| Title: | Read Data Stored by 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', 'dBase', ... |
|---|---|
| Description: | Reading and writing data stored by some versions of 'Epi Info', 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', and for reading and writing some 'dBase' files. |
| Authors: | R Core Team [aut, cph, cre], Roger Bivand [ctb, cph], Vincent J. Carey [ctb, cph], Saikat DebRoy [ctb, cph], Stephen Eglen [ctb, cph], Rajarshi Guha [ctb, cph], Swetlana Herbrandt [ctb], Nicholas Lewin-Koh [ctb, cph], Mark Myatt [ctb, cph], Michael Nelson [ctb], Ben Pfaff [ctb], Brian Quistorff [ctb], Frank Warmerdam [ctb, cph], Stephen Weigand [ctb, cph], Free Software Foundation, Inc. [cph] |
| Maintainer: | R Core Team <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.8-87 |
| Built: | 2024-06-26 09:42:22 UTC |
| Source: | CRAN |
Scans a file as a SAS XPORT format library and returns a list containing information about the SAS library.
lookup.xport(file)lookup.xport(file)
file |
character variable with the name of the file to read. The file must be in SAS XPORT format. |
A list with one component for each dataset in the XPORT format library.
Saikat DebRoy
SAS Technical Support document TS-140: “The Record Layout of a Data Set in SAS Transport (XPORT) Format” available as https://support.sas.com/content/dam/SAS/support/en/technical-papers/record-layout-of-a-sas-version-5-or-6-data-set-in-sas-transport-xport-format.pdf
## Not run: ## no XPORT file is installed. lookup.xport("test.xpt") ## End(Not run)## Not run: ## no XPORT file is installed. lookup.xport("test.xpt") ## End(Not run)
Reads data from Weka Attribute-Relation File Format (ARFF) files.
read.arff(file)read.arff(file)
file |
a character string with the name of the ARFF file to read
from, or a |
A data frame containing the data from the ARFF file.
Attribute-Relation File Format https://waikato.github.io/weka-wiki/formats_and_processing/arff/.
write.arff;
functions write.arff and read.arff in package
RWeka which provide some support for logicals via conversion to
or from factors.
The function reads a DBF file into a data frame, converting character fields to factors, and trying to respect NULL fields.
The DBF format is documented but not much adhered to. There is is no guarantee this will read all DBF files.
read.dbf(file, as.is = FALSE)read.dbf(file, as.is = FALSE)
file |
name of input file |
as.is |
should character vectors not be converted to factors? |
DBF is the extension used for files written for the ‘XBASE’ family of database languages, ‘covering the dBase, Clipper, FoxPro, and their Windows equivalents Visual dBase, Visual Objects, and Visual FoxPro, plus some older products’ (https://www.clicketyclick.dk/databases/xbase/format/). Most of these follow the file structure used by Ashton-Tate's dBase II, III or 4 (later owned by Borland).
read.dbf is based on C code from
http://shapelib.maptools.org/ which implements the
‘XBASE’ specification. It can convert fields of type
"L" (logical), "N" and "F" (numeric and float)
and "D" (dates): all other field types are read as-is as
character vectors. A numeric field is read as an R integer vector if
it is encoded to have no decimals, otherwise as a numeric vector. However,
if the numbers are too large to fit into an integer vector, it is
changed to numeric. Note that is possible to read integers that cannot be
represented exactly even as doubles: this sometimes occurs if IDs are
incorrectly coded as numeric.
A data frame of data from the DBF file; note that the field names are
adjusted to use in R using make.names(unique=TRUE).
There is an attribute "data_type" giving the single-character
dBase types for each field.
Not to be able to read a particular ‘DBF’ file is not a bug: this is a convenience function especially for shapefiles.
Nicholas Lewin-Koh and Roger Bivand; shapelib by Frank Warmerdam
http://shapelib.maptools.org/.
x <- read.dbf(system.file("files/sids.dbf", package="foreign")[1]) str(x) summary(x)x <- read.dbf(system.file("files/sids.dbf", package="foreign")[1]) str(x) summary(x)
Reads a file in Stata version 5–12 binary format into a data frame.
Frozen: will not support Stata formats after 12.
read.dta(file, convert.dates = TRUE, convert.factors = TRUE, missing.type = FALSE, convert.underscore = FALSE, warn.missing.labels = TRUE)read.dta(file, convert.dates = TRUE, convert.factors = TRUE, missing.type = FALSE, convert.underscore = FALSE, warn.missing.labels = TRUE)
file |
a filename or URL as a character string. |
convert.dates |
Convert Stata dates to |
convert.factors |
Use Stata value labels to create factors? (Version 6.0 or later). |
missing.type |
For version 8 or later, store information about different types of missing data? |
convert.underscore |
Convert |
warn.missing.labels |
Warn if a variable is specified with value labels and those value labels are not present in the file. |
If the filename appears to be a URL (of schemes ‘http:’, ‘ftp:’ or ‘https:’) the URL is first downloaded to a temporary file and then read. (‘https:’ is only supported on some platforms.)
