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Background
I was looking at some breast cancer data recently, and was analyzing the ER (estrogen receptor) status variable. It turned out that there were three possible outcomes in the data: Positive, Negative and Indeterminate. I had imported this data as a factor, and wanted to convert the Indeterminate level to a missing value, i.e. NA.
My usual method for numeric variables created a rather singular result:
x <- as.factor(c('Positive','Negative','Indeterminate'))
x1 <- ifelse(x=='Indeterminate', NA, x)
str(x1)
## int [1:3] 3 2 NA
This process converted it to an integer!! Not the end of the world, but not ideal by any means.
Further investigation revealed two other tidyverse strategies.
dplyr::na_if
This method changes the values to NA, but keeps the original level in the factor’s levels
x2 <- dplyr::na_if(x, 'Indeterminate') str(x2) ## Factor w/ 3 levels "Indeterminate",..: 3 2 NA x2 ## [1] Positive Negative <NA> ## Levels: Indeterminate Negative Positive
dplyr::recode
This method drops the level that I’m deeming to be missing from the factor
x3 <- dplyr::recode(x, Indeterminate = NA_character_) str(x3) ## Factor w/ 2 levels "Negative","Positive": 2 1 NA x3 ## [1] Positive Negative <NA> ## Levels: Negative Positive
This method can also work more generally to change all values not listed to missing values.
x4 <- dplyr::recode(x, Positive='Positive', Negative='Negative',
.default=NA_character_)
x4
## [1] Positive Negative <NA>
## Levels: Negative Positive
Other strategies are welcome in the comments.
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