Output a correlation table for item battery and item battery

Usage

tab_metrics_items_cor_items(
  data,
  cols,
  cross,
  method = "pearson",
  digits = 2,
  ci = FALSE,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

data

A tibble.

cols

The source columns.

cross

The target columns or NULL to calculate correlations within the source columns.

method

The output metrics, pearson = Pearson's R, spearman = Spearman's rho.

digits

The number of digits to print.

ci

Whether to calculate 95% confidence intervals of the correlation coefficient.

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_metrics.

Value

A volker tibble.

Examples

library(volker)
data <- volker::chatgpt

tab_metrics_items_cor_items(
  data,
  starts_with("cg_adoption_adv"),
  starts_with("use"),
  metric = TRUE
)
#> 
#> 
#> |Expectations                                                |                   Usage| Pearson's r|
#> |:-----------------------------------------------------------|-----------------------:|-----------:|
#> |ChatGPT has clear advantages compared to similar offerings. |      in private context|        0.50|
#> |ChatGPT has clear advantages compared to similar offerings. | in professional context|        0.27|
#> |Using ChatGPT brings financial benefits.                    |      in private context|        0.17|
#> |Using ChatGPT brings financial benefits.                    | in professional context|        0.53|
#> |Using ChatGPT is advantageous in many tasks.                |      in private context|        0.34|
#> |Using ChatGPT is advantageous in many tasks.                | in professional context|        0.35|
#> |Compared to other systems, using ChatGPT is more fun.       |      in private context|        0.47|
#> |Compared to other systems, using ChatGPT is more fun.       | in professional context|        0.27|
#> 
#> 2 missing case(s) omitted.
#>