1. Overview

summarytools provides a coherent set of functions centered on data exploration and simple reporting. At its core reside the following four functions:

Function	Description
freq()	Frequency Tables featuring counts, proportions, as well as missing data information
ctable()	Cross-Tabulations (joint frequencies) between pairs of discrete/categorical variables, featuring marginal sums as well as row, column or total proportions
descr()	Descriptive (Univariate) Statistics for numerical data, featuring common measures of central tendency and dispersion
dfSummary()	Extensive Data Frame Summaries featuring type-specific information for all variables in a data frame: 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

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 packages
Offer flexibility in terms of output formats and contents
Integrate well with software and tools commonly used for reporting (the RStudio IDE, Rmarkdown, and knitr) while also allowing for stand-alone, simple report generation

1.2 Redirecting Outputs

Results can be

Displayed in the R console as plain text
Rendered as html and shown in RStudio’s Viewer or in a Web Browser
Written to / appended to plain text, markdown, or html files
Used in Rmarkdown reports

1.3 Other Characteristics

Pipe-Friendly:
- The %>% and %$% operators from the magrittr package are supported
- The %>>% operator from the pipeR package is also supported
Multilingual:
- Built-in translations exist for French, Portuguese, Spanish, Russian and Turkish
- Users can easily add custom translations or modify existing sets of translations as needed
Weights-ready: except for dfSummary(), all core functions support sampling weights
Flexible:
- Default values for most function arguments can be modified using st_options(); this simplifies coding and minimizes redundancy
- Pander options can be used for text / markdown tables
- Bootstrap and user-defined CSS classes can be used for html tables

2. The Four Core Functions

2.1 Frequency Tables With freq()

The freq() function generates frequency tables with counts, proportions, as well as missing data information.

freq(iris$Species, plain.ascii = FALSE, style = "rmarkdown")

Frequencies

iris$Species
Type: Factor

	Freq	% Valid	% Valid Cum.	% Total	% Total Cum.
setosa	50	33.33	33.33	33.33	33.33
versicolor	50	33.33	66.67	33.33	66.67
virginica	50	33.33	100.00	33.33	100.00
<NA>	0			0.00	100.00
Total	150	100.00	100.00	100.00	100.00

In this first example, the plain.ascii and style arguments were specified. However, since we have defined them globally with st_options() in the setup chunk, they are redundant and will be omitted from hereon. See section 13 for more details on this vignette’s setup.

2.1.1 Ignoring Missing Data

The report.nas argument can be set to FALSE in order to ignore missing values (NA’s). Doing so has the following effects on the resulting table:

The <NA> row is omitted
The % Total and % Total Cum. columns are also omitted
The % Valid column simply becomes %
The % Valid Cum. column simply becomes % Cum.

freq(iris$Species, report.nas = FALSE, headings = FALSE)

	Freq	%	% Cum.
setosa	50	33.33	33.33
versicolor	50	33.33	66.67
virginica	50	33.33	100.00
Total	150	100.00	100.00

Note that the headings = FALSE parameter suppresses the heading section.

2.1.2 Minimal Frequency Tables

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)

	Freq	%
setosa	50	33.33
versicolor	50	33.33
virginica	50	33.33

2.1.3 Multiple Frequency Tables

To generate frequency tables for all variables in a data frame, no need to use lapply(); freq() handles whole data frames, too:

freq(tobacco)

To avoid cluttering the results, numerical columns having more than 25 distinct values will be discarded. This threshold of 25 can be changed by using for example st_options(freq.ignore.threshold = 10).

Note: the tobacco data frame contains simulated data and is included in the package.

2.1.4 Subsetting (Filtering) Frequency Tables

The rows parameter allows subsetting frequency tables; we can use this parameter it 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 filter rows by name, we can use
- a character vector specifying the exact row names we wish to keep in the results
- a single character string which will be used as a regular expression to select the matching column(s); see ?regex for more information on regular expressions

Used in combination with the order argument, the subsetting feature can be quite practical. For a character variable containing a large number of distinct values, showing only the most frequent is easily done:

freq(tobacco$disease, order = "freq", rows = 1:5)

Frequencies

tobacco$disease
Type: Character

	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.

To account for the frequencies of unshown values, the “(Other)” row is automatically added.

2.1.5 Collapsible Sections

When generating html results, use the collapse = TRUE argument with print() or view() 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)

2.2 Cross-Tabulations with ctable()

ctable() generates cross-tabulations (joint frequencies) for pairs of categorical variables.

Since markdown does not support multiline table headings (but does accept html code), we’ll use the html rendering feature for this section.

