Turn a sub-table variable into columns

Turns each level of a tab_vars variable into a block of columns: fewer rows, more columns, and every column stores which sub-population it belongs to (col_group) beside the variable it shows (col_var). Every total row merges into ONE, named totname — the remaining tab_vars are still index columns of their own, so the label does not repeat them. A total table's own line joins that row when no tab_vars is left to hold it, and is dropped when one is. tab()'s spread_vars calls it for you, and takes care of the totals beforehand.

Usage

tab_spread(tabs, spread_vars, names_prefix, names_sort = FALSE, totname = NULL)

Arguments

tabs

A tibble of class tab, made with tab, tab_reg or tab_plain.

spread_vars

<tidy-select> The tab variables to pass to column, with a syntax of type c(var1, var2, ...).

names_prefix

String added to the start of every variable name.

names_sort

If no names_prefix is given, new names takes the form spread_var_col_var_level. Should then the column names be sorted ? If FALSE, the default, column names are ordered by first appearance.

totname

The name the merged total row takes, as a single string. NULL (default) uses the one options(tabxplor.total_names) declares.

Value

A tibble of class tab, with less rows and more columns.

Examples

 data <- forcats::gss_cat |> dplyr::filter(year %in% c(2000, 2014))

tabs <-
  tab(data, relig, marital, c(year, race), pct = "row", totaltab = "no",
      color = "difference", tot = "row", other_if_less_than = 30)

tabs |>
  dplyr::select(year, race, relig, Married) |>
  tab_spread(race)
#> # A tabxplor tab: 14 × 10
#> # Groups:         year [2]
#>    year  relig      Married_Other Married_Black Married_White Married_Total
#>                            <row%>        <row%>        <row%>        <row%>
#>  1 2000  Other                                            31%              
#>  2 2000  None                               12%           42%              
#>  3 2000  Jewish                                           49%              
#>  4 2000  Catholic             44%           20%           49%              
#>  5 2000  Protestant                         32%           51%              
#>  6 2000  Others               46%           17%           47%              
#>  7 2000  Total                45%           28%           49%           45%
#> 
#>  8 2014  Christian                          31%           49%              
#>  9 2014  None                 44%           19%           39%              
#> 10 2014  Jewish                                           54%              
#> 11 2014  Catholic             40%                         49%              
#> 12 2014  Protestant           39%           25%           57%              
#> 13 2014  Others               51%           30%           44%              
#> 14 2014  Total                43%           25%           50%           46%
#> # ℹ 4 more variables: n_Other <n>, n_Black <n>, n_White <n>, n_Total <n>
#> # difference (Total): -30 -15 -5 +5 +15 +30