This functions imports data from various file types. It is a small wrapper around haven::read_spss(), haven::read_stata(), haven::read_sas(), readxl::read_excel() and data.table::fread() resp. readr::read_delim() (the latter if package data.table is not installed). Thus, supported file types for importing data are data files from SPSS, SAS or Stata, Excel files or text files (like '.csv' files). All other file types are passed to rio::import(). data_write() works in a similar way.

## Usage

data_read(
path,
path_catalog = NULL,
encoding = NULL,
convert_factors = TRUE,
verbose = TRUE,
...
)

data_write(
data,
path,
delimiter = ",",
convert_factors = FALSE,
save_labels = FALSE,
verbose = TRUE,
...
)

## Arguments

path

Character string, the file path to the data file.

path_catalog

Character string, path to the catalog file. Only relevant for SAS data files.

encoding

The character encoding used for the file. Usually not needed.

convert_factors

If TRUE (default), numeric variables, where all values have a value label, are assumed to be categorical and converted into factors. If FALSE, no variable types are guessed and no conversion of numeric variables into factors will be performed. See also section 'Differences to other packages'. For data_write(), this argument only applies to the text (e.g. .txt or .csv) or spreadsheet file formats (like .xlsx). Converting to factors might be useful for these formats because labelled numeric variables are then converted into factors and exported as character columns - else, value labels would be lost and only numeric values are written to the file.

verbose

Toggle warnings and messages.

...

Arguments passed to the related read_*() or write_*() functions.

data

The data frame that should be written to a file.

delimiter

For CSV-files, specifies the delimiter. Defaults to ",", but in particular in European regions, ";" might be a useful alternative, especially when exported CSV-files should be opened in Excel.

save_labels

Only applies to CSV files. If TRUE, value and variable labels (if any) will be saved as additional CSV file. This file has the same file name as the exported CSV file, but includes a "_labels" suffix (i.e. when the file name is "mydat.csv", the additional file with value and variable labels is named "mydat_labels.csv").

A data frame.

## Supported file types

• data_read() is a wrapper around the haven, data.table, readr readxl and rio packages. Currently supported file types are .txt, .csv, .xls, .xlsx, .sav, .por, .dta and .sas (and related files). All other file types are passed to rio::import().

• data_write() is a wrapper around haven, readr and rio packages, and supports writing files into all formats supported by these packages.

## Compressed files (zip) and URLs

data_read() can also read the above mentioned files from URLs or from inside zip-compressed files. Thus, path can also be a URL to a file like "http://www.url.com/file.csv". When path points to a zip-compressed file, and there are multiple files inside the zip-archive, then the first supported file is extracted and loaded.

## General behaviour

data_read() detects the appropriate read_*() function based on the file-extension of the data file. Thus, in most cases it should be enough to only specify the path argument. However, if more control is needed, all arguments in ... are passed down to the related read_*() function. The same applies to data_write(), i.e. based on the file extension provided in path, the appropriate write_*() function is used automatically.

## SPSS specific behaviour

data_read() does not import user-defined ("tagged") NA values from SPSS, i.e. argument user_na is always set to FALSE when importing SPSS data with the haven package. Use convert_to_na() to define missing values in the imported data, if necessary. Furthermore, data_write() compresses SPSS files by default. If this causes problems with (older) SPSS versions, use compress = "none", for example data_write(data, "myfile.sav", compress = "none").

## Differences to other packages that read foreign data formats

data_read() is most comparable to rio::import(). For data files from SPSS, SAS or Stata, which support labelled data, variables are converted into their most appropriate type. The major difference to rio::import() is that data_read() automatically converts fully labelled numeric variables into factors, where imported value labels will be set as factor levels. If a numeric variable has no value labels or less value labels than values, it is not converted to factor. In this case, value labels are preserved as "labels" attribute. Character vectors are preserved. Use convert_factors = FALSE to remove the automatic conversion of numeric variables to factors.