()) , or on top of each
other (with tbl_stack()). The table merging and stacking abilities
allows analysts to easily synthesize and compare output from several
tables and share information in a compact format. All tables in this
manuscript were created using gtsummary v1.4.1.
2 Data Summaries
To showcase gtsummary functions, we will use a simulated clinical
trial data set containing baseline characteristics of 200 patients who
received Drug A or Drug B, as well as the outcomes of tumor response and
death. Each variable in the data frame has been assigned an attribute
label with the labelled package (Larmarange 2020), e.g.,
trial %>% set_variable_labels(age = "Age"), that will be shown in the
summary tables. These labels are displayed in the gtsummary tables by
default, and had labels not been assigned, the variable name would have
been shown.
| colname | label | class | values |
|---|---|---|---|
trt |
Chemotherapy Treatment | character | Drug A, Drug B |
age |
Age | numeric | 6, 9, 10, 17, ... |
marker |
Marker Level (ng/mL) | numeric | 0.003, 0.005, 0.013, 0.015, ... |
stage |
T Stage | factor | T1, T2, T3, T4 |
grade |
Grade | factor | I, II, III |
response |
Tumor Response | integer | 0, 1 |
death |
Patient Died | integer | 0, 1 |
ttdeath |
Months to Death/Censor | numeric | 3.53, 5.33, 6.32, 7.27, ... |
tbl_summary()
The default output from tbl_summary() is meant to be
publication-ready. 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. This is often the first table of clinical
manuscripts and describes the characteristics of the study cohort. A
simple example is shown below. Notably, by specifying the by=
argument, you can stratify the summary table. In the example below, we
have split the table by the treatment a patient received.
trial %>%
select(age, grade, response, trt) %>%
tbl_summary(by = trt)
The function is highly customizable, and
it is initiated with sensible default settings. Specifically,
tbl_summary() detects variable types of input data and calculates
descriptive statistics accordingly. For example, variables coded as
0/1, TRUE/FALSE, and Yes/No are presented dichotomously.
Additionally, NA values are recognized as missing and listed as
unknown, and if a data set is labeled, the label attributes are
utilized.
Default settings may be customized using the tbl_summary() function
arguments.
| 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 |
include= |
list of variables to include in summary table |
For continuous variables, tables display one row of statistics per
variable by default. This can be customized, and in the example below,
the age variable is cast to "continuous2" type, meaning the continuous
summary statistics will appear on two or more rows in the table. This
allows the number of non-missing observations and the mean to be
displayed on separate lines.
In the example below, the "age" variable’s label is updated to
"Patient Age". Default summary statistics for both continuous and
categorical variables are updated using the statistic= argument.
gtsummary uses glue
(Hester 2020) syntax to construct the statistics displayed in the table.
Function names appearing in curly brackets will be replaced by the
evaluated value. The digits= argument is used to increase the number
of decimal places to which the statistics are rounded, and the missing
row is omitted with missing = "no".
trial %>%
select(age, grade, response, trt) %>%
tbl_summary(
by = trt,
type = age ~ "continuous2",
label = age ~ "Patient Age",
statistic = list(age ~ c("{N_nonmiss}", "{mean} ({sd})"),
c(grade, response) ~ "{n} / {N} ({p}%)"),
digits = c(grade, response) ~ c(0, 0, 1),
missing = "no"
)
A note about notation: Throughout
the gtsummary package, you will find function arguments that accept a
list of formulas (or a single formula) as the input. In the example
above, the label for the age variable was updated using
label = age\(\sim\)"Patient Age"—equivalently,
label = list(age\(\sim\)"Patient Age"). To select groups of
variables, utilize the select helpers from the tidyselect and
gtsummary packages. The all_continuous() selector is a convenient
way to select all continuous variables. In the example above, it could
have been used to change the summary statistics for all continuous
variables—all_continuous()\(\sim\)c("{N_nonmiss}", "{mean} ({sd})").
