This is a guide to importing and exporting data to and from R.
The current version of this document is 1.7.1 (2003-06-16). ISBN 3-901167-53-6
The relational databases part of this manual is based in part on an earlier manual by Douglas Bates and Saikat DebRoy. The principal author of this manual was Brian Ripley.
Many volunteers have contributed to the packages used here. The principal authors of the packages mentioned are
CORBA Duncan Temple Lang e1071 Friedrich Leisch foreign Thomas Lumley, Saikat DebRoy, Douglas Bates and Duncan Murdoch hdf5 Marcus Daniels Java John Chambers and Duncan Temple Lang netCDF Thomas Lumley RmSQL Torsten Hothorn RMySQL David James and Saikat DebRoy RODBC Michael Lapsley and Brian Ripley RSPerl Duncan Temple Lang RPgSQL Timothy Keitt RSPython Duncan Temple Lang
Brian Ripley is the author of the support for connections.
Reading data into a statistical system for analysis and exporting the results to some other system for report writing can be frustrating tasks that can take far more time than the statistical analysis itself, even though most readers will find the latter far more appealing.
This manual describes the import and export facilities available either in R itself or via packages which are available from CRAN. Some of the packages described are still under development but they already provide useful functionality.
Unless otherwise stated, everything described in this manual is available for both Unix/Linux and Windows versions of R. (Much is not yet available for the classic Macintosh port.)
In general, statistical systems like R are not particularly well
suited to manipulations of large-scale data. Some other systems are
better than R at this, and part of the thrust of this manual is to
suggest that rather than duplicating functionality in R we can make
the other system do the work! (For example Therneau & Grambsch (2000)
comment that they prefer to do data manipulation in SAS and then use
survival in S for the analysis.) Several recent packages
allow functionality developed in languages such as
python to be directly integrated with R code,
making the use of facilities in these languages even more
appropriate. (See the Java, RSPerl and RSPython
It is also worth remembering that R like S comes from the Unix
tradition of small re-usable tools, and it can be rewarding to use tools
perl to manipulate data before import or
after export. The case study in Becker, Chambers & Wilks (1988, Chapter
9) is an example of this, where Unix tools were used to check and
manipulate the data before input to S. R itself takes that
perl to manipulate its databases of help files
rather than R itself, and the function
read.fwf used a call to
perl script until it was decided not to require
run-time. The traditional Unix tools are now much more widely
available, including on Windows (but not on classic Macintosh).
The easiest form of data to import into R is a simple text file, and
this will often be acceptable for problems of small or medium scale.
The primary function to import from a text file is
scan, and this
underlies most of the more convenient functions discussed in
However, all statistical consultants are familiar with being presented by a client with a floppy disc or CD-R of data in some proprietary binary format, for example `an Excel spreadsheet' or `an SPSS file'. Often the simplest thing to do is to use the originating application to export the data as a text file (and statistical consultants will have copies of the commonest applications on their computers for that purpose). However, this is not always possible, and Importing from other statistical systems discusses what facilities are available to access such files directly from R.
In a few cases, data have been stored in a binary form for compactness and speed of access. One application of this that we have seen several times is imaging data, which is normally stored as a stream of bytes as represented in memory, possibly preceded by a header. Such data formats are discussed in Binary files and Binary connections.
For much larger databases it is common to handle the data using a database management system (DBMS). There is once again the option of using the DBMS to extract a plain file, but for many such DBMSs the extraction operation can be done directly from an R package: See Relational databases. Importing data via network connections is discussed in Network interfaces.
Exporting results from R is usually a less contentious task, but there are still a number of pitfalls. There will be a target application in mind, and normally a text file will be the most convenient interchange vehicle. (If a binary file is required, see Binary files.)
cat underlies the functions for exporting data. It
file argument, and the
append argument allows a
text file to be written via successive calls to
The commonest task is to write a matrix or data frame to file as a
rectangular grid of numbers, possibly with row and column labels. This
can be done by the functions
write just writes out a matrix or vector in a specified
number of columns (and transposes a matrix). Function
write.table is more convenient, and writes out a data frame (or
an object that can be coerced to a data frame) with row and column
There are a number of issues that need to be considered in writing out a data frame to a text file.
These functions are based on
options(digits). It may be necessary to increase this
to avoid losing precision. For more control, use
format on a
data frame, possibly column-by-column.
R prefers the header line to have no entry for the row names, so the file looks like
dist climb time Greenmantle 2.5 650 16.083 ...
Some other systems require a (possibly empty) entry for the row names, which
write.table will provide if argument
col.names = NA
is specified. Excel is one such system.
A common field separator to use in the file is a comma, as that is
unlikely to appear in any of the fields, in English-speaking countries.
Such files are known as CSV (comma separated values) files.
In some locales the comma is used as the decimal point
(set this in
dec = ",") and there CSV files
use the semicolon as the field separator.
Using a semicolon or tab (
sep = "\t") are probably the safest
By default missing values are output as
NA, but this may be
changed by argument
na. Note that
NaNs are treated as
write.table, but not by
By default strings are quoted (including the row and column names).
quote controls quoting of character and factor variables.
Some care is needed if the strings contain embedded quotes. Three useful forms are
> df <- data.frame(a = I("a \" quote")) > write.table(df) "a" "1" "a \" quote" > write.table(df, qmethod = "double") "a" "1" "a "" quote" > write.table(df, quote = FALSE, sep = ",") a 1,a " quote
The second is the form of escape commonly used by spreadsheets.
write.table is often not the best way to write out
very large matrices, for which it can use excessively large amounts of
write.matrix in package MASS provides a
specialized interface for writing matrices, with the option of writing
them in blocks and thereby reducing memory usage.
It is possible to use
sink to divert the standard R output to
a file, and thereby capture the output of (possibly implicit)
options(width) setting may need to be increased.
When reading data from text files, it is the responsibility of the user to know and to specify the conventions used to create that file, e.g. the comment character, whether a header line is present, the value separator, the representation for missing values (and so on) described in Export to text files. A markup language which can be used to describe not only content but also the structure of the content can make a file self-describing, so that one need not provide these details to the software reading the data.
