# Creating missing values in factors

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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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