Convenience function to select a set of variables from a correlation matrix
to keep as the columns, and exclude these or all other variables from the rows. This
function will take a correlate correlation matrix, and
expression(s) suited for dplyr::select(). The selected variables will remain
in the columns, and these, or all other variables, will be excluded from the
rows based on `same. For a complete list of methods for using this
function, see select.
Arguments
- x
cor_df. See
correlate.- ...
One or more unquoted expressions separated by commas. Variable names can be used as if they were positions in the data frame, so expressions like `x:y`` can be used to select a range of variables.
- mirror
Boolean. Whether to mirror the selected columns in the rows or not.
- .dots
Use focus_ to do standard evaluations. See
select.
Value
A tbl or, if mirror = TRUE, a cor_df (see correlate).
Examples
library(dplyr)
x <- correlate(mtcars)
#> Correlation computed with
#> • Method: 'pearson'
#> • Missing treated using: 'pairwise.complete.obs'
focus(x, mpg, cyl) # Focus on correlations of mpg and cyl with all other variables
#> # A tibble: 9 × 3
#> term mpg cyl
#> <chr> <dbl> <dbl>
#> 1 disp -0.848 0.902
#> 2 hp -0.776 0.832
#> 3 drat 0.681 -0.700
#> 4 wt -0.868 0.782
#> 5 qsec 0.419 -0.591
#> 6 vs 0.664 -0.811
#> 7 am 0.600 -0.523
#> 8 gear 0.480 -0.493
#> 9 carb -0.551 0.527
focus(x, -disp, -mpg, mirror = TRUE) # Remove disp and mpg from columns and rows
#> # A tibble: 9 × 10
#> term cyl hp drat wt qsec vs am gear carb
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 cyl NA 0.832 -0.700 0.782 -0.591 -0.811 -0.523 -0.493 0.527
#> 2 hp 0.832 NA -0.449 0.659 -0.708 -0.723 -0.243 -0.126 0.750
#> 3 drat -0.700 -0.449 NA -0.712 0.0912 0.440 0.713 0.700 -0.0908
#> 4 wt 0.782 0.659 -0.712 NA -0.175 -0.555 -0.692 -0.583 0.428
#> 5 qsec -0.591 -0.708 0.0912 -0.175 NA 0.745 -0.230 -0.213 -0.656
#> 6 vs -0.811 -0.723 0.440 -0.555 0.745 NA 0.168 0.206 -0.570
#> 7 am -0.523 -0.243 0.713 -0.692 -0.230 0.168 NA 0.794 0.0575
#> 8 gear -0.493 -0.126 0.700 -0.583 -0.213 0.206 0.794 NA 0.274
#> 9 carb 0.527 0.750 -0.0908 0.428 -0.656 -0.570 0.0575 0.274 NA
x <- correlate(iris[-5])
#> Correlation computed with
#> • Method: 'pearson'
#> • Missing treated using: 'pairwise.complete.obs'
focus(x, -matches("Sepal")) # Focus on correlations of non-Sepal
#> # A tibble: 2 × 3
#> term Petal.Length Petal.Width
#> <chr> <dbl> <dbl>
#> 1 Sepal.Length 0.872 0.818
#> 2 Sepal.Width -0.428 -0.366
# variables with Sepal variables.