The eXtensible Markup Language - more commonly known simply as XML - can be used to provide such structure, not only for standard datasets but also more complex data structures. XML is becoming extremely popular and is emerging as a standard for general data markup and exchange. It is being used by different communities to describe geographical data such as maps, graphical displays, mathematics and so on.
The XML package provides general facilities for reading and writing XML documents within both R and S-PLUS in the hope that we can easily make use of this technology as it evolves. Several people are exploring how we can use XML for, amongst other things, representing datasets to be shared across different applications; storing R and S-PLUS objects so they can be shared by both systems; representing plots via SVG (Scalable Vector Graphics, a dialect of XML); representing function documentation; generating "live" analyses/reports that contain text, data and code.
A description of the facilities of the XML package is outside the scope of this document: see the package's Web page at http://www.omegahat.org/RSXML for details and examples.
In Export to text files we saw a number of variations on the format of a spreadsheet-like text file, in which the data are presented in a rectangular grid, possibly with row and column labels. In this section we consider importing such files into R.
read.table is the most convenient way to read in a
rectangular grid of data. Because of the many possibilities, there are
several other functions that call
read.table but change a group
of default arguments.
read.table is an inefficient way to read in
very large numerical matrices: see
Some of the issues to consider are:
We recommend that you specify the
header argument explicitly,
Conventionally the header line has entries only for the columns and not
for the row labels, so is one field shorter than the remaining lines.
(If R sees this, it sets
header = TRUE.) If presented with a
file that has a (possibly empty) header field for the row labels, read
it in by something like
read.table("file.dat", header = TRUE, row.names = 1)
Column names can be given explicitly via the
names override the header line (if present).
Normally looking at the file will determine the field separator to be
used, but with white-space separated files there may be a choice between
sep = "" which uses any white space (spaces, tabs or
newlines) as a separator,
sep = " " and
sep = "\t". Note
that the choice of separator affects the input of quoted strings.
If you have a tab-delimited file containing empty fields be sure to use
sep = "\t".
By default character strings can be quoted by either
', and in each case all the characters up to a matching quote are
taken as part of the character string. The set of valid quoting
characters (which might be none) is controlled by the
sep = "\n" the default is changed to
If no separator character is specified, quotes can be escaped within
quoted strings by immediately preceding them by
If a separator character is specified, quotes can be escaped within quoted strings by doubling them as is conventional in spreadsheets. For example
'One string isn''t two',"one more"
can be read by
read.table("testfile", sep = ",")
This does not work with the default separator.
By default the file is assumed to contain the character string
to represent missing values, but this can be changed by the argument
na.strings, which is a vector of one or more character
representations of missing values.
Empty fields in numeric columns are also regarded as missing values.
It is quite common for a file exported from a spreadsheet to have all
trailing empty fields (and their separators) omitted. To read such
fill = TRUE.
If a separator is specified, leading and trailing white space in
character fields is regarded as part of the field. To strip the space,
strip.white = TRUE.
read.table ignores empty lines. This can be changed
blank.lines.skip = FALSE, which will only be useful in
fill = TRUE, perhaps to indicate missing data in
a regular layout.
Unless you take any special action,
read.table tries to select a
suitable class for each variable in the data frame. It tries in turn
moving on if any entry is not missing and cannot be
converted.1 If all of these fail, the
variable is converted to a factor.
as.is provide greater control.
as.is suppresses conversion of character vectors to factors
colClasses allows the desired class to be set for
each column in the input.
as.is are specified per
column, not per variable, and so include the column of row names
# as a comment character,
and if this is encountered (except in quoted strings) the rest of the
line is ignored. Lines containing only white space and a comment are
treated as blank lines.
If it is known that there will be no comments in the data file, it is
safer (and may be faster) to use
comment.char = "".
read.table appropriate for CSV and tab-delimited
files exported from spreadsheets in English-speaking locales. The
read.delim2 are appropriate for
use in countries where the comma is used for the decimal point.
If the options to
read.table are specified incorrectly, the error
message will usually be of the form
Error in scan(file = file, what = what, sep = sep, : line 1 did not have 5 elements
Error in read.table("files.dat", header = TRUE) : more columns than column names
This may give enough information to find the problem, but the auxiliary
count.fields can be useful to investigate further.
Efficiency can be important when reading large data grids. It will help
colClasses as one of the atomic vector types (logical,
integer, numeric, complex or character) for each column, and to give
nrows, the number of rows to be read (and a mild over-estimate is
better than not specifying this at all).
Sometimes data files have no field delimiters but have fields in pre-specified columns. This was very common in the days of punched cards, and is still sometimes used to save file space.
read.fwf provides a simple way to read such files,
specifying a vector of field widths. The function reads the file into
memory as whole lines, splits the resulting character strings, writes
out a temporary tab-separated file and then calls
This is adequate for small files, but for anything more complicated we
recommend using the facilities of a language like
pre-process the file.
scan to read the
file, and then process the results of
scan. They are very
convenient, but sometimes it is better to use
scan has many arguments, most of which we have already
read.table. The most crucial argument is
what, which specifies a list of modes of variables to be read
from the file. If the list is named, the names are used for the
components of the returned list. Modes can be numeric, character or
complex, and are usually specified by an example, e.g.
0i. For example
cat("2 3 5 7", "11 13 17 19", file="ex.dat", sep="\n") scan(file="ex.dat", what=list(x=0, y="", z=0), flush=TRUE)
returns a list with three components and discards the fourth column in the file.
There is a function
readLines which will be more convenient if
all you want is to read whole lines into R for further processing.
One common use of
scan is to read in a large numeric matrix.
matrix.dat just contains the numbers for a 200 x
2000 matrix. Then we can use
A <- matrix(scan("matrix.dat", n = 200*2000), 200, 2000, byrow = TRUE)
On one test this took 2 seconds whereas
A <- as.matrix(read.table("matrix.dat"))
took 32 seconds (and a lot more memory). (Using
"" saved 3 seconds.)
Sometimes spreadsheet data is in a compact format that gives the covariates for each subject followed by all the observations on that subject. R's modelling functions need observations in a single column. Consider the following sample of data from repeated MRI brain measurements
Status Age V1 V2 V3 V4 P 23646 45190 50333 55166 56271 CC 26174 35535 38227 37911 41184 CC 27723 25691 25712 26144 26398 CC 27193 30949 29693 29754 30772 CC 24370 50542 51966 54341 54273 CC 28359 58591 58803 59435 61292 CC 25136 45801 45389 47197 47126
There are two covariates and up to four measurements on each subject.
