library(knitr) opts_chunk$set(comment = NA, prompt = FALSE, cache = FALSE, results = 'asis') library(kableExtra) library(summarytools) library(magrittr) st_options(plain.ascii = FALSE, style = "rmarkdown", footnote = NA, subtitle.emphasis = FALSE, lang = "en")
st_css(main = TRUE, global = TRUE, bootstrap = FALSE)
summarytools provides a coherent set of functions centered on data exploration and simple reporting. At its core reside the following four functions:
txt <- data.frame( Function = c('<a href="#freq"><strong><code>freq()</strong></code></a>', '<a href="#ctable"><strong><code>ctable()</strong></code></a>', '<a href="#descr"><strong><code>descr()</strong></code></a>', '<a href="#dfsummary"><strong><code>dfSummary()</strong></code></a>'), Description = c(paste("**Frequency Tables** featuring counts, proportions, cumulative", "statistics as well as missing data reporting"), paste("**Cross-Tabulations** (joint frequencies) between pairs", "of discrete/categorical variables, featuring marginal sums", "as well as row, column or total proportions"), paste("**Univariate ('Descriptive') Statistics** for numerical data,", "with common measures of central tendency and dispersion"), paste("**Data Frame Summaries** featuring type-specific", "information for all variables: univariate", "statistics and/or frequency distributions, bar charts or", "histograms, as well as missing data counts and proportions.", "Very useful to quickly, detect anomalies and identify trends", "at a glance")) ) kable(txt, format = "html", escape = FALSE, align = c('l', 'l')) %>% kable_paper(full_width = FALSE, position = "left") %>% column_spec(1, extra_css = "vertical-align:top") %>% column_spec(2, extra_css = "vertical-align:top")
The package was developed with the following objectives in mind:
Results can be
When creating R Markdown documents, make sure to
results = "asis"
plain.ascii = FALSE
dfSummary()
freq()
, ctable()
and descr()
support sampling
weights %>%
, %$%
) and
pipeR (%>>%
) are fully
supported; the native |>
introduced in R 4.0 is supported as well and
in most cases is a safer bet as far as objects' name searching goes.st_options()
to minimize redundancy in function calls stby()
function which is a slightly modified version of base::by()
, but
dplyr::group_by()
is also supported freq()
, and
in dfSummary()
when all values are labelled. A future release will have
ctable()
behave similarly. na.val
allows treating a special value as NA
in freq()
,
ctable()
and dfSummary()
(feature introduced in version 1.1.0). format()
arguments are also supported by summarytools'
print
method / view()
function; this can be used to set a
thousands separator or modify the decimal separator, among other
possibilities (see help("format")
)
The freq()
function generates frequency tables with counts,
proportions, as well as missing data information. Side note: the very idea
for creating this package stemmed from the absence of such a function in
base R.
freq(iris$Species, plain.ascii = FALSE, style = "rmarkdown")
In this first example, the plain.ascii
and style
arguments were
specified. However, since we have defined them globally for this document
using st_options()
, they are redundant and will be omitted from hereon
(section 16 contains a detailed description of this
vignette's configuration).
One of summarytools' main purposes is to help cleaning and preparing data
for further analysis. But in some circumstances, we don't need (or already have)
information about missing data. Using report.nas = FALSE
makes the output
table smaller by one row and two columns:
freq(iris$Species, report.nas = FALSE, headings = FALSE)
 | The `headings = FALSE` parameter suppresses the heading section. |
By "switching off" all optional elements, a much simpler table will be produced:
freq(iris$Species, report.nas = FALSE, totals = FALSE, cumul = FALSE, headings = FALSE)
While the output is much simplified, the syntax is not; I blame it on
Tesler's law of conservation of complexity!
Thankfully, st_options()
is there to accommodate everyone's preferences (see
the section on package options).
To generate frequency tables for all variables in a data frame, we could
(and in the earliest versions, needed to) use lapply()
. However, this
is not required since freq()
accepts data frames as the main argument:
freq(tobacco)
To avoid cluttering the results, numerical columns having more
than 25 distinct values are ignored. This threshold value can be changed via st_options()
(example: st_options(freq.ignore.threshold = 10)
).
