\input texinfo
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@setfilename R-data.info
@settitle R Data Import/Export
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@dircategory Programming
@direntry
* R Data: (R-data).      R Data Import/Export.
@end direntry

@finalout

@include R-defs.texi
@include version.texi

@copying
This manual is for R, version @value{VERSION}.

@Rcopyright{2000}

@quotation
@permission{}
@end quotation
@end copying

@titlepage
@title R Data Import/Export
@subtitle Version @value{VERSION}
@author R Core Team
@page
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@c @ifnothtml
@contents
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@ifnottex
@node Top
@top R Data Import/Export

This is a guide to importing and exporting data to and from R.

@insertcopying

@end ifnottex



@node Acknowledgements
@unnumbered Acknowledgements

The relational databases part of this manual is based in part on an
earlier manual by @I{Douglas Bates} and @I{Saikat DebRoy}.  The
principal author of this manual was @I{Brian Ripley}.

Many volunteers have contributed to the packages used here.  The
principal authors of the packages mentioned are

@quotation
@table @asis
@item @CRANpkg{DBI}:
@I{David A. James}

@item @CRANpkg{dataframes2xls}:
@I{Guido van Steen}

@item @CRANpkg{foreign}:
@I{Thomas Lumley},
@I{Saikat DebRoy},
@I{Douglas Bates},
@I{Duncan Murdoch} and 
@I{Roger Bivand}

@item @CRANpkg{gdata}:
@I{Gregory R. Warnes}

@item @CRANpkg{ncdf4}:
@I{David Pierce}

@item @CRANpkg{rJava}:
@I{Simon Urbanek}

@item @CRANpkg{RJDBC}:
@I{Simon Urbanek}

@item @CRANpkg{RMySQL}:
@I{David James} and
@I{Saikat DebRoy}

@item @CRANpkg{RNetCDF}:
@I{Pavel Michna}

@item @CRANpkg{RODBC}:
@I{Michael Lapsley} and
@I{Brian Ripley}

@item @CRANpkg{ROracle}:
@I{David A. James}

@item @CRANpkg{RPostgreSQL}:
@I{Sameer Kumar Prayaga} and
@I{Tomoaki Nishiyama}

@item @pkg{RSPerl}:
@I{Duncan Temple Lang}

@item @pkg{RSPython}:
@I{Duncan Temple Lang}

@item @CRANpkg{RSQLite}:
@I{David A. James}

@item @pkg{SJava}:
@I{John Chambers} and
@I{Duncan Temple Lang}

@item @CRANpkg{WriteXLS}:
@I{Marc Schwartz}

@item @CRANpkg{XLConnect}:
@I{Mirai Solutions GmbH}

@item @CRANpkg{XML}:
@I{Duncan Temple Lang}
@end table
@end quotation

@I{Brian Ripley} is the author of the support for connections.



@node Introduction
@chapter Introduction

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 @acronym{CRAN}
or elsewhere.

Unless otherwise stated, everything described in this manual is (at
least in principle) available on all platforms running @R{}.

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
another system do the work!  (For example @I{Therneau} & @I{Grambsch} (2000)
commented that they preferred to do data manipulation in SAS and then
use package @CRANpkg{survival} in @Sl{} for the analysis.)  Database
manipulation systems are often very suitable for manipulating and
extracting data: several packages to interact with @abbr{DBMS}s are discussed
here.

There are packages to allow functionality developed in languages such as
@code{Java}, @code{perl} and @code{python} to be directly integrated
with @R{} code, making the use of facilities in these languages even
more appropriate.  (See the @CRANpkg{rJava} package from @acronym{CRAN}.)
@c and the @pkg{SJava}, @pkg{RSPerl} and @pkg{RSPython} packages from the
@c Omegahat project, @uref{http://www.omegahat.net}.)


@cindex Unix tools
@cindex @I{awk}
@cindex @I{perl}
It is also worth remembering that @R{} like @Sl{} comes from the Unix
tradition of small re-usable tools, and it can be rewarding to use tools
such as @code{awk} and @code{perl} to manipulate data before import or
after export.  The case study in @I{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 @Sl{}. The traditional Unix tools
are now much more widely available, including for Windows.

This manual was first written in 2000, and the number of scope of @R{}
packages has increased a hundredfold since.  For specialist data formats
it is worth searching to see if a suitable package already exists.


@node Imports
@section Imports
@findex scan

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 @code{scan}, and this
underlies most of the more convenient functions discussed in
@ref{Spreadsheet-like data}.

However, all statistical consultants are familiar with being presented
by a client with a memory stick (formerly, 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 most common applications
on their computers for that purpose).  However, this is not always
possible, and @ref{Importing from other statistical systems} discusses
what facilities are available to access such files directly from @R{}.
For Excel spreadsheets, the available methods are summarized in
@ref{Reading Excel spreadsheets}.  
@c For ODS spreadsheets from Open
@c Office, see the Omegahat package@footnote{Currently not available from
@c that repository but as a source package for download from
@c @url{http://www.omegahat.net/ROpenOffice/}.} @pkg{ROpenOffice}.

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 @ref{Binary files} and @ref{Binary connections}.

For much larger databases it is common to handle the data using a
database management system (@abbr{DBMS}).  There is once again the option of
using the @abbr{DBMS} to extract a plain file, but for many such @abbr{DBMS}s the
extraction operation can be done directly from an @R{} package:
@xref{Relational databases}.  Importing data via network connections is
discussed in @ref{Network interfaces}.


@c Keep this single-item menu to avoid a spurious warning about
@c an unreferenced node when making info with Texinfo 6.8 and 7.0.
@menu
* Encodings::                   
@end menu

@node Encodings
@subsection Encodings
@cindex Encodings

Unless the file to be imported from is entirely in @acronym{ASCII}, it
is usually necessary to know how it was encoded.  For text files, a good
way to find out something about its structure is the @command{file}
command-line tool (for Windows, included in @code{Rtools}).  This
reports something like

@example
text.Rd: UTF-8 Unicode English text
text2.dat: ISO-8859 English text
text3.dat: Little-endian UTF-16 Unicode English character data,
   with CRLF line terminators
intro.dat: UTF-8 Unicode text
intro.dat: UTF-8 Unicode (with BOM) text
@end example

@noindent
Modern Unix-alike systems, including macOS, are likely to produce
UTF-8 files.  Windows may produce what it calls `Unicode' files
(@code{UCS-2LE} or just possibly @code{UTF-16LE}@footnote{the
distinction is subtle,
@uref{https://en.wikipedia.org/wiki/UTF-16/UCS-2}, and the use of
surrogate pairs is very rare.}).  Otherwise most files will be in a
8-bit encoding unless from a Chinese/Japanese/Korean locale (which have
a wide range of encodings in common use).  It is not possible to
automatically detect with certainty which 8-bit encoding (although
guesses may be possible and @command{file} may guess as it did in the
example above), so you may simply have to ask the originator for some
clues (e.g.@: `Russian on Windows').

`@abbr{BOM}s' (Byte Order Marks,
@uref{https://en.wikipedia.org/wiki/Byte_order_mark}) cause problems for
Unicode files.  In the Unix world @abbr{BOM}s are rarely used, whereas in the
Windows world they almost always are for @I{UCS-2}/UTF-16 files, and often
are for UTF-8 files.  The @command{file} utility will not even recognize
@I{UCS-2} files without a @abbr{BOM}, but many other utilities will refuse to read
files with a @abbr{BOM} and the @abbr{IANA} standards for @code{UTF-16LE}
and @code{UTF-16BE} prohibit it.  We have too often been reduced to
looking at the file with the command-line utility @command{od} or a hex
editor to work out its encoding.

Note that @code{utf8} is not a valid encoding name (@code{UTF-8} is),
and @code{macintosh} is the most portable name for what is sometimes
called `Mac Roman' encoding. 


@node Export to text files
@section Export to text files
@cindex Exporting to a text file

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 often a text file will be the most convenient
interchange vehicle.  (If a binary file is required, see @ref{Binary
files}.)

@findex cat
Function @code{cat} underlies the functions for exporting data.  It
takes a @code{file} argument, and the @code{append} argument allows a
text file to be written via successive calls to @code{cat}.  Better,
especially if this is to be done many times, is to open a @code{file}
connection for writing or appending, and @code{cat} to that connection,
then @code{close} it.

@findex write
@findex write.table
The most common 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 @code{write.table} and @code{write}.
Function @code{write} just writes out a matrix or vector in a specified
number of columns (and transposes a matrix).  Function
@code{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
labels.

There are a number of issues that need to be considered in writing out a
data frame to a text file.