The data were exported from Excel as a file
We can use
stack to help manipulate these data to give a single
zz <- read.csv("mr.csv", strip.white = TRUE) zzz <- cbind(zz[gl(nrow(zz), 1, 4*nrow(zz)), 1:2], stack(zz[, 3:6]))
Status Age values ind X1 P 23646 45190 V1 X2 CC 26174 35535 V1 X3 CC 27723 25691 V1 X4 CC 27193 30949 V1 X5 CC 24370 50542 V1 X6 CC 28359 58591 V1 X7 CC 25136 45801 V1 X11 P 23646 50333 V2 ...
unstack goes in the opposite direction, and may be
useful for exporting data.
Another way to do this is to use the function
> reshape(zz, idvar="id",timevar="var", varying=list(c("V1","V2","V3","V4")),direction="long") Status Age var V1 id 1.1 P 23646 1 45190 1 2.1 CC 26174 1 35535 2 3.1 CC 27723 1 25691 3 4.1 CC 27193 1 30949 4 5.1 CC 24370 1 50542 5 6.1 CC 28359 1 58591 6 7.1 CC 25136 1 45801 7 1.2 P 23646 2 50333 1 2.2 CC 26174 2 38227 2 ...
reshape has a more complicated syntax than
can be used for data where the `long' form has more than the one column
in this example. With
reshape can also
perform the opposite transformation.
Displaying higher-dimensional contingency tables in array form typically
is rather inconvenient. In categorical data analysis, such information
is often represented in the form of bordered two-dimensional arrays with
leading rows and columns specifying the combination of factor levels
corresponding to the cell counts. These rows and columns are typically
"ragged" in the sense that labels are only displayed when they change,
with the obvious convention that rows are read from top to bottom and
columns are read from left to right. In R, such "flat" contingency
tables can be created using
which creates objects of class
"ftable" with an appropriate print
As a simple example, consider the R standard data set
UCBAdmissions which is a 3-dimensional contingency table
resulting from classifying applicants to graduate school at UC Berkeley
for the six largest departments in 1973 classified by admission and sex.
> data(UCBAdmissions) > ftable(UCBAdmissions) Dept A B C D E F Admit Gender Admitted Male 512 353 120 138 53 22 Female 89 17 202 131 94 24 Rejected Male 313 207 205 279 138 351 Female 19 8 391 244 299 317
The printed representation is clearly more useful than displaying the data as a 3-dimensional array.
There is also a function
read.ftable for reading in flat-like
contingency tables from files.
This has additional arguments for dealing with variants on how exactly
the information on row and column variables names and levels is
represented. The help page for
read.ftable has some useful
examples. The flat tables can be converted to standard contingency
tables in array form using
Note that flat tables are characterized by their "ragged" display of
row (and maybe also column) labels. If the full grid of levels of the
row variables is given, one should instead use
read.table to read
in the data, and create the contingency table from this using
In this chapter we consider the problem of reading a binary data file written by another statistical system. This is often best avoided, but may be unavoidable if the originating system is not available.
The recommended package foreign provides import facilities for
files produced by these statistical systems, and for export to Stata. In
some cases these function may require substantially less memory than
.dta files are a binary file format. Files from versions
5.0, 6.0 and 7.0 of Stata can be read and written by functions
write.dta. Stata variables with value labels
are optionally converted to (and from) R factor.
EpiInfo versions 5 and 6 stored data in a self-describing fixed-width
read.epiinfo will read these
.REC files into
an R data frame.
read.mtp imports a `Minitab Portable Worksheet'. This
returns the components of the worksheet as an R list.
read.xport reads a file in SAS Transport (XPORT) format
and return a list of data frames. If SAS is available on your system,
read.ssd can be used to create and run a SAS script that
saves a SAS permanent dataset (
Transport format. It then calls
read.xport to read the resulting
read.spss can read files created by the `save' and
`export' commands in SPSS. It returns a list with one
component for each variable in the saved data set. SPSS
variables with value labels are optionally converted to R factors.
read.S which can read binary objects produced by S-PLUS
3.x, 4.x or 2000 on (32-bit) Unix or Windows (and can read them on a
different OS). This is able to read many but not all S objects: in
particular it can read vectors, matrices and data frames and lists
data.restore reads S-PLUS data dumps (created by
data.dump) with the same restrictions (except that dumps from the
Alpha platform can also be read). It should be possible to read data
dumps from S-PLUS 5.x and 6.x written with
If you have access to S-PLUS, it is usually more reliable to
the object(s) in S-PLUS and
source the dumpfile in R. For
S-PLUS 5.x and 6.x you may need to use
dump(..., oldStyle=T), and
to read in very large objects it may be preferable to use the dumpfile
as a batch script rather than
Octave is a numerical linear algebra system, and function
read.octave in package e1071 can read the first vector or
matrix from an Octave ASCII data file created using the Octave command
There are limitations on the types of data that R handles well. Since all data being manipulated by R are resident in memory, and several copies of the data can be created during execution of a function, R is not well suited to extremely large data sets. Data objects that are more than a few (tens of) megabytes in size can cause R to run out of memory.
R does not easily support concurrent access to data. That is, if more than one user is accessing, and perhaps updating, the same data, the changes made by one user will not be visible to the others.
R does support persistence of data, in that you can save a data object or an entire worksheet from one session and restore it at the subsequent session, but the format of the stored data is specific to R and not easily manipulated by other systems.
Database management systems (DBMSs) and, in particular, relational DBMSs (RDBMSs) are designed to do all of these things well. Their strengths are
The sort of statistical applications for which DBMS might be used are to extract a 10% sample of the data, to cross-tabulate data to produce a multi-dimensional contingency table, and to extract data group by group from a database for separate analysis.
