Center Variables (Mean Centering)

Centers variables by subtracting the mean. When used on a grouped data frame (via dplyr::group_by()), this becomes group-mean centering — the R equivalent of separate centering within each group.

Usage

center(data, ..., weights = NULL, suffix = NULL, na.rm = TRUE)

Arguments

data: A data frame or numeric vector.
...: Variables to center (tidyselect). Only used when data is a data frame.
weights: Optional survey weights (unquoted column name or numeric vector). When provided, the weighted mean is subtracted instead of the unweighted mean.
suffix: A character string appended to column names (e.g., "_c"). If NULL (default), original columns are overwritten.
na.rm: Remove missing values before computing the mean? Default: TRUE.

Value

If data is a vector, a centered numeric vector. If data is a data frame, the modified data frame (invisibly).

Details

Grand-Mean vs. Group-Mean Centering

Grand-mean centering (ungrouped): Subtracts the overall mean. A centered value of 0 means the respondent is at the sample average.
Group-mean centering (grouped): Subtracts the group mean. Useful in multilevel models to separate within-group and between-group effects. This replaces sjmisc's separate de_mean() function.

Weighted Centering

When weights is provided, the weighted mean is used for centering. This accounts for survey design in the centering computation.

Examples

library(dplyr)
data(survey_data)

# Grand-mean centering
data <- center(survey_data, income, age, suffix = "_c")

# Weighted centering
data <- center(survey_data, income, age,
               weights = sampling_weight, suffix = "_c")

# Group-mean centering (replaces sjmisc::de_mean)
data <- survey_data %>%
  group_by(region) %>%
  center(income, age, suffix = "_gmc")