Performs a grand-mean centering of data.

## Usage

center(x, ...)

centre(x, ...)

# S3 method for numeric
center(
x,
robust = FALSE,
weights = NULL,
reference = NULL,
center = NULL,
verbose = TRUE,
...
)

# S3 method for data.frame
center(
x,
select = NULL,
exclude = NULL,
robust = FALSE,
weights = NULL,
reference = NULL,
center = NULL,
force = FALSE,
remove_na = c("none", "selected", "all"),
append = FALSE,
ignore_case = FALSE,
verbose = TRUE,
regex = FALSE,
...
)

## Arguments

x

A (grouped) data frame, a (numeric or character) vector or a factor.

...

Currently not used.

robust

Logical, if TRUE, centering is done by subtracting the median from the variables. If FALSE, variables are centered by subtracting the mean.

weights

Can be NULL (for no weighting), or:

• For data frames: a numeric vector of weights, or a character of the name of a column in the data.frame that contains the weights.

• For numeric vectors: a numeric vector of weights.

reference

A data frame or variable from which the centrality and deviation will be computed instead of from the input variable. Useful for standardizing a subset or new data according to another data frame.

center

Numeric value, which can be used as alternative to reference to define a reference centrality. If center is of length 1, it will be recycled to match the length of selected variables for centering. Else, center must be of same length as the number of selected variables. Values in center will be matched to selected variables in the provided order, unless a named vector is given. In this case, names are matched against the names of the selected variables.

verbose

Toggle warnings and messages.

select

Variables that will be included when performing the required tasks. Can be either

• a variable specified as a literal variable name (e.g., column_name),

• a string with the variable name (e.g., "column_name"), or a character vector of variable names (e.g., c("col1", "col2", "col3")),

• a formula with variable names (e.g., ~column_1 + column_2),

• a vector of positive integers, giving the positions counting from the left (e.g. 1 or c(1, 3, 5)),

• a vector of negative integers, giving the positions counting from the right (e.g., -1 or -1:-3),

• one of the following select-helpers: starts_with(), ends_with(), contains(), a range using : or regex(""). starts_with(), ends_with(), and contains() accept several patterns, e.g starts_with("Sep", "Petal").

• or a function testing for logical conditions, e.g. is.numeric() (or is.numeric), or any user-defined function that selects the variables for which the function returns TRUE (like: foo <- function(x) mean(x) > 3),

• ranges specified via literal variable names, select-helpers (except regex()) and (user-defined) functions can be negated, i.e. return non-matching elements, when prefixed with a -, e.g. -ends_with(""), -is.numeric or -(Sepal.Width:Petal.Length). Note: Negation means that matches are excluded, and thus, the exclude argument can be used alternatively. For instance, select=-ends_with("Length") (with -) is equivalent to exclude=ends_with("Length") (no -). In case negation should not work as expected, use the exclude argument instead.

If NULL, selects all columns. Patterns that found no matches are silently ignored, e.g. find_columns(iris, select = c("Species", "Test")) will just return "Species".

exclude

See select, however, column names matched by the pattern from exclude will be excluded instead of selected. If NULL (the default), excludes no columns.

force

Logical, if TRUE, forces centering of factors as well. Factors are converted to numerical values, with the lowest level being the value 1 (unless the factor has numeric levels, which are converted to the corresponding numeric value).

remove_na

How should missing values (NA) be treated: if "none" (default): each column's standardization is done separately, ignoring NAs. Else, rows with NA in the columns selected with select / exclude ("selected") or in all columns ("all") are dropped before standardization, and the resulting data frame does not include these cases.

append

Logical or string. If TRUE, centered variables get new column names (with the suffix "_c") and are appended (column bind) to x, thus returning both the original and the centered variables. If FALSE, original variables in x will be overwritten by their centered versions. If a character value, centered variables are appended with new column names (using the defined suffix) to the original data frame.

ignore_case

Logical, if TRUE and when one of the select-helpers or a regular expression is used in select, ignores lower/upper case in the search pattern when matching against variable names.

regex

Logical, if TRUE, the search pattern from select will be treated as regular expression. When regex = TRUE, select must be a character string (or a variable containing a character string) and is not allowed to be one of the supported select-helpers or a character vector of length > 1. regex = TRUE is comparable to using one of the two select-helpers, select = contains("") or select = regex(""), however, since the select-helpers may not work when called from inside other functions (see 'Details'), this argument may be used as workaround.

## Value

The centered variables.

## Note

Difference between centering and standardizing: Standardized variables are computed by subtracting the mean of the variable and then dividing it by the standard deviation, while centering variables involves only the subtraction.

