One- and two-way frequency tables

tda_freq counts occurrences of each combination of values across one or more variables; tda_freq1 counts occurrences of each value of one variable; tda_freq2 cross-tabulates two. TDA's manual documents all three (freq, freq1, freq2) on one shared page, since they mostly share the same options (maxcat=/fmt=/tfmt=/df=) and differ mainly in shape. Their R outputs are shaped differently too – tda_freq/tda_freq1 return a table, tda_freq2 a matrix of counts – but all three describe a frequency distribution, so they sit together here as TDA's own manual does.

Usage

tda_freq(..., maxcat = NULL, dir = tempfile("tda"))

tda_freq1(..., maxcat = NULL, options = list(), dir = tempfile("tda"))

tda_freq2(
  ...,
  contingency = FALSE,
  maxcat = NULL,
  options = list(),
  dir = tempfile("tda")
)

Arguments

...

a data frame, a matrix, or vectors. tda_freq2 needs exactly two.

maxcat

the maximum number of categories per variable (all three commands' own maxcat=, default 1000).

dir

working directory.

options

a named list of further TDA options, passed through.

contingency

for tda_freq2, also calculate chi-square and the other contingency measures (freq2's sc=1, a flag rather than a real option – there is no other value). Reported in measures, a named numeric vector, not just printed.

Value

An object carrying a table for tda_freq/ tda_freq1, a matrix for tda_freq2, and, with contingency = TRUE, a measures named vector (chi-square, Cramer's V, lambda, Somers' D, and the rest).

See also

Other descriptive statistics: tda_brr(), tda_cov(), tda_dstat(), tda_independence(), tda_ineq(), tda_loglin(), tda_quant(), tda_rcorr(), tda_segr(), tda_subm()

Examples

set.seed(48)
d <- data.frame(g = sample(1:3, 80, TRUE), h = sample(1:2, 80, TRUE))
tda_freq1(d["g"])$table
#>   index value count percent cum.count cum.percent
#> 1     1     1    32   40.00        32       40.00
#> 2     2     2    21   26.25        53       66.25
#> 3     3     3    27   33.75        80      100.00
tda_freq(d)$table  # a joint table over both variables at once, unlike
#>   index g h count percent cum_count cum_percent
#> 1     1 1 1    18   22.50        18       22.50
#> 2     2 1 2    14   17.50        32       40.00
#> 3     3 2 1    12   15.00        44       55.00
#> 4     4 2 2     9   11.25        53       66.25
#> 5     5 3 1    14   17.50        67       83.75
#> 6     6 3 2    13   16.25        80      100.00
                   # tda_freq1's single-variable count
tda_freq2(d)$matrix  # a cross-tabulation, rows by g, columns by h
#>    h
#> g    1  2
#>   1 18 14
#>   2 12  9
#>   3 14 13
tda_freq2(d, contingency = TRUE)$measures[c("Cramer's V", "Phi")]
#> Cramer's V        Phi 
#> 0.04572763 0.04572763