R/wide2long_omv.R
wide2long_omv.RdConverts .omv-files for the statistical spreadsheet 'jamovi' (https://www.jamovi.org) from wide to long format
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
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
List / set of variables that are to be transformed into single (time-varying) variables in long format (default: c())
Name of the variable(s) should be excluded from the transformation, typically this will be between-subject-variable(s) (default: c())
Name(s) of one or more variables that (is created to) identify the same group / individual (if empty, "ID" is added with row numbers identifying cases; default: NULL)
Name of the variable that (is created to) differentiate multiple records from the same group / individual (default: "cond"; a counter is added for each time-varying part)
Character that separates the variables in varLst into a time-varying part and a part that forms the variable name in long format ("" in "VAR_1", "VAR_2", default: "")
Whether to arrange the variables before the transformation, so that they are in accordance with the different split levels (default: TRUE)
Variable(s) that are used to sort the data frame (see Details; if empty, the order returned from reshape is kept; default: c())
Integer (or vector of integers) determining which parts of the variable names in varLst shall not be transformed (default: NULL), see Details below
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
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
a data frame (only returned if fleOut is empty) where the input data set is converted from wide to long format
If varLst is empty, it is tried to generate it using all variables in the data frame except those defined by varExc and varID. The variable(s) in
varID need to be unique identifiers (in the original dataset), those in varExc don't have this requirement. It is recommended that the variable names
in varExc and varID should not contain the variable separator (defined in varSep; default: "_").
varOrd determines whether the variables are rearranged to match the order of split levels. Consider the varLst X_1, Y_1, X_2, Y_2. If varOrd were
set to FALSE, the original order would be preserved and the second part of the variable name (1, 2, ...) would become condition 1, and the first part
condition 2. In most cases, leaving varOrd set to TRUE is recommended.
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).
exclLvl points to a part of the variable names in varLst to be excluded. For example, if the variable name is PART1_PART2_PART3 (split at _), then
excLvl = 1 would exclude PART1 from the transformation. Quite often, one has more that one variable of a particular type (e.g., responses, reaction
times, etc.). Those would typically be the first part of each variable name in varLst (the conditions then being PART2, PART3, and so on). excLvl = 1
would exclude those variable types / categories from being transformed into long (i.e., they would be kept as separate columns).
The ellipsis-parameter (...) can be used to submit arguments / parameters to the functions that are used for transforming or 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.
The transformation from long to wide uses reshape: varID matches (~) idvar in reshape, varTme ~ timevar, varLst ~ varying, 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).
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.
if (FALSE) {
# generate a test dataframe with 100 (imaginary) participants / units of
# observation (ID), and 8 repeated measurements of variable (X_1, X_2, ...)
dtaInp <- cbind(data.frame(ID = as.character(seq(1:100))),
stats::setNames(
as.data.frame(matrix(runif(800, -10, 10), nrow = 100)),
paste0("X_", 1:8)))
cat(str(dtaInp))
# 'data.frame': 100 obs. of 9 variables:
# $ ID : chr "1" "2" "3" "4" ...
# $ X_1: num ...
# $ X_2: num ...
# $ X_3: num ...
# $ X_4: num ...
# $ X_5: num ...
# $ X_6: num ...
# $ X_7: num ...
# $ X_8: num ...
# this data set is stored as (temporary) RDS-file and later processed by wide2long
nmeInp <- tempfile(fileext = ".rds")
nmeOut <- tempfile(fileext = ".omv")
saveRDS(dtaInp, nmeInp)
jmvReadWrite::wide2long_omv(dtaInp = nmeInp, fleOut = nmeOut, varID = "ID",
varTme = "measure", varLst = setdiff(names(dtaInp), "ID"),
varSrt = c("ID", "measure"))
# it is required to give at least the arguments dtaInp (if dtaInp is a data frame,
# fleOut needs to be provided too) and varID
# "reshape" then assigns all variables expect the variable defined by varID to
# varLst (but throws a warning)
# varSrt enforces sorting the data set after the transformation (sorted, the
# measurements within one person come after another; unsorted all measurements
# for one repetition would come after another)
# 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)
# -> 6939 (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 long (and each the measurements is now
# indicated by the "measure")
# 'data.frame': 800 obs. of 3 variables:
# $ ID : Factor w/ 100 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 2 2 ...
# ..- attr(*, "missingValues")= list()
# $ measure: Factor w/ 8 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 1 2 ...
# ..- attr(*, "missingValues")= list()
# $ X : num ...
# ..- attr(*, "missingValues")= list()
unlink(nmeInp)
unlink(nmeOut)
}