Traditionally there have been large (and expensive) commercial RDBMSs (Informix; Oracle; Sybase; IBM's DB/2; Microsoft SQL Server on Windows) and academic and small-system databases (such as MySQL, PostgreSQL, Microsoft Access, ...), the former marked out by much greater emphasis on data security features. The line is blurring, with the Open Source PostgreSQL having more and more high-end features, and `free' versions of Informix, Oracle and Sysbase being made available on Linux.
There are other commonly used data sources, including spreadsheets, non-relational databases and even text files (possibly compressed). Open Database Connectivity (ODBC) is a standard to use all of these data sources. It originated on Windows (see http://www.microsoft.com/data/odbc/) but is also implemented on Linux.
All of the packages described later in this chapter provide clients to client/server databases. The database can reside on the same machine or (more often) remotely. There is an ISO standard (in fact several: SQL92 is ISO/IEC 9075, also known as ANSI X3.135-1992, and SQL99 is coming into use) for an interface language called SQL (Structured Query Language, sometimes pronounced `sequel': see Bowman et al. 1996 and Kline and Kline 2001) which these DBMSs support to varying degrees.
The more comprehensive R interfaces generate SQL behind the scenes for common operations, but direct use of SQL is needed for complex operations in all. Conventionally SQL is written in upper case, but many users will find it more convenient to use lower case in the R interface functions.
A relational DBMS stores data as a database of tables (or relations) which are rather similar to R data frames, in that they are made up of columns or fields of one type (numeric, character, date, currency, ...) and rows or records containing the observations for one entity.
SQL `queries' are quite general operations on a relational database. The classical query is a SELECT statement of the type
SELECT State, Murder FROM USArrests WHERE rape > 30 ORDER BY Murder SELECT t.sch, c.meanses, t.sex, t.achieve FROM student as t, school as c WHERE t.sch = c.id SELECT sex, COUNT(*) FROM student GROUP BY sex SELECT sch, AVG(sestat) FROM student GROUP BY sch LIMIT 10
The first of these selects two columns from the R data frame
USArrests that has been copied across to a database table,
subsets on a third column and asks the results be sorted. The second
performs a database join on two tables
school and returns four columns. The third and fourth queries do
some cross-tabulation and return counts or averages. (The five
aggregation functions are COUNT(*) and SUM, MAX, MIN and AVG, each
applied to a single column.)
SELECT queries use FROM to select the table, WHERE to specify a condition for inclusion (or more than one condition separated by AND or OR), and ORDER BY to sort the result. Unlike data frames, rows in RDBMS tables are best thought of as unordered, and without an ORDER BY statement the ordering is indeterminate. You can sort (in lexicographical order) on more than one column by separating them by commas. Placing DESC after an ORDER BY puts the sort in descending order.
SELECT DISTINCT queries will only return one copy of each distinct row in the selected table.
The GROUP BY clause selects subgroups of the rows according to the criterion. If more than one column is specified (separated by commas) then multi-way cross-classifications can be summarized by one of the five aggregation functions. A HAVING clause allows the select to include or exclude groups depending on the aggregated value.
If the SELECT statement contains an ORDER BY statement that produces a unique ordering, a LIMIT clause can be added to select (by number) a contiguous block of output rows. This can be useful to retrieve rows a block at a time. (It may not be reliable unless the ordering is unique, as the LIMIT clause can be used to optimize the query.)
There are queries to create a table (CREATE TABLE, but usually one copies a data frame to the database in these interfaces), INSERT or DELETE or UPDATE data. A table is destroyed by a DROP TABLE `query'.
Kline and Kline (2001) discuss the details of the implementation of SQL in SQL Server 2000, Oracle, MySQL and PostgreSQL.
Data can be stored in a database in various data types. The range of data types is DBMS-specific, but the SQL standard defines many types, including the following that are widely implemented (often not by the SQL name).
There are variants on
The more comprehensive of the R interface packages hide the type conversion issues from the user.
There are several packages available on CRAN to help R communicate with DBMSs. They provide different levels of abstraction. Some provide means to copy whole data frames to and from databases. All have functions to select data within the database via SQL queries, and (except RmSQL) to retrieve the result as a whole as a data frame or in pieces (usually as groups of rows, but RPgSQL can retrieve columns).
All except RODBC are tied to one DBMS, but work is in progress
towards a unified `front-end' package DBI
(http://developer.r-project.org/db) in conjuction with a
`back-end', the most developed of which is RMySQL. Also on CRAN
are the back-ends ROracle and RSQLite (which works with the
Package RMySQL on CRAN provides an interface to the
MySQL database system (see http://www.mysql.com and Dubois,
2000.). The description here applies to version
versions had a substantially different interface. The current version
requires the DBI package, and this description will apply with
minor changes to all the other back-ends to DBI.
MySQL exists on Unix/Linux and Windows: as from version 3.23.x (Jan 2001) it is released under GPL. MySQL is a `light and lean' database. (It preserves the case of names where the operating file system is case-sensitive, so not on Windows.) Package RMySQL has been used on both Linux and Windows.
dbDriver("MySQL") returns a database connection
manager object, and then a call to
dbConnect opens a database
connection which can subsequently be closed by a call to the generic
dbDriver("SQLite") with those
DBMSs and ROracle or RSQLite respectively.
SQL queries can be sent by either
dbGetquery sends the query and retrieves the
results as a data frame.
dbSendQuery sends the query and returns
an object of class inheriting from
"DBIResult" which can be used
to retrieve the results, and subsequently used in a call to
dbClearResult to remove the result.
fetch is used to retrieve some or all of the rows in the
query result, as a list. The function
dbHasCompleted indicates if
all the rows have been fetched, and
dbGetRowCount returns the
number of rows in the result.
