Putting spearman and pearson correlations in upper/lower triangles

corrr_dif_methods

library(tidyverse)
library(corrr)

d <- mtcars

stretch_triangle <- function(cordf, upper) {
  cordf %>% shave(upper = upper) %>% stretch(na.rm = TRUE)
}

spearman <- correlate(d, method = "spearman") %>% stretch_triangle(upper = FALSE)
#> 
#> Correlation method: 'spearman'
#> Missing treated using: 'pairwise.complete.obs'

pearson <- correlate(d, method = "pearson") %>% stretch_triangle(upper = TRUE)
#> 
#> Correlation method: 'pearson'
#> Missing treated using: 'pairwise.complete.obs'

pearson %>% bind_rows(spearman) %>% spread(y, r) %>% select(-x) %>% as_cordf()
#> # A tibble: 11 x 12
#>    rowname       am     carb     cyl    disp     drat    gear      hp
#>    <chr>      <dbl>    <dbl>   <dbl>   <dbl>    <dbl>   <dbl>   <dbl>
#>  1 am       NA        0.0575  -0.522  -0.624   0.687    0.794  -0.362
#>  2 carb     -0.0644  NA        0.580   0.540  -0.125    0.115   0.733
#>  3 cyl      -0.523    0.527   NA       0.902  -0.700   -0.493   0.832
#>  4 disp     -0.591    0.395    0.928  NA      -0.710   -0.556   0.791
#>  5 drat      0.713   -0.0908  -0.679  -0.684  NA        0.700  -0.520
#>  6 gear      0.808    0.274   -0.564  -0.594   0.745   NA      -0.331
#>  7 hp       -0.243    0.750    0.902   0.851  -0.449   -0.126  NA    
#>  8 mpg       0.600   -0.551   -0.852  -0.848   0.681    0.480  -0.776
#>  9 qsec     -0.230   -0.656   -0.572  -0.460   0.0919  -0.213  -0.667
#> 10 vs        0.168   -0.570   -0.814  -0.724   0.447    0.206  -0.752
#> 11 wt       -0.692    0.428    0.858   0.898  -0.750   -0.583   0.775
#> # ... with 4 more variables: mpg <dbl>, qsec <dbl>, vs <dbl>, wt <dbl>