Cross-tabulate two survey questions

Builds a crosstab from two categorical columns: x forms the rows and y the columns. The cell contents are chosen with cell – counts or row/column/total percentages – or, when a numeric value column is supplied, an aggregate of that column (e.g. the mean spend) for each x by y combination. Registered orders (see register_order()) are applied to x and y automatically, and a factor column keeps its own level order when no order is registered.

Usage

crosstab(
  data = NULL,
  x,
  y,
  cell = c("count", "row_pct", "col_pct", "total_pct"),
  value = NULL,
  fn = mean,
  wide = TRUE,
  digits = NULL,
  na_rm = TRUE,
  drop = NULL,
  weights = NULL
)

Arguments

data

A data frame.

x

Row variable (unquoted).

y

Column variable (unquoted).

cell

What to put in each cell when value is not given: "count" (default), "row_pct", "col_pct" or "total_pct".

value

Optional numeric column (unquoted) to aggregate per cell instead of counting.

fn

Aggregation function used with value. Default mean().

wide

If TRUE (default), return one row per x with a column per y level; if FALSE, return the tidy long form.

digits

Decimal places for percentage / aggregated cells. Default 0 for percentages, 2 when value is supplied.

na_rm

Drop blanks / non-answers in x, y (and NA in value). Default TRUE.

drop

Optional character vector of answer values to remove from both x and y before tabulating (e.g. c("Other")), matched case-insensitively. Defaults to the drop_answers option. See drop_items().

weights

Survey weighting: NULL (default) uses the session scheme from set_weights() if set; FALSE forces unweighted; or pass an ad-hoc scheme. When weighting is active the cells default to weighted values – weighted counts, weighted percentages, or (for a numeric value) the weighted mean.

Value

A tibble: wide (x plus one column per y level) or long (x, y, value). The long form refuses an x or y that is itself called value, since the two columns would share a name.

Details

Choose what the cells mean with cell: "row_pct" makes each row sum to 100 (the distribution of y within each x), "col_pct" makes each column sum to 100, "total_pct" is the share of the whole table, and "count" is the raw frequency. Supplying a numeric value switches the cells to an aggregate of that column – by default the mean – which answers questions like "what is the average NPS for each region by gender?". Blanks and non-answers in x/y are dropped when na_rm = TRUE. Any registered orders (register_order()) set the row/column ordering automatically; a factor without one keeps its own level order; anything else stays in data order.

See also

calc_percentage(), compare_values(), register_order().

Other summaries: calc_percentage(), calc_percentage_batch(), calc_percentage_multi(), calc_summary(), clear_checkpoints(), crosstab_banner()

Examples

# counts
crosstab(podracing_survey, demo_gender, region)
#> # A tibble: 3 × 6
#>   demo_gender  Asia Europe `Latin America` `North America` Oceania
#>   <chr>       <dbl>  <dbl>           <dbl>           <dbl>   <dbl>
#> 1 Female         23    120              33             208      15
#> 2 Male           31    177              42             253      22
#> 3 Non-binary      3      7              NA              14       3

# row percentages: gender split within each region (each row ~ 100)
crosstab(podracing_survey, region, demo_gender, cell = "row_pct")
#> # A tibble: 5 × 4
#>   region        Female  Male `Non-binary`
#>   <chr>          <dbl> <dbl>        <dbl>
#> 1 Asia              40    54            5
#> 2 Europe            39    58            2
#> 3 Latin America     44    56           NA
#> 4 North America     44    53            3
#> 5 Oceania           38    55            8

# mean NPS for each region x gender cell
crosstab(podracing_survey, region, demo_gender, value = nps_value, fn = mean)
#> # A tibble: 5 × 4
#>   region        Female  Male `Non-binary`
#>   <chr>          <dbl> <dbl>        <dbl>
#> 1 Asia            7.7   8            9.33
#> 2 Europe          7.57  7.53         8.29
#> 3 Latin America   7.67  7.71        NA   
#> 4 North America   7.42  7.57         6.79
#> 5 Oceania         7.53  8            7.67