Similarly, users may utilize all_categorical() (from gtsummary) or
any of the tidyselect helpers used throughout the tidyverse
packages, such as starts_with(), contains(), etc.
In addition to summary statistics, the gtsummary package has several
functions to add additional 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_difference() |
add column for difference between two group, confidence interval, and p-value |
add_stat_label() |
add label for the summary statistics shown in each row |
add_stat() |
generic function to add a column with user-defined values |
add_q() |
add a column of q-values to control for multiple comparisons |
In the example below, descriptive statistics are shown by the treatment
received and overall, as well as a p-value comparing the values
between the treatments. Default statistical tests are chosen based on
data type, and the statistical test performed can be customized in the
add_p() function. p-value formatting can be adjusted using the
pvalue_fun= argument, which accepts both a proper function, as well
the formula shortcut notation used throughout the tidyverse packages.
trial %>%
select(age, grade, response, trt) %>%
tbl_summary(by = trt) %>%
add_overall() %>%
add_p(test = all_continuous() ~ "t.test",
pvalue_fun = ~style_pvalue(., digits = 2))
tbl_svysummary()
The tbl_svysummary() function is analogous to tbl_summary(), except
a survey (Lumley 2020) object
is supplied rather than a data frame. The summary statistics presented
take into account the survey weights, as do any p-values presented.
# convert trial data frame to survey object
svy_trial <- survey::svydesign(data = trial, ids = ~ 1, weights = ~ 1)
tbl_svysummary_1 <-
svy_trial %>%
tbl_svysummary(by = trt, include = c(trt, age, grade)) %>%
add_p()
tbl_cross()
The tbl_cross() function is a wrapper for tbl_summary() and creates
a simple, publication-ready cross tabulation.
trial %>%
tbl_cross(row = stage, col = trt, percent = "cell") %>%
add_p(source_note = TRUE)
tbl_survfit()
The tbl_survfit() function parses and tabulates survival::survfit()
objects presenting survival percentile estimates and survival
probabilities at specified times.
library(survival)
list(survfit(Surv(ttdeath, death) ~ trt, trial),
survfit(Surv(ttdeath, death) ~ grade, trial)) %>%
tbl_survfit(times = c(12, 24),
label_header = "**{time} Month**") %>%
add_p()
Customization
The gtsummary package includes functions specifically made to modify and format the summary tables. These functions work with any table constructed with gtsummary. The most common uses are changing the column headers and footnotes or modifying the look of tables through bolding and italicization.
| Function | Description |
|---|---|
modify_header() |
update column headers |
modify_footnote() |
update column footnote |
modify_spanning_header() |
update spanning headers |
modify_caption() |
update table caption/title |
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 |
The gtsummary package utilizes the gt package to print the summary
tables. The gt package exports approximately one hundred functions to
customize and style tables. When you need to add additional details or
styling not available within gtsummary, use the as_gt() function to
convert the gtsummary object to gt and continue customization.
The example below is a common table reported in clinical trials and
observational research where two treatments are compared. The treatment
differences were added with the add_difference() function. The table
includes customization using both gtsummary and gt functions. The
gtsummary functions are utilized to bold the variable labels, update
the column headers, and add a spanning header. Additional gt
customization was utilized to add table captions and source notes.
trial %>%
select(marker, response, trt) %>%
tbl_summary(by = trt,
missing = "no",
statistic = marker ~ "{mean} ({sd})") %>%
add_difference() %>%
add_n() %>%
add_stat_label() %>%
bold_labels() %>%
modify_header(list(label ~ "**Variable**", all_stat_cols() ~ "**{level}**")) %>%
modify_spanning_header(all_stat_cols() ~ "**Randomization Assignment**") %>%
as_gt() %>%
gt::tab_header(
title = gt::md("**Table 1. Treatment Differences**"),
subtitle = gt::md("_Highly Confidential_")
) %>%
gt::tab_source_note("Data updated June 26, 2015")
3 Model Summaries
Regression modeling is one of the most common tools of medical research.