@enumerate
@findex format
@item @strong{Precision}

Most of the conversions of real/complex numbers done by these functions
is to full precision, but those by @code{write} are governed by the
current setting of @code{options(digits)}.  For more control, use
@code{format} on a data frame, possibly column-by-column.

@item @strong{Header line}

@R{} prefers the header line to have no entry for the row names, so the
file looks like

@example
                dist    climb   time
Greenmantle     2.5     650     16.083
   ...
@end example

@noindent
Some other systems require a (possibly empty) entry for the row names, which
is what @code{write.table} will provide if argument @code{col.names = NA}
is specified.  Excel is one such system.

@item @strong{Separator}
@cindex CSV files
@cindex comma separated values
@findex write.csv
@findex write.csv2

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, and wrapper
function @code{write.csv} provides appropriate defaults.  In some
locales the comma is used as the decimal point (set this in
@code{write.table} by @code{dec = ","}) and there CSV files use the
semicolon as the field separator: use @code{write.csv2} for appropriate
defaults.  There is an @abbr{IETF} standard for CSV files (which mandates
commas and CRLF line endings, for which use @code{eol = "\r\n"}), RFC4180
(see @uref{https://www.rfc-editor.org/rfc/rfc4180}), but what is more
important in practice is that the file is readable by the application it
is targeted at.

Using a semicolon or tab (@code{sep = "\t"}) are probably the safest
options.

@item @strong{Missing values}
@cindex Missing values

By default missing values are output as @code{NA}, but this may be
changed by argument @code{na}.  Note that @code{NaN}s are treated as
@code{NA} by @code{write.table}, but not by @code{cat} nor @code{write}.

@item @strong{Quoting strings}
@cindex Quoting strings

By default strings are quoted (including the row and column names).
Argument @code{quote} controls if character and factor variables are
quoted: some programs, for example @pkg{Mondrian}
(@uref{https://en.wikipedia.org/wiki/Mondrian_(software)}), do not accept
quoted strings.

Some care is needed if the strings contain embedded quotes.  Three
useful forms are

@example
> 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
@end example

@noindent
The second is the form of escape commonly used by spreadsheets.

@item @strong{Encodings}
@cindex Encodings

Text files do not contain metadata on their encodings, so for
non-@acronym{ASCII} data the file needs to be targetted to the
application intended to read it.  All of these functions can write to a
@emph{connection} which allows an encoding to be specified for the file,
and @code{write.table} has a @code{fileEncoding} argument to make this
easier.

The hard part is to know what file encoding to use.  For use on Windows,
it is best to use what Windows calls `Unicode'@footnote{Even then,
Windows applications may expect a Byte Order Mark which the
implementation of @code{iconv} used by @R{} may or may not add depending
on the platform.}, that is @code{"UTF-16LE"}.  Using UTF-8 is a good way
to make portable files that will not easily be confused with any other
encoding, but even macOS applications (where UTF-8 is the system
encoding) may not recognize them, and Windows applications are most
unlikely to.  Apparently Excel:mac 2004/8 expected @code{.csv} files in
@code{"macroman"} encoding (the encoding used in much earlier versions
of Mac OS).

@end enumerate

@findex write.matrix
Function @code{write.matrix} in package @CRANpkg{MASS} provides a
specialized interface for writing matrices, with the option of writing
them in blocks and thereby reducing memory usage.

@findex sink
It is possible to use @code{sink} to divert the standard @R{} output to
a file, and thereby capture the output of (possibly implicit)
@code{print} statements.  This is not usually the most efficient route,
and the @code{options(width)} setting may need to be increased.

@findex write.foreign
Function @code{write.foreign} in package @CRANpkg{foreign} uses
@code{write.table} to produce a text file and also writes a code file
that will read this text file into another statistical package. There is
currently support for export to @code{SAS}, @code{SPSS} and @code{Stata}.


@node XML
@section XML
@cindex XML

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 @ref{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 @I{eXtensible Markup Language} -- more commonly known simply as
@acronym{XML} -- can be used to provide such structure, not only for
standard datasets but also more complex data structures.
@acronym{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.

@acronym{XML} provides a way to specify the file's encoding, e.g.@:

@example
<?xml version="1.0" encoding="UTF-8"?>
@end example

@noindent
although it does not require it.

The @CRANpkg{XML} package provides general facilities for reading and
writing @acronym{XML} documents within @R{}. 
@c A description of the facilities of the @CRANpkg{XML} package is outside
@c the scope of this document: see the package's Web page at
@c @uref{http://www.omegahat.net/RSXML} for details and examples.
Package @CRANpkg{StatDataML} on @acronym{CRAN} is one example building
on @CRANpkg{XML}.  Another interface to the @pkg{libxml2} C library is
provided by package @CRANpkg{xml2}.

@cindex YAML
@acronym{YAML} is another system for structuring text data, with
emphasis on human-readability: it is supported by package
@CRANpkg{yaml}.



@node Spreadsheet-like data
@chapter Spreadsheet-like data
@cindex Spreadsheet-like data


In @ref{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{}.


@node Variations on read.table
@section  Variations on @code{read.table}
@findex read.table

The function @code{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 @code{read.table} but change a group
of default arguments.

Beware that @code{read.table} is an inefficient way to read in
very large numerical matrices: see @code{scan} below.

Some of the issues to consider are:

@enumerate
@item @strong{Header line}

We recommend that you specify the @code{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 @code{header = TRUE}.)  If presented with a
file that has a (possibly empty) header field for the row labels, read
it in by something like

@example
read.table("file.dat", header = TRUE, row.names = 1)
@end example

Column names can be given explicitly via the @code{col.names}; explicit
names override the header line (if present).

@item @strong{Separator}

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
the default @code{sep = ""} which uses any white space (spaces, tabs or
newlines) as a separator, @code{sep = " "} and @code{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
@code{sep = "\t"}.


@item @strong{Quoting}
@cindex Quoting strings

By default character strings can be quoted by either @samp{"} or
@samp{'}, 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 @code{quote}
argument.  For @code{sep = "\n"} the default is changed to @code{quote =
""}.

If no separator character is specified, quotes can be escaped within
quoted strings by immediately preceding them by @samp{\}, C-style.

If a separator character is specified, quotes can be escaped within
quoted strings by doubling them as is conventional in spreadsheets.  For
example

@example
'One string isn''t two',"one more"
@end example

@noindent
can be read by

@example
read.table("testfile", sep = ",")
@end example

@noindent
This does not work with the default separator.

@item @strong{Missing values}
@cindex Missing values

By default the file is assumed to contain the character string @code{NA}
to represent missing values, but this can be changed by the argument
@code{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.

In numeric columns, the values @code{NaN}, @code{Inf} and @code{-Inf} are
accepted.

@item @strong{Unfilled lines}

It is quite common for a file exported from a spreadsheet to have all
trailing empty fields (and their separators) omitted.  To read such
files set @code{fill = TRUE}.

@item @strong{White space in character fields}

If a separator is specified, leading and trailing white space in
character fields is regarded as part of the field.  To strip the space,
use argument @code{strip.white = TRUE}.

@item @strong{Blank lines}

By default, @code{read.table} ignores empty lines.  This can be changed
by setting @code{blank.lines.skip = FALSE}, which will only be useful in
conjunction with @code{fill = TRUE}, perhaps to use blank rows to
indicate missing cases in a regular layout.

@item @strong{Classes for the variables}

Unless you take any special action, @code{read.table} reads all the
columns as character vectors and then tries to select a suitable class
for each variable in the data frame.  It tries in turn @code{logical},
@code{integer}, @code{numeric} and @code{complex}, moving on if any
entry is not missing and cannot be converted.@footnote{This is normally
fast as looking at the first entry rules out most of the possibilities.}
If all of these fail, the variable is converted to a factor.

Arguments @code{colClasses} and @code{as.is} provide greater control.
Specifying @code{as.is = TRUE} suppresses conversion of character
vectors to factors (only).  Using @code{colClasses} allows the desired
class to be set for each column in the input: it will be faster and use
less memory.

Note that @code{colClasses} and @code{as.is} are specified @emph{per} 
column, not @emph{per} variable, and so include the column of row names
(if any).

@item @strong{Comments}

By default, @code{read.table} uses @samp{#} 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 @code{comment.char = ""}.

@item @strong{Escapes}

Many OSes have conventions for using backslash as an escape character in
text files, but Windows does not (and uses backslash in path names).
It is optional in @R{} whether such conventions are applied to data files.

