Correlation tables annoy me

I’m lazy

And so are most people.

I like to focus on as few things as possible at any given moment and correlation tables get in the way of that.

Correlation tables contain useless data

corrs <- cor(mtcars)

corrplot::corrplot(corrs, order = "hclust")

I don’t need to be reminded that each predictor is perfectly correlated with itself.

I guess this could be useful because the border it forms warns me that I am about to see the same exact information for the second time.

Which brings me to…

Correlation tables duplicate information

A solution part 1: Eliminate the obvious

After taking a quick look at the table (just the first half :) )to get an idea of the largest correlation values I establish a cut-off point to look at the pairs with the strongest values.

I also remove values where a predictor is correlated with itself.

cut_off <- 0.8

corrs <- cor(mtcars)

# probably *shouldn't* be using melt() here b/c reshape2 is
# depreciated but it 
# is easy and I like easy because I am lazy
correlated <- reshape2::melt(corrs) %>% 
    dplyr::filter(abs(value) > cut_off,
           #remove entries for a variable correlated to itself
           Var1 != Var2) %>% 
    dplyr::arrange(desc(abs(value))) #not necessary just sorting to demo pairwise dups

correlated

##    Var1 Var2      value
## 1  disp  cyl  0.9020329
## 2   cyl disp  0.9020329
## 3    wt disp  0.8879799
## 4  disp   wt  0.8879799
## 5    wt  mpg -0.8676594
## 6   mpg   wt -0.8676594
## 7   cyl  mpg -0.8521620
## 8   mpg  cyl -0.8521620
## 9  disp  mpg -0.8475514
## 10  mpg disp -0.8475514
## 11   hp  cyl  0.8324475
## 12  cyl   hp  0.8324475
## 13   vs  cyl -0.8108118
## 14  cyl   vs -0.8108118

Notice each successive pair of rows is a pairwise duplicate.

A solution part 2: Eliminate the duplicates

correlated %>% 
    dplyr::mutate(
        combo = dplyr::if_else(
            # > and < comparison doesnt work with factors
            as.character(Var1) > as.character(Var2), #if
                               stringr::str_c(Var1, Var2), #then 
                               stringr::str_c(Var2, Var1) #else
        )
    ) %>% 
    dplyr::distinct(combo, .keep_all= TRUE) %>% 
    dplyr::select(-combo)

##   Var1 Var2      value
## 1 disp  cyl  0.9020329
## 2   wt disp  0.8879799
## 3   wt  mpg -0.8676594
## 4  cyl  mpg -0.8521620
## 5 disp  mpg -0.8475514
## 6   hp  cyl  0.8324475
## 7   vs  cyl -0.8108118

There you have it - only the unique predictor pairs!

I arrived at this solution after finding this stackoverflow post