Means table — tab_num • tabxplor

Cross categorical variables with numeric ones, and get a table of means. Superseded by tab(), which builds the same table whenever col_vars holds numeric variables – and everything around it (both kinds of variable at once, colours, totals, tests). It stays the smallest entry point into the numeric aggregate core, and takes the same arguments resolved by the same rules, so its numbers agree with tab()'s cell for cell.

Usage

tab_num(
  data,
  row_var,
  col_vars,
  tab_vars,
  wt,
  ...,
  num = FALSE,
  df = FALSE,
  .fine = NULL,
  .by_table = FALSE
)

Arguments

data: A data frame.
row_var: Singular aliases of row_vars/col_vars (which now accept several variables). Kept working.
col_vars: <tidy-select> The column variable(s) — see row_vars. An interaction is written a*b, as in tab_reg(), and only col_vars takes one: two factors give one column per observed cell of the pair, a number crossed with a factor one mean column per level. See vignette("tabxplor").
tab_vars: <tidy-select> Tab variables: one subtable per combination of their levels. Leave empty for a simple cross-table.
wt: A weight variable, of class numeric. Leave empty for unweighted results.
...: Every other argument of tab() – color, ci, tot, digits, ... – passed by name. See tab(); a typo gets a suggestion.
num: Set to TRUE to obtain a table with normal numeric vectors (not fmt).
df: Set to TRUE to obtain a plain data.frame (not a tibble), with normal numeric vectors (not fmt). Useful, for example, to pass the table to correspondence analysis with FactoMineR.
.fine, .by_table: Internal. .fine is a pre-computed count-aggregate to roll up from instead of scanning the raw data (used by tab_counts and the scan-fusion path); .by_table forces the table-by-table path.

Value

A tibble of class tabxplor_tab. If ... (tab_vars) are provided, a tab of class tabxplor_grouped_tab. All non-text columns are fmt vectors of class tabxplor_fmt, storing all the data necessary to print formats and colors. Columns with row_var and tab_vars are of class factor : every added factor will be considered as a tab_vars and used for grouping. To add text columns without using them in calculations, be sure they are of class character.

Examples

# \donttest{
data <- dplyr::storms |> dplyr::filter(!is.na(wind))
tab_num(data, category, wind, tot = "row",
        color = "difference", color_signif = "guaranteed_effect")
#> # A tabxplor tab: 7 × 2
#>   category         wind
#>             <mean (cv)>
#> 1 1         71 (cv  8%)
#> 2 2         89 (cv  4%)
#> 3 3        104 (cv  4%)
#> 4 4        122 (cv  5%)
#> 5 5        146 (cv  4%)
#> 6 NA        38 (cv 31%)
#> 7 Total     50 (cv 51%)
#> # standardized difference (Total): -0.4 -0.2 -0.1 -0 +0 +0.1 +0.2 +0.4 [all that is significant is colored, error-adjusted]
# }