Both @code{read.table} and @code{scan} have a logical argument
@code{allowEscapes}.  This is false by default, and backslashes are then
only interpreted as (under circumstances described above) escaping
quotes.  If this set to be true, C-style escapes are interpreted, namely
the control characters @code{\a, \b, \f, \n, \r, \t, \v} and octal and
hexadecimal representations like @code{\040} and @code{\0x2A}.  Any
other escaped character is treated as itself, including backslash.  Note
that Unicode escapes such as @code{\u@var{xxxx}} are never interpreted.

@item @strong{Encoding}

If the file contains non-@acronym{ASCII} character fields, ensure that
it is read in the correct encoding.  This is mainly an issue for reading
legacy Latin-1 files in a UTF-8 locale, which can be done by something like

@example
read.table("file.dat", fileEncoding = "latin1")
@end example

@noindent
Note that this will work in any locale which can represent Latin-1
strings, but not many Greek/Russian/Chinese/Japanese @dots{} locales.

Other examples for the @code{fileEncoding} argument include:

@example
fileEncoding = "UCS-2LE"    # Windows 'Unicode' files
fileEncoding = "UTF-8"
@end example

@noindent
If you know (correctly) the file's encoding this will almost always
work.  However, we know of one exception, UTF-8 files with a @abbr{BOM}.  Some
people claim that UTF-8 files should never have a @abbr{BOM}, but some software
(apparently including Excel:mac) uses them, and many Unix-alike OSes do
not accept them.  So faced with a file which @command{file} reports as

@example
intro.dat: UTF-8 Unicode (with BOM) text
@end example

@noindent
it can be read on Windows by

@example
read.table("intro.dat", fileEncoding = "UTF-8")
@end example

@noindent
but on a Unix-alike might need

@example
read.table("intro.dat", fileEncoding = "UTF-8-BOM")
@end example

@noindent
(This would most likely work without specifying an encoding in a UTF-8 locale.)

@c Another problem with this (real-life) example is that whereas
@c @command{file-5.03} reported the BOM, @command{file-4.17} found on OS
@c 10.5 (Leopard) did not.
@end enumerate

@findex read.csv
@findex read.csv2
@findex read.delim
@findex read.delim2
@cindex CSV files
@findex Sys.localeconv
@cindex locales
Convenience functions @code{read.csv} and @code{read.delim} provide
arguments to @code{read.table} appropriate for CSV and tab-delimited
files exported from spreadsheets in English-speaking locales.  The
variations @code{read.csv2} and @code{read.delim2} are appropriate for
use in those locales where the comma is used for the decimal point and
(for @code{read.csv2}) for spreadsheets which use semicolons to separate
fields.

If the options to @code{read.table} are specified incorrectly, the error
message will usually be of the form

@example
Error in scan(file = file, what = what, sep = sep, : 
        line 1 did not have 5 elements
@end example

@noindent
or

@example
Error in read.table("files.dat", header = TRUE) : 
        more columns than column names
@end example

@findex count.fields

@noindent
This may give enough information to find the problem, but the auxiliary
function @code{count.fields} can be useful to investigate further.

Efficiency can be important when reading large data grids.  It will help
to specify @code{comment.char = ""}, @code{colClasses} as one of the
atomic vector types (logical, integer, numeric, complex, character or
perhaps raw) for each column, and to give @code{nrows}, the number of
rows to be read (and a mild over-estimate is better than not specifying
this at all).  See the examples in later sections.


@node Fixed-width-format files
@section Fixed-width-format files
@cindex Fixed-width-format files

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.

@findex read.fwf
Function @code{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 @code{read.table}.
This is adequate for small files, but for anything more complicated we
recommend using the facilities of a language like @code{perl} to
pre-process the file.
@cindex @I{perl}

@findex read.fortran
Function @code{read.fortran} is a similar function for fixed-format files,
using Fortran-style column specifications.


@node Data Interchange Format (DIF)
@section Data Interchange Format (DIF)
@cindex Data Interchange Format (DIF)

An old format sometimes used for spreadsheet-like data is @abbr{DIF}, or Data Interchange 
format.  

@findex read.DIF
Function @code{read.DIF} provides a simple way to read such files.  It takes 
arguments similar to @code{read.table} for assigning types to each of the columns.

On Windows, spreadsheet programs often store spreadsheet data copied to
the clipboard in this format; @code{read.DIF("clipboard")} can read it
from there directly.  It is slightly more robust than
@code{read.table("clipboard")} in handling spreadsheets with empty
cells.


@node Using scan directly
@section  Using @code{scan} directly
@findex scan

Both @code{read.table} and @code{read.fwf} use @code{scan} to read the
file, and then process the results of @code{scan}.  They are very
convenient, but sometimes it is better to use @code{scan} directly.

Function @code{scan} has many arguments, most of which we have already
covered under @code{read.table}.  The most crucial argument is
@code{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.@: @code{0},
@code{""} or @code{0i}.  For example

@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)
@end example

@noindent
returns a list with three components and discards the fourth column in
the file.

@findex readLines
There is a function @code{readLines} which will be more convenient if
all you want is to read whole lines into @R{} for further processing.

One common use of @code{scan} is to read in a large matrix.  Suppose
file @file{matrix.dat} just contains the numbers for a 200 x 2000
matrix. Then we can use
@c write.table(matrix(rnorm(200*2000), 200), "matrix.dat", row.names=F, col.names=F)

@example
A <- matrix(scan("matrix.dat", n = 200*2000), 200, 2000, byrow = TRUE)
@end example

@noindent
On one test this took 1 second (under Linux, 3 seconds under Windows on
the same machine) whereas

@example
A <- as.matrix(read.table("matrix.dat"))
@end example

@noindent
took 10 seconds (and more memory), and 

@example
A <- as.matrix(read.table("matrix.dat", header = FALSE, nrows = 200,
                          comment.char = "", colClasses = "numeric"))
@end example

@noindent
took 7 seconds.  The difference is almost entirely due to the overhead
of reading 2000 separate short columns: were they of length 2000,
@code{scan} took 9 seconds whereas @code{read.table} took 18 if used
efficiently (in particular, specifying @code{colClasses}) and 125 if
used naively.


Note that timings can depend on the type read and the data. 
Consider reading a million distinct integers:
@example
writeLines(as.character((1+1e6):2e6), "ints.dat")
xi <- scan("ints.dat", what=integer(0), n=1e6)   # 0.77s
xn <- scan("ints.dat", what=numeric(0), n=1e6)   # 0.93s
xc <- scan("ints.dat", what=character(0), n=1e6) # 0.85s
xf <- as.factor(xc)                              # 2.2s
DF <- read.table("ints.dat")                     # 4.5s
@end example
@noindent
and a million examples of a small set of codes:
@example
code <- c("LMH", "SJC", "CHCH", "SPC", "SOM")
writeLines(sample(code, 1e6, replace=TRUE), "code.dat")
y <- scan("code.dat", what=character(0), n=1e6)  # 0.44s
yf <- as.factor(y)                               # 0.21s
DF <- read.table("code.dat")                     # 4.9s
DF <- read.table("code.dat", nrows=1e6)          # 3.6s
@end example

Note that these timings depend heavily on the operating system (the
basic reads in Windows take at least as twice as long as these Linux
times) and on the precise state of the garbage collector.


@node Re-shaping data
@section Re-shaping data
@cindex Re-shaping data

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

@example
 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
@end example

@noindent
There are two covariates and up to four measurements on each subject.
The data were exported from Excel as a file @file{mr.csv}.

@findex stack
We can use @code{stack} to help manipulate these data to give a single
response.

@example
zz <- read.csv("mr.csv", strip.white = TRUE)
zzz <- cbind(zz[gl(nrow(zz), 1, 4*nrow(zz)), 1:2], stack(zz[, 3:6]))
@end example

@noindent
with result

@example
      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
...
@end example

@findex unstack
Function @code{unstack} goes in the opposite direction, and may be
useful for exporting data.

@findex reshape
Another way to do this is to use the function
@code{reshape}, by

@example
> 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
...
@end example

The @code{reshape} function has a more complicated syntax than
@code{stack} but can be used for data where the `long' form has more
than the one column in this example. With @code{direction="wide"},
@code{reshape} can also perform the opposite transformation.

Some people prefer the tools in packages @CRANpkg{reshape},
@CRANpkg{reshape2} and @CRANpkg{plyr}.


@node Flat contingency tables
@section Flat contingency tables
@cindex Flat contingency tables

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 @code{ftable},
@findex ftable
which creates objects of class @code{"ftable"} with an appropriate print
method.

As a simple example, consider the @R{} standard data set
@code{UCBAdmissions} which is a 3-dimensional contingency table
resulting from classifying applicants to graduate school at @I{UC Berkeley}
for the six largest departments in 1973 classified by admission and sex.

@example
> 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
@end example

@noindent
The printed representation is clearly more useful than displaying the
data as a 3-dimensional array.

