How to mutate a new variable in R dyplyr that is based on criteria from 2 columns?-CodePudding

I have a dataset which looks like this:

Recipient  ID
(chr)       (chr)  
Smith       C
Wells       S
Wells       S
Jones       S
Jones       N
Wu          C
Wu          N
Wu          S

I want to mutate a new variable, which is either "Unique" or "Multiple", based on if Recipient appears once (Unique), Recipient appears more than once but has the same ID for each occurence (Unique), Recipient appears more than once AND has 1 or more IDs (Multiple). I've tried to use:

df %>%
 group_by(Recipient, ID) %>%
 mutuate(Freq = case_when(
                str_count(Recipient) == 1 & str_count(ID) == 1 ~ "Unique",
                str_count(Recipient) > 2 & str_count(ID) == 1 ~ "Unique",
                str_count(Recipient) > 2 & str_count(ID) > 1 ~ "Multiple"))

When I did this, all the values were multiple:

Recipient  ID     Freq
(chr)      (chr)  (chr)
Smith       C     Multiple (should be Unique)
Wells       S     Multiple (should be Unique)
Wells       S     Multiple (should be Unique)
Jones       S     Multiple
Jones       N     Multiple
Wu          C     Multiple
Wu          N     Multiple
Wu          S     Multiple

I've tried multiple times, but can't crack it. Can anyone help to solve this, or recommend an easier way to code this? Thanks!

CodePudding user response：

A possible solution with n_distinct():

library(dplyr)

df %>%
  group_by(Recipient) %>%
  mutate(Freq = ifelse(n_distinct(ID) == 1, "unique", "multiple")) %>%
  ungroup()

# A tibble: 8 x 3
  Recipient ID    Freq
  <chr>     <chr> <chr>
1 Smith     C     unique
2 Wells     S     unique
3 Wells     S     unique
4 Jones     S     multiple
5 Jones     N     multiple
6 Wu        C     multiple
7 Wu        N     multiple
8 Wu        S     multiple

Data

df <- structure(list(Recipient = c("Smith", "Wells", "Wells", "Jones", 
"Jones", "Wu", "Wu", "Wu"), ID = c("C", "S", "S", "S", "N", "C",
"N", "S")), class = "data.frame", row.names = c(NA, -8L))

CodePudding user response：

Here is the update after clarification:

library(dplyr)

df %>% 
  group_by(Recipient) %>% 
  mutate(Freq = paste(Recipient, ID),
         Freq = ifelse(Freq %in% Freq[duplicated(Freq)], "unique", "multiple"),
         Freq = ifelse(Recipient %in% Recipient[duplicated(Recipient)], Freq, "unique"))

  Recipient ID    Freq    
  <chr>     <chr> <chr>   
1 Smith     C     unique  
2 Wells     S     unique  
3 Wells     S     unique  
4 Jones     S     multiple
5 Jones     N     multiple
6 Wu        C     multiple
7 Wu        N     multiple
8 Wu        S     multiple