read.sas

read.sas is a general function for reading sas7bdat files. It supports a variety of complex sas7bdat files, x86 and x64, big and small endian, and both compression types. It has been tested with numeric and character data, and provides helper functions for converting sas7bdat to R types Date and POSIXct. Time variables are converted to characters. Conversion to date variables is applied if a known date, datetime or time format is found in the sas7bdat file. For user-defined formats, the package provides functions to convert from sas7bdat to R.

Input files may contain deleted rows that are marked as deleted instead of being removed from the input data. These are removed on import, if you still need them look at remove_deleted. Formats, labels and additional file information are available with attributes().

read.sas(
  file,
  debug = FALSE,
  convert_dates = TRUE,
  recode = TRUE,
  select.rows = NULL,
  select.cols = NULL,
  remove_deleted = TRUE,
  rownames = FALSE,
  empty_to_na = FALSE
)

Arguments

file: file to read
debug: print debug information
convert_dates: default is TRUE
recode: default is TRUE
select.rows: integer. Vector of rows to import. Minimum 0. Rows imported are sorted. If 0 is in select.rows, zero rows are returned.
select.cols: character: Vector of variables to select.
remove_deleted: logical if deleted rows should be removed from data
rownames: first column will be used as rowname and removed from data
empty_to_na: logical. In SAS empty characters are missing. this option allows to convert "" to NA_character_ when importing.

Examples

fl <- system.file("extdata", "cars.sas7bdat", package = "readsas")
read.sas(fl)
#>    speed dist
#> 1      4    2
#> 2      4   10
#> 3      7    4
#> 4      7   22
#> 5      8   16
#> 6      9   10
#> 7     10   18
#> 8     10   26
#> 9     10   34
#> 10    11   17
#> 11    11   28
#> 12    12   14
#> 13    12   20
#> 14    12   24
#> 15    12   28
#> 16    13   26
#> 17    13   34
#> 18    13   34
#> 19    13   46
#> 20    14   26
#> 21    14   36
#> 22    14   60
#> 23    14   80
#> 24    15   20
#> 25    15   26
#> 26    15   54
#> 27    16   32
#> 28    16   40
#> 29    17   32
#> 30    17   40
#> 31    17   50
#> 32    18   42
#> 33    18   56
#> 34    18   76
#> 35    18   84
#> 36    19   36
#> 37    19   46
#> 38    19   68
#> 39    20   32
#> 40    20   48
#> 41    20   52
#> 42    20   56
#> 43    20   64
#> 44    22   66
#> 45    23   54
#> 46    24   70
#> 47    24   92
#> 48    24   93
#> 49    24  120
#> 50    25   85

Arguments

See also

Examples