Make categorical variable out of a continuous one is sometimes needed in biostatistics, especially if you encounter issues with distribution. Here is an example of some code where I do that using an NHANES dataset as demonstration (which you can read about here).
If you watch the video, you’ll see that I’m working with a blood pressure dataset called BLPS_a make categorical variables. The continuous variable I’m using is a systolic blood pressure (SBP) reading named BPXOSY1. Before I make variable, I want to view the distribution, so I use the hist command to get a histogram, and the quantile command to get quartiles.
hist(BLPS_a$BPXOSY1) quantile(BLPS_a$BPXOSY1, na.rm = TRUE)
Here is what the output looks like.
You can use this output to determine data-driven cutpoints to turn your continuous variable to make categorical variable. However, we want to make empirical cutpoints. These are based on the American Heart Association classifications for SBP.
- These are the classifications we are using.
- It can be helpful to use empirical classifications even if they are not data-driven.
- That way, they can be compared to other data classified the same way.
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Free consultationYou will see in the code below how I executed those boundaries using the conditions in the code. I make the variable BPLSGrp through executing those criteria. Then I check it with a table command.
BLPS_a$BLPSGrp <- 9 BLPS_a$BLPSGrp[BLPS_a$BPXOSY1 < 120] <- 1 BLPS_a$BLPSGrp[BLPS_a$BPXOSY1 >= 120 & BLPS_a$BPXOSY1 < 130] <- 2 BLPS_a$BLPSGrp[BLPS_a$BPXOSY1 >= 130] <- 3 table(BLPS_a$BPXOSY1, BLPS_a$BLPSGrp, useNA = c("always"))
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