# How to compute the z-score with R

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Sometimes it is necessary to standardize the data due to its distribution or simply because we need to have a fair comparison of a value (e.g, body weight) with a reference population (e.g., school, city, state, country). The calculation of z-score is simple, but less information we can find on the web for its purpose and mean.

In this post, I will explain what the z-score means, how it is calculated with an example, and how to create a new z-score variable in R. As usual, I will use the data from National Health and Nutrition Examination Survey (NHANES).

### What is Z-score

In short, the z-score is a measure that shows how much away (below or above) of the mean is a specific value (individual) in a given dataset. In the example below, I am going to measure the z value of body mass index (BMI) in a dataset from NHANES.

### Get the data and packages

Loading packages and creating the dataset:

library(tidyverse) library(RNHANES) dat = nhanes_load_data("DEMO_E", "2007-2008") %>% select(SEQN, RIAGENDR) %>% left_join(nhanes_load_data("BMX_E", "2007-2008"), by="SEQN") %>% select(SEQN, RIAGENDR, BMXBMI) %>% filter(RIAGENDR == "1", !is.na(BMXBMI)) %>% transmute(SEQN, Gender = RIAGENDR, BMI = BMXBMI) dat SEQN Gender BMI 1 41475 2 58.04 2 41476 2 15.18 3 41477 1 30.05 4 41479 1 27.56 5 41480 1 17.93 6 41481 1 23.34 7 41482 1 33.64 8 41483 1 44.06 9 41485 2 25.99 10 41486 2 31.21

### How to calculate the z-score for BMI

To calculate the z-score of BMI, we need to have the average of BMI, the standard deviation of BMI.

Mean of BMI:

mean(dat$BMI) ## [1] 25.70571

Standard deviation of BMI:

sd(dat$BMI) ## [1] 7.608628

Suppose we want to calculate the z-score of the first and third participant in the dataset `dat`. The calculation will be: I take the actual BMI (58.04), substract the mean (25.70571), and divide the difference by the standard deviation (7.608628). The result is 4.249687. This indicate that z score is 4.249687 standard deviations above the average of population.

(58.04 - 25.70571)/7.608628 = 4.249687

### How to calculate the z-score in R

dat %>% mutate(zscore = (BMI - mean(BMI))/sd(BMI)) SEQN Gender BMI zscore 1 41475 2 58.04 4.249687006 2 41476 2 15.18 -1.383391690 3 41477 1 30.05 0.570968558 4 41479 1 27.56 0.243708503 5 41480 1 17.93 -1.021959902 6 41481 1 23.34 -0.310925004 7 41482 1 33.64 1.042801328 8 41483 1 44.06 2.412299228 9 41485 2 25.99 0.037363810 10 41486 2 31.21 0.723427057

Now we see the z-score for each individual, and the values corresponded to what we calculated above.

If you calculate the mean and standard deviation of the `zscore`

above, you will find that mean is 0, and standard deviation is 1.

Feel free to comment!

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