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 = ".", numerals = c("allow.loss", "warn.loss", "no.loss"), 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, skipNul = FALSE)
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 = "", …)
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,
getwd(). Tilde-expansion is performed where supported.
This can be a compressed file (see
file can be a readable text-mode
connection (which will be opened for reading if
necessary, and if so
closed (and hence destroyed) at
the end of the function call). (If
stdin() is used,
the prompts for lines may be somewhat confusing. Terminate input
with a blank line or an EOF signal,
Ctrl-D on Unix and
Ctrl-Z on Windows. Any pushback on
stdin() will be
cleared before return.)
file can also be a complete URL. (For the supported URL
schemes, see the ‘URLs’ section of the help for
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:
header is set to
if and only if the first row contains one fewer field than the
number of columns.
the field separator character. Values on each line of the
file are separated by this character. If
sep = "" (the
read.table) the separator is ‘white space’,
that is one or more spaces, tabs, newlines or carriage returns.
the set of quoting characters. To disable quoting
quote = "". See
scan for the
behaviour on quotes embedded in quotes. Quoting is only considered
for columns read as character, which is all of them unless
colClasses is specified.
the character used in the file for decimal points.
string indicating how to convert numbers whose conversion
to double precision would lose accuracy, see
Can be abbreviated. (Applies also to complex-number inputs.)
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
row.names is missing, the rows are
row.names = NULL forces row numbering. Missing or
row.names generate row names that are considered
to be ‘automatic’ (and not preserved by
a vector of optional names for the variables.
The default is to use
"V" followed by the column number.
the default behavior of
read.table is to convert
character variables (which are not converted to logical, numeric or
complex) to factors. The variable
as.is controls the
conversion of columns not otherwise specified by
Its value is either a vector of logicals (values are recycled if
necessary), or a vector of numeric or character indices which
specify which columns should not be converted to factors.
Note: to suppress all conversions including those of numeric
colClasses = "character".
as.is is specified per column (not per
variable) and so includes the column of row names (if any) and any
columns to be skipped.
a character vector of strings which are to be
NA values. Blank fields are also
considered to be missing values in logical, integer, numeric and
complex fields. Note that the test happens after
white space is stripped from the input, so
values may need their own white space stripped in advance.
character. A vector of classes to be assumed for
the columns. If unnamed, recycled as necessary. If named, names
are matched with unspecified values being taken to be
Possible values are
NA (the default, when
type.convert is used),
"NULL" (when the column
is skipped), one of the atomic vector classes (logical, integer,
numeric, complex, character, raw), or
"POSIXct". Otherwise there needs to be an
method (from package methods) for conversion from
"character" to the specified formal class.
colClasses is specified per column (not per
variable) and so includes the column of row names (if any).
integer: the maximum number of rows to read in. Negative and other invalid values are ignored.
integer: the number of lines of the data file to skip before beginning to read data.
TRUE then the names of the
variables in the data frame are checked to ensure that they are
syntactically valid variable names. If necessary they are adjusted
make.names) so that they are, and also to ensure
that there are no duplicates.
TRUE then in case the rows have unequal
length, blank fields are implicitly added. See ‘Details’.
logical. Used only when
been specified, and allows the stripping of leading and trailing
white space from unquoted
character fields (
are always stripped). See
scan for further details
(including the exact meaning of ‘white space’),
remembering that the columns may include the row names.
TRUE blank lines in the
input are ignored.
character: a character vector of length one
containing a single character or an empty string. Use
turn off the interpretation of comments altogether.
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
scan will flush to the
end of the line after reading the last of the fields requested.
This allows putting comments after the last field.
logical: should character vectors be converted
to factors? Note that this is overridden by
colClasses, both of which allow finer control.
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
file, the ‘R Data Import/Export Manual’ and
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
Encoding): it is not used to
re-encode the input, but allows R to handle encoded strings in
their native encoding (if one of those two). See ‘Value’
character string: if
file is not supplied and this is,
then data are read from the value of
text via a text connection.
Notice that a literal string can be used to include (small) data sets
within R code.
logical: should nuls be skipped?
Further arguments to be passed to
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
Character strings in the result (including factor levels) will have a
declared encoding if
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.
nrows, even as a mild over-estimate, will help memory
comment.char = "" will be appreciably faster than the
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.
scan instead for matrices.
This function is the principal means of reading tabular data into R.
colClasses is specified, all columns are read as
character columns and then converted using
to logical, integer, numeric, complex or (depending on
factor as appropriate. Quotes are (by default) interpreted in all
fields, so a column of values like
"42" will result in an
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.
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 input 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
blank.lines.skip are true, so specify
necessary (as in the ‘Examples’).
read.csv2 are identical to
read.table except for the defaults. They are intended for
reading ‘comma separated value’ files (
read.csv2) the variant used in countries that use a comma as
decimal point and a semicolon as field separator. Similarly,
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
blank.lines.skip = TRUE; however, comment lines prior
to the header must have the comment character in the first non-blank
Quoted fields with embedded newlines are supported except after a
comment character. Embedded nuls are unsupported: skipping them (with
skipNul = TRUE) may work.
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.
read.fwf for reading fixed width
count.fields can be useful to determine problems with
reading files which result in reports of incorrect record lengths (see
the ‘Examples’ below).
https://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 = paste0("V", seq_len(ncol))) 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 ")
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