Missing values in nested ifelse statements in R - r

Question

At some point in time, I encountered this problem...and solved it. However, as it is a recurring problem and I've now forgotten the solution, hopefully this question will offer clarification to others as well as me :)
I am creating a variable that is based answers to several questions. Each question can have three values: 1, 2, or NA. 1's and 2's are mutually exclusive for each observation.
I simply want to create a variable that is a composite of the choice coded with "1" for each person, and give it a value based on that code.
So let's say I have this df:
ID var1 var2 var3 var4
1 1 2 NA NA
2 NA NA 2 1
3 2 1 NA NA
4 2 NA 1 NA
I then try to recode based on the following statement:
df$var <-
ifelse(
as.numeric(df$var1) == 1,
"Gut instinct",
ifelse(
as.numeric(df$var2) == 1,
"Data",
ifelse(
as.numeric(df$var3) == 1,
"Science",
ifelse(
as.numeric(df$var4) == 1,
"Philosophy",
NA
)
)
)
)
However, this code only PARTIALLY codes based on the "ifelse". For example, df$var might have observation of 'Gut instinct' and 'Philosophy', but the codings for when var2 and var3==1 are still NA.
Any thoughts on why this might be happening?

Answer 1

An alternative that will be quicker than apply (using #MrFlick's data):
vals <- c("Gut", "Data", "Science", "Phil")
intm <- dd[-1]==1 & !is.na(dd[-1])
dd$resp <- NA
dd$resp[row(intm)[intm]] <- vals[col(intm)[intm]]
How much quicker? On 1 million rows:
#row/col assignment
user system elapsed
0.99 0.02 1.02
#apply
user system elapsed
11.98 0.04 12.30
And giving the same results when tried on identical datasets:
identical(flick$resp,latemail$resp)
#[1] TRUE

Answer 2

This is because ifelse (and ==) has special behavior for NA. Specifically, R doesn't want to tell you that NA is different from 1 (or anything else), because often NA is used to represent a value that could be anything, maybe even 1.
> 1 == NA
[1] NA
> ifelse(NA == 1, "yes", "no")
[1] NA
With your code, if an NA occurs before a 1 (like for ID 2), then that ifelse statement will just return NA, and the nested FALSE ifelse will never be called.

Answer 3

Here's a way to do with without the nested ifelse statements
#your data
dd<-data.frame(ID = 1:4,
var1 = c(1, NA, 2, 2),
var2 = c(2, NA, 1, NA),
var3 = c(NA, 2, NA, 2),
var4 = c(NA, 1, NA, NA)
)
resp <- c("Gut","Data","Sci","Phil")[apply(dd[,-1]==1,1,function(x) which(x)[1])]
cbind(dd, resp)
I use apply to scan across the rows to find the first 1 and use that index to subset the response values. Using which helps to deal with the NA values.

Answer 4

To answer your question it is due to the NAs in your data. This should sort your problem out
df <- data.frame( ID=1:4, var1= c(1, NA, 2, 2), var2= c(2, NA, 1, NA),
var3=c(NA,2,NA,2), var4=c(NA, 1, NA, NA))
df$var<-ifelse(as.numeric(df$var1)==1&!is.na(df$var1),"Gut instinct",
ifelse(as.numeric(df$var2)==1&!is.na(df$var2),"Data",
ifelse(as.numeric(df$var3)==1&!is.na(df$var3),"Science",
ifelse(as.numeric(df$var4)==1&!is.na(df$var4),"Philosophy",NA))))
However, I would find it easier to reshape the data into a 'matrix' rather than a table and do it using a vector.
data <- df
library(reshape2)
long <- melt(data, id.vars="ID")
long
This would give you a matrix. Convert the var titles to something more meaningful.
library(stringr)
long$variable <- str_replace(long$variable, "var1", "Gut Instinct")
long$variable <- str_replace(long$variable, "var2", "Data")
long$variable <- str_replace(long$variable, "var3", "Science")
long$variable <- str_replace(long$variable, "var4", "Philosophy")
And now you can decide what to do based on each result
long$var <- ifelse(long$value==1, long$variable, NA)
and convert it back to something like the original if you want it that way
reshape(data=long, timevar="ID",idvar=c("var", "variable"), v.names = "value", direction="wide")
HTH