Table of Contents
Data centering is a statistical technique used to adjust the mean of a dataset to 0, making it more suitable for analysis. In R, this can be achieved by subtracting the mean from each data point using the “scale” function. For example, if we have a vector of values [2, 4, 6, 8], the centered data would be [-3, -1, 1, 3] as the mean of the original data is 5 and subtracting 5 from each value gives us the centered data. This can also be applied to data frames and matrices in R by specifying the “scale” argument in relevant functions.
Center Data in R (With Examples)
To center a dataset means to subtract the mean value from each individual observation in the dataset.
For example, suppose we have the following dataset:
It turns out that the mean value is 14. Thus, to center this dataset we would subtract 14 from each individual observation:
Note that the mean value of the centered dataset is zero.
This tutorial provides several examples of how to center data in R.
Example 1: Center the Values of a Vector
The following code shows how to use the function from base R to center the values in a vector:
#create vector
data <- c(4, 6, 9, 13, 14, 17, 18, 19, 19, 21)
#subtract the mean value from each observation in the vector
scale(data, scale=FALSE)
[,1]
[1,] -10
[2,] -8
[3,] -5
[4,] -1
[5,] 0
[6,] 3
[7,] 4
[8,] 5
[9,] 5
[10,] 7
attr(,"scaled:center")
[1] 14
The resulting values are the centered values of the dataset. The scale() function also tells us that the mean value of the dataset is 14.
Note that the scale() function, by default, subtracts the mean from each individual observation and then divides by the standard deviation.
By specifying scale=FALSE, we tell R not to divide by the standard deviation.
Example 2: Center the Columns in a Data Frame
The following code shows how to use the function and the function from base R to center the values of each column of a data frame:
#create data frame
df <- data.frame(x = c(1, 4, 5, 6, 6, 8, 9),
y = c(7, 7, 8, 8, 8, 9, 12),
z = c(3, 3, 4, 4, 6, 7, 7))
#center each column in the data frame
df_new <- sapply(df, function(x) scale(x, scale=FALSE))
#display data frame
df_new
x y z
[1,] -4.5714286 -1.4285714 -1.8571429
[2,] -1.5714286 -1.4285714 -1.8571429
[3,] -0.5714286 -0.4285714 -0.8571429
[4,] 0.4285714 -0.4285714 -0.8571429
[5,] 0.4285714 -0.4285714 1.1428571
[6,] 2.4285714 0.5714286 2.1428571
[7,] 3.4285714 3.5714286 2.1428571
We can verify that the mean of each column in the new data frame is equal to zero by using the colMeans() function:
colMeans(df_new)
x y z
2.537653e-16 -2.537653e-16 3.806479e-16
The values are shown in scientific notation, but each value is essentially equal to zero.