Transform skewed variables (aiming at they conform to a normal distribution) in .omv-files for the statistical spreadsheet 'jamovi' (https://www.jamovi.org)
Source:R/transform_vars_omv.R
transform_vars_omv.RdTransform skewed variables (aiming at they conform to a normal distribution) in .omv-files for the statistical spreadsheet 'jamovi' (https://www.jamovi.org)
Usage
transform_vars_omv(
dtaInp = NULL,
fleOut = "",
varXfm = NULL,
psvAnl = FALSE,
usePkg = c("foreign", "haven"),
selSet = "",
...
)Arguments
- dtaInp
Either a data frame or the name of a data file to be read (including the path, if required; "FILENAME.ext"; default: NULL); files can be of any supported file type, see Details below.
- fleOut
Name of the data set / file to be written (including the path, if required; "FILE_OUT.omv"; default: ""); if empty, the resulting data frame is returned instead.
- varXfm
Named list variable where the name indicates which transformation is to be carried out and where each list entry points to one or more variables to be transformed using this transformation. See Details for more information.
- psvAnl
Whether analyses that are contained in the input file shall be transferred to the output file (TRUE / FALSE; default: FALSE)
- usePkg
Name of the package: "foreign" or "haven" that shall be used to read SPSS, Stata, and SAS files; "foreign" is the default (it is included in base R), but "haven" is newer and more comprehensive; you may have to install using
install.packages("haven", dep = TRUE).- selSet
Name of the object / data set that is to be selected from the workspace (only relevant when reading .RData-files which can contain several objects / data sets)
- ...
Additional arguments passed on to methods; see Details below
Value
a data frame (only returned if fleOut is empty) where the order of variables / columns of the input data
set is re-arranged
Details
varXfmhas to be a named list variable where the names can either indicate the type of transformation or the kind and degree of skewness that shall be corrected. For the type of transformation, the following names are valid:posSqr,negSqr,posLog,negLog,posInv,negInv; where the second part of the name indicates the transformation to be carried out:...Sqr- square root,...Log- logarithm to the basis 10,...Inv- inversion, i.e., 1 / original value), and where the first part of the name indicates whether the original value is used (pos...) or whether the original value is subtracted from the maximum value of that variable (neg...; a constant of 1 is added to the maximum value for...Logand...Invtransformations). For the degree and kind of skewness, the following names are valid:mdrPos,strPos,svrPos,mdrNeg,strNeg,svrNeg(degree: moderate, strong, severe; kind: positive or negative).The ellipsis-parameter (
...) can be used to submit arguments / parameters to the functions that are used for reading the data. By clicking on the respective function under “See also”, you can get a more detailed overview over which parameters each of those functions take.
See also
transform_vars_omv internally uses the following functions for reading and writing data files in different
formats: read_omv() and write_omv() for jamovi-files, utils::read.table() for
CSV / TSV files, load() for reading .RData-files, readRDS() for .rds-files, haven::read_sav() or
foreign::read.spss() for SPSS-files, haven::read_dta() or foreign::read.dta() for Stata-files,
haven::read_sas() for SAS-data-files, and haven::read_xpt() or foreign::read.xport() for SAS-transport-files.
Examples
# generate skewed variables
set.seed(335)
dtaInp <- data.frame(MP = rnorm(1000) * 1e-1 + rexp(1000, 2) * (1 - 1e-1),
MN = rnorm(1000) * 1e-1 - rexp(1000, 2) * (1 - 1e-1),
SP = rnorm(1000) * 1e-2 + rexp(1000, 2) * (1 - 1e-2),
SN = rnorm(1000) * 1e-2 - rexp(1000, 2) * (1 - 1e-2),
EP = rnorm(1000) * 1e-4 + rexp(1000, 2) * (1 - 1e-4),
EN = rnorm(1000) * 1e-4 - rexp(1000, 2) * (1 - 1e-4))
jmv::descriptives(data = dtaInp, skew = TRUE, sw = TRUE)
#>
#> DESCRIPTIVES
#>
#> Descriptives
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> MP MN SP SN EP EN
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> N 1000 1000 1000 1000 1000 1000
#> Missing 0 0 0 0 0 0
#> Mean 0.4480184 -0.4646020 0.4781537 -0.4923456 0.5047809 -0.4990873
#> Median 0.3380596 -0.3269513 0.3197633 -0.3401608 0.3404835 -0.3466678
#> Standard deviation 0.4304019 0.4796432 0.4789503 0.4825046 0.5134605 0.5080463
#> Minimum -0.2340510 -2.634444 -0.01486472 -3.474560 0.001293439 -4.066375
