Summary of a data frame consisting of: variable names and types, labels if any, factor levels, frequencies and/or numerical summary statistics, barplots/histograms, and valid/missing observation counts and proportions.
dfSummary(
x,
round.digits = 1,
varnumbers = st_options("dfSummary.varnumbers"),
class = st_options("dfSummary.class"),
labels.col = st_options("dfSummary.labels.col"),
valid.col = st_options("dfSummary.valid.col"),
na.col = st_options("dfSummary.na.col"),
graph.col = st_options("dfSummary.graph.col"),
graph.magnif = st_options("dfSummary.graph.magnif"),
style = st_options("dfSummary.style"),
plain.ascii = st_options("plain.ascii"),
justify = "l",
na.val = st_options("na.val"),
col.widths = NA,
headings = st_options("headings"),
display.labels = st_options("display.labels"),
max.distinct.values = 10,
trim.strings = FALSE,
max.string.width = 25,
split.cells = 40,
split.tables = Inf,
tmp.img.dir = st_options("tmp.img.dir"),
keep.grp.vars = FALSE,
silent = st_options("dfSummary.silent"),
...
)A data frame.
Number of significant digits to display. Defaults to
1. Does not affect proportions, which always show 1 digit.
Logical. Show variable numbers in the first column.
Defaults to TRUE. Can be set globally with st_options,
option “dfSummary.varnumbers”.
Logical. Show data classes in Variable column.
TRUE by default.
Logical. If TRUE, variable labels (as defined with
rapportools, Hmisc or summarytools' label
functions, among others) will be displayed. TRUE by default, but
the labels column is only shown if a label exists for at least one
column. Can be set globally with st_options, option
“dfSummary.labels.col”.
Logical. Include column indicating count and proportion of
valid (non-missing) values. TRUE by default; can be set
globally with st_options, option “dfSummary.valid.col”.
Logical. Include column indicating count and proportion of
missing (NA) values. TRUE by default; can be set globally
with st_options, option “dfSummary.na.col”.
Logical. Display barplots/histograms column. TRUE
by default; can be set globally with st_options,
option “dfSummary.graph.col”.
Numeric. Magnification factor for graphs column. Useful
if the graphs show up too large (then use a value such as .75) or too small
(use a value such as 1.25). Must be positive. Defaults to 1.
Can be set globally with st_options, option
“dfSummary.graph.magnif”.
Character. Argument used by pander.
Defaults to “multiline”. The only other valid option
is “grid”. Style “rmarkdown” will fallback to
“multiline”.
Logical. pander argument; when
TRUE, no markup characters will be used (useful when printing to
console). Defaults to TRUE. Set to FALSE when in context of
markdown rendering. To change the default value globally, see
st_options.
String indicating alignment of columns; one of “l” (left) “c” (center), or “r” (right). Defaults to “l”.
Character. For factors and character vectors, consider this
value as NA. Ignored if there are actual NA values. NULL
by default.
Numeric or character. Vector of column widths. If numeric,
values are assumed to be numbers of pixels. Otherwise, any CSS-supported
units can be used. NA by default, meaning widths are calculated
automatically.
Logical. Set to FALSE to omit headings. To change this
default value globally, see st_options.
Logical. Should data frame label be displayed in the
title section? Default is TRUE. To change this default value
globally, see st_options.
The maximum number of values to display frequencies for. If variable has more distinct values than this number, the remaining frequencies will be reported as a whole, along with the number of additional distinct values. Defaults to 10.
Logical; for character variables, should leading and
trailing white space be removed? Defaults to FALSE. See
details section.
Limits the number of characters to display in the
frequency tables. Defaults to 25.
A numeric argument passed to pander.
It is the number of characters allowed on a line before splitting the cell.
Defaults to 40.
pander argument which determines the maximum width
of a table. Keeping the default value (Inf) is recommended.
Character. Directory used to store temporary images when rendering dfSummary() with `method = "pander"`, `plain.ascii = TRUE` and `style = "grid"`. See Details.
Logical. When using group_by,
keep rows corresponding to grouping variable(s) in output table.
When FALSE (default), variable numbers still reflect the
the ordering in the full data frame (in other words, some numbers will
be skipped in the variable number column).
