Chapter 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.

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.

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.

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.

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)   # .77s
xn <- scan("ints.dat", what=numeric(0), n=1e6)   # .93s
xc <- scan("ints.dat", what=character(0), n=1e6) # .85s
xf <- as.factor(xc)                              # .2s
DF <- read.table("ints.dat")                     # .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)  # .44s
yf <- as.factor(y)                               # .21s
DF <- read.table("code.dat")                     # .9s
DF <- read.table("code.dat", nrows=1e6)          # .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.

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.

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.