Collapsing Levels of a Factor Variable in one column while summing the counts in another - r

Question

I originally had a vary wide data (4 rows with 158 columns) which I used reshape::melt() on to create a long data set (624 rows x 3 columns).
Now, however, I have a data set like this:
demo <- data.frame(region = as.factor(c("North", "South", "East", "West")),
criteria = as.factor(c("Writing_1_a", "Writing_2_a", "Writing_3_a", "Writing_4_a",
"Writing_1_b", "Writing_2_b", "Writing_3_b", "Writing_4_b")),
counts = as.integer(c(18, 27, 99, 42, 36, 144, 99, 9)))
Which produces a table similar to the one below:
region criteria counts
North Writing_1_a 18
South Writing_2_a 27
East Writing_3_a 99
West Writing_4_a 42
North Writing_1_b 36
South Writing_2_b 144
East Writing_3_b 99
West Writing_4_b 9
Now what I want to create is something like this:
goal <- data.frame(region = as.factor(c("North", "South", "East", "West")),
criteria = as.factor(c("Writing_1", "Writing_2", "Writing_3", "Writing_4")),
counts = as.integer(c(54, 171, 198, 51)))
Meaning that when I collapse the criteria columns it sums the counts:
region criteria counts
North Writing_1 54
South Writing_2 171
East Writing_3 198
West Writing_4 51
I have tried using forcats::fct_collapse and forcats::recode()but to no avail - I'm positive I'm just not doing it right. Thank you in advance for any assistance you can provide.

Answer 1

You can think about what exactly you're trying to do to change factor levels—fct_collapse would manually collapse several levels into one level, and fct_recode would manually change the labels of individual levels. What you're trying to do is change all the labels based on applying some function, in which case fct_relabel is appropriate.
You can write out an anonymous function when you call fct_relabel, or just pass it the name of a function and that function's argument(s). In this case, you can use stringr::str_remove to find and remove a regex pattern, and regex such as _[a-z]$ to remove any underscore and then lowercase letter that appear at the end of a string. That way it should scale well with your real data, but you can adjust it if not.
library(tidyverse)
...
new_crits <- demo %>%
mutate(crit_no_digits = fct_relabel(criteria, str_remove, "_[a-z]$"))
new_crits
#> region criteria counts crit_no_digits
#> 1 North Writing_1_a 18 Writing_1
#> 2 South Writing_2_a 27 Writing_2
#> 3 East Writing_3_a 99 Writing_3
#> 4 West Writing_4_a 42 Writing_4
#> 5 North Writing_1_b 36 Writing_1
#> 6 South Writing_2_b 144 Writing_2
#> 7 East Writing_3_b 99 Writing_3
#> 8 West Writing_4_b 9 Writing_4
Verifying that this new variable has only the levels you want:
levels(new_crits$crit_no_digits)
#> [1] "Writing_1" "Writing_2" "Writing_3" "Writing_4"
And then summarizing based on that new factor:
new_crits %>%
group_by(crit_no_digits) %>%
summarise(counts = sum(counts))
#> # A tibble: 4 x 2
#> crit_no_digits counts
#> <fct> <int>
#> 1 Writing_1 54
#> 2 Writing_2 171
#> 3 Writing_3 198
#> 4 Writing_4 51
Created on 2018-11-04 by the reprex package (v0.2.1)

Answer 2

A dplyr solution using regular expressions:
demo %>%
mutate(criteria = gsub("(_a)|(_b)", "", criteria)) %>%
group_by(region, criteria) %>%
summarize(counts = sum(counts)) %>%
arrange(criteria) %>%
as.data.frame
region criteria counts
1 North Writing_1 54
2 South Writing_2 171
3 East Writing_3 198
4 West Writing_4 51