Count values by a metric column that will be split into groups

Usage

tab_counts_one_cor(
  data,
  col,
  cross,
  prop = "total",
  percent = TRUE,
  values = c("n", "p"),
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

data: A tibble.
col: The column holding factor values.
cross: The metric column that will be split into groups at the median.
prop: The basis of percent calculation: "total" (the default), "cols", or "rows".
percent: Proportions are formatted as percent by default. Set to FALSE to get bare proportions.
values: The values to output: n (frequency) or p (percentage) or both (the default).
labels: If TRUE (default) extracts labels from the attributes, see codebook.
clean: Prepare data by data_clean.
...: Placeholder to allow calling the method with unused parameters from tab_counts.

Value

A volker tibble.

Examples

library(volker)
data <- volker::chatgpt

tab_counts_one_cor(data, adopter, sd_age)
#> 
#> 
#> |Innovator type                                    |      total|  Low Age| High Age|
#> |:-------------------------------------------------|----------:|--------:|--------:|
#> |I try new offers immediately                      |   15% (15)| 10% (10)|   5% (5)|
#> |I try new offers rather quickly                   |   62% (63)| 24% (24)| 39% (39)|
#> |I wait until offers establish themselves          |   22% (22)| 12% (12)| 10% (10)|
#> |I only use new offers when I have no other choice |     1% (1)|   0% (0)|   1% (1)|
#> |total                                             | 100% (101)| 46% (46)| 54% (55)|
#> 
#> Age split at median 38.
#>