Using the tobacco data frame, we’ll cross-tabulate the two categorical variables smoker and diseased.

print(ctable(x = tobacco$smoker, y = tobacco$diseased, prop = "r"),
      method = "render")

Cross-Tabulation, Row Proportions

smoker * diseased
Data Frame: tobacco

	diseased
smoker	Yes				No				Total
Yes	125	(	41.9%	)	173	(	58.1%	)	298	(	100.0%	)
No	99	(	14.1%	)	603	(	85.9%	)	702	(	100.0%	)
Total	224	(	22.4%	)	776	(	77.6%	)	1000	(	100.0%	)

2.2.1 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".

2.2.2 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"))

	diseased
smoker	Yes	No
Yes	125	173
No	99	603

2.2.3 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 will be shown; to use alternate confidence levels, use for example OR = .90.

To show how pipes can be used with summarytools, we’ll use magrittr’s %$% and %>% operators:

library(magrittr)
tobacco %$%  # Acts like with(tobacco, ...)
  ctable(smoker, diseased,
         chisq = TRUE, OR = TRUE, RR = TRUE,
         headings = FALSE) %>%
  print(method = "render")

	diseased
smoker	Yes				No				Total
Yes	125	(	41.9%	)	173	(	58.1%	)	298	(	100.0%	)
No	99	(	14.1%	)	603	(	85.9%	)	702	(	100.0%	)
Total	224	(	22.4%	)	776	(	77.6%	)	1000	(	100.0%	)
Χ² = 91.7088 df = 1 p = .0000 O.R. (95% C.I.) = 4.40 (3.22 - 6.02) R.R. (95% C.I.) = 2.97 (2.37 - 3.73)

2.3 Descriptive Statistics With 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)

Non-numerical variable(s) ignored: Species

Descriptive Statistics

iris
N: 150

	Petal.Length	Petal.Width	Sepal.Length	Sepal.Width
Mean	3.76	1.20	5.84	3.06
Std.Dev	1.77	0.76	0.83	0.44
Min	1.00	0.10	4.30	2.00
Q1	1.60	0.30	5.10	2.80
Median	4.35	1.30	5.80	3.00
Q3	5.10	1.80	6.40	3.30
Max	6.90	2.50	7.90	4.40
MAD	1.85	1.04	1.04	0.44
IQR	3.50	1.50	1.30	0.50
CV	0.47	0.64	0.14	0.14
Skewness	-0.27	-0.10	0.31	0.31
SE.Skewness	0.20	0.20	0.20	0.20
Kurtosis	-1.42	-1.36	-0.61	0.14
N.Valid	150.00	150.00	150.00	150.00
Pct.Valid	100.00	100.00	100.00	100.00

2.3.1 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)

Non-numerical variable(s) ignored: Species

	Mean	Std.Dev
Petal.Length	3.76	1.77
Petal.Width	1.20	0.76
Sepal.Length	5.84	0.83
Sepal.Width	3.06	0.44

See ?descr for a list of all available statistics. Special values “all”, “fivenum”, and “common” are also valid values for the stats argument. The default value is “all”.

2.4 Data Frame Summaries With 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), we use the view() function:

view(dfSummary(iris))

2.4.1 Using dfSummary() in Rmarkdown Documents

When using dfSummary() in Rmarkdown 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 Rmarkdown documents, except for html rendering. The explanation for this can be found further below.

2.4.2 Advanced Features

This function

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

2.4.3 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

3. Grouped Statistics Using 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))

Non-numerical variable(s) ignored: Species

Descriptive Statistics

iris
Group: Species = setosa
N: 50

	Mean	Std.Dev	Min	Median	Max	N.Valid	Pct.Valid
Petal.Length	1.46	0.17	1.00	1.50	1.90	50.00	100.00
Petal.Width	0.25	0.11	0.10	0.20	0.60	50.00	100.00
Sepal.Length	5.01	0.35	4.30	5.00	5.80	50.00	100.00
Sepal.Width	3.43	0.38	2.30	3.40	4.40	50.00	100.00

Group: Species = versicolor
N: 50

	Mean	Std.Dev	Min	Median	Max	N.Valid	Pct.Valid
Petal.Length	4.26	0.47	3.00	4.35	5.10	50.00	100.00
Petal.Width	1.33	0.20	1.00	1.30	1.80	50.00	100.00
Sepal.Length	5.94	0.52	4.90	5.90	7.00	50.00	100.00
Sepal.Width	2.77	0.31	2.00	2.80	3.40	50.00	100.00

