1.1 Motivation

The package was developed with the following objectives in mind:

• Provide a coherent set of easy-to-use descriptive functions that are akin to those included in commercial statistical software suites such as SAS, SPSS, and Stata
• Offer flexibility in terms of output format & content
  
• Integrate well with commonly used software & tools for reporting (the RStudio IDE, Rmarkdown, and knitr) while also allowing for standalone, simple report generation using any R interface

1.2 Directing Output

Results can be

• Displayed in the R console as plain text
• Rendered as html and shown in a Web browser or in RStudio’s Viewer Pane
• Written / appended to plain text, markdown, or html files

When creating R Markdown documents, make sure to

• Use chunk option results="asis"
• Une the function argument plain.ascii=FALSE
• Set the style parameter to “rmarkdown”, or “grid” for dfSummary()

1.3 Other Characteristics

• Weights-enabled: freq(), ctable() and descr() support sampling weights
• Multilingual:
  • Built-in translations exist for French, Portuguese, Spanish, Russian, and Turkish. Users can easily add custom translations or modify existing ones as needed
• Flexible and extensible:
  • The built-in features used to support alternate languages provide a way to modify a great number of terms used in outputs (headings and tables)
  • Pipe operators from magrittr (%>%, %$%) and pipeR (%>>%) are fully supported; the native |> introduced in R 4.0 is supported as well
  • Default values for a good number of function parameters can be modified using st_options() to minimize redundancy in function calls
  • By-group processing is easily achieved using the package’s stby() function which is a slightly modified version of base::by(), but dplyr::group_by() is also supported
  • Pander options can be used to customize or enhance plain text and markdown tables
  • Base R’s format() parameters are also supported; this can be used to set thousands separator or modify the decimal separator, among other possibilities (see help("format"))
  • Bootstrap CSS is used by default with html output, and user-defined classes can be added at will

Frequencies

iris$Species
Type: Factor

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)

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).

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:

• To filter rows by their order of appearance, we use a numerical vector; rows = 1:10 will show the frequencies for the first 10 values only. To account for the frequencies of unshown values, the “(Other)” row is automatically added
• To filter rows by name, we can use either
  • a character vector specifying all the row names we wish to keep
    
  • a single character string, which will be used as a regular expression (see ?regex for more information on this topic)

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)

	Freq	% Valid	% Valid Cum.	% Total	% Total Cum.
Hypertension	36	16.22	16.22	3.60	3.60
Cancer	34	15.32	31.53	3.40	7.00
Cholesterol	21	9.46	40.99	2.10	9.10
Heart	20	9.01	50.00	2.00	11.10
Pulmonary	20	9.01	59.01	2.00	13.10
(Other)	91	40.99	100.00	9.10	22.20
<NA>	778			77.80	100.00
Total	1000	100.00	100.00	100.00	100.00

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 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)

Cross-Tabulation, Row Proportions

smoker * diseased
Data Frame: tobacco

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.

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")

Descriptive Statistics

iris
N: 150

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")

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.

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"
)

Variable	Stats / Values	Freqs (% of Valid)	Graph	Valid
Sepal.Length [numeric]	Mean (sd) : 5.8 (0.8) min ≤ med ≤ max: 4.3 ≤ 5.8 ≤ 7.9 Q1 - Q3 : 5.1 - 6.4	35 distinct values		150 (100.0%)
Sepal.Width [numeric]	Mean (sd) : 3.1 (0.4) min ≤ med ≤ max: 2 ≤ 3 ≤ 4.4 Q1 - Q3 : 2.8 - 3.3	23 distinct values		150 (100.0%)
Petal.Length [numeric]	Mean (sd) : 3.8 (1.8) min ≤ med ≤ max: 1 ≤ 4.3 ≤ 6.9 Q1 - Q3 : 1.6 - 5.1	43 distinct values		150 (100.0%)
Petal.Width [numeric]	Mean (sd) : 1.2 (0.8) min ≤ med ≤ max: 0.1 ≤ 1.3 ≤ 2.5 Q1 - Q3 : 0.3 - 1.8	22 distinct values		150 (100.0%)
Species [factor]	1. setosa 2. versicolor 3. virginica	50 ( 33.3% ) 50 ( 33.3% ) 50 ( 33.3% )		150 (100.0%)

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
)

expression(
  paste(
    paste0(
      trs("iqr"),
      " (", trs("cv"),
      ") : "
    ),
    format_number(
      IQR(column_data, na.rm = TRUE),
      round.digits
    ),
    " (", format_number(
      sd(column_data, na.rm = TRUE)/mean(column_data, na.rm = TRUE),
      round.digits
    ),
    ")", collapse = "", sep = ""
  )
)

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

• Reports the number of duplicate records in the heading section
  
• Detects UPC/EAN codes (barcode numbers) and doesn’t calculate irrelevant statistics for them
  
• Detects email addresses and reports counts of valid, invalid and duplicate addresses; note that the proportions of valid and invalid sum up to 100%; the duplicates proportion is calculated independently, which is why in the bar chart (html version), the bar for this category is shown with a different color
• Allows the display of “windowed” results by using the max.tbl.height parameter; This is especially convenient if the analyzed data frame has numerous variables; see vignette("rmarkdown", package = "summarytools") for more details