Logical. Hide console messages. FALSE by default. To
change this value globally, see st_options.
Additional arguments passed to pander.
A data frame with additional class summarytools containing as
many rows as there are columns in x, with attributes to inform
print method. Columns in the output data frame are:
Number indicating the order in which column appears in the data frame.
Name of the variable, along with its class(es).
Label of the variable (if applicable).
For factors, a list of their values, limited by the
max.distinct.values parameter. For character variables, the most
common values (in descending frequency order), also limited by
max.distinct.values. For numerical variables, common univariate
statistics (mean, std. deviation, min, med, max, IQR and CV).
For factors and character variables, the
frequencies and proportions of the values listed in the previous
column. For numerical vectors, number of distinct values, or frequency
of distinct values if their number is not greater than
max.distinct.values.
An ASCII histogram for numerical variables, and ASCII barplot for factors and character variables.
An html encoded graph, either barplot or histogram.
Number and proportion of valid values.
Number and proportion of missing (NA and NAN) values.
The default value plain.ascii = TRUE is intended to
facilitate interactive data exploration. When using the package for
reporting with rmarkdown, make sure to set this option to
FALSE.
When trim.strings is set to TRUE, trimming is done
before calculating frequencies, be aware that those will
be impacted accordingly.
Specifying tmp.img.dir allows producing results consistent with
pandoc styling while also showing png graphs. Due to the fact that
in Pandoc, column widths are determined by the length of cell contents
even if said content is merely a link to an image, using standard
R temporary directory to store the images would cause columns to be
exceedingly wide. A shorter path is needed. On Mac OS and Linux,
using “/tmp” is a sensible choice, since this directory is cleaned
up automatically on a regular basis. On Windows however, there is no such
convenient directory, so the user has to choose a directory and cleanup the
temporary images manually after the document has been rendered. Providing
a relative path such as “img”, omitting “./”, is recommended.
The maximum length for this parameter is set to 5 characters. It can be set
globally with st_options (e.g.:
st_options(tmp.img.dir = ".").
It is possible to control which statistics are shown in the
Stats / Values column. For this, see the Details and
Examples sections of st_options.
Several packages provide functions for defining variable labels, summarytools being one of them. Some packages (Hmisc in particular) employ special classes for labelled objects, but summarytools doesn't use nor look for any such classes.
data("tobacco")
saved_x11_option <- st_options("use.x11")
st_options(use.x11 = FALSE)
dfSummary(tobacco)
#> Data Frame Summary
#> tobacco
#> Dimensions: 1000 x 9
#> Duplicates: 2
#>
#> --------------------------------------------------------------------------------------------------------------
#> No Variable Stats / Values Freqs (% of Valid) Graph Valid Missing
#> ---- -------------- ------------------------- --------------------- --------------------- ---------- ---------
#> 1 gender 1. F 489 (50.0%) IIIIIIIIII 978 22
#> [factor] 2. M 489 (50.0%) IIIIIIIIII (97.8%) (2.2%)
#>
#> 2 age Mean (sd) : 49.6 (18.3) 63 distinct values . . . . . : 975 25
#> [numeric] min < med < max: : : : : : . : : : : (97.5%) (2.5%)
#> 18 < 50 < 80 : : : : : : : : : :
#> IQR (CV) : 32 (0.4) : : : : : : : : : :
#> : : : : : : : : : :
#>
#> 3 age.gr 1. 18-34 258 (26.5%) IIIII 975 25
#> [factor] 2. 35-50 241 (24.7%) IIII (97.5%) (2.5%)
#> 3. 51-70 317 (32.5%) IIIIII
#> 4. 71 + 159 (16.3%) III
#>
#> 4 BMI Mean (sd) : 25.7 (4.5) 974 distinct values : 974 26
#> [numeric] min < med < max: : : : (97.4%) (2.6%)
#> 8.8 < 25.6 < 39.4 : : :
#> IQR (CV) : 5.7 (0.2) : : : : :
#> . : : : : : .