There is also a function @code{read.ftable} for reading in flat-like
contingency tables from files.
@findex read.ftable
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 @code{read.ftable} has some useful
examples.  The flat tables can be converted to standard contingency
tables in array form using @code{as.table}.

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 @code{read.table} to read
in the data, and create the contingency table from this using
@code{xtabs}.



@node Importing from other statistical systems
@chapter Importing from other statistical systems
@cindex Importing from other statistical systems

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. 

In all cases the facilities described were written for data files from
specific versions of the other system (often in the early 2000s), and
have not necessarily been updated for the most recent versions of the
other system.


@node EpiInfo Minitab SAS S-PLUS SPSS Stata Systat
@section @I{EpiInfo}, @I{Minitab}, S-PLUS, SAS, SPSS, @I{Stata}, @I{Systat}

The recommended package @CRANpkg{foreign} provides import facilities for
files produced by these statistical systems, and for export to @I{Stata}. In
some cases these functions may require substantially less memory than
@code{read.table} would. @code{write.foreign} (See @ref{Export to text
files}) provides an export mechanism with support currently for
@code{SAS}, @code{SPSS} and @code{Stata}.

@cindex @I{EpiInfo}
@cindex @I{EpiData}
@findex read.epiinfo
@I{EpiInfo} versions 5 and 6 stored data in a  self-describing fixed-width
text format. @code{read.epiinfo} will read these @file{.REC} files into
an @R{} data frame. @I{EpiData} also produces data in this format.

@cindex @I{Minitab}
@findex read.mtp
Function @code{read.mtp} imports a `@I{Minitab} Portable Worksheet'.  This
returns the components of the worksheet as an @R{} list.

@cindex SAS
@findex read.xport
Function @code{read.xport} reads a file in SAS Transport (@I{XPORT}) format
and return a list of data frames.  If SAS is available on your system,
function @code{read.ssd} can be used to create and run a SAS script that
saves a SAS permanent dataset (@file{.ssd} or @file{.sas7bdat}) in
Transport format.  It then calls @code{read.xport} to read the resulting
file.  (Package @CRANpkg{Hmisc} has a similar function @code{sas.get}, also
running SAS.)  For those without access to SAS but running on Windows,
the SAS System Viewer (a zero-cost download) can be used to open SAS
datasets and export them to e.g.@: @file{.csv} format.

@cindex S-PLUS
@findex read.S
@findex data.restore

Function @code{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 @Sl{} objects: in
particular it can read vectors, matrices and data frames and lists
containing those.

Function @code{data.restore} reads S-PLUS data dumps (created by
@code{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 later written with @code{data.dump(oldStyle=T)}.

If you have access to S-PLUS, it is usually more reliable to @code{dump}
the object(s) in S-PLUS and @code{source} the dump file in @R{}.  For
S-PLUS 5.x and later you may need to use @code{dump(..., oldStyle=T)},
and to read in very large objects it may be preferable to use the dump
file as a batch script rather than use the @code{source} function.

@cindex SPSS
@cindex SPSS Data Entry
@findex read.spss
Function @code{read.spss} can read files created by the `save' and
`export' commands in @acronym{SPSS}.  It returns a list with one
component for each variable in the saved data set. @acronym{SPSS}
variables with value labels are optionally converted to @R{} factors.

@acronym{SPSS} Data Entry is an application for creating data entry
forms.  By default it creates data files with extra formatting
information that @code{read.spss} cannot handle, but it is possible to
export the data in an ordinary @acronym{SPSS} format.

Some third-party applications claim to produce data `in SPSS format' but
with differences in the formats: @code{read.spss} may or may not be able
to handle these.

@cindex @I{Stata}
@findex read.dta
@findex write.dta
@I{Stata} @file{.dta} files are a binary file format. Files from versions 5
up to 12 of @I{Stata} can be read and written by functions @code{read.dta}
and @code{write.dta}.  @I{Stata} variables with value labels are optionally
converted to (and from) @R{} factors. For  @I{Stata} versions 13 and later
see @acronym{CRAN} packages @CRANpkg{readstata13} and @CRANpkg{haven}.


@cindex @I{Systat}
@findex read.systat
@code{read.systat} reads those @I{Systat} @code{SAVE} files that are
rectangular data files (@code{mtype = 1}) written on little-endian
machines (such as from Windows).  These have extension @file{.sys}
or (more recently) @file{.syd}.


@node Octave
@section Octave
@cindex Octave
@findex read.octave

Octave is a numerical linear algebra system
(@uref{https://octave.org/}), and function @code{read.octave} in
package @CRANpkg{foreign} can read in files in Octave text data format
created using the Octave command @command{save -ascii}, with support for
most of the common types of variables, including the standard atomic
(real and complex scalars, matrices, and @math{N}-d arrays, strings,
ranges, and boolean scalars and matrices) and recursive (structs, cells,
and lists) ones.



@node Relational databases
@chapter Relational databases

@cindex Relational databases
@cindex DBMS


@node Why use a database?
@section Why use a database?

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) hundred megabytes in size can cause
@R{} to run out of memory, particularly on a 32-bit operating system.

@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 (@abbr{DBMS}s) and, in particular, relational
@abbr{DBMS}s (@abbr{RDBMS}s) @emph{are} designed to do all of these things well.
Their strengths are

@enumerate
@item
To provide fast access to selected parts of large databases.

@item
Powerful ways to summarize and cross-tabulate columns in databases.

@item
Store data in more organized ways than the rectangular grid model of
spreadsheets and @R{} data frames.

@item
Concurrent access from multiple clients running on multiple hosts while
enforcing security constraints on access to the data.

@item
Ability to act as a server to a wide range of clients.
@end enumerate

The sort of statistical applications for which @abbr{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.

Increasingly OSes are themselves making use of @abbr{DBMS}s for these reasons,
so it is nowadays likely that one will be already installed on your
(non-Windows) OS.  @uref{https://en.wikipedia.org/wiki/Akonadi, Akonadi}
is used by KDE4 to store personal information.  Several macOS
applications, including Mail and Address Book, use SQLite.
@c https://www.actualtech.com/sqlite_applications.php


@node Overview of RDBMSs
@section Overview of RDBMSs

Traditionally there had been large (and expensive) commercial @abbr{RDBMS}s
(@uref{https://www.ibm.com/products/informix, Informix};
@uref{https://www.oracle.com, Oracle}; 
@I{Sybase};
@uref{https://www.ibm.com/db2, IBM's DB2};
@uref{https://www.microsoft.com/sql-server/, Microsoft @acronym{SQL}
Server} on Windows) and academic and small-system databases (such as
MySQL@footnote{and forks, notably MariaDB.}, PostgreSQL, Microsoft
Access, @dots{}), the former marked out by much greater emphasis on data
security features.  The line is blurring, with MySQL and PostgreSQL
having more and more high-end features, and free `express' versions
being made available for the commercial @abbr{DBMS}s.

@cindex ODBC
@cindex Open Database Connectivity
There are other commonly used data sources, including spreadsheets,
non-relational databases and even text files (possibly compressed).
Open Database Connectivity (@abbr{ODBC}) is a standard to use all of
these data sources.  It originated on Windows (see
@uref{https://learn.microsoft.com/en-us/sql/odbc/microsoft-open-database-connectivity-odbc})
but is also implemented on Linux/Unix/macOS.

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 @acronym{ISO} standard (in fact
several: @acronym{SQL}92 is @acronym{ISO}/@acronym{IEC}@tie{}9075, also known as
@acronym{ANSI} X3.135-1992, and @acronym{SQL}99 is coming into use) for
an interface language called @acronym{SQL} (Structured Query Language,
sometimes pronounced `sequel': see Bowman @emph{@I{et al.}@:} 1996 and Kline
and Kline 2001) which these @abbr{DBMS}s support to varying degrees.


@node SQL queries
@subsection @acronym{SQL} queries
@cindex SQL queries

The more comprehensive @R{} interfaces generate @acronym{SQL} behind the
scenes for common operations, but direct use of @acronym{SQL} is needed
for complex operations in all.  Conventionally @acronym{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 @abbr{DBMS} stores data as a database of @emph{tables} (or
@emph{relations}) which are rather similar to @R{} data frames, in that
they are made up of @emph{columns} or @emph{fields} of one type
(numeric, character, date, currency, @dots{}) and @emph{rows} or
@emph{records} containing the observations for one entity.

@acronym{SQL} `queries' are quite general operations on a relational
database.  The classical query is a SELECT statement of the type

@example
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
@end example

@noindent
The first of these selects two columns from the @R{} data frame
@code{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 @emph{join} on two tables @code{student} and
@code{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 @abbr{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 @I{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 Microsoft SQL Server 2000, Oracle, MySQL and PostgreSQL.

