Read a delimited file (including csv & tsv) into a tibble

Source: R/read_delim.R

read_delim.Rd

read_csv() and read_tsv() are special cases of the generalread_delim(). They're useful for reading the most common types offlat file data, comma separated values and tab separated values,respectively. read_csv2() uses ; for the field separator and , for thedecimal 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)

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` willbe automatically uncompressed. Files starting with `http://`,`https://`, `ftp://`, or `ftps://` will be automaticallydownloaded. Remote gz files can also be automatically downloaded anddecompressed. Literal data is most useful for examples and tests. It must contain atleast one new line to be recognised as data (instead of a path) or be avector of greater than length 1. Using a value of `clipboard()` will read from the system clipboard.
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 specialcharacters? This is more general than `escape_double` as backslashescan be used to escape the delimiter character, the quote character, orto add special characters like `\n`.
escape_double	Does the file escape quotes by doubling them?i.e. If this option is `TRUE`, the value `""""` representsa single quote, `\"`.
col_names	Either `TRUE`, `FALSE` or a character vectorof column names. If `TRUE`, the first row of the input will be used as the columnnames, and will not be included in the data frame. If `FALSE`, columnnames will be generated automatically: X1, X2, X3 etc. If `col_names` is a character vector, the values will be used as thenames of the columns, and the first row of the input will be read intothe first row of the output data frame. Missing (`NA`) column names will generate a warning, and be filledin with dummy names `X1`, `X2` etc. Duplicate column nameswill generate a warning and be made unique with a numeric prefix.
col_types	One of `NULL`, a `cols()` specification, ora string. See `vignette("readr")` for more details. If `NULL`, all column types will be imputed from the first 1000 rowson the input. This is convenient (and fast), but not robust. If theimputation fails, you'll need to supply the correct types yourself. If a column specification created by `cols()`, it must containone column specification for each column. If you only want to read asubset of the columns, use `cols_only()`. Alternatively, you can use a compact string representation where eachcharacter represents one column:c = character, i = integer, n = number, d = double,l = logical, f = factor, 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 likethe default time zone, encoding, decimal mark, big mark, and day/monthnames.
na	Character vector of strings to interpret as missing values. Set thisoption to `character()` to indicate no missing values.
quoted_na	Should missing values inside quotes be treated as missingvalues (the default) or strings.
comment	A string used to identify comments. Any text after thecomment characters will be silently ignored.
trim_ws	Should leading and trailing whitespace be trimmed fromeach 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 displayin an interactive session and not while knitting a document. The displayis updated every 50,000 values and will only display if estimated readingtime is 5 seconds or more. The automatic progress bar can be disabled bysetting option `readr.show_progress` to `FALSE`.
skip_empty_rows	Should blank rows be ignored altogether? i.e. If thisoption is `TRUE` then blank rows will not be represented at all. If it is`FALSE` then they will be represented by `NA` values in all the columns.

Value

A tibble(). If there are parsing problems, a warning tells youhow many, and you can retrieve the details with problems().

Examples

# Input sources -------------------------------------------------------------# Read from a pathread_csv(readr_example("mtcars.csv"))
#> Parsed with column specification:#> cols(#>  mpg = col_double(),#>  cyl = col_double(),#>  disp = col_double(),#>  hp = col_double(),#>  drat = col_double(),#>  wt = col_double(),#>  qsec = col_double(),#>  vs = col_double(),#>  am = col_double(),#>  gear = col_double(),#>  carb = col_double()#> )
#> # A tibble: 32 x 11#> mpg cyl disp hp drat wt qsec vs am gear carb#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4#> # … with 22 more rows
read_csv(readr_example("mtcars.csv.zip"))
See Also
read_delim function - RDocumentationData import with the tidyverse :: Cheatsheetread_delim: Read a delimited file (including CSV and TSV) into a tibble in readr: Read Rectangular Text DataFast Reading of Data From TXT|CSV Files into R: readr package - Easy Guides - Wiki
#> Parsed with column specification:#> cols(#>  mpg = col_double(),#>  cyl = col_double(),#>  disp = col_double(),#>  hp = col_double(),#>  drat = col_double(),#>  wt = col_double(),#>  qsec = col_double(),#>  vs = col_double(),#>  am = col_double(),#>  gear = col_double(),#>  carb = col_double()#> )
#> # A tibble: 32 x 11#> mpg cyl disp hp drat wt qsec vs am gear carb#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2#> 10 19.2 6 168. 123 3.92 3.44 18.3 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_double(),#>  disp = col_double(),#>  hp = col_double(),#>  drat = col_double(),#>  wt = col_double(),#>  qsec = col_double(),#>  vs = col_double(),#>  am = col_double(),#>  gear = col_double(),#>  carb = col_double()#> )
#> # A tibble: 32 x 11#> mpg cyl disp hp drat wt qsec vs am gear carb#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4#> # … with 22 more rows
if (FALSE) {# Including remote pathsread_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")
#> # A tibble: 2 x 2#> x y#> <dbl> <dbl>#> 1 1 2#> 2 3 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")
#> # A tibble: 2 x 2#> x y #> <dbl> <chr>#> 1 1 2 #> 2 3 4 
See Also
readr package - RDocumentation
# 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 x 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 x 1#> x#> <dbl>#> 1 1#> 2 2#> 3 NA
problems(y)
#> # A tibble: 1 x 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 x 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 x 2#> a b#> <dbl> <dbl>#> 1 1 2
read_tsv("a\tb\n1.0\t2.0")
#> # A tibble: 1 x 2#> a b#> <dbl> <dbl>#> 1 1 2
read_delim("a|b\n1.0|2.0", delim = "|")
#> # A tibble: 1 x 2#> a b#> <dbl> <dbl>#> 1 1 2

Read a delimited file (including csv & tsv) into a tibble — read_delim (2024)

Arguments

Value

Examples