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())

Arguments

file

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 and 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 delimiter character, the quote character, 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.

Missing (NA) column names will generate a warning, and be filled in with dummy names X1, X2 etc. Duplicate column names will generate a warning and be made unique with a numeric prefix.

col_types

One of NULL, a cols() specification, or 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 column specification 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.

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 readr.show_progress to FALSE.

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(readr_example("mtcars.csv"))
#> Parsed with column specification: #> cols( #> mpg = col_double(), #> cyl = col_integer(), #> disp = col_double(), #> hp = col_integer(), #> drat = col_double(), #> wt = col_double(), #> qsec = col_double(), #> vs = col_integer(), #> am = col_integer(), #> gear = col_integer(), #> carb = col_integer() #> )
#> # A tibble: 32 × 11 #> mpg cyl disp hp drat wt qsec vs am gear carb #> <dbl> <int> <dbl> <int> <dbl> <dbl> <dbl> <int> <int> <int> <int> #> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> # ... with 22 more rows
read_csv(readr_example("mtcars.csv.zip"))
#> Parsed with column specification: #> cols( #> mpg = col_double(), #> cyl = col_integer(), #> disp = col_double(), #> hp = col_integer(), #> drat = col_double(), #> wt = col_double(), #> qsec = col_double(), #> vs = col_integer(), #> am = col_integer(), #> gear = col_integer(), #> carb = col_integer() #> )
#> # A tibble: 32 × 11 #> mpg cyl disp hp drat wt qsec vs am gear carb #> <dbl> <int> <dbl> <int> <dbl> <dbl> <dbl> <int> <int> <int> <int> #> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> # ... with 22 more rows
read_csv(readr_example("mtcars.csv.bz2"))
#> Parsed with column specification: #> cols( #> mpg = col_double(), #> cyl = col_integer(), #> disp = col_double(), #> hp = col_integer(), #> drat = col_double(), #> wt = col_double(), #> qsec = col_double(), #> vs = col_integer(), #> am = col_integer(), #> gear = col_integer(), #> carb = col_integer() #> )
#> # A tibble: 32 × 11 #> mpg cyl disp hp drat wt qsec vs am gear carb #> <dbl> <int> <dbl> <int> <dbl> <dbl> <dbl> <int> <int> <int> <int> #> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> # ... with 22 more rows
read_csv("https://github.com/tidyverse/readr/raw/master/inst/extdata/mtcars.csv")
#> Parsed with column specification: #> cols( #> mpg = col_double(), #> cyl = col_integer(), #> disp = col_double(), #> hp = col_integer(), #> drat = col_double(), #> wt = col_double(), #> qsec = col_double(), #> vs = col_integer(), #> am = col_integer(), #> gear = col_integer(), #> carb = col_integer() #> )
#> # A tibble: 32 × 11 #> mpg cyl disp hp drat wt qsec vs am gear carb #> <dbl> <int> <dbl> <int> <dbl> <dbl> <dbl> <int> <int> <int> <int> #> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> # ... with 22 more rows
# Or directly from a string (must contain a newline) read_csv("x,y\n1,2\n3,4")
#> # A tibble: 2 × 2 #> x y #> <int> <int> #> 1 1 2 #> 2 3 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")
#> # A tibble: 2 × 2 #> x y #> <dbl> <chr> #> 1 1 2 #> 2 3 4
# Or with a list of column types: read_csv("x,y\n1,2\n3,4", col_types = list(col_double(), col_character()))
#> # A tibble: 2 × 2 #> x y #> <dbl> <chr> #> 1 1 2 #> 2 3 4
# 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()))
#> Warning: 1 parsing failure. #> row col expected actual file #> 3 x a double b literal data
y
#> # A tibble: 3 × 1 #> x #> <dbl> #> 1 1 #> 2 2 #> 3 NA
#> # A tibble: 1 × 5 #> row col expected actual file #> <int> <chr> <chr> <chr> <chr> #> 1 3 x a double b literal data
# File types ---------------------------------------------------------------- read_csv("a,b\n1.0,2.0")
#> # A tibble: 1 × 2 #> a b #> <dbl> <dbl> #> 1 1 2
read_csv2("a;b\n1,0;2,0")
#> Using ',' as decimal and '.' as grouping mark. Use read_delim() for more control.
#> # A tibble: 1 × 2 #> a b #> <dbl> <dbl> #> 1 1 2
read_tsv("a\tb\n1.0\t2.0")
#> # A tibble: 1 × 2 #> a b #> <dbl> <dbl> #> 1 1 2
read_delim("a|b\n1.0|2.0", delim = "|")
#> # A tibble: 1 × 2 #> a b #> <dbl> <dbl> #> 1 1 2