Group: Species = virginica
N: 50

	Mean	Std.Dev	Min	Median	Max	N.Valid	Pct.Valid
Petal.Length	5.55	0.55	4.50	5.55	6.90	50.00	100.00
Petal.Width	2.03	0.27	1.40	2.00	2.50	50.00	100.00
Sepal.Length	6.59	0.64	4.90	6.50	7.90	50.00	100.00
Sepal.Width	2.97	0.32	2.20	3.00	3.80	50.00	100.00

3.1 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")))

Descriptive Statistics

BMI by age.gr
Data Frame: tobacco
N: 258

	18-34	35-50	51-70	71 +
Mean	23.84	25.11	26.91	27.45
Std.Dev	4.23	4.34	4.26	4.37
Min	8.83	10.35	9.01	16.36
Median	24.04	25.11	26.77	27.52
Max	34.84	39.44	39.21	38.37

3.2 Using stby() With ctable()

The syntax is a little trickier for this one, so here is an example (results not shown):

stby(list(x = tobacco$smoker, y = tobacco$diseased), 
     INDICES = tobacco$gender, FUN = ctable)

# or equivalently
with(tobacco, 
     stby(list(x = smoker, y = diseased), 
          INDICES = gender, FUN = ctable))

4. Grouped Statistics Using dplyr::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 variables as a valid category, albeit with a warning message 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")

Non-numerical variable(s) ignored: age.gr, smoker, diseased, disease

Descriptive Statistics

tobacco
Group: gender = F
N: 489

	BMI	age	cigs.per.day	samp.wgts
Min	9.01	18.00	0.00	0.86
Q1	22.98	34.00	0.00	0.86
Median	25.87	50.00	0.00	1.04
Q3	29.48	66.00	10.50	1.05
Max	39.44	80.00	40.00	1.06

Group: gender = M
N: 489

	BMI	age	cigs.per.day	samp.wgts
Min	8.83	18.00	0.00	0.86
Q1	22.52	34.00	0.00	0.86
Median	25.14	49.50	0.00	1.04
Q3	27.96	66.00	11.00	1.05
Max	36.76	80.00	40.00	1.06

Group: gender = (Missing)
N: 22

	BMI	age	cigs.per.day	samp.wgts
Min	20.24	19.00	0.00	0.86
Q1	24.97	36.00	0.00	1.04
Median	27.16	55.50	0.00	1.05
Q3	30.23	64.00	10.00	1.05
Max	32.43	80.00	28.00	1.06

5. Creating Tidy Tables With 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()

# A tibble: 4 x 8
  variable      mean    sd   min   med   max n.valid pct.valid
  <chr>        <dbl> <dbl> <dbl> <dbl> <dbl>   <dbl>     <dbl>
1 Petal.Length  3.76 1.77    1    4.35   6.9     150       100
2 Petal.Width   1.20 0.762   0.1  1.3    2.5     150       100
3 Sepal.Length  5.84 0.828   4.3  5.8    7.9     150       100
4 Sepal.Width   3.06 0.436   2    3      4.4     150       100

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

# A tibble: 3 x 3
  Species     freq   pct
  <fct>      <dbl> <dbl>
1 setosa        50  33.3
2 versicolor    50  33.3
3 virginica     50  33.3

By definition, no total rows are part of tidy tables, and the row names are converted to a regular column. Note that for displaying tibbles using Rmarkdown, the knitr chunk option ‘results’ should be set to “markup” instead of “asis”.

5.1 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 x 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 x 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 x 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.

5.2 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(iris, iris$Species, descr, stats = "fivenum") %>%
  tb(order = 3) %>%
  kable(format = "html", digits = 2) %>%
  collapse_rows(columns = 1, valign = "top")

variable	Species	min	q1	med	q3	max
Petal.Length	setosa	1.0	1.4	1.50	1.6	1.9
	versicolor	3.0	4.0	4.35	4.6	5.1
	virginica	4.5	5.1	5.55	5.9	6.9
Petal.Width	setosa	0.1	0.2	0.20	0.3	0.6
	versicolor	1.0	1.2	1.30	1.5	1.8
	virginica	1.4	1.8	2.00	2.3	2.5
Sepal.Length	setosa	4.3	4.8	5.00	5.2	5.8
	versicolor	4.9	5.6	5.90	6.3	7.0
	virginica	4.9	6.2	6.50	6.9	7.9
Sepal.Width	setosa	2.3	3.2	3.40	3.7	4.4
	versicolor	2.0	2.5	2.80	3.0	3.4
	virginica	2.2	2.8	3.00	3.2	3.8

6. Redirecting Output to Files

Using the file argument with print() or view(), 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 About 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, but with a caveat: displaying graphs with dfSummary() will cause vertical misalignment (we hope to resolve this issue in a future version).