@node Data types
@subsection Data types

Data can be stored in a database in various data types.  The range of
data types is @abbr{DBMS}-specific, but the @acronym{SQL} standard defines many
types, including the following that are widely implemented (often not by
the @acronym{SQL} name).

@table @code
@item float(@var{p})
Real number, with optional precision.  Often called @code{real} or
@code{double} or @code{double precision}.
@item integer
32-bit integer.  Often called @code{int}.
@item smallint
16-bit integer
@item character(@var{n})
fixed-length character string.  Often called @code{char}.
@item character varying(@var{n})
variable-length character string.  Often called @code{varchar}.  Almost
always has a limit of 255 chars.
@item boolean
true or false.  Sometimes called @code{bool} or @code{bit}.
@item date
calendar date
@item time
time of day
@item timestamp
date and time
@end table

@noindent
There are variants on @code{time} and @code{timestamp}, @code{with
timezone}.  Other types widely implemented are @code{text} and
@code{blob}, for large blocks of text and binary data, respectively.

The more comprehensive of the @R{} interface packages hide the type
conversion issues from the user.


@node R interface packages
@section R interface packages

There are several packages available on @acronym{CRAN} to help @R{}
communicate with @abbr{DBMS}s.  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 @acronym{SQL}
queries, and to retrieve the result as a whole as a
data frame or in pieces (usually as groups of rows).  

All except @CRANpkg{RODBC} are tied to one @abbr{DBMS}, but there has been a
proposal for a unified `front-end' package @CRANpkg{DBI}
(@uref{https://developer.r-project.org/db/}) in conjunction with a
`back-end', the most developed of which is @CRANpkg{RMySQL}.  Also on
@acronym{CRAN} are the back-ends @CRANpkg{ROracle},
@CRANpkg{RPostgreSQL} and @CRANpkg{RSQLite} (which works with the
bundled @abbr{DBMS} @code{SQLite}, @uref{https://www.sqlite.org/index.html}) and
@CRANpkg{RJDBC} (which uses Java and can connect to any @abbr{DBMS} that has a
@abbr{JDBC} driver).

@c The BioConductor project has updated @pkg{RdbiPgSQL} (formerly on
@c @acronym{CRAN} ca 2000), a first-generation interface to PostgreSQL.

@pkg{PL/R} (@uref{https://github.com/postgres-plr/plr}) is a project to
embed R into PostgreSQL.

Package @CRANpkg{RMongo} provides an @R{} interface to a Java client for
`MongoDB' (@uref{https://en.wikipedia.org/wiki/MongoDB}) databases,
which are queried using JavaScript rather than SQL.  Package
@CRANpkg{mongolite} is another client using @pkg{mongodb}'s C driver.


@node DBI
@subsection Packages using DBI
@cindex MySQL database system

Package @CRANpkg{RMySQL} on @acronym{CRAN} provides an interface to the
MySQL database system (see @uref{https://www.mysql.com} and @I{Dubois},
2000) or its fork @I{MariaDB} (see @uref{https://mariadb.org/}).  The
description here applies to versions @code{0.5-0} and later: earlier
versions had a substantially different interface.  The current version
requires the @CRANpkg{DBI} package, and this description will apply with
minor changes to all the other back-ends to @CRANpkg{DBI}.

MySQL exists on Unix/Linux/macOS and Windows: there is a `Community
Edition' released under GPL but commercial licenses are also available.
MySQL was originally a `light and lean' database.  (It preserves the
case of names where the operating file system is case-sensitive, so not
on Windows.)


@findex dbDriver
@findex dbConnect
@findex dbDisconnect
The call @code{dbDriver("MySQL")} returns a database connection manager
object, and then a call to @code{dbConnect} opens a database connection
which can subsequently be closed by a call to the generic function
@code{dbDisconnect}.  Use @code{dbDriver("Oracle")},
@code{dbDriver("PostgreSQL")} or @code{dbDriver("SQLite")} with those
@abbr{DBMSs} and packages @CRANpkg{ROracle}, @CRANpkg{RPostgreSQL} or @CRANpkg{RSQLite}
respectively.

@findex dbSendQuery
@findex dbClearResult
@findex dbGetQuery
@acronym{SQL} queries can be sent by either @code{dbSendQuery} or
@code{dbGetQuery}.  @code{dbGetquery} sends the query and retrieves the
results as a data frame.  @code{dbSendQuery} sends the query and returns
an object of class inheriting from @code{"DBIResult"} which can be used
to retrieve the results, and subsequently used in a call to
@code{dbClearResult} to remove the result.

@findex fetch
Function @code{fetch} is used to retrieve some or all of the rows in the
query result, as a list.  The function @code{dbHasCompleted} indicates if
all the rows have been fetched, and @code{dbGetRowCount} returns the
number of rows in the result.

@findex dbReadTable
@findex dbWriteTable
@findex dbExistsTable
@findex dbRemoveTable
These are convenient interfaces to read/write/test/delete tables in the
database.  @code{dbReadTable} and @code{dbWriteTable} copy to and from
an @R{} data frame, mapping the row names of the data frame to the field
@code{row_names} in the @code{MySQL} table.

@smallexample
> 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)
[1] "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
> dbGetQuery(con, paste("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)
@end smallexample

@node RODBC
@subsection Package @pkg{RODBC}
@cindex ODBC
@cindex Open Database Connectivity

Package @CRANpkg{RODBC} on @acronym{CRAN} provides an interface to
database sources supporting an @abbr{ODBC} interface.  This is very
widely available, and allows the same @R{} code to access different
database systems.  @CRANpkg{RODBC} runs on Unix/Linux, Windows and macOS,
and almost all database systems provide support for @abbr{ODBC}.  We
have tested Microsoft SQL Server, Access, MySQL, PostgreSQL, Oracle and
IBM DB2 on Windows and MySQL, MariaDB, Oracle, PostgreSQL and SQLite on
Linux.

@abbr{ODBC} is a client-server system, and we have happily connected to a @abbr{DBMS}
running on a Unix server from a Windows client, and @emph{vice versa}.

On Windows @abbr{ODBC} support is part of the OS.  On Unix/Linux you will need
an @abbr{ODBC} Driver Manager such as @I{unixODBC}
(@uref{https://www.unixodbc.org/}) or @abbr{iODBC} (@uref{https://www.iodbc.org/}:
this is pre-installed in macOS) and an installed driver for your
database system.

@cindex Excel
@cindex @I{dBase}
@findex .dbf
Windows provides drivers not just for @abbr{DBMS}s but also for Excel
(@file{.xls}) spreadsheets, @I{dBase} (@file{.dbf}) files and even text
files.  (The named applications do @emph{not} need to be
installed. Which file formats are supported depends on the versions of
the drivers.)  There are versions for Excel and Access 2007/2010 (go to
@uref{https://www.microsoft.com/en-us/download}, and
search for `Office @abbr{ODBC}', which will lead to
@file{AccessDatabaseEngine.exe}), the `2007 Office System Driver' (the
latter has a version for 64-bit Windows, and that will also read earlier
versions).

On macOS the Actual Technologies
(@url{https://www.actualtech.com/product_access.php}) drivers provide
@abbr{ODBC} interfaces to Access databases and to Excel spreadsheets (not
including Excel 2007/2010).

@findex odbcConnect
@findex odbcDriverConnect
@findex odbcGetInfo
Many simultaneous connections are possible.  A connection is opened by a
call to @code{odbcConnect} or @code{odbcDriverConnect} (which on the
Windows GUI allows a database to be selected via dialog boxes) which
returns a handle used for subsequent access to the database.  Printing a
connection will provide some details of the @abbr{ODBC} connection, and calling
@code{odbcGetInfo} will give details on the client and server.

 
@findex odbcClose
@findex close
A connection is closed by a call to @code{close} or @code{odbcClose},
and also (with a warning) when not R object refers to it and at the end
of an R session.

@findex sqlTables
Details of the tables on a connection can be found using
@code{sqlTables}.

@findex sqlFetch
@findex sqlSave
Function @code{sqlSave} copies an @R{} data frame to a table in the
database, and @code{sqlFetch} copies a table in the database to an @R{}
data frame.

@findex sqlQuery
@findex sqlCopy
@findex odbcQuery
@findex sqlGetResults
@findex sqlFetchMore
An @acronym{SQL} query can be sent to the database by a call to
@code{sqlQuery}.  This returns the result in an @R{} data frame.
(@code{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
calling @code{odbcQuery} and then @code{sqlGetResults} to fetch the
results.  The latter can be used within a loop to retrieve a limited
number of rows at a time, as can function @code{sqlFetchMore}.