The variables in the Stata data set become the columns of the data frame. Missing values are correctly handled. The data label, variable labels, timestamp, and variable/dataset characteristics are stored as attributes of the data frame.
By default Stata dates (%d and %td formats) are converted to R's
Date class, and variables with Stata value labels are
converted to factors. Ordinarily, read.dta will not convert
a variable to a factor unless a label is present for every level. Use
convert.factors = NA to override this. In any case the value
label and format information is stored as attributes on the returned
data frame. Stata's date formats are sketchily documented: if
necessary use convert.dates = FALSE and examine the attributes
to work out how to post-process the dates.
Stata 8 introduced a system of 27 different missing data values. If
missing.type is TRUE a separate list is created with the
same variable names as the loaded data. For string variables the list
value is NULL. For other variables the value is NA
where the observation is not missing and 0–26 when the observation is
missing. This is attached as the "missing" attribute of the
returned value.
The default file format for Stata 13, format-115, is
substantially different from those for Stata 5–12.
A data frame with attributes. These will include "datalabel",
"time.stamp", "formats", "types",
"val.labels", "var.labels" and "version" and may
include "label.table" and "expansion.table".
Possible versions are 5, 6, 7,
-7 (Stata 7SE, ‘format-111’), 8 (Stata 8 and 9,
‘format-113’), 10 (Stata 10 and 11, ‘format-114’).
and 12 (Stata 12, ‘format-115’).
The value labels in attribute "val.labels" name a table for
each variable, or are an empty string. The tables are elements of the
named list attribute "label.table": each is an integer vector with
names.
Thomas Lumley and R-core members: support for value labels by Brian Quistorff.
Stata Users Manual (versions 5 & 6), Programming manual (version 7), or online help (version 8 and later) describe the format of the files. Or directly at https://www.stata.com/help.cgi?dta_114 and https://www.stata.com/help.cgi?dta_113, but note that these have been changed since first published.
Different approaches are available in package memisc (see its
help for Stata.file), function read_dta in package
haven and package readstata13.
write.dta,
attributes,
Date,
factor
write.dta(swiss,swissfile <- tempfile()) read.dta(swissfile)write.dta(swiss,swissfile <- tempfile()) read.dta(swissfile)
Reads data files in the .REC format used by Epi Info versions 6
and earlier and by EpiData. Epi Info is a public domain database and
statistics package produced by the US Centers for Disease Control
and EpiData is a freely available data entry and validation system.
read.epiinfo(file, read.deleted = FALSE, guess.broken.dates = FALSE, thisyear = NULL, lower.case.names = FALSE)read.epiinfo(file, read.deleted = FALSE, guess.broken.dates = FALSE, thisyear = NULL, lower.case.names = FALSE)
file |
A filename, URL, or connection. |
read.deleted |
Deleted records are read if |
guess.broken.dates |
Attempt to convert dates with 0 or 2 digit year information (see ‘Details’). |
thisyear |
A 4-digit year to use for dates with no year. Defaults to the current year. |
lower.case.names |
Convert variable names to lowercase? |
Epi Info allows dates to be specified with no year or with a 2 or 4
digits. Dates with four-digit years are always converted to
Date class. With the guess.broken.dates option the function
will attempt to convert two-digit years using the operating system's
default method (see Date) and will use the current
year or the thisyear argument for dates with no year
information.
If read.deleted is TRUE the "deleted" attribute
of the data frame indicates the deleted records.
A data frame.
Some later versions of Epi Info use the Microsoft Access file format to store data. That may be readable with the RODBC package.
https://www.cdc.gov/epiinfo/, http://www.epidata.dk
## Not run: ## That file is not available read.epiinfo("oswego.rec", guess.broken.dates = TRUE, thisyear = "1972") ## End(Not run)## Not run: ## That file is not available read.epiinfo("oswego.rec", guess.broken.dates = TRUE, thisyear = "1972") ## End(Not run)
Return a list with the data stored in a file as a Minitab Portable Worksheet.
read.mtp(file)read.mtp(file)
file |
character variable with the name of the file to read. The file must be in Minitab Portable Worksheet format. |
A list with one component for each column, matrix, or constant stored in the Minitab worksheet.
This function was written around 1990 for the format current then. Later versions of Minitab appear to have added to the format.
