When I added facet_wrap to a pipeline after ggradar I had to use facet_wrap(~group) even though the name of the variable I was using was category_of_interest.
When I tried test_data %>% ggradar + facet_wrap(~category_of_interest) I got:
Error incombine_vars(): ! At least one layer must contain all faceting variables:category_of_interest`.
Plot is missing category_of_interest
Layer 1 is missing category_of_interest
Layer 2 is missing category_of_interest
Layer 3 is missing category_of_interest
Layer 4 is missing category_of_interest
Layer 5 is missing category_of_interest
Layer 6 is missing category_of_interest
Layer 7 is missing category_of_interest
Layer 8 is missing category_of_interest
Layer 9 is missing category_of_interest
Layer 10 is missing category_of_interest
Layer 11 is missing category_of_interest
Layer 12 is missing category_of_interest
Layer 13 is missing category_of_interest
Run rlang::last_error() to see where the error occurred.
`
I added some code so that the name of the grouping variable is preserved so that it can be used in the facet_wrap call.
I also added a line of code to drop dead levels from the grouping variable. I ran into this problem when I filtered the data on the way into ggradar. This line adds a dependency on forcats. I thought this was okay since forcats is part of the tidyverse.
When I added facet_wrap to a pipeline after ggradar I had to use facet_wrap(~group) even though the name of the variable I was using was category_of_interest.
When I tried
test_data %>% ggradar + facet_wrap(~category_of_interest)I got:Error incombine_vars(): ! At least one layer must contain all faceting variables:category_of_interest`.category_of_interestcategory_of_interestcategory_of_interestcategory_of_interestcategory_of_interestcategory_of_interestcategory_of_interestcategory_of_interestcategory_of_interestcategory_of_interestcategory_of_interestcategory_of_interestcategory_of_interestcategory_of_interestRunrlang::last_error()to see where the error occurred. ` I added some code so that the name of the grouping variable is preserved so that it can be used in the facet_wrap call. I also added a line of code to drop dead levels from the grouping variable. I ran into this problem when I filtered the data on the way into ggradar. This line adds a dependency on forcats. I thought this was okay since forcats is part of the tidyverse.make_test_data.R.txt