#> Maximum 3.068939 0.1981469 3.250690 0.01231214 3.872901 -3.689731e-5
#> Skewness 1.526837 -1.538163 1.730588 -1.869945 1.954079 -2.093286
#> Std. error skewness 0.07734382 0.07734382 0.07734382 0.07734382 0.07734382 0.07734382
#> Shapiro-Wilk W 0.8884226 0.8645966 0.8252225 0.8271758 0.8121985 0.8008598
#> Shapiro-Wilk p < .0000001 < .0000001 < .0000001 < .0000001 < .0000001 < .0000001
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
crrXfm <- list(posSqr = c("MP"), negSqr = c("MN"), posLog = c("MP", "SP"), negLog = c("SN"),
posInv = c("MP", "SP", "EP"), negInv = c("EN"))
dtaOut <- jmvReadWrite::transform_vars_omv(dtaInp = dtaInp, varXfm = crrXfm)
jmv::descriptives(data = dtaOut, skew = TRUE, sw = TRUE)
#>
#> DESCRIPTIVES
#>
#> Descriptives
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> MP MN SP SN EP EN MP_SQR MN_SQR MP_LOG SP_LOG SN_LOG MP_INV SP_INV EP_INV EN_INV
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> N 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000
#> Missing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> Mean 0.4480184 -0.4646020 0.4781537 -0.4923456 0.5047809 -0.4990873 0.7886966 0.7667946 0.2135253 0.1558936 0.1594012 0.6272808 0.7231910 0.7235345 0.7235556
#> Median 0.3380596 -0.3269513 0.3197633 -0.3401608 0.3404835 -0.3466678 0.7563799 0.7246364 0.1964831 0.1253601 0.1311286 0.6360876 0.7492730 0.7467201 0.7425942
#> Standard deviation 0.4304019 0.4796432 0.4789503 0.4825046 0.5134605 0.5080463 0.2451266 0.2735867 0.1005615 0.1201892 0.1197994 0.1348379 0.1761416 0.1832728 0.1792720
#> Minimum -0.2340510 -2.634444 -0.01486472 -3.474560 0.001293439 -4.066375 0.000000 0.000000 0.000000 0.000000 0.000000 0.2323965 0.2344361 0.2052711 0.1973812
#> Maximum 3.068939 0.1981469 3.250690 0.01231214 3.872901 -3.689731e-5 1.817413 1.683030 0.6337704 0.6299755 0.6519437 1.000000 1.000000 1.000000 1.000000
#> Skewness 1.526837 -1.538163 1.730588 -1.869945 1.954079 -2.093286 0.5789399 0.6582018 0.7445348 0.9976868 0.9711986 -0.1693792 -0.4780434 -0.4496755 -0.4746157
#> Std. error skewness 0.07734382 0.07734382 0.07734382 0.07734382 0.07734382 0.07734382 0.07734382 0.07734382 0.07734382 0.07734382 0.07734382 0.07734382 0.07734382 0.07734382 0.07734382
#> Shapiro-Wilk W 0.8884226 0.8645966 0.8252225 0.8271758 0.8121985 0.8008598 0.9795875 0.9697485 0.9640099 0.9125518 0.9233129 0.9934879 0.9567561 0.9590432 0.9606668
#> Shapiro-Wilk p < .0000001 < .0000001 < .0000001 < .0000001 < .0000001 < .0000001 < .0000001 < .0000001 < .0000001 < .0000001 < .0000001 0.0002339 < .0000001 < .0000001 < .0000001
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
crrXfm <- list(mdrPos = c("MP"), mdrNeg = c("MN"), strPos = c("SP"), strNeg = c("SN"),
svrPos = c("EP"), svrNeg = c("EN"))
dtaOut <- jmvReadWrite::transform_vars_omv(dtaInp = dtaInp, varXfm = crrXfm)
jmv::descriptives(data = dtaOut, skew = TRUE, sw = TRUE)
#>
#> DESCRIPTIVES
#>
#> Descriptives
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> MP MN SP SN EP EN MP_SQR MN_SQR SP_LOG SN_LOG EP_INV EN_INV
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> N 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000
#> Missing 0 0 0 0 0 0 0 0 0 0 0 0
#> Mean 0.4480184 -0.4646020 0.4781537 -0.4923456 0.5047809 -0.4990873 0.7886966 0.7667946 0.1558936 0.1594012 0.7235345 0.7235556
#> Median 0.3380596 -0.3269513 0.3197633 -0.3401608 0.3404835 -0.3466678 0.7563799 0.7246364 0.1253601 0.1311286 0.7467201 0.7425942
#> Standard deviation 0.4304019 0.4796432 0.4789503 0.4825046 0.5134605 0.5080463 0.2451266 0.2735867 0.1201892 0.1197994 0.1832728 0.1792720
#> Minimum -0.2340510 -2.634444 -0.01486472 -3.474560 0.001293439 -4.066375 0.000000 0.000000 0.000000 0.000000 0.2052711 0.1973812
#> Maximum 3.068939 0.1981469 3.250690 0.01231214 3.872901 -3.689731e-5 1.817413 1.683030 0.6299755 0.6519437 1.000000 1.000000
#> Skewness 1.526837 -1.538163 1.730588 -1.869945 1.954079 -2.093286 0.5789399 0.6582018 0.9976868 0.9711986 -0.4496755 -0.4746157
#> Std. error skewness 0.07734382 0.07734382 0.07734382 0.07734382 0.07734382 0.07734382 0.07734382 0.07734382 0.07734382 0.07734382 0.07734382 0.07734382
#> Shapiro-Wilk W 0.8884226 0.8645966 0.8252225 0.8271758 0.8121985 0.8008598 0.9795875 0.9697485 0.9125518 0.9233129 0.9590432 0.9606668
#> Shapiro-Wilk p < .0000001 < .0000001 < .0000001 < .0000001 < .0000001 < .0000001 < .0000001 < .0000001 < .0000001 < .0000001 < .0000001 < .0000001
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>