Conditionally changing only some cells in a data frame - ifelse() failure? - r

Question

I am trying to conditionally change some items when cleaning survey data.
I've got two questions, Question X and Question Y. If they respond 1 or 2 for Question X, they go on to answer Question Y. If they answer 3 or 4 for Question X, they skip Question Y.
If they answer X with 1 or 2 and then skip Y, I want to record their 'NULL!' entries as NA - they just didn't answer the question when they should have.
If they answer X with 3 or 4 and then skip Y, I want to record their 'NULL!' entries as 0 - they weren't supposed to answer the question, so they didn't.
Here's a reproducible dataset I made:
set.seed(1)
df <- data.frame(
X = as.factor(sample(c("1.00", "2.00", "3.00", "4.00"), 10, replace = TRUE)),
Y = as.factor(sample(c("1.00", "2.00", "#NULL!"), 10, replace = TRUE))
)
df
I'm trying to replace the aforementioned 'NULL!' fields with either NA or 0 respectively. I've been trying it with ifelse() and have had little luck - it appears to return anything that is 1.00 or 2.00 as NA and 3.00 or 4.00 as 0. Is there a better way to do this? What am I doing wrong?
levels(df$Y) <- c(levels(df$Y), 0)
df$Y <- ifelse(df$X == '3.00'| df$X == '4.00', df$Y[df$y == 'NULL!'] <- 0, df$Y[df$Y == '#NULL!'] <- NA)
df
Thank you for your help!

Answer 1

You're doing a couple of things the hard way. First, using factors constrains one to only use levels that exist in the a particular factor, which may not be what you want. Second, you have levels of "#NULL!" but are attempting (unsuccessfully) to test for a level of "NULL!". I'm guessing you wanted them to be the same level. Third; You are attempting to use "<-" inside the second and third arguments of ifelse. That will not succeed in manner you intended. The LHS of such an expression is not evaluated by ifelse.
You can instead use nested ifelse:
df$Y <- ifelse( (df$X == '3.00'| df$X == '4.00') & df$Y == "#NULL!", 0,
ifelse( df$Y == "#NULL!", NA, df$Y) ) # only mess with "Nulls"
df
X Y
1 2.00 1.00
2 2.00 1.00
3 3.00 0
4 4.00 2.00
5 1.00 <NA>
6 4.00 2.00
7 4.00 0
8 3.00 0
9 3.00 2.00
10 1.00 <NA>
To prevent the missing levels problem which you handled by adding a "0" level, I instead made my dataframe so it contained character vectors:
set.seed(1)
df <- data.frame(X = sample(c("1.00", "2.00", "3.00", "4.00"), 10, replace== TRUE),
Y = sample(c("1.00", "2.00", "#NULL!"), 10, replace = TRUE),
stringsAsFactors=FALSE)
Earlier tidyverse code:
library(tidyverse)
df %>% mutate(Y = case_when(
X == "3.00" ~ "0",
X == "4.00" ~ "0",
TRUE ~ as.character(Y)))

Answer 2

How about this?
set.seed(1)
df <- data.frame(
X = as.factor(sample(c("1.00", "2.00", "3.00", "4.00"), 10, replace = TRUE)),
Y = as.factor(sample(c("1.00", "2.00", "#NULL!"), 10, replace = TRUE))
)
df$X <- as.character(df$X)
df$Y <- as.character(df$Y)
df$Y <- ifelse(df$X=="1.00" | df$X=="2.00" & df$Y == "#NULL!", NA, df$Y)
df$Y <- ifelse(df$X=="3.00" | df$X=="4.00", "0.00", df$Y)
df
X Y
1 2.00 1.00
2 2.00 1.00
3 3.00 0.00
4 4.00 0.00
5 1.00 <NA>
6 4.00 0.00
7 4.00 0.00
8 3.00 0.00
9 3.00 0.00
10 1.00 <NA>