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Tools to Quickly and Neatly Summarize Data

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Introduction to summarytools"

Introduction to summarytools"
In summarytools: Tools to Quickly and Neatly Summarize Data 

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)

1. Overview {#overview}

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("**Descriptive (Univariate) Statistics** for numerical data, featuring",
            "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")

1.1 Motivation

The package was developed with the following objectives in mind:

1.2 Directing Output

Results can be

When creating R Markdown documents, make sure to

1.3 Other Characteristics


<< 1. Overview | TOC | 3. Cross-Tabulations: ctable() >>


2. Frequency Tables: freq()

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).

2.1 Missing Data

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)
![](assets/lightbulb.svg) The `headings = FALSE` parameter suppresses the heading section.

2.2 Simplest Expression

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 section on package options).

2.3 Multiple Frequency Tables At Once

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 of 25 can be changed by using st_options(); for example, to change it to 10, we'd use st_options(freq.ignore.threshold = 10).

![](assets/lightbulb.svg) 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*).

2.4 Subsetting (Filtering) Frequency Tables

The rows parameter allows subsetting frequency tables; we can use this parameter in different ways:

Showing The Most Common Values

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.

![](assets/lightbulb.svg) Notice the "**(Other)**" row, which is automatically generated.

2.5 Collapsible Sections

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)


<< 2. Frequency Tables: freq() | TOC | 4. Descriptive Statistics: descr() >>


3. Cross-Tabulations: ctable()

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.

3.1 Row, Column, or Total Proportions

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".

3.2 Minimal Cross-Tabulations

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")
)

3.3 Chi-Square (𝛘2), Odds Ratio and Risk Ratio

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.

![](assets/lightbulb.svg) 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")


<< 3. Cross-Tabs: ctable() | TOC | 5. Data Frame Summaries: dfSummary() >>


4. Descriptive Statistics: descr()

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 silent = TRUE. It is possible to set that option globally, which we will do here, so it won't be displayed in the remaining of this vignette.

st_options(descr.silent = TRUE)

4.1 Transposing and Selecting Statistics

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. The default value is “all”, and it can be modified using st_options():

st_options(descr.stats = "common")


<< 4. Descriptive Statistics with descr() | TOC | 6. Grouped Statistics: stby() >>


5. Data Frame Summaries: dfSummary()

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))

![](assets/lightbulb.svg) Be careful to use `view()` and not `View()`. If you use the latter, 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()` works properly. Otherwise, `stview()` is always there as a foolproof alternative.

5.1 Using dfSummary() in R Markdown Documents

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 redundant, we can drop either one of them.

dfSummary(tobacco, 
          plain.ascii  = FALSE, 
          style        = "grid", 
          graph.magnif = 0.75, 
          valid.col    = FALSE,
          tmp.img.dir  = "/tmp")


The tmp.img.dir parameter is mandatory when generating dfSummaries in R Markdown documents, except for html rendering. The explanation for this can be found further below.

![](assets/exclamation-diamond.svg) 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}`.

5.2 Optional Statistics

This feature has been requested several times since the package was released. Introduced in version 1.0.0, it provides control over which statistics to shown in the Stats/Values column. Namely, 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 = .8),
  method = "render"
)

If we had used dfSummary.custom.2 instead of dfSummary.custom.1, a fourth row would have been added under the default IQR (CV) row.

Note that instead of round(), it is possible to use the internal format_number(), which ensures the number is formatted according to all specified arguments (rounding digits, decimal mark and thousands mark, etc.). The internal 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 / indent 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
)
![](assets/lightbulb.svg) Don't forget to specify `na.rm = TRUE` for all functions that use this parameter (most of base R functions do).

5.3 Other Notable Features

The dfSummary() function also

5.4 Excluding Columns

Although 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


<< 5. Data Frame Summaries | TOC | 7. Grouped Statistics: group_by() >>


6. Grouped Statistics: stby()

To produce optimal results, summarytools has its own version of the base by() function. It's called stby(), and we use it exactly as we would by():

(iris_stats_by_species <- stby(data      = iris, 
                               INDICES   = iris$Species, 
                               FUN       = descr, 
                               stats     = "common", 
                               transpose = TRUE))

6.1 Special Case of descr() with stby()

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"))
)

6.2 Using stby() with ctable()

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))


<< 6. Grouped Statistics : group_by() | TOC | 8. Tidy Tables : tb() >>


7. Grouped Statistics: group_by()

To create grouped statistics with freq(), descr() or dfSummary(), it is possible to use dplyr's group_by() as an alternative to stby(). Syntactic differences aside, one key distinction is that group_by() considers NA values on the grouping variable(s) as a valid category, albeit with a warning suggesting the use of forcats::fct_explicit_na to make NA's explicit in factors. Following this advice, we get:

library(dplyr)
tobacco$gender %<>% forcats::fct_explicit_na()
tobacco %>% 
  group_by(gender) %>% 
  descr(stats = "fivenum")

suppressPackageStartupMessages(library(dplyr))
library(magrittr)
tobacco$gender %<>% forcats::fct_explicit_na()
tobacco %>% group_by(gender) %>% descr(stats = "fivenum")


<< 7. Grouped Statistics : group_by() | TOC | 9. Directing Output to Files >>


8. Tidy Tables : tb()

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()

iris$Species %>% 
  freq(cumul = FALSE, report.nas = FALSE) %>% 
  tb()

By definition, no total rows are part of tidy tables, and the row names are converted to a regular column.

