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.

focus(x, ..., mirror = FALSE)

focus_(x, ..., .dots, mirror)

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 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 x 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 x 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 method: 'pearson' #> Missing treated using: 'pairwise.complete.obs'
focus(x, -matches("Sepal")) # Focus on correlations of non-Sepal
#> # A tibble: 2 x 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.