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Frequency table for one column

Source: R/eda.R
frequency_table.Rd

Counts, percentages, and percentages of the non-missing values, with missing counted as its own row. The counterpart of janitor::tabyl().

Usage

frequency_table(data, column = NULL, max_levels = 25L, sort = TRUE)

Arguments

data

A data frame, or a vector.

column

Column name, when data is a data frame.

max_levels

Maximum rows to return, most frequent first (default 25). The remainder are folded into one "(other)" row so the percentages still sum to 100.

sort

Sort by descending count (default TRUE) ; FALSE keeps the natural order of the values.

Value

A data frame of class bricklayer_freq: value, n, pct, pct_valid.

Details

Two percentage columns, because both questions get asked and conflating them is how missingness gets hidden: pct is the share of ALL rows, pct_valid the share of rows where the value is present. When a column is 40% missing those two differ enormously, and only the second describes the values that are actually there.

See also

profile_columns() for every column at once.

Examples

df <- data.frame(grade = c("a", "b", "b", "c", NA, "b"),
                 stringsAsFactors = FALSE)
frequency_table(df, "grade")
#> ── Frequency table ─────────────────────────────────────────────── 
#>        value n  pct pct_valid                  bar
#>            b 3 50.0      60.0 ####################
#>            a 1 16.7      20.0              #######
#>            c 1 16.7      20.0              #######
#>  – (missing) 1 16.7         –              #######
#> ────────────────────────────────────────────────────────────────── 

# The two percentage columns differ exactly by the missingness.
frequency_table(df, "grade")[, c("pct", "pct_valid")]
#>        pct pct_valid
#> 1 50.00000        60
#> 2 16.66667        20
#> 3 16.66667        20
#> 4 16.66667        NA

# A vector works directly.
frequency_table(c(1, 1, 2, 3, 3, 3))
#> ── Frequency table ─────────────────────────────────────────────── 
#>  value n  pct pct_valid                  bar
#>      3 3 50.0      50.0 ####################
#>      1 2 33.3      33.3        #############
#>      2 1 16.7      16.7              #######
#> ────────────────────────────────────────────────────────────────── 

# Natural order rather than frequency order.
frequency_table(c("c", "a", "b", "a"), sort = FALSE)
#> ── Frequency table ─────────────────────────────────────────────── 
#>  value n  pct pct_valid                  bar
#>      a 2 50.0      50.0 ####################
#>      b 1 25.0      25.0           ##########
#>      c 1 25.0      25.0           ##########
#> ────────────────────────────────────────────────────────────────── 

# Long tails are folded so the percentages still total 100.
set.seed(1)
ft <- frequency_table(sample(letters, 500, TRUE), max_levels = 5)
ft
#> ── Frequency table ─────────────────────────────────────────────── 
#>    value   n  pct pct_valid                  bar
#>        y  25  5.0       5.0                    #
#>        e  24  4.8       4.8                    #
#>        t  24  4.8       4.8                    #
#>        a  23  4.6       4.6                    #
#>        f  23  4.6       4.6                    #
#>  (other) 381 76.2      76.2 ####################
#> ────────────────────────────────────────────────────────────────── 
sum(ft$pct)
#> [1] 100