Douglas M. Bates
## Not run: read.mtp("ex1-10.mtp") ## End(Not run)## Not run: read.mtp("ex1-10.mtp") ## End(Not run)
Read a file in Octave text data format into a list.
read.octave(file)read.octave(file)
file |
a character string with the name of the file to read. |
This function is used to read in files in Octave text data format, as
created by save -text in Octave. It knows about most of the
common types of variables, including the standard atomic (real and
complex scalars, matrices, and -d arrays, strings, ranges, and
boolean scalars and matrices) and recursive (structs, cells, and
lists) ones, but has no guarantee to read all types. If a type is not
recognized, a warning indicating the unknown type is issued, it is
attempted to skip the unknown entry, and NULL is used as its
value. Note that this will give incorrect results, and maybe even
errors, in the case of unknown recursive data types.
As Octave can read MATLAB binary files, one can make the contents of such files available to R by using Octave's load and save (as text) facilities as an intermediary step.
A list with one named component for each variable in the file.
Stephen Eglen [email protected] and Kurt Hornik
read.spss reads a file stored by the SPSS save or
export commands.
This was orignally written in 2000 and has limited support for changes in SPSS formats since (which have not been many).
read.spss(file, use.value.labels = TRUE, to.data.frame = FALSE, max.value.labels = Inf, trim.factor.names = FALSE, trim_values = TRUE, reencode = NA, use.missings = to.data.frame, sub = ".", add.undeclared.levels = c("sort", "append", "no"), duplicated.value.labels = c("append", "condense"), duplicated.value.labels.infix = "_duplicated_", ...)read.spss(file, use.value.labels = TRUE, to.data.frame = FALSE, max.value.labels = Inf, trim.factor.names = FALSE, trim_values = TRUE, reencode = NA, use.missings = to.data.frame, sub = ".", add.undeclared.levels = c("sort", "append", "no"), duplicated.value.labels = c("append", "condense"), duplicated.value.labels.infix = "_duplicated_", ...)
file |
character string: the name of the file or URL to read. |
use.value.labels |
logical: convert variables with value labels
into R factors with those levels? This is only done if there are
at least as many labels as values of the variable (when values
without a matching label are returned as |
to.data.frame |
logical: return a data frame? |
max.value.labels |
logical: only variables with value labels and
at most this many unique values will be converted to factors if
|
trim.factor.names |
logical: trim trailing spaces from factor levels? |
trim_values |
logical: should values and value labels have
trailing spaces ignored when matching for |
reencode |
logical: should character strings be re-encoded to the
current locale. The default, |
use.missings |
logical: should information on user-defined
missing values be used to set the corresponding values to |
sub |
character string: If not |
add.undeclared.levels |
character:
specify how to handle variables with at least one value label and further
non-missing values that have no value label (like a factor levels in R).
For |
duplicated.value.labels |
character: what to do with duplicated value
labels for different levels.
For |
duplicated.value.labels.infix |
character: the infix used for labels of
factor levels with duplicated value labels in SPSS (default |
... |
passed to |
This uses modified code from the PSPP project (http://www.gnu.org/software/pspp/ for reading the SPSS formats.
If the filename appears to be a URL (of schemes ‘http:’,
‘ftp:’ or ‘https:’) the URL is first downloaded to a
temporary file and then read. (‘https:’ is supported where
supported by download.file with its current default
method.)
Occasionally in SPSS, value labels will be added to some values of a
continuous variable (e.g. to distinguish different types of missing
data), and you will not want these variables converted to factors. By
setting max.value.labels you can specify that variables with a
large number of distinct values are not converted to factors even if
they have value labels.
If SPSS variable labels are present, they are returned as the
"variable.labels" attribute of the answer.
Fixed length strings (including value labels) are padded on the right
with spaces by SPSS, and so are read that way by R. The default
argument trim_values=TRUE causes trailing spaces to be ignored
when matching to value labels, as examples have been seen where the
strings and the value labels had different amounts of padding. See
the examples for sub for ways to remove trailing spaces
in character data.
URL https://learn.microsoft.com/en-us/windows/win32/intl/code-page-identifiers
provides a list of translations from Windows codepage numbers to
encoding names that iconv is likely to know about and so
suitable values for reencode. Automatic re-encoding is
attempted for apparent codepages of 200 or more in a UTF-8 or latin-1 locale:
some other high-numbered codepages can be re-encoded on most systems,
but the encoding names are platform-dependent (see
iconvlist).
A list (or optionally a data frame) with one component for each variable in the saved data set.
If what looks like a Windows codepage was recorded in the SPSS file,
it is attached (as a number) as attribute "codepage" to the
result.
There may be attributes "label.table" and
"variable.labels". Attribute "label.table" is a named
list of value labels with one element per variable, either NULL
or a named character vector. Attribute "variable.labels" is a
named character vector with names the short variable names and
elements the long names.