These are convenient interfaces to read/write/test/delete tables in the
dbWriteTable copy to and from
an R data frame, mapping the row names of the data frame to the field
row_names in the
> library(RMySQL) # will load DBI as well ## open a connection to a MySQL database > con <- dbConnect(dbDriver("MySQL"), dbname = "test") ## list the tables in the database > dbListTables(con) ## load a data frame into the database, deleting any existing copy > data(USArrests) > dbWriteTable(con, "arrests", USArrests, overwrite = TRUE) TRUE > dbListTables(con)  "arrests" ## get the whole table > dbReadTable(con, "arrests") Murder Assault UrbanPop Rape Alabama 13.2 236 58 21.2 Alaska 10.0 263 48 44.5 Arizona 8.1 294 80 31.0 Arkansas 8.8 190 50 19.5 ... ## Select from the loaded table: all on one line > dbGetQuery(con, "select row_names, Murder from arrests where Rape > 30 order by Murder") row_names Murder 1 Colorado 7.9 2 Arizona 8.1 3 California 9.0 4 Alaska 10.0 5 New Mexico 11.4 6 Michigan 12.1 7 Nevada 12.2 8 Florida 15.4 > dbRemoveTable(con, "arrests") > dbDisconnect(con)
Package RODBC on CRAN provides an interface to database sources supporting an ODBC interface. This is very widely available, and allows the same R code to access different database systems. RODBC runs on both Linux and Windows, and many database systems provide support for ODBC, including most of those on Windows (such as Microsoft Access), and MySQL, Oracle and PostgreSQL on Unix/Linux.
On Windows ODBC support is normally installed, and current versions are available from http://www.microsoft.com/data/odbc/ as part of MDAC. On Unix/Linux you will need an ODBC Driver Manager such as unixODBC (http://www.unixODBC.org) or iOBDC (http://www.iODBC.org) and an installed driver for your database system. The FreeODBC project (http://www.jepstone.net/FreeODBC/) is a repository of information related to ODBC.
Two groups of interface functions are provided. The
provide a low-level interface to the basic ODBC functions: see
the help page (
?RODBC) for details. The
provide an interface between R data frames and SQL tables.
Up to 16 simultaneous connections are possible. A connection is opened
by a call to
odbcDriverConnect (which on
Windows allows a database to be selected via dialog boxes) which returns
a handle used for subsequent access to the database. A connection is
odbcClose. Details of the tables on a
connection can be found using
sqlSave copies an R data frame to a table in the
sqlFetch copies a table in the database to an R
An SQL query can be sent to the database by a call to
sqlQuery. This returns the result in an R data frame.
sqlCopy sends a query to the database and saves the result as a
table in the database.) A finer level of control is attained by first
odbcQuery and then
sqlGetResults to fetch the
results. The latter can be used within a loop to retrieve a limited
number of rows at a time.
Here is an example using PostgreSQL, for which the ODBC driver
maps column and data frame names to lower case. We use a database
testdb we created earlier, and had the DSN (data source name) set
unixODBC. Exactly the same code
worked using MyODBC to access a MySQL database under Linux or Windows
(where MySQL also maps names to lowercase). Under Windows,
DSNs are set up in the ODBC applet in the Control
Panel (`Data Sources (ODBC)' in the `Administrative Tools' section on
> library(RODBC) ## tell it to map names to l/case > channel <- odbcConnect("testdb", uid="ripley", case="tolower") ## load a data frame into the database > data(USArrests) > sqlSave(channel, USArrests, rownames = "state") > rm(USArrests) ## list the tables in the database > sqlTables(channel) TABLE_QUALIFIER TABLE_OWNER TABLE_NAME TABLE_TYPE REMARKS 1 usarrests TABLE ## list it > sqlFetch(channel, "USArrests", rownames = "state") murder assault urbanpop rape Alabama 13.2 236 58 21.2 Alaska 10.0 263 48 44.5 ... ## an SQL query, originally on one line > sqlQuery(channel, "select state, murder from USArrests where rape > 30 order by murder") state murder 1 Colorado 7.9 2 Arizona 8.1 3 California 9.0 4 Alaska 10.0 5 New Mexico 11.4 6 Michigan 12.1 7 Nevada 12.2 8 Florida 15.4 ## remove the table > sqlDrop(channel, "USArrests") ## close the connection > odbcClose(channel)
As a simple example of using ODBC under Windows with a Excel
spreadsheet, suppose that an DSN for spreadsheet
bdr.xls has been set up in the Control Panel. Then we can read
from the spreadsheet by
> library(RODBC) > channel <- odbcConnect("bdr.xls") ## list the spreadsheets > sqlTables(channel) TABLE_CAT TABLE_SCHEM TABLE_NAME TABLE_TYPE REMARKS 1 C:\\bdr NA Sheet1$ SYSTEM TABLE NA 2 C:\\bdr NA Sheet2$ SYSTEM TABLE NA 3 C:\\bdr NA Sheet3$ SYSTEM TABLE NA 4 C:\\bdr NA Sheet1$Print_Area TABLE NA ## retrieve the contents of sheet 1, by either of > sh1 <- sqlFetch(channel, "Sheet1") > sh1 <- sqlQuery(channel, "select * from [Sheet1$]")
Notice that the specification of the table is different from the name
sqlTables: as from version 0.9-1
able to map the differences.
The ODBC interface to Excel spreadsheets is read-only; you cannot alter the spreadsheet data.
Package RPgSQL at http://rpgsql.sourceforge.net/ and in the
Devel area on CRAN provides an interface to
PostgreSQL. Development appears to
PostgreSQL is described by its developers as `the most advanced open source database server' (Momjian, 2000). It would appear to be buildable for most Unix-alike OSes and Windows (under Cygwin or U/Win). PostgreSQL has most of the features of the commercial RDBMSs.
To make use of RPgSQL, first open a connection to a database using
db.connect. (Currently only one connection can be open at a
time.) Once a connection is open an R data frame can be copied to a
PostgreSQL table by
copies a PostgreSQL table to an R data frame.
RPgSQL has the interesting concept of a proxy data frame.
A data frame proxy is an R object that inherits from the
"data.frame" class, but contains no data. All accesses to the
proxy data frame generate the appropriate SQL query and
retrieve the resulting data from the database. A proxy data frame is
set up by a call to
bind.db.proxy. To remove the proxy, just
remove the object which
A finer level of control is available via sending SQL queries
to the PostgreSQL server via
db.execute. This leaves a result in
PostgreSQL's result cache, unless flushed by
clear = TRUE (the
default). Once a result is in the cache,
db.fetch.result can be
used to fetch the whole result as a data frame. Functions such as
db.result.rows will report the
number of columns and rows in the cached table, and
db.read.column will fetch a single column (as a vector). An
individual cell in the result can be read by
db.clear.result will clear the result cache.
