Strongest pairwise correlations in a data frame

Ranks the numeric column pairs by the strength of their association, so a wide table's structure can be read without squinting at a correlation matrix.

Usage

top_correlations(data, n = 10L, method = c("spearman", "pearson"), min_abs = 0)

Arguments

data

A data frame; non-numeric columns are ignored.

n

Number of pairs to return (default 10).

method

"spearman" (default) or "pearson".

min_abs

Report only pairs whose absolute correlation reaches this (default 0).

Value

A data frame of class bricklayer_correlations with x, y, correlation and abs_correlation, strongest first.

Details

Spearman is the default deliberately. Pearson measures LINEAR association only, so it understates a relationship that is perfectly monotone but curved, and a single outlier can manufacture or destroy it. On data you have not yet inspected – which is the situation this function is for – the rank correlation is the safer question to ask.

See also

core_cor_spearman(), core_cov()

Examples

set.seed(1)
n <- 200
df <- data.frame(
  a = stats::rnorm(n),
  b = stats::rnorm(n),
  grade = sample(letters[1:3], n, TRUE)
)
df$c <- df$a * 2 + stats::rnorm(n, sd = 0.1)   # strongly related to a
df$d <- exp(df$a)                              # monotone but curved

top_correlations(df)
#> ── Top correlations (spearman) ─────────────────────────────────── 
#>  x y correlation                  plot
#>  a d       1.000           |##########
#>  a c       0.998           |##########
#>  c d       0.998           |##########
#>  a b      -0.062          #|          
#>  b d      -0.062          #|          
#>  b c      -0.060          #|          
#> ────────────────────────────────────────────────────────────────── 

# The curved pair is ranked correctly by Spearman and understated by
# Pearson, which is why Spearman is the default.
tc <- top_correlations(df, method = "spearman")
tc[tc$x == "a" & tc$y == "d", "correlation"]
#> [1] 1
tp <- top_correlations(df, method = "pearson")
tp[tp$x == "a" & tp$y == "d", "correlation"]
#> [1] 0.8496382

# Filter to the pairs worth looking at.
top_correlations(df, min_abs = 0.5)
#> ── Top correlations (spearman) ─────────────────────────────────── 
#>  x y correlation                  plot
#>  a d       1.000           |##########
#>  a c       0.998           |##########
#>  c d       0.998           |##########
#> ──────────────────────────────────────────────────────────────────