I recently encountered a new problem in R which I did not see before. I have a set of data with a dependent variable Accuracy which has only two values, "0" and "1". Before, I use data$Accuracy=as.numeric(data$Accuracy) to turn these two levels to numbers and it works.
This time, however, when I did the same thing. "0"s turned to "1"s and "1"s turned to "2"s. Is this due to the new changes made in R? How do I work around this issue?
Thanks!!
It could be that the columns are factor class and when we use as.numeric, we get the integer storage mode values (in R, indexing starts from 1). In that case, we can convert to character and then to numeric
data$Accuracy <- as.numeric(as.character(data$Accuracy))
If it is a factor the manual recommends
as.numeric(levels(data$Accuracy))[data$Accuracy]
to transform it to approximately its original numeric values.
I guess there could be a problem with the dataframe definition or reading from a file. If original data where only 0 and 1 data$Accuracy should be class integer. But any no numeric character in just one row will create a factor column. As example:
> zz<-data.frame(c(0, 0, 1, 1))
> zz
c.0..0..1..1.
1 0
2 0
3 1
4 1
> zz<-data.frame(c(0, 0, 1, 1, "")) # an empty space
> zz
c.0..0..1..1.....
1 0
2 0
3 1
4 1
5
> class(zz$c.0..0..1..1.....)
[1] "factor"
> zz<-data.frame(c(0, 0, 1, 1, NA)) # empty numeric data
> zz
c.0..0..1..1..NA.
1 0
2 0
3 1
4 1
5 NA
> class(zz$c.0..0..1..1..NA.)
[1] "numeric"
dputand show the expected output for the same. Please read the info about how to ask a good question and how to give a reproducible example.