![](assets/lightbulb.svg) When displaying *tibbles* using **rmarkdown**, the **knitr** chunk option `results` should be set to 'markup' instead of 'asis'.

8.1 Tidy Split-Group Statistics

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)

For more details, see ?tb.

8.2 A Bridge to Other Packages

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")


<< 8. Tidy Tables : tb() | TOC | 10. Global Options >>


9. Directing Output to Files

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 by the package 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.

9.1 Appending Output Files

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.


<< 9. Directing Output to Files | TOC | 11. Format Attributes >>


10. Package Options

The following options can be set globally with st_options():

10.1 General 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 | | 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()

10.2 Function-Specific Options

| 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 row and “valid” columns | | freq.ignore.threshold (1) | 25 | Used to determine which vars to ignore | | freq.silent | FALSE | Hide console messages | | ctable.prop | “r” | Display row proportions by default | | ctable.totals | TRUE | Show marginal totals | | ctable.round.digits | 1 | Number of decimals to show in ctable() | | 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

Examples

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


<< 10. Global Options | TOC | 12. Fine-Tuning Looks : CSS >>


11. Format Attributes

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().

11.1 Overriding Function-Specific Arguments

The following table indicates what arguments can be used with print() or view() to override formatting attributes. Base R's format() function arguments can also be used (although 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

11.2 Overriding Heading Contents

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 | | |

Example

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")

11.3 Order of Priority for Parameters / Options

  1. print() or view() parameters have precedence (overriding feature)
  2. freq() / ctable() / descr() / dfSummary() parameters come second
  3. Global options set with st_options() come third and act as default

The logic for the evaluation of the various parameter values can be summarized as follows:


<< 11. Format Attributes | TOC | 13. Shiny Apps >>


12. Fine-Tuning Looks : CSS

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.

Example

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")


<< 12. Fine-Tuning Looks : CSS | TOC | 14. Graphs in R Markdown >>


13. Shiny Apps

To successfully include summarytools functions in Shiny apps,

Example (results not shown)

print(dfSummary(somedata, 
                varnumbers   = FALSE, 
                valid.col    = FALSE, 
                graph.magnif = 0.8), 
      method   = 'render',
      headings = FALSE,
      bootstrap.css = FALSE)


<< 13. Shiny Apps | TOC | 15. Languages & Term Customization >>


14. Graphs in R Markdown {#tmp-img-dir}

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" and can be left to NA. It is only necessary to define it when a transitory markdown table must be created, as shown below. Note how narrow the Graph column is -- this is actually required, since the width of the rendered column is determined by the number of characters in the cell, rather than the width of the image itself:

+---------------+--------|----------------------+---------+
| Variable      | stats  |  Graph               | Valid   |
+===============+========|======================+=========+
| age\          |  ...   | ![](/tmp/ds0001.png) | 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, therefore consenting to having content written outside R's predefined temporary zone.

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 ".").


<< 14. Graphs in R Markdown | TOC | 16. Vignette Setup >>


15. Languages & Term Customization

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)

15.1 Non-UTF-8 Locales

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")

15.2 Defining and Using Custom Terms

Using the function use_custom_lang(), it is possible to add your own set of translations or personalized terms. To achieve this, get the csv template, customize one, many or all of the +/- 70 terms, and call use_custom_lang(), giving it as sole argument the path to the edited csv template. Note that such custom language settings will not persist across R sessions. This means that you should always have this csv file handy for future use.

15.3 Defining Only Specific Keywords

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 heading sections.

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 we can edit all the terms we want.

After closing the edit window, a dialogue box gives the option to save the newly created custom language to a csv file (even though we changed just a few keywords, the package considers the terms as a whole). We can later reload into memory the custom language file by calling use_custom_lang("path-to-custom-language-file.csv").

See ?define_keywords for a list of all customizable terms in the package.

To revert all changes, we can simply use st_options(lang = "en").

15.4 Power-Tweaking Headings

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
  )


<< 15. Translations & Term Customization | TOC | 17. Conclusion >>


16. Vignette Setup

Knowing how this vignette is configured can help you get started with using summarytools in R Markdown documents.

16.1 The YAML Section

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")
---

16.2 The Setup Chunk

```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
```

16.3 Including summarytools' CSS

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)
```


<< 16. Vignette Setup | TOC


17. Conclusion

The package comes with no guarantees. It is a work in progress and feedback is always welcome. Please open an issue on GitHub if you find a bug or wish to submit a feature request.

Stay Up to Date

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.


TOC



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summarytools documentation built on May 20, 2022, 9:06 a.m.

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