 | The *tobacco* data frame contains simulated data and is included in the package. Another simulated data frame is included: *exams*. Both have French versions (*tabagisme*, *examens*). |
The rows
argument allows subsetting frequency tables; we can
use it in different ways:
rows = 1:10
will show the frequencies for the first 10 values only. An
“(Other)” row is automatically added to account for the frequency of other
values?regex
for more information on this topic) By combining the order
and rows
parameters, we can easily filter
the results to show, for example, the 5 most common values in a factor:
freq(tobacco$disease, order = "freq", rows = 1:5, headings = FALSE)
Instead of "freq"
, we can use "-freq"
to reverse the ordering and
get results ranked from lowest to highest in frequency.
 | Notice the "**(Other)**" row, which is automatically added |
When generating html results, use the collapse = TRUE
argument with
print()
or view()
/ stview()
to get collapsible sections; clicking
on the variable name in the heading section will collapse / reveal the
frequency table (results not shown).
view(freq(tobacco), collapse = TRUE)
ctable()
generates cross-tabulations (joint frequencies) for
pairs of categorical variables.
Using the tobacco simulated data frame, we'll cross-tabulate the two categorical variables smoker and diseased.
ctable(x = tobacco$smoker, y = tobacco$diseased, prop = "r") # Show row proportions
As can be seen, since markdown does not fully support multiline table headings, pander does what it can to display this particular type of table. To get better results, the "render" method is recommended and will be used in the next examples.
Row proportions are shown by default. To display column or total
proportions, use prop = "c"
or prop = "t"
, respectively. To omit
proportions altogether, use prop = "n"
.
By "switching off" all optional features, we get a simple 2 x 2 table:
with(tobacco, print(ctable(x = smoker, y = diseased, prop = 'n', totals = FALSE, headings = FALSE), method = "render") )
To display the chi-square statistic, set chisq = TRUE
. For
2 x 2 tables, use OR
and RR
to show odds ratio and
risk ratio (also called relative risk), respectively. Those can
be set to TRUE
, in which case 95% confidence intervals are
shown; to use different confidence levels, use for example OR = .90
.
 | Using pipes generally makes it easier to generate `ctable()` results. |
library(magrittr) tobacco %$% # Acts like with(tobacco, ...) ctable(x = smoker, y = diseased, chisq = TRUE, OR = TRUE, RR = TRUE, headings = FALSE) %>% print(method = "render")
descr()
generates descriptive / univariate statistics, i.e. common central
tendency statistics and measures of dispersion. It accepts single
vectors as well as data frames; in the latter case, all non-numerical columns
are ignored, with a message to that effect.
descr(iris)
To turn off the variable-type messages, use the package's print()
method
silent = TRUE
parameter. It is possible to set that option globally,
which we will do here, so it won't be displayed in the remaining portion of this vignette.
st_options(descr.silent = TRUE)
Results can be transposed by using transpose = TRUE
, and statistics
can be selected using the stats
argument:
descr(iris, stats = c("mean", "sd"), transpose = TRUE, headings = FALSE)
See ?descr
for a list of all available statistics. Special values
“all”, “fivenum”, and “common” are also valid, and can be used in conjunction
with specific statistics, to add or remove them. For example,
stats= c("common", "", "-pct.valid")
adds N to, and
excludes Pct. Valid from, common statistics. The default value is “all”,
and it can be modified using st_options()
:
st_options(descr.stats = "common")
keywords (all, fivenum, and
common) can be used in conjunction with statistics, to add or
remove them. stats= c("common", "n", "-pct.valid")
adds N to, and
excludes Pct. Valid from, common statistics.
dfSummary()
creates a summary table with statistics, frequencies and graphs
for all variables in a data frame. The information displayed is
type-specific (character, factor, numeric, date) and also varies according to
the number of distinct values.
To see the results in RStudio's Viewer (or in the default Web browser if
working in another IDE or from a terminal window), use the view()
function,
or its twin stview()
in case of name conflicts:
view(dfSummary(iris))
 | Be careful to use `view()` (or `stview()`) and not `View()` with capital `V'. Otherwise, results will be shown in the data viewer. Also, be mindful of the order in which the packages are loaded. Some packages redefine `view()` to point to `View()`; loading **summarytools** *after* these packages will ensure its own `view()` function works properly. Otherwise, `stview()` is always there as a foolproof alternative. |
When using dfSummary()
in R Markdown documents, it is generally a good
idea to exclude a column or two to avoid margin overflow. Since the Valid
and Missing columns are complementary (and therefore redundant), we can
safely drop either one of them.
dfSummary(tobacco, plain.ascii = FALSE, style = "grid", graph.magnif = 0.82, varnumbers = FALSE, valid.col = FALSE, tmp.img.dir = "/tmp")
The tmp.img.dir
parameter is mandatory when generating dfSummaries in
R Markdown documents, except for method "render". The explanation for this can be found further below.
 | Some users reported repeated X11 warnings; those can be avoided by setting the `warning` chunk option to `FALSE`: `{r chunk_name, results="asis", warning=FALSE}`. |
Introduced in version 1.0.0 in response to feature requests, a mechanism
provides control over which statistics to shown in the Stats/Values column.
