R Data Import/Export

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R Data Import/Export

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

This manual is for R, version 3.5.1 Patched (2018-09-11).

Copyright © 2000–2018 R Core Team

Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies.

Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one.

Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team.


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Acknowledgements

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

DBIDavid A. James
dataframes2xlsGuido van Steen
foreignThomas Lumley, Saikat DebRoy, Douglas Bates, Duncan Murdoch and Roger Bivand
gdataGregory R. Warnes
ncdf4David Pierce
rJavaSimon Urbanek
RJDBCSimon Urbanek
RMySQLDavid James and Saikat DebRoy
RNetCDFPavel Michna
RODBCMichael Lapsley and Brian Ripley
ROracleDavid A, James
RPostgreSQLSameer Kumar Prayaga and Tomoaki Nishiyama
RSPerlDuncan Temple Lang
RSPythonDuncan Temple Lang
RSQLiteDavid A, James
SJavaJohn Chambers and Duncan Temple Lang
WriteXLSMarc Schwartz
XLConnectMirai Solutions GmbH
XMLDuncan Temple Lang

Brian Ripley is the author of the support for connections.


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1 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 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 Therneau & Grambsch (2000) commented that they preferred to do data manipulation in SAS and then use package survival in S for the analysis.) Database manipulation systems are often very suitable for manipulating and extracting data: several packages to interact with DBMSs are discussed here.

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

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 such as awk and 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. 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.


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1.1 Imports

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 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 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 Reading Excel spreadsheets.

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.


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1.1.1 Encodings

Unless the file to be imported from is entirely in 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 file command-line tool (for Windows, included in Rtools). This reports something like

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

Modern Unix-alike systems, including macOS, are likely to produce UTF-8 files. Windows may produce what it calls ‘Unicode’ files (UCS-2LE or just possibly UTF-16LE1). 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 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’).

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

Note that utf8 is not a valid encoding name (UTF-8 is), and macintosh is the most portable name for what is sometimes called ‘Mac Roman’ encoding.


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1.2 Export to text files

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 Binary files.)

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

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 write.table and write. Function 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 labels.

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

  1. Precision

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

  2. Header line

    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 is what write.table will provide if argument col.names = NA is specified. Excel is one such system.

  3. Separator

    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 write.csv provides appropriate defaults. In some locales the comma is used as the decimal point (set this in write.table by dec = ",") and there CSV files use the semicolon as the field separator: use write.csv2 for appropriate defaults. There is an IETF standard for CSV files (which mandates commas and CRLF line endings, for which use eol = "\r\n"), RFC4180 (see https://tools.ietf.org/html/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 (sep = "\t") are probably the safest options.

  4. Missing values

    By default missing values are output as NA, but this may be changed by argument na. Note that NaNs are treated as NA by write.table, but not by cat nor write.

  5. Quoting strings

    By default strings are quoted (including the row and column names). Argument quote controls if character and factor variables are quoted: some programs, for example Mondrian (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

    > 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.

  6. Encodings

    Text files do not contain metadata on their encodings, so for non-ASCII data the file needs to be targetted to the application intended to read it. All of these functions can write to a connection which allows an encoding to be specified for the file, and write.table has a 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’2, that is "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 .csv files in "macroman" encoding (the encoding used in much earlier versions of Mac OS).

Function 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) print statements. This is not usually the most efficient route, and the options(width) setting may need to be increased.

Function write.foreign in package foreign uses 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 SAS, SPSS and Stata.


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1.3 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 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.

XML provides a way to specify the file’s encoding, e.g.

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

although it does not require it.

The XML package provides general facilities for reading and writing XML documents within R. Package StatDataML on CRAN is one example building on XML. Another interface to the libxml2 C library is provided by package xml2.

yaml is another system for structuring text data, with emphasis on human-readability: it is supported by package yaml.


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2 Spreadsheet-like data

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.


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2.1 Variations on read.table

The function 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.

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

Some of the issues to consider are:

  1. Encoding

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

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

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

  2. Header line

    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 col.names; explicit names override the header line (if present).

  3. 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 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".

  4. Quoting

    By default character strings can be quoted by either ‘"’ or ‘'’, 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 quote argument. For sep = "\n" the default is changed to quote = "".

    If no separator character is specified, quotes can be escaped within quoted strings by immediately preceding them by ‘\’, 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

    'One string isn''t two',"one more"
    

    can be read by

    read.table("testfile", sep = ",")
    

    This does not work with the default separator.

  5. Missing values

    By default the file is assumed to contain the character string NA 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.

    In numeric columns, the values NaN, Inf and -Inf are accepted.

  6. 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 fill = TRUE.

  7. 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 strip.white = TRUE.

  8. Blank lines

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

  9. Classes for the variables

    Unless you take any special action, 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 logical, integer, numeric and complex, moving on if any entry is not missing and cannot be converted.3 If all of these fail, the variable is converted to a factor.

