Correlation Data Frame

An implementation of stats::cor(), which returns a correlation data frame rather than a matrix. See details below. Additional adjustment include the use of pairwise deletion by default.

correlate(
  x,
  y = NULL,
  use = "pairwise.complete.obs",
  method = "pearson",
  diagonal = NA,
  quiet = FALSE
)

Arguments

x	a numeric vector, matrix or data frame.
y	NULL (default) or a vector, matrix or data frame with compatible dimensions to x. The default is equivalent to y = x (but more efficient).
use	an optional character string giving a method for computing covariances in the presence of missing values. This must be (an abbreviation of) one of the strings "everything", "all.obs", "complete.obs", "na.or.complete", or "pairwise.complete.obs".
method	a character string indicating which correlation coefficient (or covariance) is to be computed. One of "pearson" (default), "kendall", or "spearman": can be abbreviated.
diagonal	Value (typically numeric or NA) to set the diagonal to
quiet	Set as TRUE to suppress message about method and use parameters.

Value

A correlation data frame cor_df

Details

• A tibble (see tibble)

• An additional class, "cor_df"

• A "term" column

• Standardized variances (the matrix diagonal) set to missing values by default (NA) so they can be ignored in calculations.

As of version 0.4.3, the first column of a cor_df object is named "term". In previous versions this first column was named "rowname".

Examples

if (FALSE) {
correlate(iris)
}

correlate(iris[-5])
#> 
#> Correlation method: 'pearson'
#> Missing treated using: 'pairwise.complete.obs'
#> # A tibble: 4 x 5
#>   term         Sepal.Length Sepal.Width Petal.Length Petal.Width
#>   <chr>               <dbl>       <dbl>        <dbl>       <dbl>
#> 1 Sepal.Length       NA          -0.118        0.872       0.818
#> 2 Sepal.Width        -0.118      NA           -0.428      -0.366
#> 3 Petal.Length        0.872      -0.428       NA           0.963
#> 4 Petal.Width         0.818      -0.366        0.963      NA    

correlate(mtcars)
#> 
#> Correlation method: 'pearson'
#> Missing treated using: 'pairwise.complete.obs'
#> # A tibble: 11 x 12
#>    term     mpg    cyl   disp     hp    drat     wt    qsec     vs      am
#>    <chr>  <dbl>  <dbl>  <dbl>  <dbl>   <dbl>  <dbl>   <dbl>  <dbl>   <dbl>
#>  1 mpg   NA     -0.852 -0.848 -0.776  0.681  -0.868  0.419   0.664  0.600 
#>  2 cyl   -0.852 NA      0.902  0.832 -0.700   0.782 -0.591  -0.811 -0.523 
#>  3 disp  -0.848  0.902 NA      0.791 -0.710   0.888 -0.434  -0.710 -0.591 
#>  4 hp    -0.776  0.832  0.791 NA     -0.449   0.659 -0.708  -0.723 -0.243 
#>  5 drat   0.681 -0.700 -0.710 -0.449 NA      -0.712  0.0912  0.440  0.713 
#>  6 wt    -0.868  0.782  0.888  0.659 -0.712  NA     -0.175  -0.555 -0.692 
#>  7 qsec   0.419 -0.591 -0.434 -0.708  0.0912 -0.175 NA       0.745 -0.230 
#>  8 vs     0.664 -0.811 -0.710 -0.723  0.440  -0.555  0.745  NA      0.168 
#>  9 am     0.600 -0.523 -0.591 -0.243  0.713  -0.692 -0.230   0.168 NA     
#> 10 gear   0.480 -0.493 -0.556 -0.126  0.700  -0.583 -0.213   0.206  0.794 
#> 11 carb  -0.551  0.527  0.395  0.750 -0.0908  0.428 -0.656  -0.570  0.0575
#> # … with 2 more variables: gear <dbl>, carb <dbl>

if (FALSE) {

# Also supports DB backend and collects results into memory

library(sparklyr)
sc <- spark_connect(master = "local")
mtcars_tbl <- copy_to(sc, mtcars)
mtcars_tbl %>%
  correlate(use = "pairwise.complete.obs", method = "spearman")
spark_disconnect(sc)

}