Simple summary statistics

Source: R/sumstats.R

Generate a simple set of summary statistics for a numeric vector or the numeric columns of a data.frame over various subsets. There are MANY other ways to do this but this function is suitable to my needs.

sumstats(data, ...)

# S3 method for numeric
sumstats(data, digits = 2, ...)

# S3 method for data.frame
sumstats(data, f = NULL, digits = 2, order = TRUE,
  ...)

Arguments

data

a data.frame or numeric vector.
...

additional arguments passed onto various methods.
digits

an integer to specify the number of decimal places desired for the resulting summary statistics. Passed directly to round().
f

a formula. See the examples and aggregate for details.
order

a logical value. If TRUE (default), the rows of the resulting data.frame will be first sorted alphabetically by the variables specified in the left-hand side of f then according to the permutations of the grouping factors specified in the right-hand side of f. If FALSE, the rows will be sorted only by permutations of the grouping factors.

Value

a data.frame in which each row represents a variable-grouping permutation.

Examples

# Numeric vector
sumstats(mtcars$disp)
#>              n   mean     sd  min    p25   p50 p75 max
#> mtcars$disp 32 230.72 123.94 71.1 120.83 196.3 326 472
# All variables summarized and no grouping
sumstats(mtcars)
#>    variable  n   mean     sd   min    p25    p50    p75    max
#> 1        am 32   0.41   0.50  0.00   0.00   0.00   1.00   1.00
#> 2      carb 32   2.81   1.62  1.00   2.00   2.00   4.00   8.00
#> 3       cyl 32   6.19   1.79  4.00   4.00   6.00   8.00   8.00
#> 4      disp 32 230.72 123.94 71.10 120.83 196.30 326.00 472.00
#> 5      drat 32   3.60   0.53  2.76   3.08   3.70   3.92   4.93
#> 6      gear 32   3.69   0.74  3.00   3.00   4.00   4.00   5.00
#> 7        hp 32 146.69  68.56 52.00  96.50 123.00 180.00 335.00
#> 8       mpg 32  20.09   6.03 10.40  15.43  19.20  22.80  33.90
#> 9      qsec 32  17.85   1.79 14.50  16.89  17.71  18.90  22.90
#> 10       vs 32   0.44   0.50  0.00   0.00   0.00   1.00   1.00
#> 11       wt 32   3.22   0.98  1.51   2.58   3.33   3.61   5.42
sumstats(iris)
#>       variable   n mean   sd min p25  p50 p75 max
#> 1 Petal.Length 150 3.76 1.77 1.0 1.6 4.35 5.1 6.9
#> 2  Petal.Width 150 1.20 0.76 0.1 0.3 1.30 1.8 2.5
#> 3 Sepal.Length 150 5.84 0.83 4.3 5.1 5.80 6.4 7.9
#> 4  Sepal.Width 150 3.06 0.44 2.0 2.8 3.00 3.3 4.4
# Only 'Petal.Width' and 'Sepal.Width' summarize and no grouping
sumstats(iris, cbind(Petal.Width, Sepal.Width) ~ NULL)
#>      variable   n mean   sd min p25 p50 p75 max
#> 1 Petal.Width 150 1.20 0.76 0.1 0.3 1.3 1.8 2.5
#> 2 Sepal.Width 150 3.06 0.44 2.0 2.8 3.0 3.3 4.4
# All variables summarized and grouped by 'Species'
sumstats(iris, . ~ Species)
#>        variable    Species  n mean   sd min  p25  p50  p75 max
#> 1  Petal.Length     setosa 50 1.46 0.17 1.0 1.40 1.50 1.58 1.9
#> 2  Petal.Length versicolor 50 1.33 0.20 1.0 1.20 1.30 1.50 1.8
#> 3  Petal.Length  virginica 50 6.59 0.64 4.9 6.23 6.50 6.90 7.9
#> 4   Petal.Width     setosa 50 3.43 0.38 2.3 3.20 3.40 3.68 4.4
#> 5   Petal.Width versicolor 50 4.26 0.47 3.0 4.00 4.35 4.60 5.1
#> 6   Petal.Width  virginica 50 2.03 0.27 1.4 1.80 2.00 2.30 2.5
#> 7  Sepal.Length     setosa 50 5.01 0.35 4.3 4.80 5.00 5.20 5.8
#> 8  Sepal.Length versicolor 50 2.77 0.31 2.0 2.52 2.80 3.00 3.4
#> 9  Sepal.Length  virginica 50 5.55 0.55 4.5 5.10 5.55 5.88 6.9
#> 10  Sepal.Width     setosa 50 0.25 0.11 0.1 0.20 0.20 0.30 0.6
#> 11  Sepal.Width versicolor 50 5.94 0.52 4.9 5.60 5.90 6.30 7.0
#> 12  Sepal.Width  virginica 50 2.97 0.32 2.2 2.80 3.00 3.18 3.8
# 'mpg' and 'wt' summarized by 'cyl' and 'vs'
sumstats(mtcars, cbind(mpg, wt) ~ cyl + vs)
#>    variable cyl vs  n  mean   sd   min   p25   p50   p75   max
#> 1       mpg   4  0  1 26.00   NA 26.00 26.00 26.00 26.00 26.00
#> 2       mpg   4  1 10  2.30 0.60  1.51  1.86  2.26  2.70  3.19
#> 3       mpg   6  0  3  2.75 0.13  2.62  2.70  2.77  2.82  2.88
#> 4       mpg   6  1  4 19.12 1.63 17.80 18.03 18.65 19.75 21.40
#> 5       mpg   8  0 14 15.10 2.56 10.40 14.40 15.20 16.25 19.20
#> 6        wt   4  0  1  2.14   NA  2.14  2.14  2.14  2.14  2.14
#> 7        wt   4  1 10 26.73 4.75 21.40 22.80 25.85 30.40 33.90
#> 8        wt   6  0  3 20.57 0.75 19.70 20.35 21.00 21.00 21.00
#> 9        wt   6  1  4  3.39 0.12  3.21  3.38  3.44  3.45  3.46
#> 10       wt   8  0 14  4.00 0.76  3.17  3.53  3.75  4.01  5.42
sumstats(mtcars, cbind(mpg, wt) ~ cyl + vs, order = FALSE)
#>    variable cyl vs  n  mean   sd   min   p25   p50   p75   max
#> 1       mpg   4  0  1 26.00   NA 26.00 26.00 26.00 26.00 26.00
#> 2        wt   6  0  3 20.57 0.75 19.70 20.35 21.00 21.00 21.00
#> 3       mpg   8  0 14 15.10 2.56 10.40 14.40 15.20 16.25 19.20
#> 4        wt   4  1 10 26.73 4.75 21.40 22.80 25.85 30.40 33.90
#> 5       mpg   6  1  4 19.12 1.63 17.80 18.03 18.65 19.75 21.40
#> 6        wt   4  0  1  2.14   NA  2.14  2.14  2.14  2.14  2.14
#> 7       mpg   6  0  3  2.75 0.13  2.62  2.70  2.77  2.82  2.88
#> 8        wt   8  0 14  4.00 0.76  3.17  3.53  3.75  4.01  5.42
#> 9       mpg   4  1 10  2.30 0.60  1.51  1.86  2.26  2.70  3.19
#> 10       wt   6  1  4  3.39 0.12  3.21  3.38  3.44  3.45  3.46