The third row, which displays IQR (CV)
, can be modified to show any available
statistics in R. An additional "slot" (unused by default) is also made
available. To use this feature, define dfSummary.custom.1
and/or
dfSummary.custom.2
using st_options()
in the following way, encapsulating
the code in an expression()
:
st_options( dfSummary.custom.1 = expression( paste( "Q1 - Q3 :", round( quantile(column_data, probs = .25, type = 2, names = FALSE, na.rm = TRUE), digits = 1 ), " - ", round( quantile(column_data, probs = .75, type = 2, names = FALSE, na.rm = TRUE), digits = 1 ) ) ) ) print( dfSummary(iris, varnumbers = FALSE, na.col = FALSE, style = "multiline", plain.ascii = FALSE, headings = FALSE, graph.magnif = .82), method = "render" )
If we had used dfSummary.custom.2
instead of dfSummary.custom.1
,
a fourth line would have been added under IQR (CV)
.
Note that instead of round()
, it is possible to use the internal
function format_number()
, which will ensure correct formatting of numbers
according to all specified arguments (rounding digits, decimal mark,
big/small mark, etc.). The variable round.digits
, which contains
the value of st_options("round.digits")
can also be used.
This is how the default IQR (CV)
is defined -- here we set the first custom
stat back to its default value and then display its definition (formatR::tidy_source()
is used to format the expression):
library(formatR) st_options(dfSummary.custom.1 = "default") formatR::tidy_source( text = deparse(st_options("dfSummary.custom.1")), indent = 2, args.newline = TRUE )
 | Don't forget to specify `na.rm = TRUE` for all functions that use this parameter (most of base R functions do). |
The dfSummary()
function also
max.tbl.height
parameter; This is especially convenient if the analyzed data frame has
numerous variables; see
vignette("rmarkdown", package = "summarytools")
for more detailsAlthough most columns can be excluded using the function's parameters, it is also possible to delete them with the following syntax (results not shown):
dfs <- dfSummary(iris) dfs$Variable <- NULL # This deletes the Variable column
To produce optimal results, summarytools has its own version of
the base by()
function. It's called stby()
, and we use it as we
would by()
, with a notable difference: set the useNA
parameter to TRUE
to create an additional group for observations containing NA
s on the grouping variable(s) (see example in section 6.2).
(iris_stats_by_species <- stby(data = iris, INDICES = iris$Species, FUN = descr, stats = "common", transpose = TRUE))
When used to produce split-group statistics for a single variable, stby()
assembles everything into a single table instead of displaying a series
of one-column tables.
with(tobacco, stby(data = BMI, INDICES = age.gr, FUN = descr, stats = c("mean", "sd", "min", "med", "max"), useNA = TRUE) )
The syntax is a little trickier for this combination, so here is an example (results not shown):
stby(data = list(x = tobacco$smoker, y = tobacco$diseased), INDICES = tobacco$gender, FUN = ctable) # or equivalently with(tobacco, stby(data = list(x = smoker, y = diseased), INDICES = gender, FUN = ctable))
To create grouped statistics with freq()
, descr()
or dfSummary()
, it is
possible to use dplyr's group_by()
as an alternative to stby()
.
Usings forcats::fct_na_value_to_level
to make NA
's explicit in factors is
recommended:
library(dplyr) tobacco$gender %<>% forcats::fct_na_value_to_level() tobacco %>% group_by(gender) %>% descr(stats = "fivenum")
suppressPackageStartupMessages(library(dplyr)) library(magrittr) tobacco$gender %<>% forcats::fct_na_value_to_level("(Missing)") tobacco %>% group_by(gender) %>% descr(stats = "fivenum")
When generating freq()
or descr()
tables, it is possible to turn the
results into "tidy" tables with the use of the tb()
function (think of
tb as a diminutive for tibble). For example:
library(magrittr) iris %>% descr(stats = "common") %>% tb() %>% knitr::kable() iris$Species %>% freq(cumul = FALSE, report.nas = FALSE) %>% tb() %>% knitr::kable()
By definition, no total rows are part of tidy tables, and the row names are turned into a regular column.
 | When displaying *tibbles* using **rmarkdown**, the **knitr** chunk option `results` should be set to 'markup' instead of 'asis'. |
Not all tables generated by tb() are strictly speaking tidy; you can choose
for instance to not recalculate proportions/valid proportions of grouped
freq outputs, using recalculate = FALSE
(TRUE by default).
