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Cross-tables from already-aggregated counts

Source: R/tab-counts.R
tab_counts.Rd

tab_counts() builds the same color-coded cross-table as tab(), but from data that is already cross-tabulated — a dplyr::count() result, a contingency table, or a published table of counts or percentages — instead of microdata (one row per individual). Percentages, differences, confidence intervals, chi-squared, colors and totals are all computed from the counts, and the result is identical to the table tab() would build from the microdata behind them.

It accepts four input shapes:

  • Long tidy counts (the default): one row per row_var \(\times\) col_var (\(\times\) tab_vars) combination, with the count in counts (and the weighted count in wt_counts).

  • A wide data.frame: a label (row_var) column plus one column per col_var level — select those level columns with cols and name the column variable with col_name.

  • A table / xtabs / matrix object: melted automatically, the row and column variables read from the dimnames (or set with row_var / col_var).

  • Frequencies + base N: the wide shape, plus input = "pct" and base (the column of row sample sizes); the integer counts are rebuilt from the percentages and the base.

With weighted data, give the real (unweighted) count in counts and the weighted count in wt_counts: estimates use the weighted counts, while confidence intervals and tests use the real unweighted sample size. Counts that are not whole numbers (weighted-only or frequency-only input) disable confidence intervals and chi-squared, with a message.

Usage

tab_counts(
  data,
  row_var,
  col_var,
  tab_vars,
  counts,
  wt_counts,
  cols,
  col_name = "variable",
  base,
  input = c("counts", "pct"),
  ...
)

Arguments

data

A data frame.

row_var, col_var

[Deprecated] Singular aliases of row_vars/col_vars (which now accept several variables). Kept working.

tab_vars

<tidy-select> Tab variables: one subtable per combination of their levels. Leave empty for a simple cross-table.

counts

The column holding the unweighted count for each cell (long tidy shape).

wt_counts

Optional column holding the weighted count for each cell. Leave empty for an unweighted table.

cols

<tidy-select> For a wide data.frame: the columns holding the col_var levels.

col_name

Name of the (synthesised) column variable when cols is used.

base

For input = "pct": the column holding each row's sample size N.

input

"counts" (default) or "pct" (with cols and base: the level columns hold frequencies, and counts are rebuilt from them and base).

...

Every other argument of tab() – pct, color, ci, tot, ... – passed by name. See tab(); a typo gets a suggestion.

Value

A tabxplor_tab (or tabxplor_grouped_tab when tab_vars are provided).

Examples

# Long tidy counts (as from dplyr::count()) reproduce the microdata table :
counts <- dplyr::count(forcats::gss_cat, marital, race)
tab_counts(counts, marital, race, counts = n, pct = "row")
#> # A tabxplor tab: 7 × 5
#>   marital        Other  Black  White         Total
#>                 <row%> <row%> <row%>    <row% (n)>
#> 1 No answer        12%    12%    76% 100% (    17)
#> 2 Never married    12%    24%    64% 100% ( 5 416)
#> 3 Separated        15%    26%    59% 100% (   743)
#> 4 Divorced          6%    15%    79% 100% ( 3 383)
#> 5 Widowed           4%    14%    82% 100% ( 1 807)
#> 6 Married           9%     9%    82% 100% (10 117)
#> 7 Total             9%    15%    76% 100% (21 483)
# tab(forcats::gss_cat, marital, race, pct = "row")   # identical

# A contingency table object :
tab_counts(table(forcats::gss_cat$marital, forcats::gss_cat$race), pct = "row")
#> # A tabxplor tab: 7 × 5
#>   Var1           Other  Black  White         Total
#>                 <row%> <row%> <row%>    <row% (n)>
#> 1 No answer        12%    12%    76% 100% (    17)
#> 2 Never married    12%    24%    64% 100% ( 5 416)
#> 3 Separated        15%    26%    59% 100% (   743)
#> 4 Divorced          6%    15%    79% 100% ( 3 383)
#> 5 Widowed           4%    14%    82% 100% ( 1 807)
#> 6 Married           9%     9%    82% 100% (10 117)
#> 7 Total             9%    15%    76% 100% (21 483)

# A wide data.frame of counts :
wide <- tidyr::pivot_wider(counts, names_from = race, values_from = n)
tab_counts(wide, row_var = marital, cols = c(Other, Black, White),
           col_name = "race", pct = "row")
#> # A tabxplor tab: 7 × 5
#>   marital        Other  Black  White         Total
#>                 <row%> <row%> <row%>    <row% (n)>
#> 1 No answer        12%    12%    76% 100% (    17)
#> 2 Never married    12%    24%    64% 100% ( 5 416)
#> 3 Separated        15%    26%    59% 100% (   743)
#> 4 Divorced          6%    15%    79% 100% ( 3 383)
#> 5 Widowed           4%    14%    82% 100% ( 1 807)
#> 6 Married           9%     9%    82% 100% (10 117)
#> 7 Total             9%    15%    76% 100% (21 483)