@cindex PostgreSQL database system
Here is an example using PostgreSQL, for which the @abbr{ODBC} driver
maps column and data frame names to lower case.  We use a database
@code{testdb} we created earlier, and had the @abbr{DSN} (data source name) set
up in @file{~/.odbc.ini} under @code{unixODBC}.  Exactly the same code
worked using @I{MyODBC} to access a MySQL database under Linux or Windows
(where MySQL also maps names to lowercase).  Under Windows,
@acronym{DSN}s are set up in the @abbr{ODBC} applet in the Control
Panel (`@I{Data Sources (ODBC)}' in the `Administrative Tools' section).
@cindex MySQL database system

@smallexample
> 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", addPK = TRUE)
> 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)
@end smallexample

@cindex Excel
@findex .xls
@findex odbcConnectExcel
As a simple example of using @abbr{ODBC} under Windows with a Excel
spreadsheet, we can read from a spreadsheet by

@smallexample
> library(RODBC)
> channel <- odbcConnectExcel("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$Print_Area        TABLE      NA
## retrieve the contents of sheet 1, by either of
> sh1 <- sqlFetch(channel, "Sheet1")
> sh1 <- sqlQuery(channel, "select * from [Sheet1$]")
@end smallexample

@noindent
Notice that the specification of the table is different from the name
returned by @code{sqlTables}: @code{sqlFetch} is able to map the
differences.



@c @node RPgSQL
@c @subsection Package RPgSQL
@c @cindex PostgreSQL database system

@c Package @pkg{RPgSQL} at @uref{http://rpgsql.sourceforge.net/} and in the
@c @code{Devel} area on @acronym{CRAN} provides an interface to
@c @uref{http://www.postgresql.org, PostgreSQL}.  Development appears to
@c have ceased.

@c PostgreSQL is described by its developers as `the most advanced open
@c source database server' (Momjian, 2000).  It would appear to be buildable
@c for most Unix-alike OSes and Windows (under Cygwin or U/Win).
@c PostgreSQL has most of the features of the commercial RDBMSs.

@c @findex db.connect
@c @findex db.read.table
@c @findex db.write.table
@c To make use of @pkg{RPgSQL}, first open a connection to a database using
@c @code{db.connect}.  (Currently only one connection can be open at a
@c time.)  Once a connection is open an @R{} data frame can be copied to a
@c PostgreSQL table by @code{db.write.table}, whereas @code{db.read.table}
@c copies a PostgreSQL table to an @R{} data frame.

@c @findex bind.db.proxy
@c @cindex proxy data frame
@c @pkg{RPgSQL} has the interesting concept of a @emph{proxy data frame}.
@c A data frame proxy is an @R{} object that inherits from the
@c @code{"data.frame"} class, but contains no data.  All accesses to the
@c proxy data frame generate the appropriate @acronym{SQL} query and
@c retrieve the resulting data from the database.  A proxy data frame is
@c set up by a call to @code{bind.db.proxy}.  To remove the proxy, just
@c remove the object which @code{bind.db.proxy} created.

@c @findex db.execute
@c @findex db.result.columns
@c @findex db.result.rows
@c @findex db.read.column
@c @findex db.fetch.result
@c @findex db.clear.result
@c @findex db.result.get.value
@c A finer level of control is available via sending @acronym{SQL} queries
@c to the PostgreSQL server via @code{db.execute}.  This leaves a result in
@c PostgreSQL's result cache, unless flushed by @code{clear = TRUE} (the
@c default).  Once a result is in the cache, @code{db.fetch.result} can be
@c used to fetch the whole result as a data frame.  Functions such as
@c @code{db.result.columns} and @code{db.result.rows} will report the
@c number of columns and rows in the cached table, and
@c @code{db.read.column} will fetch a single column (as a vector).  An
@c individual cell in the result can be read by @code{db.result.get.value}.
@c @code{db.clear.result} will clear the result cache.

@c @findex sql.insert
@c @findex sql.select
@c One disadvantage is that PostgreSQL maps all table and column names to
@c lower case, so for maximal flexibility, only use lower case in @R{}
@c names.  Functions @code{sql.insert} and @code{sql.select} provide
@c convenience wrappers for the INSERT and SELECT queries.

@c We can explore these functions in a simple example.  The database
@c @file{testdb} had already been set up, and as PostgreSQL was running on
@c a standalone machine no further authentication was required to connect.

@c @smallexample
@c > library(RPgSQL)
@c > db.connect(dbname="testdb")   # add authentication as needed
@c Connected to database "testdb" on ""
@c > data(USArrests)
@c > usarrests <- USArrests
@c > names(usarrests) <- tolower(names(USArrests))
@c > db.write.table(USArrests, write.row.names = TRUE)
@c > db.write.table(usarrests, write.row.names = TRUE)
@c > rm(USArrests, usarrests)
@c ## db.ls lists tables in the database.
@c > db.ls()
@c [1] "USArrests"  "usarrests"
@c > db.read.table("USArrests")
@c                Murder Assault UrbanPop Rape
@c Alabama          13.2     236       58 21.2
@c Alaska           10.0     263       48 44.5
@c    ...
@c ## set up a proxy data frame.  Remember USArrests has been removed
@c > bind.db.proxy("USArrests")
@c ## USArrests is now a proxy, so all accesses are to the database
@c > USArrests[, "Rape"]
@c    Rape
@c 1  21.2
@c 2  44.5
@c    ...
@c > rm(USArrests) # remove proxy
@c > db.execute("SELECT rpgsql_row_names, murder FROM usarrests",
@c              "WHERE rape > 30 ORDER BY murder", clear=FALSE)
@c > db.fetch.result()
@c            murder
@c Colorado      7.9
@c Arizona       8.1
@c California    9.0
@c Alaska       10.0
@c New Mexico   11.4
@c Michigan     12.1
@c Nevada       12.2
@c Florida      15.4
@c > db.rm("USArrests", "usarrests") # use ask=FALSE to skip confirmation
@c Destroy table USArrests? y
@c Destroy table usarrests? y
@c > db.ls()
@c character(0)
@c > db.disconnect()
@c @end smallexample

@c @noindent
@c Notice how the row names are mapped if @code{write.row.names = TRUE} to
@c a field @code{rpgsql_row_names} in the database table and transparently
@c restored provided we preserve that field in the query.

@c @pkg{RPgSQL} provides means to extend its mapping between @R{} classes
@c within a data frame and PostgreSQL types.



@node Binary files
@chapter Binary files
@cindex Binary files

Binary connections (@ref{Connections}) are now the preferred way to
handle binary files.


@node Binary data formats
@section Binary data formats
@findex hdf5
@cindex Hierarchical Data Format

@findex netCDF
@cindex network Common Data Form

Packages @CRANpkg{h5}, @I{Bioconductor}'s @pkg{rhdf5}, @CRANpkg{RNetCDF} and
@CRANpkg{ncdf4} on @acronym{CRAN} provide interfaces to @acronym{NASA}'s
@abbr{HDF5} (Hierarchical Data Format, see
@uref{https://www.hdfgroup.org/wp-content/uploads/2017/07/HDF5.pdf}) and to @I{UCAR}'s @I{netCDF} data files
(network Common Data Form, see
@uref{https://www.unidata.ucar.edu/software/netcdf/}).

Both of these are systems to store scientific data in array-oriented
ways, including descriptions, labels, formats, units, @dots{}.  @abbr{HDF5} also
allows @emph{groups} of arrays, and the @R{} interface maps lists
to @abbr{HDF5} groups, and can write numeric and character vectors and
matrices.

@I{NetCDF}'s version 4 format (confusingly, implemented in @I{netCDF} 4.1.1 and
later, but not in 4.0.1) includes the use of various @abbr{HDF5} formats.  This
is handled by package @CRANpkg{ncdf4} whereas @CRANpkg{RNetCDF} handles
version 3 files.

The availability of software to support these formats is somewhat
limited by platform, especially on Windows.


@node dBase files (DBF)
@section @I{dBase} files (@I{DBF})

@cindex @I{dBase}
@cindex @I{DBF} files
@code{dBase} was a DOS program written by @I{Ashton-Tate} and later owned by
@I{Borland} which has a binary flat-file format that became popular, with
file extension @file{.dbf}.  It has been adopted for the '@I{Xbase}' family
of databases, covering @I{dBase}, Clipper, @I{FoxPro} and their Windows
equivalents @I{Visual dBase}, @I{Visual Objects} and @I{Visual FoxPro} (see
@uref{https://www.clicketyclick.dk/databases/xbase/format/}).
A @I{dBase} file contains
a header and then a series of fields and so is most similar to an @R{}
data frame.  The data itself is stored in text format, and can include
character, logical and numeric fields, and other types in later versions
(see for example
@uref{https://www.loc.gov/preservation/digital/formats/fdd/fdd000325.shtml}
and
@uref{https://www.clicketyclick.dk/databases/xbase/format/index.html}).

