Change values in data.frame conditionally - r

Question

I am trying to check the value of one variable and if it meets a certain condition the new variable gets set to 1 or else it gets set to zero.
I am having difficulty with this in R.
This simple code does not work:
attach(data)
if (Drug = 1) {
Drug_factor <- 0
} else {
if (Drug = 2) {
Drug_factor <- 1
} else Drug_factor<- 0
I do not understand why this will not work.
Why does R use such complicated conventions for doing basic stuff ?

Answer 1

You can either use ifelse
Data$Drug_factor <- with(Data, ifelse(Drug==1, 0, 1))
Or use the factor approach
Data$Drug_factor <- with(Data, as.numeric(as.character(factor(Drug,
levels=1:2, labels=0:1))))
Or
Data$Drug_factor <- c(0,1)[(Data$Drug==2)+1]
Or even shorter assuming that the 'Drug' is 'numeric'
Data$Drug_factor <- c(0,1)[Data$Drug]
All these cases, assume that there are only two unique elements in 'Drug'.
Suppose if you have more than 2 unique elements in 'Drug', from the code, it seems to me that only when 'Drug==2', the value should be returned as 1. Creating another value in 'Drug'
Data$Drug[4] <- 3
In this case, we can change the ifelse condition such that when 'Drug' is 2 return 1 and for all others to return 0.
Data$Drug_factor <- with(Data, ifelse(Drug==2, 1, 0))
A similar option by indexing is,
Data$Drug_factor <- c(0,1)[(Data$Drug==2)+1]
data
set.seed(24)
Data <- data.frame(Drug= sample(1:2, 10, replace=TRUE), val=rnorm(10))

Answer 2

There are two different kinds of problems of this kind.
In the simple case, you want to change a small number of values to some other value. For this purpose, I find that using mapvalues() from plyr is a good solution. For example:
#lets pretend we have loaded some data where missing data is coded as 99
set.seed(1) #reproducible results
test_data = sample(c(0:5, 99), size = 1000, replace = T)
#table of our dta
table(test_data)
Output:
test_data
0 1 2 3 4 5 99
138 145 150 150 127 142 148
Recode:
#recode 99 to NA
library(plyr)
test_data_noNA = mapvalues(test_data, 99, NA)
table(test_data_noNA, exclude = NULL) #also count NAs
Output:
test_data_noNA
0 1 2 3 4 5 <NA>
138 145 150 150 127 142 148
In the other case, you want to conditionally change values to some other value, but there is a large/indefinite/infinite number of values it could be.
Example:
#continuous data
set.seed(1) #reproducible results
test_data = rnorm(1000) #normally distributed data
hist(test_data) #plot with histogram
However, let's say we want to deal with outliers, which we define at beyond 2SD from the mean. However, we don't just want to exclude them, so instead we will recode them.
#change values above 2 to 2
test_data[test_data > 2] = 2
#change valuesbelow -2 to -2
test_data[test_data < -2] = -2
hist(test_data) #plot with histogram