This function prints a basic descriptive statistic, including variable labels.
descr(
x,
...,
max.length = NULL,
weights = NULL,
show = "all",
out = c("txt", "viewer", "browser"),
encoding = "UTF-8",
file = NULL
)A vector or a data frame. May also be a grouped data frame (see 'Note' and 'Examples').
Optional, unquoted names of variables that should be selected for
further processing. Required, if x is a data frame (and no
vector) and only selected variables from x should be processed.
You may also use functions like : or tidyselect's
select-helpers.
See 'Examples' or package-vignette.
Numeric, indicating the maximum length of variable labels
in the output. If variable names are longer than max.length,
they will be shortened to the last whole word within the first
max.length chars.
Bare name, or name as string, of a variable in x
that indicates the vector of weights, which will be applied to weight all
observations. Default is NULL, so no weights are used.
Character vector, indicating which information (columns) that describe
the data should be returned. May be one or more of "type", "label", "n",
"NA.prc", "mean", "sd", "se", "md", "trimmed", "range", "iqr", "skew". There are
two shortcuts: show = "all" (default) shows all information,
show = "short" just shows n, missing percentage, mean and standard
deviation.
Character vector, indicating whether the results should be printed
to console (out = "txt") or as HTML-table in the viewer-pane
(out = "viewer") or browser (out = "browser").
Character vector, indicating the charset encoding used
for variable and value labels. Default is "UTF-8". Only used
when out is not "txt".
Destination file, if the output should be saved as file.
Only used when out is not "txt".
A data frame with basic descriptive statistics.
data may also be a grouped data frame (see group_by)
with up to two grouping variables. Descriptive tables are created for each
subgroup then.
data(efc)
descr(efc, e17age, c160age)
#>
#> ## Basic descriptive statistics
#>
#> var type label n NA.prc mean sd se md trimmed range
#> e17age numeric elder' age 891 1.87 79.12 8.09 0.27 79 79.05 38 (65-103)
#> c160age numeric carer' age 901 0.77 53.46 13.35 0.44 54 53.68 71 (18-89)
#> iqr skew
#> 12 0.06
#> 19 -0.14
efc$weights <- abs(rnorm(nrow(efc), 1, .3))
descr(efc, c12hour, barthtot, weights = weights)
#>
#> ## Basic descriptive statistics
#>
#> var type label n NA.prc mean
#> c12hour numeric average number of hours of care per week 896 0.66 43.18
#> barthtot numeric Total score BARTHEL INDEX 878 2.75 64.51
#> sd se range iqr skew
#> 51.83 1.73 164 (4-168) 32.75 1.66
#> 29.64 1.00 100 (0-100) 45.00 -0.73
library(dplyr)
efc %>% select(e42dep, e15relat, c172code) %>% descr()
#>
#> ## Basic descriptive statistics
#>
#> var type label n NA.prc mean sd se md
#> e42dep numeric elder's dependency 901 0.77 2.94 0.94 0.03 3
#> e15relat numeric relationship to elder 901 0.77 2.85 2.08 0.07 2
#> c172code numeric carer's level of education 842 7.27 1.97 0.63 0.02 2
#> trimmed range iqr skew
#> 3.02 3 (1-4) 2 -0.42
#> 2.44 7 (1-8) 2 1.56
#> 1.96 2 (1-3) 0 0.02
# show just a few elements
efc %>% select(e42dep, e15relat, c172code) %>% descr(show = "short")
#>
#> ## Basic descriptive statistics
#>
#> var n NA.prc mean sd
#> e42dep 901 0.77 2.94 0.94
#> e15relat 901 0.77 2.85 2.08