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

Descriptive Statistics

iris
Group: Species = setosa
N: 50

Group: Species = versicolor
N: 50

Group: Species = virginica
N: 50

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))

Descriptive Statistics

tobacco
Group: gender = F
N: 489

Group: gender = M
N: 489

Group: gender = (Missing)
N: 22

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()

# A tibble: 12 × 9
   gender variable   mean    sd   min   med   max n.valid pct.valid
   <fct>  <chr>     <dbl> <dbl> <dbl> <dbl> <dbl>   <dbl>     <dbl>
 1 Girl   economics  72.5  7.79  62.3  70.2  89.6      14      93.3
 2 Girl   english    73.9  9.41  58.3  71.8  93.1      14      93.3
 3 Girl   french     71.1 12.4   44.8  68.4  93.7      14      93.3
 4 Girl   geography  67.3  8.26  50.4  67.3  78.9      15     100  
 5 Girl   history    71.2  9.17  53.9  72.9  86.4      15     100  
 6 Girl   math       73.8  9.03  55.6  74.8  86.3      14      93.3
 7 Boy    economics  75.2  9.40  60.5  71.7  94.2      15     100  
 8 Boy    english    77.8  5.94  69.6  77.6  90.2      15     100  
 9 Boy    french     76.6  8.63  63.2  74.8  94.7      15     100  
10 Boy    geography  73   12.4   47.2  71.2  96.3      14      93.3
11 Boy    history    74.4 11.2   54.4  72.6  93.5      15     100  
12 Boy    math       73.3  9.68  60.5  72.2  93.2      14      93.3

The order parameter controls row ordering:

grouped_descr %>% tb(order = 2)

# A tibble: 12 × 9
   gender variable   mean    sd   min   med   max n.valid pct.valid
   <fct>  <chr>     <dbl> <dbl> <dbl> <dbl> <dbl>   <dbl>     <dbl>
 1 Girl   economics  72.5  7.79  62.3  70.2  89.6      14      93.3
 2 Boy    economics  75.2  9.40  60.5  71.7  94.2      15     100  
 3 Girl   english    73.9  9.41  58.3  71.8  93.1      14      93.3
 4 Boy    english    77.8  5.94  69.6  77.6  90.2      15     100  
 5 Girl   french     71.1 12.4   44.8  68.4  93.7      14      93.3
 6 Boy    french     76.6  8.63  63.2  74.8  94.7      15     100  
 7 Girl   geography  67.3  8.26  50.4  67.3  78.9      15     100  
 8 Boy    geography  73   12.4   47.2  71.2  96.3      14      93.3
 9 Girl   history    71.2  9.17  53.9  72.9  86.4      15     100  
10 Boy    history    74.4 11.2   54.4  72.6  93.5      15     100  
11 Girl   math       73.8  9.03  55.6  74.8  86.3      14      93.3
12 Boy    math       73.3  9.68  60.5  72.2  93.2      14      93.3

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)

# A tibble: 12 × 9
   variable  gender  mean    sd   min   med   max n.valid pct.valid
   <chr>     <fct>  <dbl> <dbl> <dbl> <dbl> <dbl>   <dbl>     <dbl>
 1 economics Girl    72.5  7.79  62.3  70.2  89.6      14      93.3
 2 economics Boy     75.2  9.40  60.5  71.7  94.2      15     100  
 3 english   Girl    73.9  9.41  58.3  71.8  93.1      14      93.3
 4 english   Boy     77.8  5.94  69.6  77.6  90.2      15     100  
 5 french    Girl    71.1 12.4   44.8  68.4  93.7      14      93.3
 6 french    Boy     76.6  8.63  63.2  74.8  94.7      15     100  
 7 geography Girl    67.3  8.26  50.4  67.3  78.9      15     100  
 8 geography Boy     73   12.4   47.2  71.2  96.3      14      93.3
 9 history   Girl    71.2  9.17  53.9  72.9  86.4      15     100  
10 history   Boy     74.4 11.2   54.4  72.6  93.5      15     100  
11 math      Girl    73.8  9.03  55.6  74.8  86.3      14      93.3
12 math      Boy     73.3  9.68  60.5  72.2  93.2      14      93.3

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")

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.

10.1 General Options

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

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

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).

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():

(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:

• If an argument is explicitly supplied in the function call, it will have precedence over any stored value for the parameter (stored values are the ones that are written to the object’s attributes when using a core function, as well as the ones stored in summarytools’ global options list).

• If both a core function and the print or view function are called at once and have conflicting parameter values, print/view has precedence (they always win the argument!).

• if the parameter values cannot be found in the function calls, the stored defaults (modified with st_options() or left as they are when loading the package) will be applied.

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")

Example (results not shown)

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

Tableau de fréquences

iris$Species
Type: Facteur

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)

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
  ) 

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 setup, include=FALSE} 
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, echo=FALSE} 
st_css(main = TRUE, global = TRUE)
```

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.