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Pearson, Spearman or Kendall correlation

Source: R/pair_methods.R
pair_cor.Rd

Calculates one of either pearson, spearman or kendall correlation for every numeric variable pair in a dataset.

Usage

pair_cor(d, method = "pearson", handle.na = TRUE, ...)

Arguments

d

A dataframe

method

A character string for the correlation coefficient to be calculated. Either "pearson" (default), "spearman", or "kendall". If the value is "all", then all three correlations are calculated.

handle.na

If TRUE uses pairwise complete observations to calculate correlation coefficient, otherwise NAs not handled.

...

other arguments

Value

A tibble of class pairwise with calculated association value for every numeric variable pair, or NULL if there are not at least two numeric variables

See also

See pair_methods for other score options.

Examples

pair_cor(iris)
#> # A tibble: 6 × 6
#>   x            y            score   group  value pair_type
#>   <chr>        <chr>        <chr>   <chr>  <dbl> <chr>    
#> 1 Petal.Length Sepal.Length pearson all    0.872 nn       
#> 2 Petal.Width  Sepal.Length pearson all    0.818 nn       
#> 3 Sepal.Length Sepal.Width  pearson all   -0.118 nn       
#> 4 Petal.Length Sepal.Width  pearson all   -0.428 nn       
#> 5 Petal.Width  Sepal.Width  pearson all   -0.366 nn       
#> 6 Petal.Length Petal.Width  pearson all    0.963 nn       
pair_cor(iris, method="kendall")
#> # A tibble: 6 × 6
#>   x            y            score   group   value pair_type
#>   <chr>        <chr>        <chr>   <chr>   <dbl> <chr>    
#> 1 Petal.Length Sepal.Length kendall all    0.719  nn       
#> 2 Petal.Width  Sepal.Length kendall all    0.655  nn       
#> 3 Sepal.Length Sepal.Width  kendall all   -0.0770 nn       
#> 4 Petal.Length Sepal.Width  kendall all   -0.186  nn       
#> 5 Petal.Width  Sepal.Width  kendall all   -0.157  nn       
#> 6 Petal.Length Petal.Width  kendall all    0.807  nn       
pair_cor(iris, method="spearman")
#> # A tibble: 6 × 6
#>   x            y            score    group  value pair_type
#>   <chr>        <chr>        <chr>    <chr>  <dbl> <chr>    
#> 1 Petal.Length Sepal.Length spearman all    0.882 nn       
#> 2 Petal.Width  Sepal.Length spearman all    0.834 nn       
#> 3 Sepal.Length Sepal.Width  spearman all   -0.167 nn       
#> 4 Petal.Length Sepal.Width  spearman all   -0.310 nn       
#> 5 Petal.Width  Sepal.Width  spearman all   -0.289 nn       
#> 6 Petal.Length Petal.Width  spearman all    0.938 nn       
pair_cor(iris, method="all")
#> # A tibble: 18 × 6
#>    x            y            score    group   value pair_type
#>    <chr>        <chr>        <chr>    <chr>   <dbl> <chr>    
#>  1 Petal.Length Sepal.Length pearson  all    0.872  nn       
#>  2 Petal.Width  Sepal.Length pearson  all    0.818  nn       
#>  3 Sepal.Length Sepal.Width  pearson  all   -0.118  nn       
#>  4 Petal.Length Sepal.Width  pearson  all   -0.428  nn       
#>  5 Petal.Width  Sepal.Width  pearson  all   -0.366  nn       
#>  6 Petal.Length Petal.Width  pearson  all    0.963  nn       
#>  7 Petal.Length Sepal.Length spearman all    0.882  nn       
#>  8 Petal.Width  Sepal.Length spearman all    0.834  nn       
#>  9 Sepal.Length Sepal.Width  spearman all   -0.167  nn       
#> 10 Petal.Length Sepal.Width  spearman all   -0.310  nn       
#> 11 Petal.Width  Sepal.Width  spearman all   -0.289  nn       
#> 12 Petal.Length Petal.Width  spearman all    0.938  nn       
#> 13 Petal.Length Sepal.Length kendall  all    0.719  nn       
#> 14 Petal.Width  Sepal.Length kendall  all    0.655  nn       
#> 15 Sepal.Length Sepal.Width  kendall  all   -0.0770 nn       
#> 16 Petal.Length Sepal.Width  kendall  all   -0.186  nn       
#> 17 Petal.Width  Sepal.Width  kendall  all   -0.157  nn       
#> 18 Petal.Length Petal.Width  kendall  all    0.807  nn