Z-score calculation with R

   Standard score or z-score is a measure of standard deviations that how much below or above the element is located from the mean value.  Z-scores are usually located around -3 to 3 sigma range (based on the variance of data, it might be different). Z-scores mean value is very close to 0, and both variance and standard deviation are equal to 1.
Z-score can be calculated with below formula,

           z = ( x - μ ) / σ  

where,
    x - x vector (elements of x vector)
    μ - mean value of x vector
    σ - standard deviation of x vector

The normal distribution curve can easily explain a z-score. Z-score values are located around the curve below. Zero is a mean center value. The highest and lowest values can be found in the right and left most parts of the curve.

Let's generate some sample data and get its z-scores.

set.seed(123)

x = sample(1:50, 100, replace=T)

Getting z-scores with a formula.

m = mean(x)
s = sd(x)

zs = (x - m)/s

summary(x)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1.00   16.00   25.50   26.21   36.25   50.00

summary(zs)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-1.91447 -0.77536 -0.05392  0.00000  0.76245  1.80663 

As summary shows, the x vector was centered into 0 mean value. In 'zs', the value of x vector's 1 is equal to -1.91, and 50 is equal to 1.8 sigma value.

In R, we can use scale() command to get z-scores.

scale(x)
              [,1]
  [1,]  0.28781591
  [2,] -0.69941543
  [3,] -0.09188846
  ........
 [98,]  0.51563852
 [99,] -1.38288328
[100,]  0.21187503
attr(,"scaled:center")
[1] 26.21
attr(,"scaled:scale")
[1] 13.16814

We need the first part of a scale function result.

sc_zs = scale(x)[,1]
summary(sc_zs)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-1.91447 -0.77536 -0.05392  0.00000  0.76245  1.80663 

A summary shows that the result is the same as the one that was taken with a formula.
The scale function is often used to clean up data to remove the mean value from the series data.