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.
Usage
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 tox. The default is equivalent toy = 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
methodanduseparameters.
Details
This function returns a correlation matrix as a correlation data frame in the following format:
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.
The use argument and its possible values are inherited from stats::cor():
"everything": NAs will propagate conceptually, i.e. a resulting value will be NA whenever one of its contributing observations is NA
"all.obs": the presence of missing observations will produce an error
"complete.obs": correlations will be computed from complete observations, with an error being raised if there are no complete cases.
"na.or.complete": correlations will be computed from complete observations, returning an NA if there are no complete cases.
"pairwise.complete.obs": the correlation between each pair of variables is computed using all complete pairs of those particular variables.
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".
There is a ggplot2::autoplot() method for quickly visualizing the
correlation matrix, for more information see autoplot.cor_df().
Examples
if (FALSE) {
correlate(iris)
}
correlate(iris[-5])
#> Correlation computed with
#> • Method: 'pearson'
#> • Missing treated using: 'pairwise.complete.obs'
#> # A tibble: 4 × 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 computed with
#> • Method: 'pearson'
#> • Missing treated using: 'pairwise.complete.obs'
#> # A tibble: 11 × 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>
#> # ℹ Use `colnames()` to see all variable names
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)
}