One disadvantage is that PostgreSQL maps all table and column names to
lower case, so for maximal flexibility, only use lower case in R
convenience wrappers for the INSERT and SELECT queries.
We can explore these functions in a simple example. The database
testdb had already been set up, and as PostgreSQL was running on
a standalone machine no further authentication was required to connect.
> library(RPgSQL) > db.connect(dbname="testdb") # add authentication as needed Connected to database "testdb" on "" > data(USArrests) > usarrests <- USArrests > names(usarrests) <- tolower(names(USArrests)) > db.write.table(USArrests, write.row.names = TRUE) > db.write.table(usarrests, write.row.names = TRUE) > rm(USArrests, usarrests) ## db.ls lists tables in the database. > db.ls()  "USArrests" "usarrests" > db.read.table("USArrests") Murder Assault UrbanPop Rape Alabama 13.2 236 58 21.2 Alaska 10.0 263 48 44.5 ... ## set up a proxy data frame. Remember USArrests has been removed > bind.db.proxy("USArrests") ## USArrests is now a proxy, so all accesses are to the database > USArrests[, "Rape"] Rape 1 21.2 2 44.5 ... > rm(USArrests) # remove proxy > db.execute("SELECT rpgsql_row_names, murder FROM usarrests", "WHERE rape > 30 ORDER BY murder", clear=FALSE) > db.fetch.result() murder Colorado 7.9 Arizona 8.1 California 9.0 Alaska 10.0 New Mexico 11.4 Michigan 12.1 Nevada 12.2 Florida 15.4 > db.rm("USArrests", "usarrests") # use ask=FALSE to skip confirmation Destroy table USArrests? y Destroy table usarrests? y > db.ls() character(0) > db.disconnect()
Notice how the row names are mapped if
write.row.names = TRUE to
rpgsql_row_names in the database table and transparently
restored provided we preserve that field in the query.
RPgSQL provides means to extend its mapping between R classes within a data frame and PostgreSQL types.
Package RmSQL on CRAN provides an interface to the Mini
SQL database system (also known as mSQL,
http://www.hughes.com.au>, Yarger et al., 1999). The
package documentation describes mSQL as
Note that mSQL is NOT GPL licenced but free of charge for universities and noncommercial organisations.
RmSQL provides the most basic interface of those in this chapter, a wrapper to the C-API of mSQL with no additional functionality.
A database connection is opened by first selecting a host with
msqlConnect and then a database by
connection is closed by a call to
msqlClose. Then an
SQL query is sent by a call to
msqlQuery, and the
results stored by a call to
msqlStoreResult. When a query is
finished with, the result can be freed by
Once the result of a query has been stored, the values can be retrieved
row by row using
msqlFetchRow. This fetches the rows in order
unless the position is reset by a call to
msqlDataSeek. A call to
msqlNumRows gives the total number of rows in the result.
No example is given here as the basic interface makes any example
lengthy, but there is one in the
Example directory of the package.
Binary connections (Connections) are now the preferred way to handle binary files.
Packages hdf5 and netCDF on CRAN provide experimental interfaces to NASA's HDF5 (Hierarchical Data Format, see http://hdf.ncsa.uiuc.edu/HDF5/) and to UCAR's netCDF data files (network Common Data Form, see http://www.unidata.ucar.edu/packages/netcdf/), respectively.
Both of these are systems to store scientific data in array-oriented ways, including descriptions, labels, formats, units, .... HDF5 also allows groups of arrays, and the R interface maps lists to HDF5 groups, and can write numeric and character vectors and matrices.
The R interface can only read netCDF, not write it (yet).
Connections are used in R in the sense of Chambers (1998), a set of functions to replace the use of file names by a flexible interface to file-like objects.
The most familiar type of connection will be a file, and file
connections are created by function
file. File connections can
(if the OS will allow it for the particular file) be opened for reading
or writing or appending, in text or binary mode. In fact, files can be
opened for both reading and writing, and R keeps a separate file
position for reading and writing.
Note that by default a connection is not opened when it is created. The
rule is that a function using a connection should open a connection
(needed) if the connection is not already open, and close a connection
after use if it opened it. In brief, leave the connection in the state
you found it in. There are generic functions
close with methods to explicitly open and close connections.
Files compressed via the algorithm used by
gzip can be used as
connections created by the function
gzfile, whereas files
bzip2 can be used via
Unix programmers are used to dealing with special files
stderr. These exist as terminal
connections in R. They may be normal files, but they might also
refer to input from and output to a GUI console. (Even with the standard
Unix R interface,
stdin refers to the lines submitted from
readline rather than a file.)
The three terminal connections are always open, and cannot be opened or
stderr are conventionally used for
normal output and error messages respectively. They may normally go to
the same place, but whereas normal output can be re-directed by a call
sink, error output is sent to
stderr unless re-directed
sink, type="message"). Note carefully the language used here:
the connections cannot be re-directed, but output can be sent to other
Text connections are another source of input. They allow R
character vectors to be read as if the lines were being read from a text
file. A text connection is created and opened by a call to
textConnection, which copies the current contents of the
character vector to an internal buffer at the time of creation.
Text connections can also be used to capture R output to a character
textConnection can be asked to create a new character
object or append to an existing one, in both cases in the user's
workspace. The connection is opened by the call to
textConnection, and at all times the complete lines output to the
connection are available in the R object. Closing the connection
writes any remaining output to a final element of the character vector.
Pipes are a special form of file that connects to another
process, and pipe connections are created by the function
(currently implemented on Unix and
Rterm only). Opening a pipe
connection for writing (it makes no sense to append to a pipe) runs an
OS command, and connects its standard input to whatever R then writes
to that connection. Conversely, opening a pipe connection for input
runs an OS command and makes its standard output available for R
input from that connection.
URLs of types
file:// can be
read from using the function
url. For convenience,
will also accept these as the file specification and call
Sockets can also be used as connections via function
socketConnection on platforms which support Berkeley-like sockets
(most Unix systems, Linux and Windows but not currently classic
Macintosh). Sockets can be written to or read from, and both client and
server sockets can be used.