If there are user-defined missing values, there will be a attribute
"Missings". This is a named list with one list element per
variable. Each element has an element type, a length-one
character vector giving the type of missingness, and may also have an
element value with the values corresponding to missingness.
This is a complex subject (where the R and C source code for
read.spss is the main documentation), but the simplest cases
are types "one", "two" and "three" with a
corresponding number of (real or string) values whose labels can be
found from the "label.table" attribute. Other possibilities are
a finite or semi-infinite range, possibly plus a single value.
See also http://www.gnu.org/software/pspp/manual/html_node/Missing-Observations.html#Missing-Observations.
If SPSS value labels are converted to factors the underlying numerical
codes will not in general be the same as the SPSS numerical
values, since the numerical codes in R are always .
You may see warnings about the file encoding for SPSS save
files: it is possible such files contain non-ASCII character data
which need re-encoding. The most common occurrence is Windows codepage
1252, a superset of Latin-1. The encoding is recorded (as an integer)
in attribute "codepage" of the result if it looks like a
Windows codepage. Automatic re-encoding is done only in UTF-8 and latin-1
locales: see argument reencode.
Saikat DebRoy and the R-core team
A different interface also based on the PSPP codebase is available in
package memisc: see its help for spss.system.file.
(sav <- system.file("files", "electric.sav", package = "foreign")) dat <- read.spss(file=sav) str(dat) # list structure with attributes dat <- read.spss(file=sav, to.data.frame=TRUE) str(dat) # now a data.frame ### Now we use an example file that is not very well structured and ### hence may need some special treatment with appropriate argument settings. ### Expect lots of warnings as value labels (corresponding to R factor labels) are uncomplete, ### and an unsupported long string variable is present in the data (sav <- system.file("files", "testdata.sav", package = "foreign")) ### Examples for add.undeclared.levels: ## add.undeclared.levels = "sort" (default): x.sort <- read.spss(file=sav, to.data.frame = TRUE) ## add.undeclared.levels = "append": x.append <- read.spss(file=sav, to.data.frame = TRUE, add.undeclared.levels = "append") ## add.undeclared.levels = "no": x.no <- read.spss(file=sav, to.data.frame = TRUE, add.undeclared.levels = "no") levels(x.sort$factor_n_undeclared) levels(x.append$factor_n_undeclared) str(x.no$factor_n_undeclared) ### Examples for duplicated.value.labels: ## duplicated.value.labels = "append" (default) x.append <- read.spss(file=sav, to.data.frame=TRUE) ## duplicated.value.labels = "condense" x.condense <- read.spss(file=sav, to.data.frame=TRUE, duplicated.value.labels = "condense") levels(x.append$factor_n_duplicated) levels(x.condense$factor_n_duplicated) as.numeric(x.append$factor_n_duplicated) as.numeric(x.condense$factor_n_duplicated) ## Long Strings (>255 chars) are imported in consecutive separate variables ## (see warning about subtype 14): x <- read.spss(file=sav, to.data.frame=TRUE, stringsAsFactors=FALSE) cat.long.string <- function(x, w=70) cat(paste(strwrap(x, width=w), "\n")) ## first part: x$string_500: cat.long.string(x$string_500) ## second part: x$STRIN0: cat.long.string(x$STRIN0) ## complete long string: long.string <- apply(x[,c("string_500", "STRIN0")], 1, paste, collapse="") cat.long.string(long.string)(sav <- system.file("files", "electric.sav", package = "foreign")) dat <- read.spss(file=sav) str(dat) # list structure with attributes dat <- read.spss(file=sav, to.data.frame=TRUE) str(dat) # now a data.frame ### Now we use an example file that is not very well structured and ### hence may need some special treatment with appropriate argument settings. ### Expect lots of warnings as value labels (corresponding to R factor labels) are uncomplete, ### and an unsupported long string variable is present in the data (sav <- system.file("files", "testdata.sav", package = "foreign")) ### Examples for add.undeclared.levels: ## add.undeclared.levels = "sort" (default): x.sort <- read.spss(file=sav, to.data.frame = TRUE) ## add.undeclared.levels = "append": x.append <- read.spss(file=sav, to.data.frame = TRUE, add.undeclared.levels = "append") ## add.undeclared.levels = "no": x.no <- read.spss(file=sav, to.data.frame = TRUE, add.undeclared.levels = "no") levels(x.sort$factor_n_undeclared) levels(x.append$factor_n_undeclared) str(x.no$factor_n_undeclared) ### Examples for duplicated.value.labels: ## duplicated.value.labels = "append" (default) x.append <- read.spss(file=sav, to.data.frame=TRUE) ## duplicated.value.labels = "condense" x.condense <- read.spss(file=sav, to.data.frame=TRUE, duplicated.value.labels = "condense") levels(x.append$factor_n_duplicated) levels(x.condense$factor_n_duplicated) as.numeric(x.append$factor_n_duplicated) as.numeric(x.condense$factor_n_duplicated) ## Long Strings (>255 chars) are imported in consecutive separate variables ## (see warning about subtype 14): x <- read.spss(file=sav, to.data.frame=TRUE, stringsAsFactors=FALSE) cat.long.string <- function(x, w=70) cat(paste(strwrap(x, width=w), "\n")) ## first part: x$string_500: cat.long.string(x$string_500) ## second part: x$STRIN0: cat.long.string(x$STRIN0) ## complete long string: long.string <- apply(x[,c("string_500", "STRIN0")], 1, paste, collapse="") cat.long.string(long.string)
Generates a SAS program to convert the ssd contents to SAS transport format
and then uses read.xport to obtain a data frame.
