R doesn’t have an inbuilt function to calculate the z-score in R. To get the z-score in R, calculate the mean and standard deviation using the mean() and sd() functions in R and use mean and std. deviation values in the Z-score formula to find the z-score in R.
In this article, we will how to calculate the z score in R using the z score formula. We will calculate the mean and standard deviation for the vector using the r function and later calculate the z score.
What is a Z-Score?
Z-score is the distance of the raw score value from the mean in terms of standard deviation. Raw scores above the mean have a positive Z-score value, while a raw score below the mean has a negative z-score value.
Steps to Calculate Z-score in R
- Create the vector for the given data
- Calculate the mean of the vector using the mean() function.
- Calculate the standard deviation of the vector using the sd() function.
- Calculate the z-score using the formula. z-score = data- mean(data)) / sd(data)
- The resultant vector will give the required z-score values.
Cool Tip: Read more on how to calculate the z score on TI-84 plus!
Let’s understand how to find a z-score using R for a given vector using the step-by-step guide.
Calculate Z-score in R for the given vector
Let’s consider an example to get a z-score in R using the given vector data set.
Prepare vector in r to find the mean and standard deviation
Create a vector for given data points and assign it to the data variable.
# create the vector for given data
data <- c(19, 11, 12, 14, 15, 18, 19)
Use the mean function to calculate the mean for a given vector.
# Calculate mean
mean(data)
Use the standard deviation (sd) function to calculate the standard deviation for a given vector.
# Calculate standard deviation
sd(data)
To calculate the z-score in r put the calculated mean and standard deviation in the z score formula, it will find the z-score and assign it to the zscore variable.
# calculate z-score
zscore <- (data - mean(data)) / sd(data)
Use below R code to calculate the z-score.
Z-Score in R code
# create the vector for given data
data <- c(19, 11, 12, 14, 15, 18, 19)
# Calculate mean
mean(data)
# Calculate standard deviation
sd(data)
# calculate z-score
zscore <- (data - mean(data)) / sd(data)
zscore
#Results
mean(data)
[1] 15.42857
>
> sd(data)
[1] 3.309438
> zscore
[1] 1.0791646 -1.3381641 -1.0359980 -0.4316658
[5] -0.1294998 0.7769985 1.0791646
Interpretation of z-scores in R
- The z-score for data value 19 is 1.0791646. This means the raw score of 19 is 1.0791646 standard deviation above the mean of 15.42857.
- The z-score for data value 11 is -1.3381641. This means the raw score of 11 is -1.3381641 standard deviation below the mean of 15.42857.
- The z-score for data value 12 is -1.0359980. This means the raw score of 12 is -1.0359980 standard deviation below the mean of 15.42857.
- The z-score for data value 14 is -0.4316658. This means the raw score of 14 is -0.4316658 standard deviation below the mean of 15.42857.
Similarly, we can interpret the other z-scores.
Cool Tip: Read more on how to Calculate Z Score in Excel!
Plot the Calculated Z-score for the given vector in R
# create the vector for given data
data <- c(19, 11, 12, 14, 15, 18, 19)
# Calculate mean
mean(data)
# Calculate standard deviation
sd(data)
# calculate z-score
zscore <- (data - mean(data)) / sd(data)
zscore
# plot z-score
plot(zscore, type="o", col="green")
The resultant plot for the calculated z score for the given data set is shown below.

Conclusion
I hope the above article helps you to calculate the z score using r. We have learned how to prepare a vector for a given dataset and find the mean and standard deviation.
Using the z-score formula, we can find the z-score in r and plot the z-score on a chart.
You can read more about
How to find an area to the left for the given z
Find the area between the two z-scores.
Find the z-score for having an area of 0.09
You can find more topics about Z-Score and how to calculate z score given the area, read the z score table on the ZScoreGeek home page.