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 the field separator and ,
for the
decimal point. This is common in some European countries.
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(),
skip_empty_rows = TRUE
)
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(),
skip_empty_rows = TRUE
)
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(),
skip_empty_rows = TRUE
)
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(),
skip_empty_rows = TRUE
)
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) or be a vector of greater than length 1. Using a value of |
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:
|
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 interpret as 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 |
skip_empty_rows |
Should blank rows be ignored altogether? i.e. If this
option is |
A tibble()
. 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"))
## Not run:
# Including remote paths
read_csv("https://github.com/tidyverse/readr/raw/master/inst/extdata/mtcars.csv")
## End(Not run)
# 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 1000 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 = "|")