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

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

  10. Comments

    By default, read.table uses ‘#’ 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 = "".

  11. 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 read.table and scan have a logical argument 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 \a, \b, \f, \n, \r, \t, \v and octal and hexadecimal representations like \040 and \0x2A. Any other escaped character is treated as itself, including backslash. Note that Unicode escapes such as \uxxxx are never interpreted.

  12. Encoding

    This can be specified by the fileEncoding argument, for example

    fileEncoding = "UCS-2LE"    # Windows ‘Unicode’ files
    fileEncoding = "UTF-8"
    

    If you know (correctly) the file’s encoding this will almost always work. However, we know of one exception, UTF-8 files with a BOM. Some people claim that UTF-8 files should never have a 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 file reports as

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

    it can be read on Windows by

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

    but on a Unix-alike might need

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

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

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

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

or

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 function count.fields can be useful to investigate further.

Efficiency can be important when reading large data grids. It will help to specify comment.char = "", colClasses as one of the atomic vector types (logical, integer, numeric, complex, character or perhaps raw) 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). See the examples in later sections.


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2.2 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.

Function 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 read.table. This is adequate for small files, but for anything more complicated we recommend using the facilities of a language like perl to pre-process the file.

Function read.fortran is a similar function for fixed-format files, using Fortran-style column specifications.


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2.3 Data Interchange Format (DIF)

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

Function read.DIF provides a simple way to read such files. It takes arguments similar to 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; read.DIF("clipboard") can read it from there directly. It is slightly more robust than read.table("clipboard") in handling spreadsheets with empty cells.


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2.4 Using scan directly

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

Function scan has many arguments, most of which we have already covered under 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. 0, "" or 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 matrix. Suppose file 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 1 second (under Linux, 3 seconds under Windows on the same machine) whereas

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

took 10 seconds (and more memory), and

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

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

Note that timings can depend on the type read and the data. Consider reading a million distinct integers:

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

and a million examples of a small set of codes:

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

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.


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2.5 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

 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 mr.csv.

We can use stack to help manipulate these data to give a single response.

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

with result

      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
...

Function unstack goes in the opposite direction, and may be useful for exporting data.

Another way to do this is to use the function reshape, by

> 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
...

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

Some people prefer the tools in packages reshape, reshape2 and plyr.


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2.6 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 ftable, which creates objects of class "ftable" with an appropriate print method.

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 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 read.table to read in the data, and create the contingency table from this using xtabs.


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3 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.


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3.1 EpiInfo, Minitab, S-PLUS, SAS, SPSS, Stata, Systat

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

EpiInfo versions 5 and 6 stored data in a self-describing fixed-width text format. read.epiinfo will read these .REC files into an R data frame. EpiData also produces data in this format.

Function read.mtp imports a ‘Minitab Portable Worksheet’. This returns the components of the worksheet as an R list.

Function read.xport reads a file in SAS Transport (XPORT) format and return a list of data frames. If SAS is available on your system, function read.ssd can be used to create and run a SAS script that saves a SAS permanent dataset (.ssd or .sas7bdat) in Transport format. It then calls read.xport to read the resulting file. (Package Hmisc has a similar function 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. .csv format.

Function 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 containing those.

Function 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 later written with data.dump(oldStyle=T).

If you have access to S-PLUS, it is usually more reliable to dump the object(s) in S-PLUS and source the dump file in R. For S-PLUS 5.x and later you may need to use 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 source function.

Function 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.

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

Some third-party applications claim to produce data ‘in SPSS format’ but with differences in the formats: read.spss may or may not be able to handle these.

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

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


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3.2 Octave

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


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4 Relational databases


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4.1 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 (DBMSs) and, in particular, relational DBMSs (RDBMSs) are designed to do all of these things well. Their strengths are

  1. To provide fast access to selected parts of large databases.
  2. Powerful ways to summarize and cross-tabulate columns in databases.
  3. Store data in more organized ways than the rectangular grid model of spreadsheets and R data frames.
  4. Concurrent access from multiple clients running on multiple hosts while enforcing security constraints on access to the data.
  5. Ability to act as a server to a wide range of clients.

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.

Increasingly OSes are themselves making use of DBMSs for these reasons, so it is nowadays likely that one will be already installed on your (non-Windows) OS. Akonadi is used by KDE4 to store personal information. Several macOS applications, including Mail and Address Book, use SQLite.


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4.2 Overview of RDBMSs

Traditionally there had been large (and expensive) commercial RDBMSs (Informix; Oracle; Sybase; IBM’s DB2; Microsoft SQL Server on Windows) and academic and small-system databases (such as MySQL4, PostgreSQL, Microsoft Access, …), 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 DBMSs.

There are other commonly used data sources, including spreadsheets, non-relational databases and even text files (possibly compressed). Open Database Connectivity (