6.1 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.

7. Global options

The following options can be set with st_options():

7.1 General Options

Option name	Default	Note
style	“simple”	Set to “rmarkdown” in .Rmd documents
plain.ascii	TRUE	Set to FALSE in .Rmd documents
round.digits	2	Number of decimals to show
headings	TRUE	Formerly “omit.headings”
footnote	“default”	Personalize, or set to NA to omit
display.labels	TRUE	Show variable / data frame labels in headings
bootstrap.css (*)	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
subtitle.emphasis	TRUE	Controls headings formatting
lang	“en”	Language (always 2-letter, lowercase)

(*) Set to FALSE in Shiny apps

7.2 Function-Specific Options

Option name	Default	Note
freq.totals	TRUE	Display totals row in freq()
freq.report.nas	TRUE	Display row and “valid” columns
freq.silent	FALSE	Hide console messages
ctable.prop	“r”	Display row proportions by default
ctable.totals	TRUE	Show marginal totals
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.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	NA	Directory to store temporary images

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

8. Overriding Formatting 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().

8.1 Overriding Function-Specific Arguments

This table indicates what arguments can be used with print() or view() to override formatting attributes:

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 (*)	x	x	x	x
caption (*)	x	x	x	x

(*) These are pander options

8.2 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 override three formatting, and one heading attribute:

(age_stats <- freq(tobacco$age.gr))

Frequencies

tobacco$age.gr
Type: Factor

	Freq	% Valid	% Valid Cum.	% Total	% Total Cum.
18-34	258	26.46	26.46	25.80	25.80
35-50	241	24.72	51.18	24.10	49.90
51-70	317	32.51	83.69	31.70	81.60
71 +	159	16.31	100.00	15.90	97.50
<NA>	25			2.50	100.00
Total	1000	100.00	100.00	100.00	100.00

print(age_stats, report.nas = FALSE, totals = FALSE, display.type = FALSE,
      Variable.label = "Age Group")

Frequencies

tobacco$age.gr
Label: Age Group

	Freq	%	% Cum.
18-34	258	26.46	26.46
35-50	241	24.72	51.18
51-70	317	32.51	83.69
71 +	159	16.31	100.00

8.3 Order of Priority for Parameters / Options

print() or view() parameters have precedence (overriding feature)
freq() / ctable() / descr() / dfSummary() parameters come second
Global options set with st_options() come third

9. Fine-Tuning Looks with 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")

10. Creating Shiny apps

To successfully include summarytools functions in Shiny apps,

use html rendering
set bootstrap.css = FALSE to avoid interacting with the app’s layout
set headings = FALSE in case problems arise
adjust graph sizes with print()’s graph.magnif parameter or with the dfSummary.graph.magnif global option
if dfSummary() tables are too wide, omit a column or two (valid.col and varnumbers, for instance)
if the results are still unsatisfactory, set column widths manually with print()’s col.widths parameter

Example (results not shown)

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

11. Graphs in Markdown dfSummaries

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

Why the third element? Although R makes it really easy to create temporary files and directories, they do have long pathnames, especially on Windows. Unfortunately, Pandoc determines the final (rendered) column widths by counting characters in a cell, even if those characters are paths pointing to images.

At this time, there seems to be only one solution around this problem: cut down on characters in image paths. So instead of this:

+-----------+---------------------------------------------------------------------+---------+
| Variable  | Graph                                                               | Valid   |
+===========+=====================================================================+=========+
| gender\   | ![](C:/Users/johnny/AppData/Local/Temp/RtmpYRgetx/file5aa44d71.png) | 978\    |
| [factor]  |                                                                     | (97.8%) |
+----+---------------+------------------------------------------------------------+---------+

…we aim for this:

+---------------+----------------------+---------+
| Variable      | Graph                | Valid   |
+===============+======================+=========+
| gender\       | ![](/tmp/ds0001.png) | 978\    |
| [factor]      |                      | (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 the users need to set this 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 it’s self-cleaning. On Windows, there is no such convenient directory, so we need to pick one – be it absolute (“/tmp”) or relative (“img”, or simply “.”). Two things are to be kept in mind: it needs to be short (5 characters max) and it needs to be cleaned up manually.

12. Translations

Thanks to the R community’s efforts, the following languages can be used, in addition to English (default): French (fr), Portuguese (pt), Russian (ru), Spanish (es), and Turkish (tr).