@findex read.dbf
@findex write.dbf
Functions @code{read.dbf} and @code{write.dbf} provide ways to read and
write basic @abbr{DBF} files on all @R{} platforms.  For Windows users
@code{odbcConnectDbase} in package @CRANpkg{RODBC} provides more
comprehensive facilities to read @abbr{DBF} files @emph{via} Microsoft's @I{dBase}
@abbr{ODBC} driver (and the @I{Visual FoxPro} driver can also be used via
@code{odbcDriverConnect}).
@findex odbcConnectDbase



@node Image files
@chapter Image files

A particular class of binary files are those representing images, and a
not uncommon request is to read such a file into @R{} as a matrix.

There are many formats for image files (most with lots of variants), and
it may be necessary to use external conversion software to first convert
the image into one of the formats for which a package currently provides
an @R{} reader.  A versatile example of such software is @I{ImageMagick} and
its fork @I{GraphicsMagick}.  These provide command-line programs
@command{convert} and @command{gm convert} to convert images from one
format to another: what formats they can input is determined when they
are compiled, and the supported formats can be listed by e.g.@:
@command{convert -list format}.

Package @CRANpkg{pixmap} has a function @code{read.pnm} to read `portable
@I{anymap}' images in PBM (black/white), @abbr{PGM} (grey) and PPM (RGB colour)
formats.  These are also known as `@I{netpbm}' formats.

Packages @CRANpkg{bmp}, @CRANpkg{jpeg} and @CRANpkg{png} read the
formats after which they are named.  See also packages @CRANpkg{biOps}
and @CRANpkg{Momocs}, and Bioconductor package @pkg{EBImage}.

TIFF is more a meta-format, a wrapper within which a very large variety
of image formats can be embedded.  Packages @CRANpkg{rtiff} and
@CRANpkg{tiff} can read some of the sub-formats (depending on the
external @code{libtiff} software against which they are compiled).
There some facilities for specialized sub-formats, for example in
Bioconductor package @pkg{beadarray}.

Raster files are common in the geographical sciences, and package
@CRANpkg{rgdal} provides an interface to @I{GDAL} which provides some
facilities of its own to read raster files and links to many others.
Which formats it supports is determined when @I{GDAL} is compiled: use
@code{gdalDrivers()} to see what these are for the build you are using.
It can be useful for uncommon formats such as JPEG 2000 (which is a
different format from JPEG, and not currently supported in the macOS
nor Windows binary versions of @CRANpkg{rgdal}).



@node Connections
@chapter Connections

@cindex Connections
@emph{Connections} are used in @R{} in the sense of Chambers (1998) and
Ripley (2001), a set of functions to replace the use of file names by a
flexible interface to file-like objects.


@node Types of connections
@section Types of connections
@cindex Connections

@findex file
@cindex File connections
The most familiar type of connection will be a file, and file
connections are created by function @code{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.

@findex open
@findex close
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 @code{open} and
@code{close} with methods to explicitly open and close connections.

@findex gzfile
@findex bzfile
@cindex Compressed files
Files compressed via the algorithm used by @code{gzip} can be used as
connections created by the function @code{gzfile}, whereas files
compressed by @code{bzip2} can be used via @code{bzfile}.

@cindex Terminal connections
@findex stdin
@findex stdout
@findex stderr
Unix programmers are used to dealing with special files @code{stdin},
@code{stdout} and @code{stderr}.   These exist as @emph{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, @code{stdin} refers to the lines submitted from
@code{readline} rather than a file.)

The three terminal connections are always open, and cannot be opened or
closed.  @code{stdout} and @code{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
to @code{sink}, error output is sent to @code{stderr} unless re-directed
by @code{sink, type="message")}.  Note carefully the language used here:
the connections cannot be re-directed, but output can be sent to other
connections.

@cindex Text connections
@findex textConnection
@emph{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
@code{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
vector.  @code{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
@code{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.

@cindex Pipe connections
@findex pipe
@emph{Pipes} are a special form of file that connects to another
process, and pipe connections are created by the function @code{pipe}.
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.

@cindex URL connections
@findex url
@acronym{URL}s of types @samp{http://}, @samp{https://}, @samp{ftp://}
and @samp{file://} can be read from using the function @code{url}.  For
convenience, @code{file} will also accept these as the file
specification and call @code{url}.

@cindex Sockets
@findex socketConnection
Sockets can also be used as connections via function
@code{socketConnection} on platforms which support Berkeley-like sockets
(most Unix systems, Linux and Windows).  Sockets can be written to or
read from, and both client and server sockets can be used.


@node Output to connections
@section Output to connections
@cindex Connections

@findex cat
@findex write
@findex write.table
@findex sink
We have described functions @code{cat}, @code{write}, @code{write.table}
and @code{sink} as writing to a file, possibly appending to a file if 
argument @code{append = TRUE}, and this is what they did prior to @R{}
version 1.2.0.

The current behaviour is equivalent, but what actually happens is that
when the @code{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 @code{file = "|cmd"} (which can still be
used).

@findex writeLines
There is a function @code{writeLines} to write complete text lines
to a connection.

Some simple examples are

@example
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 further processing.
## Look at it by, e.g.,
cat(ex.lm.out, sep = "\n")
@end example


@node Input from connections
@section Input from connections

@findex scan
@findex read.table
@findex readLines
The basic functions to read from connections are @code{scan} and
@code{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
different formats.

Other functions that call @code{scan} can also make use of connections,
in particular @code{read.table}.

Some simple examples are

@example
## 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=",")
@end example

@cindex URL connections
For convenience, if the @code{file} argument specifies a FTP, HTTP or
@abbr{HTTPS} @acronym{URL}, the @acronym{URL} is opened for reading via
@code{url}.  Specifying files via @samp{file://foo.bar} is also allowed.


@c Keep this single-item menu to avoid a spurious warning about
@c an unreferenced node when making info with Texinfo 6.8 and 7.0.
@menu
* Pushback::                    
@end menu

@node Pushback
@subsection @I{Pushback}

@findex pushBack.
@cindex @I{Pushback} on a connection
C programmers may be familiar with the @code{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
to @code{pushBack}.

Pushbacks operate as a stack, so a read request first uses each line
from the most recently @I{pushbacked} text, then those from earlier
pushbacks and finally reads from the connection itself.  Once a
@I{pushbacked} line is read completely, it is cleared.  The number of
pending lines pushed back can be found via a call to
@code{pushBackLength}.
@findex pushBackLength

A simple example will show the idea.

@example
> zz <- textConnection(LETTERS)
> readLines(zz, 2)
[1] "A" "B"
> scan(zz, "", 4)
Read 4 items
[1] "C" "D" "E" "F"
> pushBack(c("aa", "bb"), zz)
> scan(zz, "", 4)
Read 4 items
[1] "aa" "bb" "G"  "H" 
> close(zz)
@end example

@I{Pushback} is only available for connections opened for input in text mode.


@node Listing and manipulating connections
@section Listing and manipulating connections
@cindex Connections

@findex showConnections
A summary of all the connections currently opened by the user can be
found by @code{showConnections()}, and a summary of all connections,
including closed and terminal connections, by @code{showConnections(all
= TRUE)}

@findex seek
@findex isSeekable
The generic function @code{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 @code{isSeekable}
reports if @code{seek} can change the position on the connection
given by its argument.

@findex truncate
The function @code{truncate} can be used to truncate a file opened for
writing at its current position.  It works only for @code{file}
connections, and is not implemented on all platforms.


@node Binary connections
@section Binary connections
@cindex Binary files

@findex readBin
@findex writeBin
Functions @code{readBin} and @code{writeBin} read to and write from
binary connections.  A connection is opened in binary mode by appending
@code{"b"} to the mode specification, that is using mode @code{"rb"} for
reading, and mode @code{"wb"} or @code{"ab"} (where appropriate) for
writing.  The functions have arguments

@example
readBin(con, what, n = 1, size = NA, endian = .Platform$endian) 
writeBin(object, con, size = NA, endian = .Platform$endian) 
@end example

In each case @code{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.
@code{object} should be an atomic vector object, that is a vector of
mode @code{numeric}, @code{integer}, @code{logical}, @code{character},
@code{complex} or @code{raw}, without attributes.  By default this is
written to the file as a stream of bytes exactly as it is represented in
memory.

