Read and write SPSS files — read

read_sav() reads both .sav and .zsav files; write_sav() creates .zsav files when compress = TRUE. read_por() reads .por files. read_spss() uses either read_por() or read_sav() based on the file extension.

read_sav(
  file,
  encoding = NULL,
  user_na = FALSE,
  col_select = NULL,
  skip = 0,
  n_max = Inf,
  .name_repair = "unique"
)

read_por(
  file,
  user_na = FALSE,
  col_select = NULL,
  skip = 0,
  n_max = Inf,
  .name_repair = "unique"
)

write_sav(data, path, compress = FALSE)

read_spss(
  file,
  user_na = FALSE,
  col_select = NULL,
  skip = 0,
  n_max = Inf,
  .name_repair = "unique"
)

Arguments

file	Either a path to a file, a connection, or literal data (either a single string or a raw vector). Files ending in `.gz`, `.bz2`, `.xz`, or `.zip` will be automatically uncompressed. Files starting with `http://`, `https://`, `ftp://`, or `ftps://` will be automatically downloaded. Remote gz files can also be automatically downloaded and decompressed. Literal data is most useful for examples and tests. It must contain at least one new line to be recognised as data (instead of a path) or be a vector of greater than length 1. Using a value of `clipboard()` will read from the system clipboard.
encoding	The character encoding used for the file. The default, `NULL`, use the encoding specified in the file, but sometimes this value is incorrect and it is useful to be able to override it.
user_na	If `TRUE` variables with user defined missing will be read into `labelled_spss()` objects. If `FALSE`, the default, user-defined missings will be converted to `NA`.
col_select	One or more selection expressions, like in `dplyr::select()`. Use `c()` or `list()` to use more than one expression. See `?dplyr::select` for details on available selection options. Only the specified columns will be read from `data_file`.
skip	Number of lines to skip before reading data.
n_max	Maximum number of lines to read.
.name_repair	Treatment of problematic column names: `"minimal"`: No name repair or checks, beyond basic existence, `"unique"`: Make sure names are unique and not empty, `"check_unique"`: (default value), no name repair, but check they are `unique`, `"universal"`: Make the names `unique` and syntactic a function: apply custom name repair (e.g., `.name_repair = make.names` for names in the style of base R). A purrr-style anonymous function, see `rlang::as_function()` This argument is passed on as `repair` to `vctrs::vec_as_names()`. See there for more details on these terms and the strategies used to enforce them.
data	Data frame to write.
path	Path to a file where the data will be written.
compress	If `TRUE`, will compress the file, resulting in a `.zsav` file. Otherwise the `.sav` file will be bytecode compressed.

Value

A tibble, data frame variant with nice defaults.

Variable labels are stored in the "label" attribute of each variable. It is not printed on the console, but the RStudio viewer will show it.

write_sav() returns the input data invisibly.

Details

Currently haven can read and write logical, integer, numeric, character and factors. See labelled_spss() for how labelled variables in SPSS are handled in R.

Examples

path <- system.file("examples", "iris.sav", package = "haven")
read_sav(path)
#> # A tibble: 150 × 5
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
#>           <dbl>       <dbl>        <dbl>       <dbl>  <dbl+lbl>
#>  1          5.1         3.5          1.4         0.2 1 [setosa]
#>  2          4.9         3            1.4         0.2 1 [setosa]
#>  3          4.7         3.2          1.3         0.2 1 [setosa]
#>  4          4.6         3.1          1.5         0.2 1 [setosa]
#>  5          5           3.6          1.4         0.2 1 [setosa]
#>  6          5.4         3.9          1.7         0.4 1 [setosa]
#>  7          4.6         3.4          1.4         0.3 1 [setosa]
#>  8          5           3.4          1.5         0.2 1 [setosa]
#>  9          4.4         2.9          1.4         0.2 1 [setosa]
#> 10          4.9         3.1          1.5         0.1 1 [setosa]
#> # … with 140 more rows

tmp <- tempfile(fileext = ".sav")
write_sav(mtcars, tmp)
read_sav(tmp)
#> # A tibble: 32 × 11
#>      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1  21       6  160    110  3.9   2.62  16.5     0     1     4     4
#>  2  21       6  160    110  3.9   2.88  17.0     0     1     4     4
#>  3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1
#>  4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1
#>  5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
#>  6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
#>  7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
#>  8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2
#>  9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2
#> 10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4
#> # … with 22 more rows