read.table {utils} | R Documentation |
Reads a file in table format and creates a data frame from it, with cases corresponding to lines and variables to fields in the file.
read.table(file, header = FALSE, sep = "", quote = "\"'", dec = ".", row.names, col.names, as.is = !stringsAsFactors, na.strings = "NA", colClasses = NA, nrows = -1, skip = 0, check.names = TRUE, fill = !blank.lines.skip, strip.white = FALSE, blank.lines.skip = TRUE, comment.char = "#", allowEscapes = FALSE, flush = FALSE, stringsAsFactors = default.stringsAsFactors(), fileEncoding = "", encoding = "unknown", text) read.csv(file, header = TRUE, sep = ",", quote="\"", dec=".", fill = TRUE, comment.char="", ...) read.csv2(file, header = TRUE, sep = ";", quote="\"", dec=",", fill = TRUE, comment.char="", ...) read.delim(file, header = TRUE, sep = "\t", quote="\"", dec=".", fill = TRUE, comment.char="", ...) read.delim2(file, header = TRUE, sep = "\t", quote="\"", dec=",", fill = TRUE, comment.char="", ...)
file |
the name of the file which the data are to be read from.
Each row of the table appears as one line of the file. If it does
not contain an absolute path, the file name is
relative to the current working directory,
Alternatively,
|
header |
a logical value indicating whether the file contains the
names of the variables as its first line. If missing, the value is
determined from the file format: |
sep |
the field separator character. Values on each line of the
file are separated by this character. If |
quote |
the set of quoting characters. To disable quoting
altogether, use |
dec |
the character used in the file for decimal points. |
row.names |
a vector of row names. This can be a vector giving the actual row names, or a single number giving the column of the table which contains the row names, or character string giving the name of the table column containing the row names. If there is a header and the first row contains one fewer field than
the number of columns, the first column in the input is used for the
row names. Otherwise if Using |
col.names |
a vector of optional names for the variables.
The default is to use |
as.is |
the default behavior of Note: to suppress all conversions including those of numeric
columns, set Note that |
na.strings |
a character vector of strings which are to be
interpreted as |
colClasses |
character. A vector of classes to be assumed for
the columns. Recycled as necessary, or if the character vector is
named, unspecified values are taken to be Possible values are Note that |
nrows |
integer: the maximum number of rows to read in. Negative and other invalid values are ignored. |
skip |
integer: the number of lines of the data file to skip before beginning to read data. |
check.names |
logical. If |
fill |
logical. If |
strip.white |
logical. Used only when |
blank.lines.skip |
logical: if |
comment.char |
character: a character vector of length one
containing a single character or an empty string. Use |
allowEscapes |
logical. Should C-style escapes such as
\n be processed or read verbatim (the default)? Note that if
not within quotes these could be interpreted as a delimiter (but not
as a comment character). For more details see |
flush |
logical: if |
stringsAsFactors |
logical: should character vectors be converted
to factors? Note that this is overridden by |
fileEncoding |
character string: if non-empty declares the
encoding used on a file (not a connection) so the character data can
be re-encoded. See the ‘Encoding’ section of the help for
|
encoding |
encoding to be assumed for input strings. It is
used to mark character strings as known to be in
Latin-1 or UTF-8 (see |
text |
character string: if |
... |
Further arguments to be passed to |
This function is the principal means of reading tabular data into R.
Unless colClasses
is specified, all columns are read as
character columns and then converted using type.convert
to logical, integer, numeric, complex or (depending on as.is
)
factor as appropriate. Quotes are (by default) interpreted in all
fields, so a column of values like "42"
will result in an
integer column.
A field or line is ‘blank’ if it contains nothing (except whitespace if no separator is specified) before a comment character or the end of the field or line.
If row.names
is not specified and the header line has one less
entry than the number of columns, the first column is taken to be the
row names. This allows data frames to be read in from the format in
which they are printed. If row.names
is specified and does
not refer to the first column, that column is discarded from such files.
The number of data columns is determined by looking at the first five lines
of input (or the whole file if it has less than five lines), or from
the length of col.names
if it is specified and
is longer. This could conceivably be wrong if fill
or
blank.lines.skip
are true, so specify col.names
if
necessary (as in the ‘Examples’).
read.csv
and read.csv2
are identical to
read.table
except for the defaults. They are intended for
reading ‘comma separated value’ files (‘.csv’) or
(read.csv2
) the variant used in countries that use a comma as
decimal point and a semicolon as field separator. Similarly,
read.delim
and read.delim2
are for reading delimited
files, defaulting to the TAB character for the delimiter. Notice that
header = TRUE
and fill = TRUE
in these variants, and
that the comment character is disabled.
The rest of the line after a comment character is skipped; quotes
are not processed in comments. Complete comment lines are allowed
provided blank.lines.skip = TRUE
; however, comment lines prior
to the header must have the comment character in the first non-blank
column.
Quoted fields with embedded newlines are supported except after a comment character.
A data frame (data.frame
) containing a representation of
the data in the file.
Empty input is an error unless col.names
is specified, when a
0-row data frame is returned: similarly giving just a header line if
header = TRUE
results in a 0-row data frame. Note that in
either case the columns will be logical unless colClasses
was
supplied.
Character strings in the result (including factor levels) will have a
declared encoding if encoding
is "latin1"
or
"UTF-8"
.
These functions can use a surprising amount of memory when reading large files. There is extensive discussion in the ‘R Data Import/Export’ manual, supplementing the notes here.
Less memory will be used if colClasses
is specified as one of
the six atomic vector classes. This can be particularly so when
reading a column that takes many distinct numeric values, as storing
each distinct value as a character string can take up to 14 times as
much memory as storing it as an integer.
Using nrows
, even as a mild over-estimate, will help memory
usage.
Using comment.char = ""
will be appreciably faster than the
read.table
default.
read.table
is not the right tool for reading large matrices,
especially those with many columns: it is designed to read
data frames which may have columns of very different classes.
Use scan
instead for matrices.
The columns referred to in as.is
and colClasses
include
the column of row names (if any).
Because this function uses pushBack
it can only handle
character strings which can be represented in the current locale. So
although fileEncoding
can be used to specify the encoding of
the input file (or a connection can be specified which re-encodes),
the implied re-encoding must be possible. This is not a problem in
UTF-8 locales, but it can be on Windows — readLines
or
scan
can be used to avoid this limitation since they have
special provisions to convert input to UTF-8.
Chambers, J. M. (1992) Data for models. Chapter 3 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
The ‘R Data Import/Export’ manual.
scan
, type.convert
,
read.fwf
for reading fixed width
formatted input;
write.table
;
data.frame
.
count.fields
can be useful to determine problems with
reading files which result in reports of incorrect record lengths (see
the ‘Examples’ below).
http://tools.ietf.org/html/rfc4180 for the IANA definition of CSV files (which requires comma as separator and CRLF line endings).
## using count.fields to handle unknown maximum number of fields ## when fill=TRUE test1 <- c(1:5, "6,7", "8,9,10") tf <- tempfile() writeLines(test1, tf) read.csv(tf, fill = TRUE) # 1 column ncol <- max(count.fields(tf, sep = ",")) read.csv(tf, fill = TRUE, header = FALSE, col.names = paste("V", seq_len(ncol), sep = "")) unlink(tf) ## "Inline" data set, using text= ## Notice that leading and trailing empty lines are auto-trimmed read.table(header=TRUE, text=" a b 1 2 3 4 ")