Skip to contents

Frequency tables, in the style of SAS PROC FREQ

Source: R/proc_freq.R
proc_freq.Rd

Produces the table SAS PROC FREQ prints for a TABLES statement: one row per observed combination of levels, with frequencies and percentages. One call is one table; a SAS step with several TABLES statements is several calls.

Usage

proc_freq(data, tables, missing = FALSE, list = FALSE, weights = NULL)

Arguments

data

A data frame.

tables

Character vector of one or more column names defining the table.

missing

Logical. FALSE (the SAS default) excludes rows with a missing value in any tables variable from the table and from every denominator, and reports their count in the frequency_missing attribute. TRUE, SAS / MISSING, treats missing as a level, with each SAS special missing value a level of its own.

list

Logical. TRUE, SAS / LIST, gives cumulative columns and percentages of the grand total for an n-way table. It has no effect on a one-way table.

weights

Character or NULL. Name of a single numeric column of weights, SAS WEIGHT. Frequency becomes the sum of weights. Rows with a missing weight are excluded entirely; non-positive weights are an error, as in proc_means.

Value

A data frame with one column per tables variable (each followed by a <var>_label column when the variable carries value labels), then:

  • for a one-way table, or list = TRUE: Frequency, Percent, Cum_Frequency, Cum_Percent;

  • for an n-way crosstab: Frequency, Percent (within stratum), Row_Percent, Col_Percent.

Frequency is integer when unweighted and double when weighted. The attribute frequency_missing holds the count (or weight) of excluded missing rows, and is 0 when none were excluded. A table with no remaining rows is returned with zero rows, not an error.

Details

The last name in tables is the column variable, the one before it the row variable, and any earlier names are strata, as in SAS TABLES a*b*c.

Percentages depend on list, not only the columns shown. For a crosstab of three or more variables, SAS prints one row-by-column table per stratum and computes Percent within that stratum. With list = TRUE, Percent is of the grand total. The two agree for one- and two-way tables and disagree from three variables on.

Missing values sort first. SAS treats missing as the smallest value, so under missing = TRUE the missing level heads the table and the cumulative columns count it first. dplyr::count() places NA last, which moves every cumulative value.

Rows follow SAS ORDER=INTERNAL: factors in declared level order, numbers by value, character values by byte value (the C locale, so "B" sorts before "a"). Unused factor levels are omitted, as SAS omits zero-count levels without SPARSE.

A haven_labelled variable is grouped on its value label, as SAS forms levels from formatted values: stored codes sharing a label form one row, shown as the smallest of those codes, and a <var>_label column holding the label follows it. A value with no label is its own row. Variable labels on the tables columns are kept.

What counts as missing. As in SAS, a blank or whitespace-only character value is missing, and so is NaN. SAS special missing values, read by haven as tagged_na, are separate missing levels in SAS order: ._, then ., then .A to .Z.

Where this differs from SAS. Unlabelled doubles are grouped on their exact values, whereas SAS groups on the printed (BEST12.) value, so values that differ past about 12 significant digits stay separate rows here. Non-positive weights are an error, whereas SAS drops zero weights and ignores negative ones; proc_means makes the same deliberate choice.

Percentages are not rounded. SAS rounds only for display, and a rounded result would fail compare_parity against SAS output.

Tests of association (CHISQ, FISHER, MEASURES and the rest) are deliberately absent, for the same reason proc_means has no inference statistics.

See also

proc_means, proc_contents

Examples

dta <- generate_survival_data(n = 200, seed = 42)

# proc freq; table dead / missing;
proc_freq(dta, "dead", missing = TRUE)
#>   dead Frequency Percent Cum_Frequency Cum_Percent
#> 1    0        92      46            92          46
#> 2    1       108      54           200         100

# proc freq; table sex * dead;
proc_freq(dta, c("sex", "dead"))
#>      sex dead Frequency Percent Row_Percent Col_Percent
#> 1 Female    0        34    17.0    44.15584    36.95652
#> 2 Female    1        43    21.5    55.84416    39.81481
#> 3   Male    0        58    29.0    47.15447    63.04348
#> 4   Male    1        65    32.5    52.84553    60.18519

# proc freq; table sex * dead / list;
proc_freq(dta, c("sex", "dead"), list = TRUE)
#>      sex dead Frequency Percent Cum_Frequency Cum_Percent
#> 1 Female    0        34    17.0            34        17.0
#> 2 Female    1        43    21.5            77        38.5
#> 3   Male    0        58    29.0           135        67.5
#> 4   Male    1        65    32.5           200       100.0