jmvReadWrite (version 0.4.10)

long2wide_omv: Converts .omv-files for the statistical spreadsheet 'jamovi' (https://www.jamovi.org) from long to wide format

Description

Converts .omv-files for the statistical spreadsheet 'jamovi' (https://www.jamovi.org) from long to wide format

Usage

long2wide_omv(
  dtaInp = NULL,
  fleOut = "",
  varTgt = c(),
  varExc = c(),
  varID = "ID",
  varTme = c(),
  varSep = "_",
  varOrd = c("times", "vars"),
  varAgg = c("mean", "first"),
  varSrt = c(),
  usePkg = c("foreign", "haven"),
  selSet = "",
  ...
)

Value

a data frame (only returned if fleOut is empty) where the input data set is converted from long to wide format

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 file to be written (including the path, if required; "FILE_OUT.omv"; default: ""); if empty, the resulting data frame is returned instead

varTgt

Names of one or more variables to be transformed / reshaped (other variables are excluded, if empty(c()) all variables except varTme, varID and varExc are included; default: c())

varExc

Name of the variable(s) should be excluded from the transformation, typically this will be between-subject-variable(s) (default: c())

varID

Names of one or more variables that identify the same group / individual (default: c())

varTme

Name of the variable(s) that differentiates multiple records from the same group / individual (default: c())

varSep

Separator character when concatenating the fixed and time-varying part of the variable name ("VAR1_1", "VAR1_2"; default: "_")

varOrd

How variables / columns are organized: for "times" (default) the steps of the time varying variable are adjacent, for "vars" the steps of the original columns in the long dataset

varAgg

How multiple occurrences of particular combinations of time varying variables are aggregated: either "mean" (calculate the mean over occurrences), or "first" (take the first occurrence)

varSrt

Variable(s) that are used to sort the data frame (see Details; if empty, the order returned from reshape is kept; default: c())

usePkg

Name of the package: "foreign" or "haven" that shall be used to read SPSS, Stata and SAS files; "foreign" is the default (it comes with base R), but "haven" is newer and more comprehensive

selSet

Name of the data set that is to be selected from the workspace (only applies when reading .RData-files)

...

Additional arguments passed on to methods; see Details below

Details

• If varTgt is empty, it is tried to generate it using all variables in the data frame except those defined by varID, varTme and varExc. The variable(s) in varID need to be unique identifiers (in the original dataset), those in varExc don't have this requirement. It is generally recommended that the variable names in varExc and varID should not contain the variable separator (defined in varSep; default: "_").

• varSrt can be either a character or a character vector (with one or more variables respectively). The sorting order for a particular variable can be inverted with preceding the variable name with "-". Please note that this doesn't make sense and hence throws a warning for certain variable types (e.g., factors).

• The ellipsis-parameter (...) can be used to submit arguments / parameters to the functions that are used for transforming, reading or writing 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.

• The transformation from long to wide uses reshape. varTgt matches (~) v.names in reshape, varID ~ idvar, varTme ~ timevar, and varSep ~ sep. The help for reshape is very explanatory, click on the link under “See also” to access it, particularly what is explained under “Details”.

• The functions for reading and writing the data are: read_omv and write_omv (for jamovi-files), read.table (for CSV / TSV files; using similar defaults as read.csv for CSV and read.delim for TSV which both are based upon read.table), load (for .RData-files), readRDS (for .rds-files), read_sav (needs R-package haven) or read.spss (needs R-package foreign) for SPSS-files, read_dta (haven) / read.dta (foreign) for Stata-files, read_sas (haven) for SAS-data-files, and read_xpt (haven) / read.xport (foreign) for SAS-transport-files. If you would like to use haven, you may need to install it using install.packages("haven", dep = TRUE).

See Also

long2wide_omv internally uses the following functions: The transformation from long to wide uses stats::reshape(). 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

if (FALSE) {
# generate a test dataframe with 100 (imaginary) participants / units of
#  observation (ID), 8 measurement (measure) of one variable (X)
dtaInp <- data.frame(ID = rep(as.character(seq(1, 100)), each = 8),
                     measure = rep(seq(1, 8), times = 100),
                     X = runif(800, -10, 10))
cat(str(dtaInp))
# the output should look like this
# 'data.frame': 800 obs. of  3 variables:
#  $ ID     : chr  "1" "1" "1" "1" ...
#  $ measure: int  1 2 3 4 5 6 7 8 1 2 ...
#  $ X      : num  ...
# this data set is stored as (temporary) RDS-file and later processed by long2wide
nmeInp <- tempfile(fileext = ".rds")
nmeOut <- tempfile(fileext = ".omv")
saveRDS(dtaInp, nmeInp)
jmvReadWrite::long2wide_omv(dtaInp = nmeInp, fleOut = nmeOut, varTgt = "X", varID = "ID",
  varTme = "measure")
# it is required to give at least the arguments dtaInp, varID and varTme
# check whether the file was created and its size
cat(list.files(dirname(nmeOut), basename(nmeOut)))
# -> "file[...].omv" ([...] contains a random combination of numbers / characters
cat(file.info(nmeOut)$size)
# -> 6851 (approximate size; size may differ in every run [in dependence of
#          how well the generated random data can be compressed])
cat(str(jmvReadWrite::read_omv(nmeOut, sveAtt = FALSE)))
# the data set is now transformed into wide (and each the measurements is now
# indicated as a suffix to X; X_1, X_2, ...)
# 'data.frame':	100 obs. of  9 variables:
#  $ ID : chr  "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" ...
#   ..- attr(*, "jmv-id")= logi TRUE
#   ..- attr(*, "missingValues")= list()
#  $ X_1: num  ...
#   ..- attr(*, "missingValues")= list()
#  $ X_2: num  ...
#   ..- attr(*, "missingValues")= list()
#  $ X_3: num  ...
#   ..- attr(*, "missingValues")= list()
#  $ X_4: num  ...
#   ..- attr(*, "missingValues")= list()
#  $ X_5: num  ...
#   ..- attr(*, "missingValues")= list()
#  $ X_6: num  ...
#   ..- attr(*, "missingValues")= list()
#  $ X_7: num  ...
#   ..- attr(*, "missingValues")= list()
#  $ X_8: num  ...
#   ..- attr(*, "missingValues")= list()

unlink(nmeInp)
unlink(nmeOut)
}