read.ssd(libname, sectionnames, tmpXport=tempfile(), tmpProgLoc=tempfile(), sascmd="sas")read.ssd(libname, sectionnames, tmpXport=tempfile(), tmpProgLoc=tempfile(), sascmd="sas")
libname |
character string defining the SAS library (usually a directory reference) |
sectionnames |
character vector giving member names. These are
files in the |
tmpXport |
character string: location where temporary xport format archive should reside – defaults to a randomly named file in the session temporary directory, which will be removed. |
tmpProgLoc |
character string: location where temporary conversion SAS program should reside – defaults to a randomly named file in session temporary directory, which will be removed on successful operation. |
sascmd |
character string giving full path to SAS executable. |
Creates a SAS program and runs it.
Error handling is primitive.
A data frame if all goes well, or NULL with warnings and some
enduring side effects (log file for auditing)
This requires SAS to be available. If you have a SAS dataset
without access to SAS you will need another product to convert it to a
format such as .csv, for example ‘Stat/Transfer’ or
‘DBMS/Copy’ or the ‘SAS System Viewer’ (Windows only).
SAS requires section names to be no more than 8 characters. This is worked by the use of symbolic links: these are barely supported on Windows.
For Unix: VJ Carey [email protected]
## if there were some files on the web we could get a real ## runnable example ## Not run: R> list.files("trialdata") [1] "baseline.sas7bdat" "form11.sas7bdat" "form12.sas7bdat" [4] "form13.sas7bdat" "form22.sas7bdat" "form23.sas7bdat" [7] "form3.sas7bdat" "form4.sas7bdat" "form48.sas7bdat" [10] "form50.sas7bdat" "form51.sas7bdat" "form71.sas7bdat" [13] "form72.sas7bdat" "form8.sas7bdat" "form9.sas7bdat" [16] "form90.sas7bdat" "form91.sas7bdat" R> baseline <- read.ssd("trialdata", "baseline") R> form90 <- read.ssd("trialdata", "form90") ## Or for a Windows example sashome <- "/Program Files/SAS/SAS 9.1" read.ssd(file.path(sashome, "core", "sashelp"), "retail", sascmd = file.path(sashome, "sas.exe")) ## End(Not run)## if there were some files on the web we could get a real ## runnable example ## Not run: R> list.files("trialdata") [1] "baseline.sas7bdat" "form11.sas7bdat" "form12.sas7bdat" [4] "form13.sas7bdat" "form22.sas7bdat" "form23.sas7bdat" [7] "form3.sas7bdat" "form4.sas7bdat" "form48.sas7bdat" [10] "form50.sas7bdat" "form51.sas7bdat" "form71.sas7bdat" [13] "form72.sas7bdat" "form8.sas7bdat" "form9.sas7bdat" [16] "form90.sas7bdat" "form91.sas7bdat" R> baseline <- read.ssd("trialdata", "baseline") R> form90 <- read.ssd("trialdata", "form90") ## Or for a Windows example sashome <- "/Program Files/SAS/SAS 9.1" read.ssd(file.path(sashome, "core", "sashelp"), "retail", sascmd = file.path(sashome, "sas.exe")) ## End(Not run)
read.systat reads a rectangular data file stored by the Systat
SAVE command as (legacy) *.sys or more recently
*.syd files.
read.systat(file, to.data.frame = TRUE)read.systat(file, to.data.frame = TRUE)
file |
character variable with the name of the file to read |
to.data.frame |
return a data frame (otherwise a list) |
The function only reads those Systat files that are rectangular data
files (mtype = 1), and warns when files have non-standard
variable name codings. The files tested were produced on MS-DOS and
Windows: files for the Mac version of Systat have a completely
different format.
