I have a large dataset and need to recode a few numeric variables to other numeric values. A portion of my dataset looks like this:
condition.10 financial.condition behavior.condition outcome Gender Race
1 6 1 3 0 Male 5
2 7 0 4 0 Female 5
3 5 0 3 1 Female 5
4 2 1 1 1 Male 2,5
5 10 1 5 0 Female 5
6 6 1 3 1 Male 5
I want to recode race into 3 "bins" of 1, 2, 3- "white", "Black", and "Other" respectively. I have managed to achieve that with this code:
mydata$Race <- NA
mydata$Race <- mydata$Q73
mydata$Race[mydata$Race==1|mydata$Race==2|mydata$Race==4|mydata$Race==6]<-6
mydata$Race[mydata$Race==3]<-2
mydata$Race[mydata$Race==5]<-1
mydata$Race[mydata$Race==6]<-3
I also tried this:
case_when(mydata$Race %in% c(1,2,4,6) ~3,
mydata$Race %in% 3 ~ 2,
mydata$Race %in% 5 ~1,
TRUE ~ as.numeric(mydata$Race))
The first bit gives me what I need, but it doesn't account for people checking two races such as that in the 4th row.
Any advice would be appreciated. I already ready recode from car package and dplyr.
Maybe it just me being so new, but it hurts not being able to do the basics.
unique(mydata$Race)
# [1] 5 2,5 2 3 6 3,5 1,5 1,2,4,5 1 1,2,5 4,6 3,6 2,3 1,3 4
# [16] 2,4,5,6 1,3,5 4,5
# Levels: 1 1,2,4,5 1,2,5 1,3 1,3,5 1,5 2 2,3 2,4,5,6 2,5 3 3,5 3,6 4 4,5 4,6 5 6
Note: I am very new to R and am looking for some guidance.
We can create a lookup table with the Race codes you're looking for. Anything not in that table, we can call "Other".
library(tidyverse)
#create a lookup table
RaceTable <- data.frame(Race = c(3, 5),
RaceName = c("White", "Black"),
stringsAsFactors = FALSE)
mydata %>%
#bring in RaceName from the lookup table
left_join(RaceTable, by = c("Race" = "Race")) %>%
#if there is no RaceName, call it "Other"
mutate(RaceName = replace(RaceName, is.na(RaceName), "Other"))
We can create a lookup named vector then loop through values:
# example data
df1 <- data.frame(Race = c("1", "2", "3", "4", "5", "5,2", "6"))
# map, named vector
lookup <- setNames(c(3, 3, 2, 3, 1, 3), 1:6)
# 1 2 3 4 5 6
# 3 3 2 3 1 3
df1$RaceClean <- sapply(as.character(df1$Race), function(i){
paste(lookup[ unlist(strsplit(i, ",")) ], collapse = ",")
})
df1
# Race RaceClean
# 1 1 3
# 2 2 3
# 3 3 2
# 4 4 3
# 5 5 1
# 6 5,2 1,3
# 7 6 3
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