Here are some examples showing how lists created using stby()
or group_by()
can be transformed into tidy tibbles.
grouped_descr <- stby(data = exams, INDICES = exams$gender, FUN = descr, stats = "common") grouped_descr %>% tb()
The order
parameter controls row ordering:
grouped_descr %>% tb(order = 2)
Setting order = 3
changes the order of the sort variables exactly as with
order = 2
, but it also reorders the columns:
grouped_descr %>% tb(order = 3)
Note that percentages will be recalculated, unless setting tb()
's
recalculate
argument to FALSE
, in which case the results won't comply with
the tidy principles.
tobacco %>% dplyr::group_by(gender) %>% freq(smoker) %>% tb()
For more details, see ?tb
.
summarytools objects are not always compatible with packages
focused on table formatting, such as
formattable or
kableExtra.
However, tb()
can be used as a "bridge", an intermediary step
turning freq()
and descr()
objects into simple tables that any
package can work with. Here is an example using kableExtra:
library(kableExtra) library(magrittr) stby(data = iris, INDICES = iris$Species, FUN = descr, stats = "fivenum") %>% tb(order = 3) %>% kable(format = "html", digits = 2) %>% collapse_rows(columns = 1, valign = "top")
Using the file
argument with print()
or view()
/ stview()
, we can
write outputs to a file, be it html, Rmd, md, or just plain text
(txt). The file extension is used to determine the type of
content to write out.
view(iris_stats_by_species, file = "~/iris_stats_by_species.html") view(iris_stats_by_species, file = "~/iris_stats_by_species.md")
A Note on PDF documents
There is no direct way to create a PDF file with summarytools. One
option is to generate an html file and convert it to PDF using
Pandoc or
WK\<html>TOpdf (the latter gives
better results than Pandoc with dfSummary()
output).
Another option is to create an Rmd document using PDF as the output
format. See vignette("rmarkdown", package = "summarytools")
for the details
on how to proceed.
The append
argument allows adding content to existing files generated by
summarytools. This is useful if we wish to include several statistical
tables in a single file. It is a quick alternative to creating an Rmd
document.
The following options can be set globally with st_options()
:
| Option name | Default | Note | | ----------------------------: | --------: | :---------------------------------------------- | | style (1) | “simple” | Set to “rmarkdown” in .Rmd documents | | plain.ascii | TRUE | Set to FALSE in .Rmd documents | | round.digits (2) | 2 | Number of decimals to show | | headings | TRUE | Formerly “omit.headings” | | footnote | “default” | Customize or set to NA to omit | | display.labels | TRUE | Show variable / data frame labels in headings | | na.val | NULL | Value to treat as NA in factor / char variables | | bootstrap.css (3) | TRUE | Include Bootstrap 4 CSS in html output files | | custom.css | NA | Path to your own CSS file | | escape.pipe | FALSE | Useful for some Pandoc conversions | | char.split (4) | 12 | Threshold for line-wrapping in column headings | | subtitle.emphasis | TRUE | Controls headings formatting | | lang | “en” | Language (always 2-letter, lowercase) |
^1^ Does not apply to dfSummary()
, which has its own style option (see next table)
^2^ Does not apply to ctable()
, which has its own round.digits
option (see next table)
^3^ Set to FALSE
in Shiny apps
^4^ Affects only html outputs for descr()
and ctable()
| Option name | Default | Note |
| ------------------------: | ----------: | :--------------------------------------- |
| freq.cumul | TRUE | Display cumulative proportions in freq() |
| freq.totals | TRUE | Display totals row in freq() |
| freq.report.nas | TRUE | Display ctable()
|
| ctable.silent | FALSE | Hide console messages |
| descr.stats | “all” | “fivenum”, “common” or vector of stats |
| descr.transpose | FALSE | Display stats in columns instead of rows |
| descr.silent | FALSE | Hide console messages |
| dfSummary.style | “multiline” | Can be set to “grid” as an alternative |
| dfSummary.varnumbers | TRUE | Show variable numbers in 1st col. |
| dfSummary.labels.col | TRUE | Show variable labels when present |
| dfSummary.graph.col | TRUE | Show graphs |
| dfSummary.valid.col | TRUE | Include the Valid column in the output |
| dfSummary.na.col | TRUE | Include the Missing column in the output |
| dfSummary.graph.magnif | 1 | Zoom factor for bar plots and histograms |
| dfSummary.silent | FALSE | Hide console messages |
| tmp.img.dir (2)| NA | Directory to store temporary images |
| use.x11 (3) | TRUE | Allow creation of Base64-encoded graphs |
^1^ See section 2.3 for details
^2^ Applies to dfSummary()
only
^3^ Set to FALSE in text-only environments
st_options() # Display all global options values st_options('round.digits') # Display the value of a specific option st_options(style = 'rmarkdown', # Set the value of one or several options footnote = NA) # Turn off the footnote for all html output
When a summarytools object is created, its formatting attributes are
stored within it. However, we can override most of them when using
print()
or view()
/ stview()
.
