R/grouped_summary.R
grouped_summary.RdDescriptive statistics for multiple variables for all grouping variable levels
grouped_summary( data, grouping.vars, measures = NULL, measures.type = "numeric", topcount.long = FALSE, k = 2L, ... )
| data | Dataframe from which variables need to be taken. |
|---|---|
| grouping.vars | A list of grouping variables. Please use unquoted
arguments (i.e., use |
| measures | List variables for which summary needs to computed. If not
specified, all variables of type specified in the argument |
| measures.type | A character indicating whether summary for numeric
("numeric") or factor/character ("factor") variables is expected
(Default: |
| topcount.long | If |
| k | Number of digits. |
| ... | Currently ignored. |
Dataframe with descriptive statistics for numeric variables (n, mean, sd, median, min, max).
# for reproducibility set.seed(123) # another possibility groupedstats::grouped_summary( data = iris, grouping.vars = Species, measures = Sepal.Length:Petal.Width, measures.type = "numeric" )#> # A tibble: 12 x 16 #> Species skim_type skim_variable missing complete mean sd min p25 #> <fct> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 setosa numeric Sepal.Length 0 1 5.01 0.352 4.3 4.8 #> 2 setosa numeric Sepal.Width 0 1 3.43 0.379 2.3 3.2 #> 3 setosa numeric Petal.Length 0 1 1.46 0.174 1 1.4 #> 4 setosa numeric Petal.Width 0 1 0.246 0.105 0.1 0.2 #> 5 versicolor numeric Sepal.Length 0 1 5.94 0.516 4.9 5.6 #> 6 versicolor numeric Sepal.Width 0 1 2.77 0.314 2 2.52 #> 7 versicolor numeric Petal.Length 0 1 4.26 0.470 3 4 #> 8 versicolor numeric Petal.Width 0 1 1.33 0.198 1 1.2 #> 9 virginica numeric Sepal.Length 0 1 6.59 0.636 4.9 6.22 #> 10 virginica numeric Sepal.Width 0 1 2.97 0.322 2.2 2.8 #> 11 virginica numeric Petal.Length 0 1 5.55 0.552 4.5 5.1 #> 12 virginica numeric Petal.Width 0 1 2.03 0.275 1.4 1.8 #> # … with 7 more variables: median <dbl>, p75 <dbl>, max <dbl>, n <int>, #> # std.error <dbl>, mean.conf.low <dbl>, mean.conf.high <dbl># if no measures are chosen, all relevant columns will be summarized groupedstats::grouped_summary( data = ggplot2::msleep, grouping.vars = vore, measures.type = "factor" )#> # A tibble: 20 x 9 #> vore skim_type skim_variable missing complete ordered n_unique top_counts #> <fct> <chr> <chr> <int> <dbl> <lgl> <int> <chr> #> 1 carni factor name 0 1 FALSE 19 Arc: 1, Bot… #> 2 carni factor genus 0 1 FALSE 16 Pan: 3, Vul… #> 3 carni factor order 0 1 FALSE 6 Car: 12, Ce… #> 4 carni factor conservation 5 0.737 FALSE 6 lc: 5, vu: … #> 5 herbi factor name 0 1 FALSE 32 Afr: 1, Arc… #> 6 herbi factor genus 0 1 FALSE 29 Spe: 3, Equ… #> 7 herbi factor order 0 1 FALSE 9 Rod: 16, Ar… #> 8 herbi factor conservation 6 0.812 FALSE 6 lc: 10, dom… #> 9 insec… factor name 0 1 FALSE 5 Big: 1, Eas… #> 10 insec… factor genus 0 1 FALSE 5 Ept: 1, Myo… #> 11 insec… factor order 0 1 FALSE 4 Chi: 2, Cin… #> 12 insec… factor conservation 2 0.6 FALSE 2 lc: 2, en: … #> 13 omni factor name 0 1 FALSE 20 Afr: 1, Afr… #> 14 omni factor genus 0 1 FALSE 20 Aot: 1, Bla… #> 15 omni factor order 0 1 FALSE 8 Pri: 10, So… #> 16 omni factor conservation 11 0.45 FALSE 2 lc: 8, dom:… #> 17 NA factor name 0 1 FALSE 7 Dee: 1, Des… #> 18 NA factor genus 0 1 FALSE 7 Cal: 1, Par… #> 19 NA factor order 0 1 FALSE 5 Rod: 3, Dip… #> 20 NA factor conservation 5 0.286 FALSE 1 lc: 2, cd: … #> # … with 1 more variable: n <int># for factors, you can also convert the dataframe to a long format with counts groupedstats::grouped_summary( data = ggplot2::msleep, grouping.vars = c(vore), measures = c(genus:order), measures.type = "factor", topcount.long = TRUE )#> # A tibble: 40 x 3 #> vore factor.level count #> <fct> <chr> <int> #> 1 carni Pan 3 #> 2 carni Vul 2 #> 3 carni Aci 1 #> 4 carni Cal 1 #> 5 carni Car 12 #> 6 carni Cet 3 #> 7 carni Cin 1 #> 8 carni Did 1 #> 9 herbi Spe 3 #> 10 herbi Equ 2 #> # … with 30 more rows