The C code was originally written for an add-on module for Systat
described in Bivand (1992 paper). Variable names retain the trailing
dollar in the list returned when to.data.frame is FALSE,
and in that case character variables are returned as is and filled up
to 12 characters with blanks on the right. The original function was
limited to reading Systat files with up to 256 variables (a Systat
limitation); it will now read up to 8192 variables.
If there is a user comment in the header this is returned as attribute
"comment". Such comments are always a multiple of 72
characters (with a maximum of 720 chars returned), normally padded with
trailing spaces.
A data frame (or list) with one component for each variable in the saved data set.
Roger Bivand
Systat Manual, 1987, 1989
Bivand, R. S. (1992) SYSTAT-compatible software for modelling spatial dependence among observations. Computers and Geosciences 18, 951–963.
summary(iris) iris.s <- read.systat(system.file("files/Iris.syd", package="foreign")[1]) str(iris.s) summary(iris.s)summary(iris) iris.s <- read.systat(system.file("files/Iris.syd", package="foreign")[1]) str(iris.s) summary(iris.s)
Reads a file as a SAS XPORT format library and returns a list of data.frames.
read.xport(file, ...)read.xport(file, ...)
file |
character variable with the name of the file to read. The file must be in SAS XPORT format. |
... |
passed to |
If there is a more than one dataset in the XPORT format library, a named
list of data frames, otherwise a data frame. The columns of the data
frames will be either numeric (corresponding to numeric in SAS) or
factor (corresponding to character in SAS). All SAS numeric missing
values (including special missing values represented by ._,
.A to .Z by SAS) are mapped to R NA.
Trailing blanks are removed from character columns before conversion to
a factor. Some sources claim that character missing values in SAS are
represented by ' ' or '': these are not treated as R
missing values.
Saikat DebRoy [email protected]
SAS Technical Support document TS-140: “The Record Layout of a Data Set in SAS Transport (XPORT) Format” available at https://support.sas.com/content/dam/SAS/support/en/technical-papers/record-layout-of-a-sas-version-5-or-6-data-set-in-sas-transport-xport-format.pdf
## Not run: ## no XPORT file is installed read.xport("test.xpt") ## End(Not run)## Not run: ## no XPORT file is installed read.xport("test.xpt") ## End(Not run)
Reads binary data files or data.dump files that were produced
in S version 3.
data.restore(file, print = FALSE, verbose = FALSE, env = .GlobalEnv) read.S(file)data.restore(file, print = FALSE, verbose = FALSE, env = .GlobalEnv) read.S(file)
file |
the filename of the S-PLUS |
print |
whether to print the name of each object as read from the file. |
verbose |
whether to print the name of every subitem within each object. |
env |
environment within which to create the restored object(s). |
read.S can read the binary files produced in some older
versions of S-PLUS on either Windows (versions 3.x, 4.x, 2000) or Unix
(version 3.x with 4 byte integers). It automatically detects whether
the file was produced on a big- or little-endian machine and adapts
itself accordingly.
data.restore can read a similar range of files produced by
data.dump and for newer versions of S-PLUS, those from
data.dump(....., oldStyle=TRUE).
Not all S3 objects can be handled in the current version. The most frequently encountered exceptions are functions and expressions; you will also have trouble with objects that contain model formulas. In particular, comments will be lost from function bodies, and the argument lists of functions will often be changed.
For read.S, an R version of the S3 object.
For data.restore, the name of the file.
Duncan Murdoch
## if you have an S-PLUS _Data file containing 'myobj' ## Not run: read.S(file.path("_Data", "myobj")) data.restore("dumpdata", print = TRUE) ## End(Not run)## if you have an S-PLUS _Data file containing 'myobj' ## Not run: read.S(file.path("_Data", "myobj")) data.restore("dumpdata", print = TRUE) ## End(Not run)
Writes data into Weka Attribute-Relation File Format (ARFF) files.
write.arff(x, file, eol = "\n", relation = deparse(substitute(x)))write.arff(x, file, eol = "\n", relation = deparse(substitute(x)))
x |
the data to be written, preferably a matrix or data frame. If not, coercion to a data frame is attempted. |
file |
either a character string naming a file, or a connection.
|
eol |
the character(s) to print at the end of each line (row). |
relation |
The name of the relation to be written in the file. |
relation will be passed through make.names before
writing to the file, in an attempt to it them acceptable to Weka, and
column names what do not start with an alphabetic character will have
X prepended.
However, the references say that ARFF files are ASCII files, and that encoding is not enforced.
