Creating categories according to several criteria's

I am trying to created a category called emotional_ipv using the following criteria:

Having experienced no IPV if all responses are “never”; an isolated incident of IPV if one response is “once”; a low frequency of violence if the response is “once” to more than one item; a mid frequency if they respond “a few times” to at least one item, but do not respond “many times” to any item; and a high frequency if there are any responses of “many times”.

df

df <- structure (list(subject_id = c("191-5467", "191-6784", "191-3457", "191-0987", "191-1245","191-2365", "191-4532", "191-9901", "191-2710", "191-5098"), ipv_q1_en = c("0", "1", "3", "0", "2", "2", "3", "2", "0", "2"), ipv_q2_en = c("0", "0", "3", "0", "2", "2", "0", "1", "0", "3"), ipv_q3_en = c("0", "1", "3", "2", "1", "2", "0", "1", "0","2"),ipv_q4_en = c("0", "0", "3", "0", "2", "2", "0", "1", "0", "3")),class = "data.frame", row.names = c (NA, -10L))

coding key...0 Never;1 Once;2 Few times;3 Many times

Desired dataset:

df1 <- structure (list(subject_id = c("191-5467", "191-6784", "191-3457", "191-0987", "191-1245",                                   "191-2365", "191-4532", "191-9901", "191-2710", "191-5098"),ipv_q1_en = c("0", "1", "3", "0", "2", "2", "3", "2", "0", "2"),ipv_q2_en = c("0", "0", "3", "0", "2", "2", "0", "1", "0", "3"), 
ipv_q3_en = c("0", "1", "3", "2", "1", "2", "0", "1", "0", "2"),ipv_q4_en = c("0", "0", "3", "0", "2", "2", "0", "1", "0", "3"),emotional_ipv = c("never", "low frequency", "high frequency", "mid frequency","mid frequency","mid frequency", "mid frequency", "high frequency", "never", "high frequency")),class = "data.frame", row.names = c (NA, -10L))

What I have tried

df %>% select(subject_id, ipv_q1_en:ipv_q4_en) %>% ifelse(ipv_q1_en == 0 & ipv_q2_en == 0 & ipv_q3_en == 0 & ipv_q4 == 0, "never", ifelse(sum(ipv_q1_en:ipv_q4_en == 1, "isolated incident")),ifelse(ipv_q1_en <= 2 & ipv_q2_en <= 2 & ipv_q3_en <= 2 & ipv_q4 <= 2, "mid frequency",ifelse())

so the above code definitely won't work but I do not know how else to do it.

Try this (and add na.rm = TRUE arguments in case you have missing values in your data):

library(tidyverse)

# define dataframe
df <-tibble(
    subject_id = c(
      "191-5467",
      "191-6784",
      "191-3457",
      "191-0987",
      "191-1245",
      "191-2365",
      "191-4532",
      "191-9901",
      "191-2710",
      "191-5098"
    ),
    ipv_q1_en = c(0L, 1L, 3L, 0L, 2L, 2L, 3L, 2L, 0L, 2L),
    ipv_q2_en = c(0L, 0L, 3L, 0L, 2L, 2L, 0L, 1L, 0L, 3L),
    ipv_q3_en = c(0L, 1L, 3L, 2L, 1L, 2L, 0L, 1L, 0L, 2L),
    ipv_q4_en = c(0L, 0L, 3L, 0L, 2L, 2L, 0L, 1L, 0L, 3L)
  )

# reshape longer
df <- df |> 
  pivot_longer(
    !subject_id,
    names_to = "question",
    names_pattern = "ipv_q(\\d+)_en",
    values_to = "answer")

# add case distinction
df |> 
  group_by(subject_id) |> 
  summarise(emotional_ipv = case_when(
    sum(answer) == 0 ~ "never",
    sum(answer == 1) == 1 ~ "isolated incident",
    sum(answer == 1) > 1 ~ "low frequency",
    sum(answer == 2) >=1 & !any(answer > 2) ~ "medium frequency",
    any(answer == 3) ~ "high frequency"
  ))
#> # A tibble: 10 × 2
#>    subject_id emotional_ipv    
#>    <chr>      <chr>            
#>  1 191-0987   medium frequency 
#>  2 191-1245   isolated incident
#>  3 191-2365   medium frequency 
#>  4 191-2710   never            
#>  5 191-3457   high frequency   
#>  6 191-4532   high frequency   
#>  7 191-5098   high frequency   
#>  8 191-5467   never            
#>  9 191-6784   low frequency    
#> 10 191-9901   low frequency

^{Created on 2023-03-03 with reprex v2.0.2}

The reason why your ifelse() statements do not work is that you need to wrap them inside mutate() if you want to modify columns. If you prefer not to make your data longer, you need rowwise() to allow aggregation across columns.