data {utils} R Documentation

## Data Sets

### Description

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

### Usage

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


### Arguments

 ... literal character strings or names. 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. overwrite logical: should existing objects of the same name in envir be replaced?

### 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, as.is=FALSE), and hence result in a data frame.

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

If more than one matching file name is found, the first on this list is used. (Files with extensions ‘.txt’, ‘.tab’ or ‘.csv’ can be compressed, with or without further extension ‘.gz’, ‘.bz2’ or ‘.xz’.)

The data sets to be loaded can be specified as a set of character strings or names, 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 third and fourth types will always result in the creation of a single variable with the same name (without extension) as the data set.

If no data sets are specified, data lists the available data sets. For each package, it looks for a data index in the ‘Meta’ subdirectory or, if this is not found, scans the ‘data’ subdirectory for data files using list_files_with_type. 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 (whether found or not), or information about all available data sets in an object of class "packageIQR" if none were specified.

### Good practice

There is no requirement for data(foo) to create an object named foo (nor to create one object), although it much reduces confusion if this convention is followed (and it is enforced if datasets are lazy-loaded).

data() was originally intended to allow users to load datasets from packages for use in their examples, and as such it loaded the datasets into the workspace .GlobalEnv. This avoided having large datasets in memory when not in use: that need has been almost entirely superseded by lazy-loading of datasets.

The ability to specify a dataset by name (without quotes) is a convenience: in programming the datasets should be specified by character strings (with quotes).

Use of data within a function without an envir argument has the almost always undesirable side-effect of putting an object in the user's workspace (and indeed, of replacing any object of that name already there). It would almost always be better to put the object in the current evaluation environment by data(..., envir = environment()). However, two alternatives are usually preferable, both described in the ‘Writing R Extensions’ manual.

• For sets of data, set up a package to use lazy-loading of data.

• For objects which are system data, for example lookup tables used in calculations within the function, use a file ‘R/sysdata.rda’ in the package sources or create the objects by R code at package installation time.

A sometimes important distinction is that the second approach places objects in the namespace but the first does not. So if it is important that the function sees mytable as an object from the package, it is system data and the second approach should be used. In the unusual case that a package uses a lazy-loaded dataset as a default argument to a function, that needs to be specified by ::, e.g., survival::survexp.us.

### Warning

This function creates objects in the envir environment (by default the user's workspace) replacing any which already existed. data("foo") can silently create objects other than foo: there have been instances in published packages where it created/replaced .Random.seed and hence change the seed for the session.

### Note

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.

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

The ‘Writing R Extensions’ for considerations in preparing the ‘data’ directory of a package.

### Examples

require(utils)
data()                         # list all available data sets
try(data(package = "rpart"), silent = TRUE) # list the data sets in the rpart package