#>
#> 5 smoker 1. Yes 298 (29.8%) IIIII 1000 0
#> [factor] 2. No 702 (70.2%) IIIIIIIIIIIIII (100.0%) (0.0%)
#>
#> 6 cigs.per.day Mean (sd) : 6.8 (11.9) 37 distinct values : 965 35
#> [numeric] min < med < max: : (96.5%) (3.5%)
#> 0 < 0 < 40 :
#> IQR (CV) : 11 (1.8) :
#> : . . . . . .
#>
#> 7 diseased 1. Yes 224 (22.4%) IIII 1000 0
#> [factor] 2. No 776 (77.6%) IIIIIIIIIIIIIII (100.0%) (0.0%)
#>
#> 8 disease 1. Hypertension 36 (16.2%) III 222 778
#> [character] 2. Cancer 34 (15.3%) III (22.2%) (77.8%)
#> 3. Cholesterol 21 ( 9.5%) I
#> 4. Heart 20 ( 9.0%) I
#> 5. Pulmonary 20 ( 9.0%) I
#> 6. Musculoskeletal 19 ( 8.6%) I
#> 7. Diabetes 14 ( 6.3%) I
#> 8. Hearing 14 ( 6.3%) I
#> 9. Digestive 12 ( 5.4%) I
#> 10. Hypotension 11 ( 5.0%)
#> [ 3 others ] 21 ( 9.5%) I
#>
#> 9 samp.wgts Mean (sd) : 1 (0.1) 0.86!: 267 (26.7%) IIIII 1000 0
#> [numeric] min < med < max: 1.04!: 249 (24.9%) IIII (100.0%) (0.0%)
#> 0.9 < 1 < 1.1 1.05!: 324 (32.4%) IIIIII
#> IQR (CV) : 0.2 (0.1) 1.06!: 160 (16.0%) III
#> ! rounded
#> --------------------------------------------------------------------------------------------------------------
# Exclude some of the columns to reduce table width
dfSummary(tobacco, varnumbers = FALSE, valid.col = FALSE)
#> Data Frame Summary
#> tobacco
#> Dimensions: 1000 x 9
#> Duplicates: 2
#>
#> ----------------------------------------------------------------------------------------------
#> Variable Stats / Values Freqs (% of Valid) Graph Missing
#> -------------- ------------------------- --------------------- --------------------- ---------
#> gender 1. F 489 (50.0%) IIIIIIIIII 22
#> [factor] 2. M 489 (50.0%) IIIIIIIIII (2.2%)
#>
#> age Mean (sd) : 49.6 (18.3) 63 distinct values . . . . . : 25
#> [numeric] min < med < max: : : : : : . : : : : (2.5%)
#> 18 < 50 < 80 : : : : : : : : : :
#> IQR (CV) : 32 (0.4) : : : : : : : : : :
#> : : : : : : : : : :
#>
#> age.gr 1. 18-34 258 (26.5%) IIIII 25
#> [factor] 2. 35-50 241 (24.7%) IIII (2.5%)
#> 3. 51-70 317 (32.5%) IIIIII
#> 4. 71 + 159 (16.3%) III
#>
#> BMI Mean (sd) : 25.7 (4.5) 974 distinct values : 26
#> [numeric] min < med < max: : : : (2.6%)
#> 8.8 < 25.6 < 39.4 : : :
#> IQR (CV) : 5.7 (0.2) : : : : :
#> . : : : : : .
#>
#> smoker 1. Yes 298 (29.8%) IIIII 0
#> [factor] 2. No 702 (70.2%) IIIIIIIIIIIIII (0.0%)
#>
#> cigs.per.day Mean (sd) : 6.8 (11.9) 37 distinct values : 35
#> [numeric] min < med < max: : (3.5%)
#> 0 < 0 < 40 :
#> IQR (CV) : 11 (1.8) :
#> : . . . . . .