@code{readBin} reads a stream of bytes from the file and interprets them
as a vector of mode given by @code{what}.  This can be either an object
of the appropriate mode (e.g.@: @code{what=integer()}) or a character
string describing the mode (one of the five given in the previous
paragraph or @code{"double"} or @code{"int"}).  Argument @code{n}
specifies the maximum number of vector elements to read from the
connection: if fewer are available a shorter vector will be returned.
Argument @code{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 @emph{vice
versa}.  This will not suffice if the data are 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.  Common PCs
(@cputype{ix86}-based and @cputype{x86_64}-based machines), Compaq Alpha
and @I{Vaxen} are @emph{little-endian}, whereas Sun @I{Sparc}, @I{mc680x0} series,
IBM R6000, @I{SGI} and most others are @emph{big-endian}.  (Network
byte-order (as used by @abbr{XDR}, @I{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 the @code{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
@code{NA}'s.

@findex readChar
@findex writeChar
Character strings are read and written in C format, that is as a string
of bytes terminated by a zero byte.  Functions @code{readChar} and
@code{writeChar} provide greater flexibility.


@c Keep this single-item menu to avoid a spurious warning about
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* Special values::              
@end menu

@node Special values
@subsection Special values

Functions @code{readBin} and @code{writeBin} will pass missing and
special values, although this should not be attempted if a size change
is involved.

The missing value for @R{} logical and integer types is @code{INT_MIN},
the smallest representable @code{int} defined in the C header
@file{limits.h}, normally corresponding to the bit pattern
@code{0x80000000}.

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 @code{Rmath} library which exports double
constants @code{NA_REAL}, @code{R_PosInf} and @code{R_NegInf}, and
include the header @file{Rmath.h} which defines the macros @code{ISNAN}
and @code{R_FINITE}.

If that is not possible, on all current platforms
@acronym{IEC}@tie{}60559 (aka IEEE@tie{}754) arithmetic is used, so
standard C facilities can be used to test for or set @code{Inf},
@code{-Inf} and @code{NaN} values.  On such platforms @code{NA} is
represented by the @code{NaN} value with low-word @code{0x7a2} (1954 in
decimal).

Character missing values are written as @code{NA}, and there are no
provision to recognize character values as missing (as this can be done
by re-assigning them once read).



@node Network interfaces
@chapter Network interfaces

Some limited facilities are available to exchange data at a lower level
across network connections.


@node Reading from sockets
@section Reading from sockets

@cindex Sockets
Base @R{} comes with some facilities to communicate @emph{via}
@acronym{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.

@findex make.socket
@findex read.socket
@findex write.socket
@findex close.socket
The earlier low-level interface is given by functions @code{make.socket},
@code{read.socket}, @code{write.socket} and @code{close.socket}.


@node Using download.file
@section Using @code{download.file}

Function @code{download.file} is provided to read a file from a Web
resource via FTP or HTTP (including @abbr{HTTPS}) and write it to a file.
Often this can be avoided, as functions such as @code{read.table} and
@code{scan} can read directly from a URL, either by explicitly using
@code{url} to open a connection, or implicitly using it by giving a URL
as the @code{file} argument.



@node Reading Excel spreadsheets
@chapter Reading Excel spreadsheets

@findex .xls
@findex .xlsx

The most common R data import/export question seems to be `how do I read
an Excel spreadsheet'.  This chapter collects together advice and
options given earlier.  Note that most of the advice is for pre-Excel
2007 spreadsheets and not the later @file{.xlsx} format.

@findex read.csv
@findex read.delim
@findex read.DIF
@findex read.table
@findex readClipboard
The first piece of advice is to avoid doing so if possible!  If you have
access to Excel, export the data you want from Excel in tab-delimited or
comma-separated form, and use @code{read.delim} or @code{read.csv} to
import it into R.  (You may need to use @code{read.delim2} or
@code{read.csv2} in a locale that uses comma as the decimal point.)
Exporting a @abbr{DIF} file and reading it using @code{read.DIF} is another
possibility.

If you do not have Excel, many other programs are able to read such
spreadsheets and export in a text format on both Windows and Unix, for
example @I{Gnumeric} (@uref{http://www.gnumeric.org}) and
OpenOffice (@uref{https://www.openoffice.org}).  You can also
cut-and-paste between the display of a spreadsheet in such a program and
R: @code{read.table} will read from the R console or, under Windows,
from the clipboard (via @code{file = "clipboard"} or
@code{readClipboard}).  The @code{read.DIF} function can also read from
the clipboard.

Note that an Excel @file{.xls} file is not just a spreadsheet: such
files can contain many sheets, and the sheets can contain formulae,
macros and so on.  Not all readers can read other than the first sheet,
and may be confused by other contents of the file.

@findex odbcConnectExcel
@findex odbcConnectExcel2007
Windows users (of 32-bit @R{}) can use @code{odbcConnectExcel} in
package @CRANpkg{RODBC}.  This can select rows and columns from any of the
sheets in an Excel spreadsheet file (at least from Excel 97--2003,
depending on your @abbr{ODBC} drivers: by calling @code{odbcConnect} directly
versions back to Excel 3.0 can be read).  The version
@code{odbcConnectExcel2007} will read the Excel 2007 formats as well as
earlier ones (provided the drivers are installed, including with 64-bit
Windows @R{}: @pxref{RODBC}).  macOS users can also use @CRANpkg{RODBC} if
they have a suitable driver (e.g.@: that from Actual Technologies).

@findex read.xls
@code{Perl} users have contributed a module
@code{OLE::SpreadSheet::ParseExcel} and a program @code{xls2csv.pl} to
convert Excel 95--2003 spreadsheets to CSV files.  Package @CRANpkg{gdata}
provides a basic wrapper in its @code{read.xls} function.  With suitable
@code{Perl} modules installed this function can also read Excel 2007
spreadsheets.

@findex dataframes2xls
@findex WriteXLS
Packages @CRANpkg{dataframes2xls} and @CRANpkg{WriteXLS} each contain a function
to @emph{write} one or more data frames to an @file{.xls} file, using
Python and Perl respectively.

@findex xlsx
Package @CRANpkg{xlsx} can read and manipulate Excel 2007 and later
spreadsheets: it requires Java.

@findex XLConnect
Package @CRANpkg{XLConnect} can read, write and manipulate both Excel
97--2003 and Excel 2007/10 spreadsheets, using Java.

@findex readxl
Package @CRANpkg{readxl} can read both Excel 97--2003 and Excel 2007/10
spreadsheets, using an included C library.



@node References
@unnumbered References

@noindent
@I{R.@: A.@: Becker}, @I{J.@: M.@: Chambers} and @I{A.@: R.@: Wilks} (1988),
@emph{The New S Language.  A Programming Environment for Data Analysis
and Graphics.}  
@I{Wadsworth & Brooks/Cole}.

@noindent
@I{J.@: Bowman}, @I{S.@: Emberson} and @I{M.@: Darnovsky} (1996),
@emph{The Practical @acronym{SQL} Handbook.  Using Structured Query Language.}
Addison-Wesley.

@noindent
@I{J.@: M.@: Chambers} (1998),
@emph{Programming with Data.  A Guide to the S Language.}
@I{Springer-Verlag}.

@noindent
@I{P.@: Dubois} (2000),
@emph{MySQL.}
New Riders.

@noindent
@I{M.@: Henning} and @I{S.@: Vinoski} (1999),
@emph{Advanced @I{CORBA} Programming with C++.}
Addison-Wesley.

@noindent
@I{K.@: Kline} and @I{D.@: Kline} (2001),
@emph{SQL in a Nutshell.} 
@I{O'Reilly}.

@noindent
@I{B.@: Momjian} (2000),
@emph{PostgreSQL: Introduction and Concepts.}
Addison-Wesley.
Also available at @uref{https://momjian.us/main/writings/pgsql/aw_pgsql_book/}.

@noindent
@I{B.@: D.@: Ripley} (2001),
Connections.
@emph{R News}, @strong{1/1}, 16--7.
@uref{https://www.r-project.org/doc/Rnews/Rnews_2001-1.pdf}

@noindent
@I{T.@: M.@: Therneau} and @I{P.@: M.@: Grambsch} (2000),
@emph{Modeling Survival Data.  Extending the Cox Model.}
@I{Springer-Verlag}.

@noindent
@I{E.@: J.@: Yarger}, @I{G.@: Reese} and @I{T.@ King} (1999),
@emph{@I{MySQL & mSQL}}.
@I{O'Reilly}.


@node Function and variable index
@unnumbered Function and variable index

@printindex vr


@node Concept index
@unnumbered Concept index

@printindex cp


@bye

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