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Merges two or more data files by adding the content of other input files as columns to the first input file and outputs them as files for the statistical spreadsheet 'jamovi' (


  dtaInp = NULL,
  fleOut = "",
  typMrg = c("outer", "inner", "left", "right"),
  varBy = list(),
  varSrt = c(),
  psvAnl = FALSE,
  usePkg = c("foreign", "haven"),
  selSet = "",



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


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


Type of merging operation: "outer" (default), "inner", "left" or "right"; see Details below


Name of the variable by which the data sets are matched, can either be a string, a character or a list (see Details below; default: list())


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


Whether analyses that are contained in the input file shall be transferred to the output file (TRUE / FALSE; default: FALSE)


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


  • 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): "outer" keeps all cases (but columns in the resulting data set may contain empty cells / missing values if same input data sets did not have a row containing the matching variable (defined in varBy). "inner" keeps only those cases where all datasets contain the same value in the matching variable, for "left" all cases from the first data set in dtaInp are kept (whereas cases that are only contained in the second or any later input data set are dropped), for "right" all cases from the second (or any higher) data set in dtaInp are kept. The behaviour of "left" and "right" may be somewhat difficult to predict in case of merging several data sets, therefore "outer" might be a safer choice if several data sets are merged.

  • The variable that is used for matching (varBy) can either be a string (if all datasets contain a matching variable with the same name), a character vector (containing more than one matching variables that are contained in / the same for all data sets) or a list with the same length as dtaInp. In such list, each cell can again contain either a string (one matching variable for each data set in dtaInp) or a character vector (several matching variables for each data set in dtaInp; NB: all character vectors in the cells of the list must have the same length as it is necessary to always use the same number of matching variables when merging).

  • 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 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.

  • Adding columns uses merge. typMrg is implemented by setting TRUE or FALSE to all.x and all.y in merge, varBy matches by.x and by.y. The help for merge can be accessed by clicking on the link under “See also”.

  • 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

merge_cols_omv internally uses the following functions: Adding columns uses merge(). 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) { # \dontrun{
dtaInp <- jmvReadWrite::bfi_sample2
nmeInp <- paste0(tempfile(), "_", 1:3, ".rds")
nmeOut <- tempfile(fileext = ".omv")
for (i in seq_along(nmeInp)) {
    saveRDS(stats::setNames(dtaInp, c("ID", paste0(names(dtaInp)[-1], "_", i))), nmeInp[i])
# save dtaInp three times (i.e., the length of nmeInp), adding "_" + 1 ... 3 as index
# to the data variables (A1 ... O5, gender, age → A1_1, ...)
jmvReadWrite::merge_cols_omv(dtaInp = nmeInp, fleOut = nmeOut, varBy = "ID")
# -> 17731 (size may differ on different OSes)
dtaOut <- jmvReadWrite::read_omv(nmeOut, sveAtt = FALSE)
# read the data set where the three original datasets were added as columns and show
# the variable names
# compared to the input data set, we have the same names (expect for "ID" which was
# used for matching and that each variable had added an indicator from which data
# set they came)
cat(dim(dtaInp), dim(dtaOut))
# the first dimension of the data sets (rows) stayed the same (250), whereas the
# second dimension is now approx. three times as large (28 -> 82):
# 28 - 1 (for "ID") = 27 * 3 + 1 (for "ID") = 82
# it's therefore not much surprise that the values of the column means for the first
# 10 variables of dtaInp and dtaOut are the same too

} # }