#>
#> diseased 1. Yes 224 (22.4%) IIII 0
#> [factor] 2. No 776 (77.6%) IIIIIIIIIIIIIII (0.0%)
#>
#> disease 1. Hypertension 36 (16.2%) III 778
#> [character] 2. Cancer 34 (15.3%) III (77.8%)
#> 3. Cholesterol 21 ( 9.5%) I
#> 4. Heart 20 ( 9.0%) I
#> 5. Pulmonary 20 ( 9.0%) I
#> 6. Musculoskeletal 19 ( 8.6%) I
#> 7. Diabetes 14 ( 6.3%) I
#> 8. Hearing 14 ( 6.3%) I
#> 9. Digestive 12 ( 5.4%) I
#> 10. Hypotension 11 ( 5.0%)
#> [ 3 others ] 21 ( 9.5%) I
#>
#> samp.wgts Mean (sd) : 1 (0.1) 0.86!: 267 (26.7%) IIIII 0
#> [numeric] min < med < max: 1.04!: 249 (24.9%) IIII (0.0%)
#> 0.9 < 1 < 1.1 1.05!: 324 (32.4%) IIIIII
#> IQR (CV) : 0.2 (0.1) 1.06!: 160 (16.0%) III
#> ! rounded
#> ----------------------------------------------------------------------------------------------
# Limit number of categories to be displayed for categorical data
dfSummary(tobacco, max.distinct.values = 5, style = "grid")
#> Data Frame Summary
#> tobacco
#> Dimensions: 1000 x 9
#> Duplicates: 2
#>
#> +----+--------------+-------------------------+---------------------+---------------------+----------+---------+
#> | No | Variable | Stats / Values | Freqs (% of Valid) | Graph | Valid | Missing |
#> +====+==============+=========================+=====================+=====================+==========+=========+
#> | 1 | gender | 1. F | 489 (50.0%) | IIIIIIIIII | 978 | 22 |
#> | | [factor] | 2. M | 489 (50.0%) | IIIIIIIIII | (97.8%) | (2.2%) |
#> +----+--------------+-------------------------+---------------------+---------------------+----------+---------+
#> | 2 | age | Mean (sd) : 49.6 (18.3) | 63 distinct values | . . . . . : | 975 | 25 |
#> | | [numeric] | min < med < max: | | : : : : : . : : : : | (97.5%) | (2.5%) |
#> | | | 18 < 50 < 80 | | : : : : : : : : : : | | |
#> | | | IQR (CV) : 32 (0.4) | | : : : : : : : : : : | | |
#> | | | | | : : : : : : : : : : | | |
#> +----+--------------+-------------------------+---------------------+---------------------+----------+---------+
#> | 3 | age.gr | 1. 18-34 | 258 (26.5%) | IIIII | 975 | 25 |
#> | | [factor] | 2. 35-50 | 241 (24.7%) | IIII | (97.5%) | (2.5%) |
#> | | | 3. 51-70 | 317 (32.5%) | IIIIII | | |
#> | | | 4. 71 + | 159 (16.3%) | III | | |
#> +----+--------------+-------------------------+---------------------+---------------------+----------+---------+
#> | 4 | BMI | Mean (sd) : 25.7 (4.5) | 974 distinct values | : | 974 | 26 |
#> | | [numeric] | min < med < max: | | : : : | (97.4%) | (2.6%) |
#> | | | 8.8 < 25.6 < 39.4 | | : : : | | |
#> | | | IQR (CV) : 5.7 (0.2) | | : : : : : | | |
#> | | | | | . : : : : : . | | |
#> +----+--------------+-------------------------+---------------------+---------------------+----------+---------+
#> | 5 | smoker | 1. Yes | 298 (29.8%) | IIIII | 1000 | 0 |
#> | | [factor] | 2. No | 702 (70.2%) | IIIIIIIIIIIIII | (100.0%) | (0.0%) |
#> +----+--------------+-------------------------+---------------------+---------------------+----------+---------+