The gtsummary package has two functions to help analysts prepare
tabular summaries of regression models: tbl_regression() and
tbl_uvregression().
tbl_regression()
The tbl_regression() function takes a regression model object in R and
returns a formatted table of regression model results. Like
tbl_summary(), tbl_regression() creates highly customizable analytic
tables with sensible defaults. Common regression models, such as
logistic regression and Cox proportional hazards regression, are
automatically identified, and the tables headers are pre-filled with
appropriate column headers (i.e., Odds Ratio and Hazard Ratio).
In the example below, the logistic regression model is summarized with
tbl_regression(). Note that a reference row for grade has been added,
and the variable labels have been carried through into the table. Using
exponentiate = TRUE, we exponentiate the regression coefficients,
yielding the odds ratios. The helper function add_global_p() was used
to replace the p-values for each term with the global p-value for
grade.
glm(response ~ age + grade, trial, family = binomial) %>%
tbl_regression(exponentiate = TRUE) %>%
add_global_p()
The tbl_regression() function
leverages the huge effort behind the broom,
parameters
(Lüdecke et al. 2020), and
broom.helpers
(Larmarange and Sjoberg 2021) packages to perform the initial formatting of the
regression object. Because tbl_regression() utilizes these packages,
there are many model types that are supported out of the box, such as
lm(), glm(), lme4::lmer(), lme4::glmer(), geepack::geeglm(),
survival::coxph(), survival::survreg(), survival::clogit(),
nnet::multinom(), rstanarm::stan_glm(), models built with the mice
package (van Buuren and Groothuis-Oudshoorn 2011), and many more. A custom tidier may be specified as
well, which is helpful when you need to present non-standard
modifications to your model results such as Wald confidence intervals or
results with modified variance-covariance standard errors.
tbl_uvregression()
The tbl_uvregression() function is a wrapper for tbl_regression()
that is useful when you need a series of univariate regression models.
The user passes a data frame to tbl_uvregression(), indicates what the
outcome is, what regression model to run, and the function will return a
formatted table of stacked univariate regression models.
trial %>%
select(response, age, grade) %>%
tbl_uvregression(
y = response,
method = glm,
method.args = list(family = binomial),
exponentiate = TRUE,
pvalue_fun = ~style_pvalue(., digits = 2)
) %>%
add_nevent() %>%
add_global_p()
4 Inline Reporting
Reproducible reports are an important part of good analytic practices.
We often need to report the results from a table in the text of an R
markdown report. The inline_text() function reports statistics from
gtsummary tables inline in an R markdown document.
Imagine you need to report the results for age from the univariate table
above. Typically, the odds ratio, confidence interval, and p-value
would be hard-coded into a report, which can lead to reproducibility
issues if the data is updated and the hard-coded statistics are not
amended. A simple call to the inline_text() function will dynamically
add the model results to an RMarkdown report.
The odds ratio for age was
r inline_text(uvreg, variable = age).
Here is how the line will appear in your report.
The odds ratio for age was 1.02 (95% CI 1.00, 1.04; p=0.091).
The default pattern to display for a regression table is
"{estimate} ({conf.level*100}% CI {conf.low}, {conf.high}; {p.value})"
(again using glue syntax), and can be modified with the
inline_text(pattern=) argument.
5 Merging and Stacking
The gtsummary tables shown above are often ready for publication as
they are; however, it is common that more complex tables need to be
constructed. This can be achieved by merging or stacking gtsummary
tables using the tbl_merge() and tbl_stack() functions. For example,
in cancer research we often report models predicting a tumor’s response
to treatment and risk of death side-by-side in publications. This type
of table is simple to construct using tbl_merge(). First, build a
table for each regression model using tbl_regression(), then merge the
two tables with tbl_merge(). Any number of gtsummary tables can be
merged with this function.
tbl1 <-
glm(response ~ age + grade, trial, family = binomial) %>%
tbl_regression(exponentiate = TRUE)
tbl2 <-
coxph(Surv(ttdeath, death) ~ age + grade, trial) %>%
tbl_regression(exponentiate = TRUE)
tbl_merge_1 <-
tbl_merge(
tbls = list(tbl1, tbl2),
tab_spanner = c("**Tumor Response**", "**Time to Death**")
)
Similarly, any number of gtsummary tables may be stacked using the
tbl_stack() function.
