This function is a wrapper around gscale() that is configured to mean-center variables without affecting the scaling of those variables.

center(data = NULL, vars = NULL, binary.inputs = "center",
  binary.factors = TRUE, weights = NULL)

Arguments

data

A data frame or survey design. Only needed if you would like to rescale multiple variables at once. If x = NULL, all columns will be rescaled. Otherwise, x should be a vector of variable names. If x is a numeric vector, this argument is ignored.

vars

If data is a data.frame or similar, you can scale only select columns by providing a vector column names to this argument.

binary.inputs

Options for binary variables. Default is center; 0/1 keeps original scale; -0.5/0.5 rescales 0 as -0.5 and 1 as 0.5; center subtracts the mean; and full subtracts the mean and divides by 2 sd.

binary.factors

Coerce two-level factors to numeric and apply scaling functions to them? Default is TRUE.

weights

A vector of weights equal in length to x. If iterating over a data frame, the weights will need to be equal in length to all the columns to avoid errors. You may need to remove missing values before using the weights.

Value

A transformed version of the data argument.

Details

Some more information can be found in the documentation for gscale()

See also

Other standardization, scaling, and centering tools: center_mod, gscale, scale_mod, standardize

Examples

# Standardize just the "qsec" variable in mtcars standardize(mtcars, vars = "qsec")
#> mpg cyl disp hp drat wt qsec vs am gear carb #> Mazda RX4 21.0 6 160.0 110 3.90 2.620 -0.77716515 0 1 4 4 #> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 -0.46378082 0 1 4 4 #> Datsun 710 22.8 4 108.0 93 3.85 2.320 0.42600682 1 1 4 1 #> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 0.89048716 1 0 3 1 #> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 -0.46378082 0 0 3 2 #> Valiant 18.1 6 225.0 105 2.76 3.460 1.32698675 1 0 3 1 #> Duster 360 14.3 8 360.0 245 3.21 3.570 -1.12412636 0 0 3 4 #> Merc 240D 24.4 4 146.7 62 3.69 3.190 1.20387148 1 0 4 2 #> Merc 230 22.8 4 140.8 95 3.92 3.150 2.82675459 1 0 4 2 #> Merc 280 19.2 6 167.6 123 3.92 3.440 0.25252621 1 0 4 4 #> Merc 280C 17.8 6 167.6 123 3.92 3.440 0.58829513 1 0 4 4 #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 -0.25112717 0 0 3 3 #> Merc 450SL 17.3 8 275.8 180 3.07 3.730 -0.13920420 0 0 3 3 #> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 0.08464175 0 0 3 3 #> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 0.07344945 0 0 3 4 #> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 -0.01608893 0 0 3 4 #> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 -0.23993487 0 0 3 4 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 0.90727560 1 1 4 1 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 0.37564148 1 1 4 2 #> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 1.14790999 1 1 4 1 #> Toyota Corona 21.5 4 120.1 97 3.70 2.465 1.20946763 1 0 3 1 #> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 -0.54772305 0 0 3 2 #> AMC Javelin 15.2 8 304.0 150 3.15 3.435 -0.30708866 0 0 3 2 #> Camaro Z28 13.3 8 350.0 245 3.73 3.840 -1.36476075 0 0 3 4 #> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 -0.44699237 0 0 3 2 #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 0.58829513 1 1 4 1 #> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 -0.64285758 0 1 5 2 #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 -0.53093460 1 1 5 2 #> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 -1.87401028 0 1 5 4 #> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 -1.31439542 0 1 5 6 #> Maserati Bora 15.0 8 301.0 335 3.54 3.570 -1.81804880 0 1 5 8 #> Volvo 142E 21.4 4 121.0 109 4.11 2.780 0.42041067 1 1 4 2