#> | 6 | cigs.per.day | Mean (sd) : 6.8 (11.9) | 37 distinct values | : | 965 | 35 |
#> | | [numeric] | min < med < max: | | : | (96.5%) | (3.5%) |
#> | | | 0 < 0 < 40 | | : | | |
#> | | | IQR (CV) : 11 (1.8) | | : | | |
#> | | | | | : . . . . . . | | |
#> +----+--------------+-------------------------+---------------------+---------------------+----------+---------+
#> | 7 | diseased | 1. Yes | 224 (22.4%) | IIII | 1000 | 0 |
#> | | [factor] | 2. No | 776 (77.6%) | IIIIIIIIIIIIIII | (100.0%) | (0.0%) |
#> +----+--------------+-------------------------+---------------------+---------------------+----------+---------+
#> | 8 | disease | 1. Hypertension | 36 (16.2%) | III | 222 | 778 |
#> | | [character] | 2. Cancer | 34 (15.3%) | III | (22.2%) | (77.8%) |
#> | | | 3. Cholesterol | 21 ( 9.5%) | I | | |
#> | | | 4. Heart | 20 ( 9.0%) | I | | |
#> | | | 5. Pulmonary | 20 ( 9.0%) | I | | |
#> | | | [ 8 others ] | 91 (41.0%) | IIIIIIII | | |
#> +----+--------------+-------------------------+---------------------+---------------------+----------+---------+
#> | 9 | samp.wgts | Mean (sd) : 1 (0.1) | 0.86!: 267 (26.7%) | IIIII | 1000 | 0 |
#> | | [numeric] | min < med < max: | 1.04!: 249 (24.9%) | IIII | (100.0%) | (0.0%) |
#> | | | 0.9 < 1 < 1.1 | 1.05!: 324 (32.4%) | IIIIII | | |
#> | | | IQR (CV) : 0.2 (0.1) | 1.06!: 160 (16.0%) | III | | |
#> | | | | ! rounded | | | |
#> +----+--------------+-------------------------+---------------------+---------------------+----------+---------+
# Using stby()
stby(tobacco, tobacco$gender, dfSummary)
#> NA detected in grouping variable(s); consider using useNA = TRUE
#> Data Frame Summary
#> tobacco
#> Group: gender = F
#> Dimensions: 489 x 9
#> Duplicates: 0
#>
#> --------------------------------------------------------------------------------------------------------------
#> No Variable Stats / Values Freqs (% of Valid) Graph Valid Missing
#> ---- -------------- ------------------------- --------------------- --------------------- ---------- ---------
#> 2 age Mean (sd) : 49.6 (18.3) 63 distinct values : . . . : . : 475 14
#> [numeric] min < med < max: : : : : : : : : : : (97.1%) (2.9%)
#> 18 < 50 < 80 : : : : : : : : : :
#> IQR (CV) : 32 (0.4) : : : : : : : : : :
#> : : : : : : : : : :
#>
#> 3 age.gr 1. 18-34 123 (25.9%) IIIII 475 14
#> [factor] 2. 35-50 118 (24.8%) IIII (97.1%) (2.9%)
#> 3. 51-70 157 (33.1%) IIIIII
#> 4. 71 + 77 (16.2%) III
#>
#> 4 BMI Mean (sd) : 26.1 (4.9) 475 distinct values : 475 14
#> [numeric] min < med < max: : : (97.1%) (2.9%)
#> 9 < 25.9 < 39.4 : : .
#> IQR (CV) : 6.5 (0.2) . : : :
#> : : : : .
#>
#> 5 smoker 1. Yes 147 (30.1%) IIIIII 489 0
#> [factor] 2. No 342 (69.9%) IIIIIIIIIIIII (100.0%) (0.0%)
#>
#> 6 cigs.per.day Mean (sd) : 6.9 (12) 37 distinct values : 468 21
#> [numeric] min < med < max: : (95.7%) (4.3%)
#> 0 < 0 < 40 :
#> IQR (CV) : 10.2 (1.8) :
#> : . . . . . .
