read_csv and read_tsv are special cases of the general
read_delim. They're useful for reading the most common types of
flat file data, comma separated values and tab separated values,
respectively. read_csv2 uses ; for separators, instead of
,. This is common in European countries which use , as the
decimal separator.
Either a path to a file, a connection, or literal data
(either a single string or a raw vector).
Files ending in .gz, .bz2, .xz, or .zip will
be automatically uncompressed. Files starting with http://,
https://, ftp://, or ftps:// will be automatically
downloaded. Remote gz files can also be automatically downloaded &
decompressed.
Literal data is most useful for examples and tests. It must contain at
least one new line to be recognised as data (instead of a path).
delim
Single character used to separate fields within a record.
quote
Single character used to quote strings.
escape_backslash
Does the file use backslashes to escape special
characters? This is more general than escape_double as backslashes
can be used to escape the delimeter character, the quote characer, or
to add special characters like \n.
escape_double
Does the file escape quotes by doubling them?
i.e. If this option is TRUE, the value """" represents
a single quote, ".
col_names
Either TRUE, FALSE or a character vector
of column names.
If TRUE, the first row of the input will be used as the column
names, and will not be included in the data frame. If FALSE, column
names will be generated automatically: X1, X2, X3 etc.
If col_names is a character vector, the values will be used as the
names of the columns, and the first row of the input will be read into
the first row of the output data frame.
col_types
One of NULL, a cols, specification of
a string. See vignette("column-types") for more details.
If NULL, all column types will be imputed from the first 1000 rows
on the input. This is convenient (and fast), but not robust. If the
imputation fails, you'll need to supply the correct types yourself.
If a column specification created by cols, it must contain
one "collector" for each column. If you only want to read a
subset of the columns, use cols_only.
Alternatively, you can use a compact string representation where each
character represents one column:
c = character, i = integer, n = number, d = double,
l = logical, D = date, T = date time, t = time, ? = guess, or
_/- to skip the column.
locale
The locale controls defaults that vary from place to place.
The default locale is US-centric (like R), but you can use
locale to create your own locale that controls things like
the default time zone, encoding, decimal mark, big mark, and day/month
names.
na
Character vector of strings to use for missing values. Set this
option to character() to indicate no missing values.
comment
A string used to identify comments. Any text after the
comment characters will be silently ignored.
skip
Number of lines to skip before reading data.
n_max
Maximum number of records to read.
progress
Display a progress bar? By default it will only display
in an interactive session. The display is updated every 50,000 values
and will only display if estimated reading time is 5 seconds or more.
trim_ws
Should leading and trailing whitespace be trimmed from
each field before parsing it?
Value
A data frame. If there are parsing problems, a warning tells you
how many, and you can retrieve the details with problems().
Examples
# Input sources -------------------------------------------------------------
# Read from a path
read_csv(system.file("extdata/mtcars.csv", package = "readr"))
read_csv(system.file("extdata/mtcars.csv.zip", package = "readr"))
read_csv(system.file("extdata/mtcars.csv.bz2", package = "readr"))
read_csv("https://github.com/hadley/readr/raw/master/inst/extdata/mtcars.csv")
# Or directly from a string (must contain a newline)
read_csv("x,y\n1,2\n3,4")
# Column types --------------------------------------------------------------
# By default, readr guess the columns types, looking at the first 100 rows.
# You can override with a compact specification:
read_csv("x,y\n1,2\n3,4", col_types = "dc")
# Or with a list of column types:
read_csv("x,y\n1,2\n3,4", col_types = list(col_double(), col_character()))
# If there are parsing problems, you get a warning, and can extract
# more details with problems()
y <- read_csv("x\n1\n2\nb", col_types = list(col_double()))
y
problems(y)
# File types ----------------------------------------------------------------
read_csv("a,b\n1.0,2.0")
read_csv2("a;b\n1,0;2,0")
read_tsv("a\tb\n1.0\t2.0")
read_delim("a|b\n1.0|2.0", delim = "|")