read_delim {readr} | R Documentation |
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.
read_delim(file, delim, quote = "\"", escape_backslash = FALSE, escape_double = TRUE, col_names = TRUE, col_types = NULL, locale = default_locale(), na = c("", "NA"), quoted_na = TRUE, comment = "", trim_ws = FALSE, skip = 0, n_max = Inf, guess_max = min(1000, n_max), progress = show_progress()) read_csv(file, col_names = TRUE, col_types = NULL, locale = default_locale(), na = c("", "NA"), quoted_na = TRUE, quote = "\"", comment = "", trim_ws = TRUE, skip = 0, n_max = Inf, guess_max = min(1000, n_max), progress = show_progress()) read_csv2(file, col_names = TRUE, col_types = NULL, locale = default_locale(), na = c("", "NA"), quoted_na = TRUE, quote = "\"", comment = "", trim_ws = TRUE, skip = 0, n_max = Inf, guess_max = min(1000, n_max), progress = show_progress()) read_tsv(file, col_names = TRUE, col_types = NULL, locale = default_locale(), na = c("", "NA"), quoted_na = TRUE, quote = "\"", comment = "", trim_ws = TRUE, skip = 0, n_max = Inf, guess_max = min(1000, n_max), progress = show_progress())
file |
Either a path to a file, a connection, or literal data (either a single string or a raw vector). Files ending in 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 |
Does the file escape quotes by doubling them?
i.e. If this option is |
col_names |
Either If If Missing ( |
col_types |
One of If If a column specification created by 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
|
locale |
The locale controls defaults that vary from place to place.
The default locale is US-centric (like R), but you can use
|
na |
Character vector of strings to use for missing values. Set this
option to |
quoted_na |
Should missing values inside quotes be treated as missing values (the default) or strings. |
comment |
A string used to identify comments. Any text after the comment characters will be silently ignored. |
trim_ws |
Should leading and trailing whitespace be trimmed from each field before parsing it? |
skip |
Number of lines to skip before reading data. |
n_max |
Maximum number of records to read. |
guess_max |
Maximum number of records to use for guessing column types. |
progress |
Display a progress bar? By default it will only display
in an interactive session and not while knitting a document. The display
is updated every 50,000 values and will only display if estimated reading
time is 5 seconds or more. The automatic progress bar can be disabled by
setting option |
A data frame. If there are parsing problems, a warning tells you
how many, and you can retrieve the details with problems()
.
# Input sources ------------------------------------------------------------- # Read from a path read_csv(readr_example("mtcars.csv")) read_csv(readr_example("mtcars.csv.zip")) read_csv(readr_example("mtcars.csv.bz2")) read_csv("https://github.com/tidyverse/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 guesses 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 = "|")