replace_triangle.RdReplace the lower or the upper triangular part of a (correlation) matrix.
replace_triangle(x, triangle = c("lower", "upper"), by = "", diagonal = FALSE) replace_upper_triangle(x, by = "", diagonal = FALSE) replace_lower_triangle(x, by = "", diagonal = FALSE)
| x | a (correlation) matrix |
|---|---|
| triangle | the triangle to replace. Allowed values are one of "upper" and "lower". |
| by | a replacement argument. Appropriate values are either "" or NA. Used to replace the upper, lower or the diagonal part of the matrix. |
| diagonal | logical. Default is FALSE. If TRUE, the matrix diagonal is included. |
an object of class cor_mat_tri, which is a data frame
replace_triangle: replaces the specified triangle by empty or NA.
replace_upper_triangle: replaces the upper triangular part of a matrix.
Returns an object of class lower_tri.
replace_lower_triangle: replaces the lower triangular part of a matrix.
Returns an object of class lower_tri
# Compute correlation matrix and pull triangles #:::::::::::::::::::::::::::::::::::::::::: # Correlation matrix cor.mat <- mtcars %>% select(mpg, disp, hp, drat, wt, qsec) %>% cor_mat() cor.mat#> # A tibble: 6 x 7 #> rowname mpg disp hp drat wt qsec #> * <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 mpg 1 -0.85 -0.78 0.68 -0.87 0.42 #> 2 disp -0.85 1 0.79 -0.71 0.89 -0.43 #> 3 hp -0.78 0.79 1 -0.45 0.66 -0.71 #> 4 drat 0.68 -0.71 -0.45 1 -0.71 0.091 #> 5 wt -0.87 0.89 0.66 -0.71 1 -0.17 #> 6 qsec 0.42 -0.43 -0.71 0.091 -0.17 1# Replace upper triangle by NA #:::::::::::::::::::::::::::::::::::::::::: cor.mat %>% replace_upper_triangle(by = NA)#> rowname mpg disp hp drat wt qsec #> 1 mpg NA NA NA NA NA NA #> 2 disp -0.85 NA NA NA NA NA #> 3 hp -0.78 0.79 NA NA NA NA #> 4 drat 0.68 -0.71 -0.45 NA NA NA #> 5 wt -0.87 0.89 0.66 -0.710 NA NA #> 6 qsec 0.42 -0.43 -0.71 0.091 -0.17 NA# Replace upper triangle by NA and reshape the # correlation matrix to have unique combinations of variables #:::::::::::::::::::::::::::::::::::::::::: cor.mat %>% replace_upper_triangle(by = NA) %>% cor_gather()#> var1 var2 cor p #> 2 disp mpg -0.850 9.38e-10 #> 3 hp mpg -0.780 1.79e-07 #> 4 drat mpg 0.680 1.78e-05 #> 5 wt mpg -0.870 1.29e-10 #> 6 qsec mpg 0.420 1.71e-02 #> 9 hp disp 0.790 7.14e-08 #> 10 drat disp -0.710 5.28e-06 #> 11 wt disp 0.890 1.22e-11 #> 12 qsec disp -0.430 1.31e-02 #> 16 drat hp -0.450 9.99e-03 #> 17 wt hp 0.660 4.15e-05 #> 18 qsec hp -0.710 5.77e-06 #> 23 wt drat -0.710 4.78e-06 #> 24 qsec drat 0.091 6.20e-01 #> 30 qsec wt -0.170 3.39e-01