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

read_delim {readr} | R Documentation

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

### Description

`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.

### Usage

    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` 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) or be a vector 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 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("readr")` 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, 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 like the default time zone, encoding, decimal mark, big mark, and day/month names.
`na` | Character vector of strings to interpret as 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`.
`skip_empty_rows` | Should blank rows be ignored altogether? i.e. If this option 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 you how many, and you can retrieve the details with `problems()`.

### Examples

    # 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 = "|")

[Package readr version 1.3.1 Index]