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 = "", …)
getwd(). Tilde-expansion is performed where supported.
This can be a compressed file (see file). Alternatively, 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
url.)
header is set to TRUE
if and only if the first row contains one fewer field than the
number of columns.sep = "" (the
default for read.table) the separator is ‘white space’,
that is one or more spaces, tabs, newlines or carriage returns.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.type.convert.
Can be abbreviated. (Applies also to complex-number inputs.) 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
numbered.
Using row.names = NULL forces row numbering. Missing or
NULL row.names generate row names that are considered
to be ‘automatic’ (and not preserved by as.matrix).
"V" followed by the column number.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 colClasses.
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
columns, set colClasses = "character".
Note that 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.
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 na.strings
values may need their own white space stripped in advance.NA. 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 "factor", "Date"
or "POSIXct". Otherwise there needs to be an as
method (from package methods) for conversion from
"character" to the specified formal class.
Note that colClasses is specified per column (not per
variable) and so includes the column of row names (if any).
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
(by 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’.sep has
been specified, and allows the stripping of leading and trailing
white space from unquoted character fields (numeric 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."" to
turn off the interpretation of comments altogether.scan.TRUE, 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.as.is and
colClasses, both of which allow finer control.file, the ‘R Data Import/Export Manual’ and
‘Note’.
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’
and ‘Note’.
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.
read.table.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".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.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 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 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. Embedded nuls are unsupported: skipping them (with
skipNul = TRUE) may work.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). 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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