data {utils}

R Documentation

Data Sets

Description

Loads specified data sets, or list the available data sets.

Usage

data(..., list = character(0), package = NULL, lib.loc = NULL,
     verbose = getOption("verbose"), envir = .GlobalEnv)

Arguments

...	a sequence of names or literal character strings.
list	a character vector.
package	a character vector giving the package(s) to look in for data sets, or NULL. By default, all packages in the search path are used, then the ‘data’ subdirectory (if present) of the current working directory.
lib.loc	a character vector of directory names of R libraries, or NULL. The default value of NULL corresponds to all libraries currently known.
verbose	a logical. If TRUE, additional diagnostics are printed.
envir	the environment where the data should be loaded.

Details

Currently, four formats of data files are supported:

1. files ending ‘.R’ or ‘.r’ are source()d in, with the R working directory changed temporarily to the directory containing the respective file. (data ensures that the utils package is attached, in case it had been run via utils::data.)

2. files ending ‘.RData’ or ‘.rda’ are load()ed.

3. files ending ‘.tab’, ‘.txt’ or ‘.TXT’ are read using read.table(..., header = TRUE), and hence result in a data frame.

4. files ending ‘.csv’ or ‘.CSV’ are read using read.table(..., header = TRUE, sep = ";"), and also result in a data frame.

If more than one matching file name is found, the first on this list is used.

The data sets to be loaded can be specified as a sequence of names or character strings, or as the character vector list, or as both.

For each given data set, the first two types (‘.R’ or ‘.r’, and ‘.RData’ or ‘.rda’ files) can create several variables in the load environment, which might all be named differently from the data set. The second two (‘.tab’, ‘.txt’, or ‘.TXT’, and ‘.csv’ or ‘.CSV’ files) will always result in the creation of a single variable with the same name as the data set.

If no data sets are specified, data lists the available data sets. It looks for a new-style data index in the ‘Meta’ or, if this is not found, an old-style ‘00Index’ file in the ‘data’ directory of each specified package, and uses these files to prepare a listing. If there is a ‘data’ area but no index, available data files for loading are computed and included in the listing, and a warning is given: such packages are incomplete. The information about available data sets is returned in an object of class "packageIQR". The structure of this class is experimental. Where the datasets have a different name from the argument that should be used to retrieve them the index will have an entry like beaver1 (beavers) which tells us that dataset beaver1 can be retrieved by the call data(beaver).

If lib.loc and package are both NULL (the default), the data sets are searched for in all the currently loaded packages then in the ‘data’ directory (if any) of the current working directory.

If lib.loc = NULL but package is specified as a character vector, the specified package(s) are searched for first amongst loaded packages and then in the default library/ies (see .libPaths).

If lib.loc is specified (and not NULL), packages are searched for in the specified library/ies, even if they are already loaded from another library.

To just look in the ‘data’ directory of the current working directory, set package = character(0) (and lib.loc = NULL, the default).

Value

a character vector of all data sets specified, or information about all available data sets in an object of class "packageIQR" if none were specified.

Note

The data files can be many small files. On some file systems it is desirable to save space, and the files in the ‘data’ directory of an installed package can be zipped up as a zip archive ‘Rdata.zip’. You will need to provide a single-column file ‘filelist’ of file names in that directory.

One can take advantage of the search order and the fact that a ‘.R’ file will change directory. If raw data are stored in ‘mydata.txt’ then one can set up ‘mydata.R’ to read ‘mydata.txt’ and pre-process it, e.g., using transform. For instance one can convert numeric vectors to factors with the appropriate labels. Thus, the ‘.R’ file can effectively contain a metadata specification for the plaintext formats.

See Also

help for obtaining documentation on data sets, save for creating the second (‘.rda’) kind of data, typically the most efficient one.

Examples

require(utils)
data()                       # list all available data sets
try(data(package = "rpart") )# list the data sets in the rpart package
data(USArrests, "VADeaths")  # load the data sets 'USArrests' and 'VADeaths'
help(USArrests)              # give information on data set 'USArrests'

---