Attribute-Relation File Format https://waikato.github.io/weka-wiki/formats_and_processing/arff/.
read.arff;
functions write.arff and read.arff in package
RWeka which provide some support for logicals via conversion to
or from factors.
write.arff(iris, file = "")write.arff(iris, file = "")
The function tries to write a data frame to a DBF file.
write.dbf(dataframe, file, factor2char = TRUE, max_nchar = 254)write.dbf(dataframe, file, factor2char = TRUE, max_nchar = 254)
dataframe |
a data frame object. |
file |
a file name to be written to. |
factor2char |
logical, default |
max_nchar |
The maximum number of characters allowed in a character field. Strings which exceed this will be truncated with a warning. See Details. |
Dots in column names are replaced by underlines in the DBF file, and names are truncated to 11 characters.
Only vector columns of classes "logical", "numeric",
"integer", "character", "factor" and
"Date" can be written. Other columns should be converted to
one of these.
Maximum precision (number of digits including minus sign and decimal sign) for numeric is 19 - scale (digits after the decimal sign) which is calculated internally based on the number of digits before the decimal sign.
The original DBASE format limited character fields to 254 bytes.
It is said that Clipper and FoxPro can read up to 32K, and it is
possible to write a reader that could accept up to 65535 bytes.
(The documentation suggests that only ASCII characters can be assumed
to be supported.) Readers expecting the older standard (which
includes Excel 2003, Access 2003 and OpenOffice 2.0) will truncate the
field to the maximum width modulo 256, so increase max_nchar
only if you are sure the intended reader supports wider character fields.
Invisible NULL.
Other applications have varying abilities to read the data types used
here. Microsoft Access reads "numeric", "integer",
"character" and "Date" fields, including recognizing
missing values, but not "logical" (read as 0,-1).
Microsoft Excel understood all possible types but did not interpret missing
values in character fields correctly (showing them as character nuls).
Nicholas J. Lewin-Koh, modified by Roger Bivand and Brian Ripley; shapelib by Frank Warmerdam.
https://www.clicketyclick.dk/databases/xbase/format/data_types.html
str(warpbreaks) try1 <- paste(tempfile(), ".dbf", sep = "") write.dbf(warpbreaks, try1, factor2char = FALSE) in1 <- read.dbf(try1) str(in1) try2 <- paste(tempfile(), ".dbf", sep = "") write.dbf(warpbreaks, try2, factor2char = TRUE) in2 <- read.dbf(try2) str(in2) unlink(c(try1, try2))str(warpbreaks) try1 <- paste(tempfile(), ".dbf", sep = "") write.dbf(warpbreaks, try1, factor2char = FALSE) in1 <- read.dbf(try1) str(in1) try2 <- paste(tempfile(), ".dbf", sep = "") write.dbf(warpbreaks, try2, factor2char = TRUE) in2 <- read.dbf(try2) str(in2) unlink(c(try1, try2))
Writes the data frame to file in the Stata binary
format. Does not write array variables unless they can be
drop-ed to a vector.
Frozen: will not support Stata formats after 10 (also used by Stata 11).
write.dta(dataframe, file, version = 7L, convert.dates = TRUE, tz = "GMT", convert.factors = c("labels", "string", "numeric", "codes"))write.dta(dataframe, file, version = 7L, convert.dates = TRUE, tz = "GMT", convert.factors = c("labels", "string", "numeric", "codes"))
dataframe |
a data frame. |
file |
character string giving filename. |
version |
integer: Stata version: 6, 7, 8 and 10 are supported, and 9 is mapped to 8, 11 to 10. |
convert.dates |
logical: convert |
tz |
timezone for date conversion. |
convert.factors |
how to handle factors. |
The major difference between supported file formats in Stata versions
is that version 7.0 and later allow 32-character variable names (5 and
6 were restricted to 8-character names). The abbreviate
function is used to trim variable names to the permitted length. A
warning is given if this is needed and it is an error for the
abbreviated names not to be unique. Each version of Stata is claimed
to be able to read all earlier formats.
The columns in the data frame become variables in the Stata data set. Missing values are handled correctly.
There are four options for handling factors. The default is to use
Stata ‘value labels’ for the factor levels. With
convert.factors = "string", the factor levels are written as
strings (the name of the value label is taken from the
"val.labels" attribute if it exists or the variable name
otherwise). With convert.factors = "numeric" the numeric values
of the levels are written, or NA if they cannot be coerced to
numeric. Finally, convert.factors = "codes" writes the
underlying integer codes of the factors. This last used to be the
only available method and is provided largely for backwards
compatibility.
If the "label.table" attribute contains value labels with
names not already attached to a variable (not the variable name or
name from "val.labels") then these will be written out as well.
If the "datalabel" attribute contains a string, it is written out
as the dataset label otherwise the dataset label is "Written by R.".