We have described functions
sink as writing to a file, possibly appending to a file if
append = TRUE, and this is what they did prior to R
The current behaviour is equivalent, but what actually happens is that
file argument is a character string, a file connection
is opened (for writing or appending) and closed again at the end of the
function call. If we want to repeatedly write to the same file, it is
more efficient to explicitly declare and open the connection, and pass
the connection object to each call to an output function. This also
makes it possible to write to pipes, which was implemented earlier in a
limited way via the syntax
file = "|cmd" (which can still be
There is a function
writeLines to write complete text lines
to a connection.
Some simple examples are
zz <- file("ex.data", "w") # open an output file connection cat("TITLE extra line", "2 3 5 7", "", "11 13 17", file = zz, sep = "\n") cat("One more line\n", file = zz) close(zz) ## convert decimal point to comma in output, using a pipe (Unix) ## both R strings and (probably) the shell need \ doubled zz <- pipe(paste("sed s/\\\\./,/ >", "outfile"), "w") cat(format(round(rnorm(100), 4)), sep = "\n", file = zz) close(zz) ## now look at the output file: file.show("outfile", delete.file = TRUE) ## capture R output: use examples from help(lm) zz <- textConnection("ex.lm.out", "w") sink(zz) example(lm, prompt.echo = "> ") sink() close(zz) ## now `ex.lm.out' contains the output for futher processing. ## Look at it by, e.g., cat(ex.lm.out, sep = "\n")
The basic functions to read from connections are
readLines. These take a character string argument and open a
file connection for the duration of the function call, but explicitly
opening a file connection allows a file to be read sequentially in
Other functions that call
scan can also make use of connections,
read.table. As from R version 1.3.0,
read.table reads the data in a single pass and so works better
with non-file connections.
Some simple examples are
## read in file created in last examples readLines("ex.data") unlink("ex.data") ## read listing of current directory (Unix) readLines(pipe("ls -1")) # remove trailing commas from an input file. # Suppose we are given a file `data' containing 450, 390, 467, 654, 30, 542, 334, 432, 421, 357, 497, 493, 550, 549, 467, 575, 578, 342, 446, 547, 534, 495, 979, 479 # Then read this by scan(pipe("sed -e s/,$// data"), sep=",")
For convenience, if the
file argument specifies a FTP or HTTP
URL, the URL is opened for reading via
file://foo.bar is also allowed.
C programmers may be familiar with the
ungetc function to push
back a character onto a text input stream. R connections have the
same idea in a more powerful way, in that an (essentially) arbitrary
number of lines of text can be pushed back onto a connection via a call
Pushbacks operate as a stack, so a read request first uses each line
from the most recently pushbacked text, then those from earlier
pushbacks and finally reads from the connection itself. Once a
pushbacked line is read completely, it is cleared. The number of
pending lines pushed back can be found via a call to
A simple example will show the idea.
> zz <- textConnection(LETTERS) > readLines(zz, 2)  "A" "B" > scan(zz, "", 4) Read 4 items  "C" "D" "E" "F" > pushBack(c("aa", "bb"), zz) > scan(zz, "", 4) Read 4 items  "aa" "bb" "G" "H" > close(zz)
Pushback is only available for connections opened for input in text mode.
A summary of all the connections currently opened by the user can be
showConnections(), and a summary of all connections,
including closed and terminal connections, by
The generic function
seek can be used to read and (on some
connections) reset the current position for reading or writing.
Unfortunately it depends on OS facilities which may be unreliable
(e.g. with text files under Windows). Function
seek can change the position on the connection
given by its argument.
truncate can be used to truncate a file opened for
writing at its current position. It works only for
connections, and is not implemented on all platforms.
writeBin read to and write from
binary connections. A connection is opened in binary mode by appending
"b" to the mode specification, that is using mode
reading, and mode
"ab" (where appropriate) for
writing. The functions have arguments
readBin(con, what, n = 1, size = NA, endian = .Platform$endian) writeBin(object, con, size = NA, endian = .Platform$endian)
In each case
con is a connection which will be opened if
necessary for the duration of the call, and if a character string is
given it is assumed to specify a file name.
It is slightly simpler to describe writing, so we will do that first.
object should be an atomic vector object, that is a vector of
complex, without attributes. By default this is written to the
file as a stream of bytes exactly as it is represented in memory.
readBin reads a stream of bytes from the file and interprets them
as a vector of mode given by
what. This can be either an object
of the appropriate mode (e.g.
what=integer()) or a character
string describing the mode (one of the five given in the previous
specifies the maximum number of vector elements to read from the
connection: if fewer are available a shorter vector will be returned.
signed allows 1-byte and 2-byte integers to be
read as signed (the default) or unsigned integers.
The remaining two arguments are used to write or read data for
interchange with another program or another platform. By default binary
data is transferred directly from memory to the connection or vice
versa. This will not suffice if the file is to be transferred to a
machine with a different architecture, but between almost all R
platforms the only change needed is that of byte-order. Intel
ix86-based machines, Compaq Alpha and Vaxen are
little-endian, whereas Sun Sparc, mc680x0 series, IBM R6000, SGI
and most others are big-endian. (Network byte-order (as used by
XDR, eXternal Data Representation) is big-endian.) To transfer to or
from other programs we may need to do more, for example to read 16-bit
integers or write single-precision real numbers. This can be done using
size argument, which (usually) allows sizes 1, 2, 4, 8 for
integers and logicals, and sizes 4, 8 and perhaps 12 or 16 for reals.
Transferring at different sizes can lose precision, and should not be
attempted for vectors containing
Character strings are read and written in C format, that is as a string
of bytes terminated by a zero byte. Functions
writeChar provide greater flexibility.
writeBin will pass missing and
special values, although this should not be attempted if a size change
The missing value for R logical and integer types is
the smallest representable
int defined in the C header
limits.h, normally corresponding to the bit pattern
The representation of the special values for R numeric and complex
types is machine-dependent, and possibly also compiler-dependent. The
simplest way to make use of them is to link an external application
against the standalone
Rmath library which exports double
include the header
Rmath.h which defines the macros
If that is not possible, on all common platforms IEC 60559 (aka IEEE
754) arithmetic is used, so standard C facilities can be used to test
for or set
NaN values. On such
NA is represented by the
NaN value with low-word
0x7a2 (1954 in decimal).
