CODEWITHSUNDEEP
rdplyrgroup-bycase-whensummarize
J.Sabree
J.Sabree

Reputation: 2536

How to use a counting function and case_when simultaneously in R?

I have a dataframe, and I want to count the number of observations for each name that meet a certain criteria:

library(dplyr)
test <- tibble(name = c("Justin", "Corey", "Sibley", "Justin", "Corey", "Sibley", "Justin", "Corey", "Sibley"),
               class = c("Bio", "Bio", "Bio", "Psych", "Pysch", "Psych", "English", "English", "English"),
               result = c("Fail", "Pass", "Pass", "Fail", "Pass", "Pass", "Fail", "Fail", "Pass"))

In the above example, the courses the students took are either STEM (e.g., "Bio" & "Psych") or literature ("English"). I want to make two new columns, one that says their STEM score, and one that contains their literature score, where the code will count 1 for each pass.

The answer should look like:

library(dplyr)
answer <- tibble(name = c("Justin", "Corey", "Sibley"),
                 stem_assessment = c(0, 2, 2),
                 lit_assessment = c(0, 0, 1))

I've tried experimenting with case_when(), group_by(), n(), summarize(), and count(), but I can't seem to crack it.

Upvotes: 0

Views: 888

Answers (2)

Ronak Shah
Ronak Shah

Reputation: 389135

group_by and summarise should do -

library(dplyr)

stem <- c("Bio", "Pysch", "Psych")
lit <- c('English')

test %>%
  group_by(name) %>%
  summarise(stem_assessment = sum(class %in% stem & result == "Pass"),
            lit_assessment = sum(class %in% lit & result == "Pass"))

#   name   stem_assessment lit_assessment
#  <chr>            <int>          <int>
#1 Corey                2              0
#2 Justin               0              0
#3 Sibley               2              1

Upvotes: 2

Zaw
Zaw

Reputation: 1474

You can use a combination of count() and pivot_longer/pivot_wider. I also coded the typo (Pysch) in test.

library(tidyverse)

test %>%
  mutate(
    stem_assessment = class %in% c("Bio", "Psych", "Pysch") & result == "Pass",
    lit_assessment = class == "English" & result == "Pass"
  ) %>% 
  select(-c(class, result)) %>%
  pivot_longer(-name, names_to = "var", values_to = "value") %>% 
  count(name, var, wt = value) %>% 
  pivot_wider(name, names_from = var, values_from = n)

Upvotes: 1

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