R, pivot wide to long while changing column names-CodePudding

I have data like this:

df<-structure(list(fname = c("Linda", "Bob"), employee_number = c("00000123456", 
"654321"), job_role = c("Dept Research Admin", "Research Regulatory Assistant"
), ActiveAccount = c("Yes", "Yes"), CanAccess = c("No", "No"), 
    oncore_roles___1 = c(1, 0), oncore_roles___2 = c(1, 0), oncore_roles___3 = c(1, 
    0), oncore_roles___4 = c(0, 0), oncore_roles___5 = c(0, 1
    ), oncore_roles___6 = c(0, 0), oncore_roles___7 = c(0, 1), 
    oncore_roles___8 = c(0, 0), oncore_roles___9 = c(0, 0), oncore_roles___10 = c(0, 
    0), oncore_roles___11 = c(0, 0), oncore_roles___12 = c(0, 
    1), oncore_roles___13 = c(0, 0), oncore_roles___14 = c(0, 
    0), oncore_roles___15 = c(0, 0), oncore_roles___16 = c(0, 
    0), oncore_roles___17 = c(0, 0)), row.names = c(NA, -2L), class = c("tbl_df", 
"tbl", "data.frame"))

The columns that start with "oncore roles" all came from this multiple choice survey option:

Where oncore_roles_1 stands for "calendar build", oncore_roles_5 stands for "principal investigator", etc... I.e. if Bob has a "1" marked in Oncore_roles_5, he was a principal investigator, if he had a zero in every other "oncore_roles" column... he was not those things.

I need to pivot my data so that it is longer and there's only one column for "Oncore Roles" which would have text that said what roles that person had, with a line for each role. So if Bob had three roles, he would get three nearly identical lines. Everything would be identical except the oncore_roles variable.

I know that's probably some version of pivot_longer but the trick (why I ask) is that I need to drop all the zero's. I.e. for this particular data, I'd be left with this:

Thank you!

CodePudding user response：

Here is one option where we create a key/value dataset based on the multiple choice question and the column names, then do a join with the reshaped data to return the mapped column

library(dplyr)
library(tidyr)
library(stringr)
keydat <- tibble(name = str_c("oncore_roles___", 1:12), 
  Oncore_role = c("Calendar Build", "Protocol Management", 
   "Subject Managment", "Financials", "Principal Investigator", 
  "Protocol Management Finance", "Regulatory", 
  "Investigational Pharmacist", "Division Director", "CTO Signoff",
   "Roles Administration", "Statistical Analysis")) 
 df %>% 
   pivot_longer(cols = starts_with('oncore_roles')) %>%
   filter(value == 1) %>% 
   inner_join(keydat) %>%
   select(-name)

-output

# A tibble: 6 × 7
  fname employee_number job_role                      ActiveAccount CanAccess value Oncore_role           
  <chr> <chr>           <chr>                         <chr>         <chr>     <dbl> <chr>                 
1 Linda 00000123456     Dept Research Admin           Yes           No            1 Calendar Build        
2 Linda 00000123456     Dept Research Admin           Yes           No            1 Protocol Management   
3 Linda 00000123456     Dept Research Admin           Yes           No            1 Subject Managment     
4 Bob   654321          Research Regulatory Assistant Yes           No            1 Principal Investigator
5 Bob   654321          Research Regulatory Assistant Yes           No            1 Regulatory            
6 Bob   654321          Research Regulatory Assistant Yes           No            1 Statistical Analysis

CodePudding user response：

If you build a small lookup table of oncore roles, say roles, you can do the following:

df %>%
  pivot_longer(cols = -(fname:CanAccess),names_prefix = "oncore_roles___",names_to = "id") %>% 
  filter(value==1) %>% 
  mutate(id=as.numeric(id)) %>% 
  left_join(roles, by="id") %>% 
  select(-(id:value))

Output (Note that my roles only has the first 5 roles, but you can make it longer, and then you could use an inner_join(), rather than left_join():

  fname employee_number job_role                      ActiveAccount CanAccess Oncore_role           
  <chr> <chr>           <chr>                         <chr>         <chr>     <chr>                 
1 Linda 00000123456     Dept Research Admin           Yes           No        Calendar Build        
2 Linda 00000123456     Dept Research Admin           Yes           No        Protocol Management   
3 Linda 00000123456     Dept Research Admin           Yes           No        Subject Management    
4 Bob   654321          Research Regulatory Assistant Yes           No        Principal Investigator
5 Bob   654321          Research Regulatory Assistant Yes           No        NA                    
6 Bob   654321          Research Regulatory Assistant Yes           No        NA

roles:


roles =tibble(
  id = 1:5,
  Oncore_role = c(
    "Calendar Build",
    "Protocol Management",
    "Subject Management",
    "Financial",
    "Principal Investigator"
))