6 Themes
We love themes. The default styling (e.g., statistics displayed in
tbl_summary(), how p-values are rounded, decimal separator, and
more) follow the reporting guidelines from European Urology, The Journal
of Urology, Urology, and the British Journal of Urology International
(Assel et al. 2019). However, you will likely submit to another
journal, or your personal preferences differ from the defaults. The
gtsummary package is unique from other table building packages with
the ability to set fine-grained customization defaults with themes.
Themes were created to make these customizations easy to navigate and
reuse across documents or projects. With themes, users can control
default settings for existing functions (e.g., always present means
instead of medians in tbl_summary()), as well as other changes that
are not modifiable with function arguments. Several themes are available
to follow various journals’ reporting guidelines, reduce cell padding
and font size, and language themes to translate gtsummary tables to
more than 14 languages.
For example, using the theme for The Journal of the American Medical
Association (JAMA), large p-values are rounded to two decimal
places, confidence intervals are shown as "lb to ub" instead of
"lb, ub", and the confidence interval is displayed in the same column
as the model coefficients.
theme_gtsummary_journal("jama")
glm(response ~ age + grade, trial, family = binomial) %>%
tbl_regression(exponentiate = TRUE)
The language theme can be used to translate the table to another
language and allows users to specify the decimal and big mark symbols.
For example,
theme_gtsummary_language(language = "es", decimal.mark = ",", big.mark = ".")
will translate the output to Spanish and format numeric results as
1.000,00 instead of 1,000.00 (the default formatting).
A custom theme was used to construct the gtsummary tables shown in this manuscript to match the R Journal font and reduce the default cell padding. Themes are an evolving feature, and we welcome additions of new journals’ reporting guidelines or other themes useful to users. A full glossary of customizable theme elements is available in the package’s themes vignette (http://www.danieldsjoberg.com/gtsummary/articles/themes.html).
7 Print Engines
Tables printed with gtsummary can be seamlessly integrated into
RMarkdown documents and knitted into various output types using a number
of print engines. The package was written to be a companion to the gt
package from RStudio and is optimized to leverage the advanced
customization features of this print engine, but offers compatibility
with a variety of popular printing methods, including knitr::kable()
(Xie 2020), flextable
(Gohel 2020), huxtable
(Hugh-Jones 2020), and
kableExtra
(Zhu 2020). While gt is used as the default for most outputs, you
can easily use your print engine of choice with the conversion helper
functions provided in the package (e.g., as_flex_table()). It is
possible to get results in HTML, PDF (via LaTeX), RTF, Microsoft Word,
PowerPoint, Excel, and others, utilizing the various print engines. The
package is designed to interact with these print engines behind the
scenes to reduce the burden on users, and you generally only need to be
aware of them if you want to add advanced customizations.
8 Summary
The functions in the gtsummary package were designed to reduce the burden of reporting and to work together to easily construct both simple and complex tables. It is our hope that the user-friendly syntax and publication-ready tables will aid analysts in preparing reproducible and high-quality findings.
CRAN packages used
gtsummary, broom, gt, magrittr, tidyselect, tidyverse, skimr, stargazer, finalfit, tableone, glue, survey, parameters, broom.helpers, flextable, huxtable, kableExtra
CRAN Task Views implied by cited packages
CausalInference, Distributions, MixedModels, OfficialStatistics, ReproducibleResearch, Survival
Note
This article is converted from a Legacy LaTeX article using the texor package. The pdf version is the official version. To report a problem with the html, refer to CONTRIBUTE on the R Journal homepage.