#>
#> 7 diseased 1. Yes 111 (22.7%) IIII 489 0
#> [factor] 2. No 378 (77.3%) IIIIIIIIIIIIIII (100.0%) (0.0%)
#>
#> 8 disease 1. Hypertension 18 (16.5%) III 109 380
#> [character] 2. Cancer 16 (14.7%) II (22.3%) (77.7%)
#> 3. Cholesterol 10 ( 9.2%) I
#> 4. Heart 9 ( 8.3%) I
#> 5. Pulmonary 9 ( 8.3%) I
#> 6. Diabetes 8 ( 7.3%) I
#> 7. Musculoskeletal 8 ( 7.3%) I
#> 8. Hypotension 7 ( 6.4%) I
#> 9. Neurological 7 ( 6.4%) I
#> 10. Vision 6 ( 5.5%) I
#> [ 3 others ] 11 (10.1%) II
#>
#> 9 samp.wgts Mean (sd) : 1 (0.1) 0.86!: 131 (26.8%) IIIII 489 0
#> [numeric] min < med < max: 1.04!: 120 (24.5%) IIII (100.0%) (0.0%)
#> 0.9 < 1 < 1.1 1.05!: 160 (32.7%) IIIIII
#> IQR (CV) : 0.2 (0.1) 1.06!: 78 (16.0%) III
#> ! rounded
#> --------------------------------------------------------------------------------------------------------------
#>
#> Group: gender = M
#> Dimensions: 489 x 9
#> Duplicates: 2
#>
#> --------------------------------------------------------------------------------------------------------------
#> No Variable Stats / Values Freqs (% of Valid) Graph Valid Missing
#> ---- -------------- ------------------------- --------------------- --------------------- ---------- ---------
#> 2 age Mean (sd) : 49.6 (18.3) 63 distinct values . . . : 478 11
#> [numeric] min < med < max: : : : : : : : : : (97.8%) (2.2%)
#> 18 < 49.5 < 80 : : : : : : : : : :
#> IQR (CV) : 32 (0.4) : : : : : : : : : :
#> : : : : : : : : : :
#>
#> 3 age.gr 1. 18-34 130 (27.2%) IIIII 478 11
#> [factor] 2. 35-50 118 (24.7%) IIII (97.8%) (2.2%)
#> 3. 51-70 151 (31.6%) IIIIII
#> 4. 71 + 79 (16.5%) III
#>
#> 4 BMI Mean (sd) : 25.3 (4) 477 distinct values : . 477 12
#> [numeric] min < med < max: : : (97.5%) (2.5%)
#> 8.8 < 25.1 < 36.8 : : : .
#> IQR (CV) : 5.4 (0.2) : : : : .
#> : : : : : :
#>
#> 5 smoker 1. Yes 143 (29.2%) IIIII 489 0
#> [factor] 2. No 346 (70.8%) IIIIIIIIIIIIII (100.0%) (0.0%)
#>
#> 6 cigs.per.day Mean (sd) : 6.7 (11.8) 36 distinct values : 475 14
#> [numeric] min < med < max: : (97.1%) (2.9%)
#> 0 < 0 < 40 :
#> IQR (CV) : 11 (1.8) :
#> : . . . . .
#>
#> 7 diseased 1. Yes 110 (22.5%) IIII 489 0
#> [factor] 2. No 379 (77.5%) IIIIIIIIIIIIIII (100.0%) (0.0%)
#>
#> 8 disease 1. Cancer 18 (16.4%) III 110 379
#> [character] 2. Hypertension 17 (15.5%) III (22.5%) (77.5%)
#> 3. Cholesterol 11 (10.0%) II
#> 4. Heart 11 (10.0%) II
#> 5. Pulmonary 11 (10.0%) II
#> 6. Musculoskeletal 10 ( 9.1%) I
#> 7. Hearing 9 ( 8.2%) I
#> 8. Digestive 7 ( 6.4%) I
#> 9. Diabetes 5 ( 4.5%)
#> 10. Hypotension 4 ( 3.6%)
#> [ 3 others ] 7 ( 6.4%) I
#>
#> 9 samp.wgts Mean (sd) : 1 (0.1) 0.86!: 131 (26.8%) IIIII 489 0
#> [numeric] min < med < max: 1.04!: 124 (25.4%) IIIII (100.0%) (0.0%)
#> 0.9 < 1 < 1.1 1.05!: 155 (31.7%) IIIIII
#> IQR (CV) : 0.2 (0.1) 1.06!: 79 (16.2%) III
#> ! rounded
#> --------------------------------------------------------------------------------------------------------------
st_options(use.x11 = saved_x11_option)
if (FALSE) { # \dontrun{
# Show in Viewer or browser - no capital V in view(); stview() is also
# available in case of conflicts with other packages)
view(dfSummary(iris))
# Rmarkdown-ready
dfSummary(tobacco, style = "grid", plain.ascii = FALSE,
varnumbers = FALSE, valid.col = FALSE, tmp.img.dir = "./img")
# Using group_by()
tobacco %>% group_by(gender) %>% dfSummary()
} # }