If the "expansion.table" attribute exists expansion fields are
written. This attribute should contain a list where each element is
character vector of length three. The first vector element contains the
name of a variable or "_dta" (meaning the dataset). The second element
contains the characeristic name. The third contains the associated data.
If the "val.labels" attribute contains a character vector with a
string label for each value then this is written as the value
labels. Otherwise the variable names are used.
If the "var.labels" attribute contains a character vector with a
string label for each variable then this is written as the variable
labels. Otherwise the variable names are repeated as variable labels.
For Stata 8 or later use the default version = 7 – the only
advantage of Stata 8 format over 7 is that it can represent multiple
different missing value types, and R doesn't have them. Stata 10/11
allows longer format lists, but R does not make use of them.
Note that the Stata formats are documented to use ASCII strings – R does not enforce this, but use of non-ASCII character strings will not be portable as the encoding is not recorded. Up to 244 bytes are allowed in character data, and longer strings will be truncated with a warning.
Stata uses some large numerical values to represent missing
values. This function does not currently check, and hence integers
greater than 2147483620 and doubles greater than
8.988e+307 may be misinterpreted by Stata.
NULL
Unless disabled by argument convert.dates = FALSE, R date and
date-time objects (POSIXt classes) are converted into the Stata
date format, the number of days since 1960-01-01. (For date-time
objects this may lose information.) Stata can be told that these are
dates by
format xdate %td;
It is possible to pass objects of class POSIXct to Stata to be
treated as one of its versions of date-times. Stata uses the number
of milliseconds since 1960-01-01, either excluding (format
%tc) or counting (format %tC) leap seconds. So
either an object of class POSICct can be passed to Stata with
convert.dates = FALSE and converted in Stata, or
315619200 should be added and then multiplied by 1000
before passing to write.dta and assigning format %tc.
Stata's comments on the first route are at
https://www.stata.com/manuals13/ddatetime.pdf, but at the time of
writing were wrong: R uses POSIX conventions and hence does not count
leap seconds.
Thomas Lumley and R-core members: support for value labels by Brian Quistorff.
Stata 6.0 Users Manual, Stata 7.0 Programming manual, Stata online help (version 8 and later, also https://www.stata.com/help.cgi?dta_114 and https://www.stata.com/help.cgi?dta_113) describe the file formats.
read.dta,
attributes,
DateTimeClasses,
abbreviate
write.dta(swiss, swissfile <- tempfile()) read.dta(swissfile)write.dta(swiss, swissfile <- tempfile()) read.dta(swissfile)
This function exports simple data frames to other statistical packages by writing the data as free-format text and writing a separate file of instructions for the other package to read the data.
write.foreign(df, datafile, codefile, package = c("SPSS", "Stata", "SAS"), ...)write.foreign(df, datafile, codefile, package = c("SPSS", "Stata", "SAS"), ...)
df |
A data frame |
datafile |
Name of file for data output |
codefile |
Name of file for code output |
package |
Name of package |
... |
Other arguments for the individual |
The work for this function is done by
foreign:::writeForeignStata, foreign:::writeForeignSAS and
foreign:::writeForeignSPSS. To add support for another package,
eg Systat, create a function writeForeignSystat with the same first
three arguments as write.foreign. This will be called from
write.foreign when package="Systat".
Numeric variables and factors are supported for all packages: dates and
times (Date, dates, date, and POSIXt
classes) and logical vectors are also supported for SAS and characters
are supported for SPSS.
For package="SAS" there are optional arguments
dataname = "rdata" taking a string that will be the SAS data set
name, validvarname taking either "V6" or "V7",
and libpath = NULL taking a string that will be the directory where
the target SAS datset will be written when the generated SAS code been
run.
For package="SPSS" there is an optional argument maxchars = 32L
taking an integer that causes the variable names (not variable labels)
to be abbreviated to not more than maxchars chars.
For compatibility with SPSS version 12 and before, change this to maxchars = 8L.
In single byte locales with SPSS versions 13 or later, this can be set to maxchars = 64L.
For package="SPSS", as a side effect, the decimal indicator is always set by
SET DECIMAL=DOT. which may override user settings of the indicator or its default
derived from the current locale.
Invisible NULL.
Thomas Lumley and Stephen Weigand
## Not run: datafile <- tempfile() codefile <- tempfile() write.foreign(esoph, datafile, codefile, package="SPSS") file.show(datafile) file.show(codefile) unlink(datafile) unlink(codefile) ## End(Not run)## Not run: datafile <- tempfile() codefile <- tempfile() write.foreign(esoph, datafile, codefile, package="SPSS") file.show(datafile) file.show(codefile) unlink(datafile) unlink(codefile) ## End(Not run)