#> c172code 842 7.27 1.97 0.63
# with grouped data frames
efc %>%
group_by(e16sex) %>%
select(e16sex, e42dep, e15relat, c172code) %>%
descr()
#>
#> ## Basic descriptive statistics
#>
#>
#> Grouped by: male
#>
#> var type label n NA.prc mean sd se md
#> e42dep numeric elder's dependency 295 0.34 2.92 0.93 0.05 3
#> e15relat numeric relationship to elder 296 0.00 2.32 1.93 0.11 2
#> c172code numeric carer's level of education 279 5.74 1.87 0.65 0.04 2
#> trimmed range iqr skew
#> 3.00 3 (1-4) 2 -0.44
#> 1.86 7 (1-8) 1 1.98
#> 1.84 2 (1-3) 1 0.14
#>
#>
#> Grouped by: female
#>
#> var type label n NA.prc mean sd se md
#> e42dep numeric elder's dependency 605 0.00 2.95 0.94 0.04 3
#> e15relat numeric relationship to elder 604 0.17 3.11 2.11 0.09 2
#> c172code numeric carer's level of education 562 7.11 2.02 0.61 0.03 2
#> trimmed range iqr skew
#> 3.03 3 (1-4) 2 -0.42
#> 2.74 7 (1-8) 2 1.45
#> 2.03 2 (1-3) 0 -0.01
# you can select variables also inside 'descr()'
efc %>%
group_by(e16sex, c172code) %>%
descr(e16sex, c172code, e17age, c160age)
#> New names:
#> • `c172code` -> `c172code...1`
#> • `e16sex` -> `e16sex...2`
#> • `e16sex` -> `e16sex...3`
#> • `c172code` -> `c172code...4`
#> • `e16sex` -> `e16sex...5`
#> • `c172code` -> `c172code...6`
#> • `e16sex` -> `e16sex...8`
#> • `c172code` -> `c172code...9`
#>
#> ## Basic descriptive statistics
#>
#> var type label n NA.prc mean sd se md
#> c172code...1 numeric carer's level of education 842 7.27 1.97 0.63 0.02 2
#> e16sex...2 numeric elder's gender 901 0.77 1.67 0.47 0.02 2
#> e16sex...3 numeric elder's gender 901 0.77 1.67 0.47 0.02 2
#> c172code...4 numeric carer's level of education 842 7.27 1.97 0.63 0.02 2
#> e16sex...5 numeric elder's gender 901 0.77 1.67 0.47 0.02 2
#> c172code...6 numeric carer's level of education 842 7.27 1.97 0.63 0.02 2
#> e17age numeric elder' age 891 1.87 79.12 8.09 0.27 79
#> e16sex...8 numeric elder's gender 901 0.77 1.67 0.47 0.02 2
#> c172code...9 numeric carer's level of education 842 7.27 1.97 0.63 0.02 2
#> c160age numeric carer' age 901 0.77 53.46 13.35 0.44 54
#> trimmed range iqr skew
#> 1.96 2 (1-3) 0 0.02
#> 1.71 1 (1-2) 1 -0.73
#> 1.71 1 (1-2) 1 -0.73
#> 1.96 2 (1-3) 0 0.02
#> 1.71 1 (1-2) 1 -0.73
#> 1.96 2 (1-3) 0 0.02
#> 79.05 38 (65-103) 12 0.06
#> 1.71 1 (1-2) 1 -0.73
#> 1.96 2 (1-3) 0 0.02
#> 53.68 71 (18-89) 19 -0.14
# or even use select-helpers
descr(efc, contains("cop"), max.length = 20)
#>
#> ## Basic descriptive statistics
#>
#> var type label n NA.prc mean sd se md trimmed
#> c82cop1 numeric do you feel you cope... 901 0.77 3.12 0.58 0.02 3 3.15
#> c83cop2 numeric do you find... 902 0.66 2.02 0.72 0.02 2 1.98
#> c84cop3 numeric does caregiving... 902 0.66 1.63 0.87 0.03 1 1.47
#> c85cop4 numeric does caregiving have... 898 1.10 1.77 0.87 0.03 2 1.63
#> c86cop5 numeric does caregiving... 902 0.66 1.39 0.67 0.02 1 1.26
#> c87cop6 numeric does caregiving... 900 0.88 1.29 0.64 0.02 1 1.13
#> c88cop7 numeric do you feel trapped... 900 0.88 1.92 0.91 0.03 2 1.80
#> c89cop8 numeric do you feel... 901 0.77 2.16 1.04 0.03 2 2.08
#> c90cop9 numeric do you feel... 888 2.20 2.93 0.96 0.03 3 3.02
#> range iqr skew
#> 3 (1-4) 0 -0.12
#> 3 (1-4) 0 0.65
#> 3 (1-4) 1 1.31
#> 3 (1-4) 1 1.06
#> 3 (1-4) 1 1.77
#> 3 (1-4) 0 2.43
#> 3 (1-4) 1 0.83
#> 3 (1-4) 2 0.32
#> 3 (1-4) 2 -0.45