## Selection of variables - the select argument

For most functions that have a select argument (including this function), the complete input data frame is returned, even when select only selects a range of variables. That is, the function is only applied to those variables that have a match in select, while all other variables remain unchanged. In other words: for this function, select will not omit any non-included variables, so that the returned data frame will include all variables from the input data frame.

If centering within-clusters (instead of grand-mean centering) is required, see demean(). For standardizing, see standardize(), and makepredictcall.dw_transformer() for use in model formulas.

## Examples

data(iris)

# entire data frame or a vector
head(iris$Sepal.Width) #> [1] 3.5 3.0 3.2 3.1 3.6 3.9 head(center(iris$Sepal.Width))
#> [1]  0.44266667 -0.05733333  0.14266667  0.04266667  0.54266667  0.84266667
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1   -0.7433333  0.44266667       -2.358  -0.9993333  setosa
#> 2   -0.9433333 -0.05733333       -2.358  -0.9993333  setosa
#> 3   -1.1433333  0.14266667       -2.458  -0.9993333  setosa
#> 4   -1.2433333  0.04266667       -2.258  -0.9993333  setosa
#> 5   -0.8433333  0.54266667       -2.358  -0.9993333  setosa
#> 6   -0.4433333  0.84266667       -2.058  -0.7993333  setosa
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1   -0.7433333  0.44266667       -2.358  -0.9993333      -1
#> 2   -0.9433333 -0.05733333       -2.358  -0.9993333      -1
#> 3   -1.1433333  0.14266667       -2.458  -0.9993333      -1
#> 4   -1.2433333  0.04266667       -2.258  -0.9993333      -1
#> 5   -0.8433333  0.54266667       -2.358  -0.9993333      -1
#> 6   -0.4433333  0.84266667       -2.058  -0.7993333      -1

# only the selected columns from a data frame
center(anscombe, select = c("x1", "x3"))
#>    x1 x2 x3 x4    y1   y2    y3    y4
#> 1   1 10  1  8  8.04 9.14  7.46  6.58
#> 2  -1  8 -1  8  6.95 8.14  6.77  5.76
#> 3   4 13  4  8  7.58 8.74 12.74  7.71
#> 4   0  9  0  8  8.81 8.77  7.11  8.84
#> 5   2 11  2  8  8.33 9.26  7.81  8.47
#> 6   5 14  5  8  9.96 8.10  8.84  7.04
#> 7  -3  6 -3  8  7.24 6.13  6.08  5.25
#> 8  -5  4 -5 19  4.26 3.10  5.39 12.50
#> 9   3 12  3  8 10.84 9.13  8.15  5.56
#> 10 -2  7 -2  8  4.82 7.26  6.42  7.91
#> 11 -4  5 -4  8  5.68 4.74  5.73  6.89
center(anscombe, exclude = c("x1", "x3"))
#>    x1 x2 x3 x4          y1         y2    y3         y4
#> 1  10  1 10 -1  0.53909091  1.6390909 -0.04 -0.9209091
#> 2   8 -1  8 -1 -0.55090909  0.6390909 -0.73 -1.7409091
#> 3  13  4 13 -1  0.07909091  1.2390909  5.24  0.2090909
#> 4   9  0  9 -1  1.30909091  1.2690909 -0.39  1.3390909
#> 5  11  2 11 -1  0.82909091  1.7590909  0.31  0.9690909
#> 6  14  5 14 -1  2.45909091  0.5990909  1.34 -0.4609091
#> 7   6 -3  6 -1 -0.26090909 -1.3709091 -1.42 -2.2509091
#> 8   4 -5  4 10 -3.24090909 -4.4009091 -2.11  4.9990909
#> 9  12  3 12 -1  3.33909091  1.6290909  0.65 -1.9409091
#> 10  7 -2  7 -1 -2.68090909 -0.2409091 -1.08  0.4090909
#> 11  5 -4  5 -1 -1.82090909 -2.7609091 -1.77 -0.6109091

# centering with reference center and scale
d <- data.frame(
a = c(-2, -1, 0, 1, 2),
b = c(3, 4, 5, 6, 7)
)

# default centering at mean
center(d)
#>    a  b
#> 1 -2 -2
#> 2 -1 -1
#> 3  0  0
#> 4  1  1
#> 5  2  2

# centering, using 0 as mean
center(d, center = 0)
#>    a b
#> 1 -2 3
#> 2 -1 4
#> 3  0 5
#> 4  1 6
#> 5  2 7

# centering, using -5 as mean
center(d, center = -5)
#>   a  b
#> 1 3  8
#> 2 4  9
#> 3 5 10
#> 4 6 11
#> 5 7 12