Recoding in R using dplyr (or something else)

Question

I am for sure no expert in R yet. I grew up with SPSS and is slowly shifting to R. I solve problems as I meet them. And seek help when I get lost.

Please look at this code:

dataset$v18[dataset$s_18 == 1] <- "Agree"
dataset$v18[dataset$s_18 == 2] <- "Partly Agree"
dataset$v18[dataset$s_18 == 3] <- "Neutral"
dataset$v18[dataset$s_18 == 4] <- "Partly disagree"
dataset$v18[dataset$s_18 == 5] <- "Disagree"

sv18x <- dataset %>%
  filter(!is.na(v18)) %>%
  group_by(v18) %>% 
  dplyr::summarise(count=n()) %>% 
  mutate(pct=count/sum(count)*100) 

sv18x$v18 <- factor(sv18x$v18,levels = c("Agree", "Partly agree", "Neutral", "Partly disagree", "Disagree uenig"))
sv18x$pct<- trunc(sv18x$pct)

I feel quite confident what this can be done in a shorter and smarter way. And I think it should be done using dplyr::recode() and something else that I probably don't know yet. I just can't figure out how to do it. Can someone give me a hint?

Answer 1

I simulated a reproducible example to help you, but it's hard to know what you want without the real dataset. The first part can be done with dplyr::case_when(), while the percentage part can be done with the janitor package.

library(dplyr)
library(janitor)

dataset <- data.frame(ola = sample(c("a", "b", "c", 150, replace = TRUE)),
                  s_18 = sample(1:5, 150, replace = TRUE))

dataset <- dataset %>%
   mutate(v18 = case_when(
          s_18 == 1 ~ "Agree",
          s_18 == 2 ~ "Partly Agree",
          s_18 == 3 ~ "Neutral",
          s_18 == 4 ~ "Partly Disagree",
          s_18 == 5 ~ "Disagree"
          ))

sv18x <- dataset %>%
  count(v18) %>%
  janitor::adorn_percentages("col") %>%
  janitor::adorn_pct_formatting()

Hope this helps!