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Merges two .omv-files for the statistical spreadsheet 'jamovi' (https://www.jamovi.org) by adding the content of the second, etc. file(s) as rows to the first file

Source: R/merge_rows_omv.R
merge_rows_omv.Rd

Merges two .omv-files for the statistical spreadsheet 'jamovi' (https://www.jamovi.org) by adding the content of the second, etc. file(s) as rows to the first file

Usage

merge_rows_omv(
  dtaInp = NULL,
  fleOut = "",
  typMrg = c("all", "common"),
  colInd = FALSE,
  rstRwN = TRUE,
  rmvDpl = FALSE,
  varSrt = NULL,
  usePkg = c("foreign", "haven"),
  selSet = "",
  ...
)

Arguments

dtaInp

Either a data frame (with the attribute "fleInp" containing the files to merge) or vector with the names of the input files (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.

typMrg

Type of merging operation: "all" (default) or "common"; see also Details.

colInd

Add a column with an indicator (the basename of the file minus the extension) marking from which input data set the respective rows are coming (default: FALSE)

rstRwN

Reset row names (i.e., do not keep the row names of the original input data sets but number them consecutively - one to the row number of all input data sets added up; default: TRUE)

rmvDpl

Remove duplicated rows (i.e., rows with the same content as a previous row in all columns; default: FALSE)

varSrt

Variable(s) that are used to sort the data frame (see Details; if empty, the order after merging is kept; default: NULL)

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 rows of all input data sets (given in the dtaInp-argument) are concatenated

Details

  • Using data frames with the input parameter dtaInp is primarily thought to be used when calling merge_cols_omv from the jamovi-modules jTransform and Rj. For the use in R, it is strongly recommended to use a character vector with the file names instead.

  • There are four different types of merging operations (defined via typMrg): "all" keeps all existing variables / columns that are contained in any of the input data sets and fills them up with NA where the variable / column doesn't exist in an input data set. "common" only keeps the variables / columns that are common to all input data sets (i.e., that are contained in all data sets).

  • 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).

  • Adding columns uses rbind (with some further operation, adding missing columns (filled with NAs), if typMrg is "all").

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

merge_rows_omv internally uses the following functions: Adding columns uses rbind(). 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

dtaInp <- jmvReadWrite::bfi_sample2
nmeInp <- paste0(tempfile(), "_", 1:3, ".rds")
nmeOut <- tempfile(fileext = ".omv")
for (i in seq_along(nmeInp)) saveRDS(dtaInp[-i - 1], nmeInp[i])
# save dtaInp three times (i.e., the length of nmeInp), removing one data columns in
# each data set (for demonstration purposes, A1 in the first, A2 in the second, ...)
jmvReadWrite::merge_rows_omv(dtaInp = nmeInp, fleOut = nmeOut, colInd = TRUE)
cat(file.info(nmeOut)$size)
#> 10933
# -> 10767 (size may differ on different OSes)
dtaOut <- jmvReadWrite::read_omv(nmeOut, sveAtt = FALSE)
unlink(nmeOut)
# read the data set where the three original datasets were added as rows and show
# the variable names
cat(names(dtaInp))
#> ID A1 A2 A3 A4 A5 C1 C2 C3 C4 C5 E1 E2 E3 E4 E5 N1 N2 N3 N4 N5 O1 O2 O3 O4 O5 gender age ID2
cat(names(dtaOut))
#> fleInd ID A2 A3 A4 A5 C1 C2 C3 C4 C5 E1 E2 E3 E4 E5 N1 N2 N3 N4 N5 O1 O2 O3 O4 O5 gender age ID2 A1
# compared to the input data set, we have the same variable names; fleInd (switched
# on by colInd = TRUE and showing from which data set the rows are coming from) is
# new and A1 is moved to the end of the list (the "original" order of variables may
# not always be preserved and columns missing from at least one of the input data
# sets may be added at the end)
cat(dim(dtaInp), dim(dtaOut))
#> 250 29 750 30
# the first dimension of the data sets (rows) is now three times of that of the input
# data set (250 -> 750), the second dimension (columns / variables) is increased by 1
# (for "fleInd")

jmvReadWrite::merge_rows_omv(dtaInp = nmeInp, fleOut = nmeOut, typMrg = "common")
# the argument typMrg = "common" removes the columns that are not present in all of
# the input data sets (i.e., A1, A2, A3)
dtaOut <- jmvReadWrite::read_omv(nmeOut, sveAtt = FALSE)
unlink(nmeOut)
# read the data set where the three original datasets were added as rows and show
# the variable names
cat(names(dtaInp))
#> ID A1 A2 A3 A4 A5 C1 C2 C3 C4 C5 E1 E2 E3 E4 E5 N1 N2 N3 N4 N5 O1 O2 O3 O4 O5 gender age ID2
cat(names(dtaOut))
#> ID A4 A5 C1 C2 C3 C4 C5 E1 E2 E3 E4 E5 N1 N2 N3 N4 N5 O1 O2 O3 O4 O5 gender age ID2
# compared to the input data set, the variables that were missing in at least one
# data set (i.e., "A1", "A2" and "A3") are removed
cat(dim(dtaInp), dim(dtaOut))
#> 250 29 750 26
# the first dimension of the data sets (rows) is now three times of that of the
# input data set (250 -> 750), the second dimension (columns / variables) is
# reduced by 3 (i.e., "A1", "A2", "A3")

unlink(nmeInp)