This function is a wrapper around gscale() that is configured
to do a conventional standardization of continuous variables,
mean-centering and dividing by one standard deviation.
Usage
standardize(
data = NULL,
vars = NULL,
binary.inputs = "center",
binary.factors = FALSE,
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,xshould be a vector of variable names. Ifxis a numeric vector, this argument is ignored.- vars
If
datais 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;0/1keeps original scale;-0.5/0.5rescales 0 as -0.5 and 1 as 0.5;centersubtracts the mean; andfullsubtracts the mean and divides by 2 sd.- binary.factors
Coerce two-level factors to numeric and apply scaling functions to them? Default is FALSE.
- 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.
Details
Some more information can be found in the documentation for
gscale()
See also
standardization, scaling, and centering tools
center(),
center_mod(),
gscale(),
scale_mod()
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