Return data used to create vis_cor plot

Create a tidy dataframe of correlations suitable for plotting

data_vis_cor(x, ...)

# Default S3 method
data_vis_cor(x, ...)

# S3 method for class 'data.frame'
data_vis_cor(
  x,
  cor_method = "pearson",
  na_action = "pairwise.complete.obs",
  ...
)

# S3 method for class 'grouped_df'
data_vis_cor(x, ...)

Arguments

x: data.frame
...: extra arguments (currently unused)
cor_method: correlation method to use, from cor: "a character string indicating which correlation coefficient (or covariance) is to be computed. One of "pearson" (default), "kendall", or "spearman": can be abbreviated."
na_action: The method for computing covariances when there are missing values present. This can be "everything", "all.obs", "complete.obs", "na.or.complete", or "pairwise.complete.obs" (default). This option is taken from the cor function argument use.

Value

data frame

tidy dataframe of correlations

Examples

data_vis_cor(airquality)
#> # A tibble: 36 × 3
#>    row_1   row_2     value
#>    <chr>   <chr>     <dbl>
#>  1 Ozone   Ozone    1     
#>  2 Ozone   Solar.R  0.348 
#>  3 Ozone   Wind    -0.602 
#>  4 Ozone   Temp     0.698 
#>  5 Ozone   Month    0.165 
#>  6 Ozone   Day     -0.0132
#>  7 Solar.R Ozone    0.348 
#>  8 Solar.R Solar.R  1     
#>  9 Solar.R Wind    -0.0568
#> 10 Solar.R Temp     0.276 
#> # ℹ 26 more rows

if (FALSE) { # \dontrun{
#return vis_dat data for each group
library(dplyr)
airquality %>%
  group_by(Month) %>%
  data_vis_cor()
} # }
data_vis_cor(airquality)
#> # A tibble: 36 × 3
#>    row_1   row_2     value
#>    <chr>   <chr>     <dbl>
#>  1 Ozone   Ozone    1     
#>  2 Ozone   Solar.R  0.348 
#>  3 Ozone   Wind    -0.602 
#>  4 Ozone   Temp     0.698 
#>  5 Ozone   Month    0.165 
#>  6 Ozone   Day     -0.0132
#>  7 Solar.R Ozone    0.348 
#>  8 Solar.R Solar.R  1     
#>  9 Solar.R Wind    -0.0568
#> 10 Solar.R Temp     0.276 
#> # ℹ 26 more rows