This function prints a basic descriptive statistic, including variable labels.
descr(x, ..., max.length = NULL, weights = NULL, out = c("txt", "viewer", "browser"))
x | 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 |
max.length | Numeric, indicating the maximum length of variable labels
in the output. If variable names are longer than |
weights | Bare name, or name as string, of a variable in |
out | Character vector, indicating whether the results should be printed
to console ( |
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) #> skew #> 0.06 #> -0.14efc$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 900 0.67 41.86 #> barthtot numeric Total score BARTHEL INDEX 880 2.84 64.93 #> sd se range #> 50.47 1.68 164 (4-168) #> 29.46 0.99 100 (0-100)#> #> ## 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 skew #> 3.02 3 (1-4) -0.42 #> 2.44 7 (1-8) 1.56 #> 1.96 2 (1-3) 0.02# with grouped data frames efc %>% group_by(e16sex) %>% select(e16sex, e42dep, e15relat, c172code) %>% descr()#> #> ## Basic descriptive statistics #> #> Grouped by: #> elder's gender: 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 skew #> 3.00 3 (1-4) -0.44 #> 1.86 7 (1-8) 1.98 #> 1.84 2 (1-3) 0.14 #> #> Grouped by: #> elder's gender: 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 skew #> 3.03 3 (1-4) -0.42 #> 2.74 7 (1-8) 1.45 #> 2.03 2 (1-3) -0.01# you can select variables also inside 'descr()' efc %>% group_by(e16sex, c172code) %>% descr(e16sex, c172code, e17age, c160age)#> #> ## Basic descriptive statistics #> #> Grouped by: #> elder's gender: male #> carer's level of education: low level of education #> var type label n NA.prc mean sd se md trimmed range #> e17age numeric elder' age 79 1.25 78.06 8.04 0.90 77 77.8 30 (65-95) #> c160age numeric carer' age 80 0.00 61.36 12.04 1.35 64 61.8 56 (31-87) #> skew #> 0.24 #> -0.38 #> #> Grouped by: #> elder's gender: male #> carer's level of education: intermediate level of education #> var type label n NA.prc mean sd se md trimmed range #> e17age numeric elder' age 155 0.64 75.19 6.61 0.53 75.0 74.82 30 (65-95) #> c160age numeric carer' age 156 0.00 53.50 15.66 1.25 54.5 53.86 61 (20-81) #> skew #> 0.45 #> -0.17 #> #> Grouped by: #> elder's gender: male #> carer's level of education: high level of education #> var type label n NA.prc mean sd se md trimmed range #> e17age numeric elder' age 43 0 77.19 8.97 1.37 78 76.66 31 (65-96) #> c160age numeric carer' age 43 0 53.35 11.15 1.70 54 53.60 46 (29-75) #> skew #> 0.34 #> -0.22 #> #> Grouped by: #> elder's gender: female #> carer's level of education: low level of education #> var type label n NA.prc mean sd se md trimmed range #> e17age numeric elder' age 98 1.01 79.77 7.78 0.79 80 79.89 33 (65-98) #> c160age numeric carer' age 99 0.00 56.59 11.75 1.18 57 56.62 51 (31-82) #> skew #> -0.08 #> -0.05 #> #> Grouped by: #> elder's gender: female #> carer's level of education: intermediate level of education #> var type label n NA.prc mean sd se md trimmed #> e17age numeric elder' age 343 2 80.24 8.06 0.43 81.0 80.38 #> c160age numeric carer' age 350 0 50.27 12.74 0.68 50.5 50.47 #> range skew #> 38 (65-103) -0.12 #> 71 (18-89) -0.09 #> #> Grouped by: #> elder's gender: female #> carer's level of education: high level of education #> var type label n NA.prc mean sd se md trimmed range #> e17age numeric elder' age 112 0.88 81.29 7.67 0.72 82.0 81.42 34 (65-99) #> c160age numeric carer' age 112 0.88 52.54 12.14 1.15 53.5 52.43 57 (28-85) #> skew #> -0.16 #> 0.13#> #> ## 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 skew #> 3 (1-4) -0.12 #> 3 (1-4) 0.65 #> 3 (1-4) 1.31 #> 3 (1-4) 1.06 #> 3 (1-4) 1.77 #> 3 (1-4) 2.43 #> 3 (1-4) 0.83 #> 3 (1-4) 0.32 #> 3 (1-4) -0.45