To switch languages, simply use

st_options(lang = "fr")

All output from the core functions will now use that language:

freq(iris$Species)

Tableau de fréquences

iris$Species
Type: Facteur

	Fréq.	% Valide	% Valide cum.	% Total	% Total cum.
setosa	50	33.33	33.33	33.33	33.33
versicolor	50	33.33	66.67	33.33	66.67
virginica	50	33.33	100.00	33.33	100.00
<NA>	0			0.00	100.00
Total	150	100.00	100.00	100.00	100.00

12.1 Non-UTF-8 Locales

On most Windows systems, it will be 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, we need to do the following:

Sys.setlocale("LC_CTYPE", "russian")
st_options(lang = 'ru')

Then to go back to default settings:

Sys.setlocale("LC_CTYPE", "")
st_options(lang = "en")

12.2 Defining and Using Custom Translations

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

12.3 Defining Specific Keywords

Sometimes, all you might want to do is change just a few keywords – for instance, you could prefer using “N” instead of “Freq” in the title row of freq() tables. For this, use define_keywords(). Calling this function without any arguments will bring up, on systems that support graphical devices (the vast majority, that is), an editable window allowing to modify only the desired item(s).

After closing the edit window, you will be able to export the resulting “custom language” into a csv file that you can reuse in the future by calling use_custom_lang().

It is also possible to programmatically define one or several keywords using define_keywords(). For instance:

define_keywords(freq = "N")

See ?define_keywords for more details.

13. This Vignette’s Setup

Knowing how this vignette is configured can help users get started with using summarytools in Rmarkdown documents.

The yaml Section

The output element is the one what matters:

## ---
## output: 
##   rmarkdown::html_vignette: 
##     css: 
##     - !expr system.file("rmarkdown/templates/html_vignette/resources/vignette.css", 
##                         package = "rmarkdown")
## ---

The Setup Chunk

## ```{r setup, include=FALSE}
## library(knitr)
## opts_chunk$set(results = 'asis',      # Can also be set at the chunk-level
##                comment = NA,
##                prompt  = FALSE,
##                cache   = FALSE)
## library(summarytools)
## st_options(plain.ascii = FALSE,        # Always use this option in Rmd documents
##            style        = "rmarkdown", # Always use this option in Rmd documents
##            footnote     = NA,          # Makes html-rendered results more concise
##            subtitle.emphasis = FALSE)  # Improves layout with some rmardown themes
## ```

Including summarytools’ CSS

The needed CSS is automatically added to html files created using print() or view() with the file argument. But in Rmarkdown documents, this needs to be done explicitly:

## ```{r, echo=FALSE}
## st_css()
## ```

14. 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.

Introduction to summarytools

Dominic Comtois

2020-03-02

1. Overview

1.1 Motivation

1.2 Redirecting Outputs

1.3 Other Characteristics

2. The Four Core Functions

2.1 Frequency Tables With freq()

Frequencies

2.1.1 Ignoring Missing Data

2.1.2 Minimal Frequency Tables

2.1.3 Multiple Frequency Tables

2.1.4 Subsetting (Filtering) Frequency Tables

Frequencies

2.1.5 Collapsible Sections

2.2 Cross-Tabulations with ctable()

Cross-Tabulation, Row Proportions

2.2.1 Row, Column or Total Proportions

2.2.2 Minimal Cross-Tabulations

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

2.3 Descriptive Statistics With descr()

Descriptive Statistics

2.3.1 Transposing and Selecting Statistics

2.4 Data Frame Summaries With dfSummary()

2.4.1 Using dfSummary() in Rmarkdown Documents

2.4.2 Advanced Features

2.4.3 Excluding Columns

3. Grouped Statistics Using stby()

Descriptive Statistics

3.1 Special Case of descr() with stby()

Descriptive Statistics

3.2 Using stby() With ctable()

4. Grouped Statistics Using dplyr::group_by()

Descriptive Statistics

5. Creating Tidy Tables With tb()

5.1 Tidy Split-Group Statistics

5.2 A Bridge to Other Packages

6. Redirecting Output to Files

6.1 Appending Output Files

7. Global options

7.1 General Options

7.2 Function-Specific Options

8. Overriding Formatting Attributes

8.1 Overriding Function-Specific Arguments

8.2 Overriding Heading Contents

Example

Frequencies

Frequencies

8.3 Order of Priority for Parameters / Options

9. Fine-Tuning Looks with CSS

Example

10. Creating Shiny apps

11. Graphs in Markdown dfSummaries

12. Translations

Tableau de fréquences

12.1 Non-UTF-8 Locales

12.2 Defining and Using Custom Translations

12.3 Defining Specific Keywords

13. This Vignette’s Setup

The yaml Section

The Setup Chunk

Including summarytools’ CSS

14. Conclusion

Stay Up-to-date

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