The following table indicates what arguments can be used with print()
or view()
/ stview()
to override formatting attributes. Base R's format()
arguments can also be used (they are not listed here).
| Argument | freq | ctable | descr | dfSummary | | --------------------------: | :----: | :----: | :---: | :-------: | | style | x | x | x | x | | round.digits | x | x | x | | | plain.ascii | x | x | x | x | | justify | x | x | x | x | | headings | x | x | x | x | | display.labels | x | x | x | x | | varnumbers | | | | x | | labels.col | | | | x | | graph.col | | | | x | | valid.col | | | | x | | na.col | | | | x | | col.widths | | | | x | | totals | x | x | | | | report.nas | x | | | | | display.type | x | | | | | missing | x | | | | | split.tables (1) | x | x | x | x | | caption (1) | x | x | x | x |
^1^ pander options
To change the information shown in the heading section,
use the following arguments with print()
or view()
:
| Argument | freq | ctable | descr | dfSummary | | -----------------: | :----: | :----: | :---: | :-------: | | Data.frame | x | x | x | x | | Data.frame.label | x | x | x | x | | Variable | x | x | x | | | Variable.label | x | x | x | | | Group | x | x | x | x | | date | x | x | x | x | | Weights | x | | x | | | Data.type | x | | | | | Row.variable | | x | | | | Col.variable | | x | | |
In the following example, we will create and display a freq()
object, and
then display it again, this time overriding three of its formatting attributes,
as well as one of its heading attributes.
(age_stats <- freq(tobacco$age.gr)) print(age_stats, report.nas = FALSE, totals = FALSE, display.type = FALSE, Variable.label = "Age Group")
print()
or view()
parameters have precedence (overriding feature) freq() / ctable() / descr() / dfSummary()
parameters come second st_options()
come third and act as default The logic for the evaluation of the various parameter values can be summarized as follows:
If an argument is explicitly supplied in the function call, it will have precedence.
If both the core function and the print()
/ view()
/ stview()
function
are called at once and have conflicting parameter values, the latter has
precedence (they always win the argument!).
If the parameter values cannot be found in the function calls, the stored
defaults (which can be modified with st_options()
) will be applied.
When creating html reports, both Bootstrap's CSS and summarytools.css are included by default. For greater control on the looks of html content, it is also possible to add class definitions in a custom CSS file.
We need to use a very small font size for a simple html report containing
a dfSummary()
. For this, we create a .css file (with the name of our
choosing) which contains the following class definition:
.tiny-text { font-size: 8px; }
Then we use print()
's custom.css
argument to specify to location of
our newly created CSS file (results not shown):
print(dfSummary(tobacco), custom.css = 'path/to/custom.css', table.classes = 'tiny-text', file = "tiny-tobacco-dfSummary.html")
To successfully include summarytools functions in Shiny apps,
bootstrap.css = FALSE
to avoid interacting with the app's layout headings = FALSE
in case problems arise graph.magnif
parameter or with the
dfSummary.graph.magnif
global option dfSummary()
tables are too wide, omit a column or two (valid.col
and
varnumbers
, for instance) col.widths
parameter col.widths
or graph.magnig
do not seem to work, try using them as
parameters for print()
rather than dfSummary()
print(dfSummary(somedata, varnumbers = FALSE, valid.col = FALSE, graph.magnif = 0.8), method = 'render', headings = FALSE, bootstrap.css = FALSE)
When using dfSummary()
in an Rmd document using markdown
styling (as opposed to html rendering), three elements are
needed in order to display the png graphs properly:
1 - plain.ascii
must be set to FALSE
2 - style
must be set to "grid"
3 - tmp.img.dir
must be defined and be at most 5 characters wide
Note that as of version 0.9.9, setting tmp.img.dir
is no longer required
when using method = "render"
. It is only necessary when a transitory
markdown output is created.