Some limited facilities are available to exchange data at a lower level across network connections.
Base R comes with some facilities to communicate via BSD sockets on systems that support them (including the common Linux, Unix and Windows ports of R). One potential problem with using sockets is that these facilities are often blocked for security reasons or to force the use of Web caches, so these functions may be more useful on an intranet than externally. For new projects it is suggested that socket connections are used instead.
The earlier low-level interface is given by functions
download.file is provided to read a file from a
Web resource via FTP or HTTP and write it to a file. Often this can be
avoided, as functions such as
scan can read
directly from a URL, either by explicitly using
url to open a
connection, or implicitly using it by giving a URL as the
DCOM is a Windows protocol for communicating between different
programs, possibly on different machines. Thomas Baier's
StatConnector program available from CRAN under
Software->Other->Non-standard provides an interface to the proxy DLL
which ships with the Windows version of R and makes an DCOM
server. This can be used to pass simple objects (vectors and matrices)
to and from R and to submit commands to R.
The program comes with a Visual Basic demonstration, and there is an Excel plug-in by Erich Neuwirth available in the same area on CRAN. This interface is in the other direction to most of those considered here in that it is another application (Excel, or written in Visual Basic) that is the client and R is the server.
CORBA (Common Object Request Broker Architecture) is similar to DCOM, allowing applications to call methods, or operations, in server objects running in other applications, potentially programmed in different languages and running on different machines. There is a CORBA package available from the Omegahat Project (at http://www.omegahat.org/RSCORBA/), currently for Unix but a Windows version looks to be possible.
This package allows R commands to be used to locate available CORBA servers, query the methods they provide, and dynamically invoke methods on these objects. R values given as arguments in these calls are exported in the call and made available to that operation invocation. Primitive data types (vectors and lists) are exported by default, while more complex objects are exported by reference. Examples of using this include communicating with the Gnumeric (http://www.gnumeric.org) spreadsheet, and also interacting with the data visualization system ggobi.
One can also create CORBA servers within R, allowing other applications to call these methods. For example, one might offer access to a particular dataset or to some of R's modelling software. This is done dynamically by combining R data objects and functions. This allows one to explicitly export data and functionality from R.
One can also use the CORBA package to achieve distributed, parallel computing in R. One R session acts as a manager and dispatches tasks to different servers running in other R worker sessions. This uses the ability to invoke asynchronous or background CORBA calls in R. More information is available from the Omegahat Project, at http://www.omegahat.org/RSCORBA/.
R. A. Becker, J. M. Chambers and A. R. Wilks (1988) The New S Language. A Programming Environment for Data Analysis and Graphics. Wadsworth & Brooks/Cole.
J. Bowman, S. Emberson and M. Darnovsky (1996) The Practical SQL Handbook. Using Structured Query Language. Addison-Wesley.
J. M. Chambers (1998) Programming with Data. A Guide to the S Language. Springer-Verlag.
P. Dubois (2000) MySQL. New Riders.
M. Henning and S. Vinoski (1999) Advanced CORBA Programming with C++. Addison-Wesley.
K. Kline and D. Kline (2001) SQL in a Nutshell. O'Reilly.
B. Momjian (2000) PostgreSQL: Introduction and Concepts. Addison-Wesley. Also downloadable at http://www.postgresql.org/docs/awbook.html.
T. M. Therneau and P. M. Grambsch (2000) Modeling Survival Data. Extending the Cox Model. Springer-Verlag.
E. J. Yarger, G. Reese and T. King (1999) MySQL & mSQL. O'Reilly.
bzfile: Types of connections
cat: Output to connections, Export to text files
close: Types of connections, RODBC
close.socket: Reading from sockets
count.fields: Variations on read.table
data.restore: EpiInfo Minitab SAS S-PLUS SPSS Stata
dbClearResult: DBI / RMySQL
dbConnect: DBI / RMySQL
dbDisconnect: DBI / RMySQL
dbDriver: DBI / RMySQL
dbExistsTable: DBI / RMySQL
dbGetQuery: DBI / RMySQL
dbReadTable: DBI / RMySQL
dbRemoveTable: DBI / RMySQL
dbSendQuery: DBI / RMySQL
dbWriteTable: DBI / RMySQL
fetch: DBI / RMySQL
file: Types of connections
format: Export to text files
ftable: Flat contingency tables
gzfile: Types of connections
hdf5: Binary data formats
isSeekable: Listing and manipulating connections
make.socket: Reading from sockets
netCDF: Binary data formats
open: Types of connections
pipe: Types of connections
read.csv: Variations on read.table
read.csv2: Variations on read.table
read.delim: Variations on read.table
read.delim2: Variations on read.table
read.dta: EpiInfo Minitab SAS S-PLUS SPSS Stata
read.epiinfo: EpiInfo Minitab SAS S-PLUS SPSS Stata
read.ftable: Flat contingency tables
read.fwf: Fixed-width-format files
read.mtp: EpiInfo Minitab SAS S-PLUS SPSS Stata
read.S: EpiInfo Minitab SAS S-PLUS SPSS Stata
read.socket: Reading from sockets
read.spss: EpiInfo Minitab SAS S-PLUS SPSS Stata
read.table: Input from connections, Variations on read.table
read.xport: EpiInfo Minitab SAS S-PLUS SPSS Stata
readBin: Binary connections
readChar: Binary connections
readLines: Input from connections, Using scan directly
reshape: Re-shaping data
scan: Input from connections, Using scan directly, Imports
seek: Listing and manipulating connections
showConnections: Listing and manipulating connections
sink: Output to connections, Export to text files
socketConnection: Types of connections
stack: Re-shaping data
stderr: Types of connections
stdin: Types of connections
stdout: Types of connections
textConnection: Types of connections
truncate: Listing and manipulating connections
unstack.: Re-shaping data
url: Types of connections
write: Output to connections, Export to text files
write.dta: EpiInfo Minitab SAS S-PLUS SPSS Stata
write.matrix: Export to text files
write.socket: Reading from sockets
write.table: Output to connections, Export to text files
writeBin: Binary connections
writeChar: Binary connections
writeLines: Output to connections
This is normally fast as looking at the first entry rules out most of the possibilities.