In markdown tables, Pandoc uses string lengths (number of characters in the cells) to set minimal column widths. A wide image can cause a column to expand as needed, but a narrow image won't make it "shrink". This is why it is important to keep the link / path as succinct as possible.
+---------------+--------|----------------------+---------+ | Variable | stats | Graph | Valid | +===============+========|======================+=========+ | age\ | ... |  | 978\ | | [numeric] | ... | | (97.8%) | +---------------+--------+----------------------+---------+
CRAN policies are really strict when it comes to writing content in the user directories, or anywhere outside R's temporary zone (for good reasons). So users need to set this temporary location themselves.
On Mac OS and Linux, using "/tmp" makes a lot of sense: it's a short path, and the directory is purged automatically. On Windows, there is no such convenient directory, so we need to pick one -- be it absolute ("/tmp") or relative ("img", or simply ".").
Thanks to the R community's efforts, the following languages can be used, in addition to English (default):
To switch languages, simply use
st_options(lang = "fr")
All output from the core functions will now use that language:
freq(iris$Species)
st_options(lang = "en")
On most Windows systems, it is necessary to change the LC_CTYPE
element of
the locale settings if the character set is not included in the system's
default locale. For instance, in order to get good results with the Russian
language in a "latin1" environment, use the following settings:
Sys.setlocale("LC_CTYPE", "russian") st_options(lang = 'ru')
To go back to default settings...
Sys.setlocale("LC_CTYPE", "") st_options(lang = "en")
Using the function use_custom_lang()
, it is possible to add your own set of
translations or custom terms. To achieve this, get the
csv template,
customize one, many or all of the +/- 80 terms, and call use_custom_lang()
with the path to the edited csv template.
The define_keywords()
makes it easy to change just one or a few terms.
For instance, you might prefer using "N" or "Count" rather than "Freq" in the
title row of freq()
tables. Or you might want to generate a document which
uses the tables' titles as sections titles.
For this, call define_keywords()
and feed it the term(s) you wish
to modify (which can themselves be stored in predefined variables). Here,
the terms we need to change are freq.title
and freq
:
section_title <- "**Species of Iris**" define_keywords(title.freq = section_title, freq = "N") freq(iris$Species)
Calling define_keywords()
without any arguments will bring up, on systems
that support graphical devices (the vast majority, that is), a window from
which you can edit all terms.
After closing the edit window, a dialogue box gives the option to save the
newly created custom language to a csv file. (Even if we only modify one or
two keywords, the package considers the terms as part of a whole "language").
We can later reload into memory the custom language file by calling
use_custom_lang("language-file.csv")
.
See ?define_keywords
for a list of all customizable terms.
To revert all changes, simply use st_options(lang = "en")
.
It is possible to further customize the headings by adding arguments to the
print()
function. Here, we use an empty string to override the value of
Variable
; this causes the second line of the heading to disappear altogether.
define_keywords(title.freq = "Types and Counts, Iris Flowers") print( freq(iris$Species, display.type = FALSE), # Variable type won't be displayed... Variable = "" # and neither will the variable name )
Knowing how this vignette is configured can help you get started with summarytools in R Markdown.
The output element is the one that matters:
--- output: rmarkdown::html_vignette: css: - !expr system.file("rmarkdown/templates/html_vignette/resources/vignette.css", package = "rmarkdown") ---
```r`r ''` library(knitr) opts_chunk$set(results = 'asis', # Can also be set at chunk level comment = NA, prompt = FALSE, cache = FALSE) library(summarytools) st_options(plain.ascii = FALSE, # Always use in Rmd documents style = "rmarkdown", # Always use in Rmd documents subtitle.emphasis = FALSE) # Improves layout w/ some themes ```
The needed CSS is automatically added to html files created
using print()
or view()
with the file
argument. But in
R Markdown documents, this needs to be done explicitly in a
setup chunk just after the YAML header (or following a first
setup chunk specifying knitr and summarytools options):
```r`r ''` st_css(main = TRUE, global = TRUE) ```
The package comes with no guarantees. It is a work in progress and feedback is welcome. Please open an issue on GitHub if you find a bug or wish to submit a feature request.
summarytools is the result of many hours of work. If it brings value to your work, please consider making a small donation using this Paypal link, or buy me a coffee.
Check out the GitHub project's page; from there you can see the latest updates and also submit feature requests.
For a preview of what's coming in the next release, have a look at the development branch.
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