vroom: Read a delimited file into a tibble
In vroom: Read and Write Rectangular Text Data Quickly

Read a delimited file into a tibble

vroom(
  file,
  delim = NULL,
  col_names = TRUE,
  col_types = NULL,
  col_select = NULL,
  id = NULL,
  skip = 0,
  n_max = Inf,
  na = c("", "NA"),
  quote = "\"",
  comment = "",
  skip_empty_rows = TRUE,
  trim_ws = TRUE,
  escape_double = TRUE,
  escape_backslash = FALSE,
  locale = default_locale(),
  guess_max = 100,
  altrep = TRUE,
  altrep_opts = deprecated(),
  num_threads = vroom_threads(),
  progress = vroom_progress(),
  show_col_types = NULL,
  .name_repair = "unique"
)

`file`	path to a local file.
`delim`	One or more characters used to delimit fields within a file. If `NULL` the delimiter is guessed from the set of `c(",", "\t", " ", "\|", ":", ";")`.
`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 `...1`, `...2` etc. Duplicate column names will generate a warning and be made unique, see `name_repair` to control how this is done.
`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 `guess_max` rows on the input interspersed throughout the file. This is convenient (and fast), but not robust. If the imputation fails, you'll need to increase the `guess_max` or supply the correct types yourself. Column specifications created by `list()` or `cols()` 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 - = skip By default, reading a file without a column specification will print a message showing what `readr` guessed they were. To remove this message, set `show_col_types = FALSE` or set 'options(readr.show_col_types = FALSE).
`col_select`	One or more selection expressions, like in `dplyr::select()`. Use `c()` or `list()` to use more than one expression. See `?dplyr::select` for details on available selection options.
`id`	Either a string or 'NULL'. If a string, the output will contain a variable with that name with the filename(s) as the value. If 'NULL', the default, no variable will be created.
`skip`	Number of lines to skip before reading data. If `comment` is supplied any commented lines are ignored after skipping.
`n_max`	Maximum number of lines to read.
`na`	Character vector of strings to interpret as missing values. Set this option to `character()` to indicate no missing values.
`quote`	Single character used to quote strings.
`comment`	A string used to identify comments. Any text after the comment characters will be silently ignored.
`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.
`trim_ws`	Should leading and trailing whitespace (ASCII spaces and tabs) be trimmed from each field before parsing it?
`escape_double`	Does the file escape quotes by doubling them? i.e. If this option is `TRUE`, the value '""' represents a single quote, '"'.
`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`.
`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.
`guess_max`	Maximum number of lines to use for guessing column types.
`altrep`	Control which column types use Altrep representations, either a character vector of types, `TRUE` or `FALSE`. See `vroom_altrep()` for for full details.
`altrep_opts`	\Sexpr[results=rd, stage=render]{lifecycle::badge("deprecated")}
`num_threads`	Number of threads to use when reading and materializing vectors. If your data contains newlines within fields the parser will automatically be forced to use a single thread only.
`progress`	Display a progress bar? By default it will only display in an interactive session and not while knitting a document. The automatic progress bar can be disabled by setting option `readr.show_progress` to `FALSE`.
`show_col_types`	Control showing the column specifications. If `TRUE` column specifications are always show, if `FALSE` they are never shown. If `NULL` (the default) they are shown only if an explicit specification is not given to `col_types`.
`.name_repair`	Handling of column names. By default, vroom ensures column names are not empty and unique. See `.name_repair` as documented in `tibble::tibble()` for additional options including supplying user defined name repair functions.

# get path to example file
input_file <- vroom_example("mtcars.csv")
input_file

# Read from a path

# Input sources -------------------------------------------------------------
# Read from a path
vroom(input_file)
# You can also use paths directly
# vroom("mtcars.csv")

## Not run: 
# Including remote paths
vroom("https://github.com/r-lib/vroom/raw/main/inst/extdata/mtcars.csv")

## End(Not run)

# Or directly from a string with `I()`
vroom(I("x,y\n1,2\n3,4\n"))

# Column selection ----------------------------------------------------------
# Pass column names or indexes directly to select them
vroom(input_file, col_select = c(model, cyl, gear))
vroom(input_file, col_select = c(1, 3, 11))

# Or use the selection helpers
vroom(input_file, col_select = starts_with("d"))

# You can also rename specific columns
vroom(input_file, col_select = list(car = model, everything()))

# Column types --------------------------------------------------------------
# By default, vroom guesses the columns types, looking at 1000 rows
# throughout the dataset.
# You can specify them explicitly with a compact specification:
vroom(I("x,y\n1,2\n3,4\n"), col_types = "dc")

# Or with a list of column types:
vroom(I("x,y\n1,2\n3,4\n"), col_types = list(col_double(), col_character()))

# File types ----------------------------------------------------------------
# csv
vroom(I("a,b\n1.0,2.0\n"), delim = ",")
# tsv
vroom(I("a\tb\n1.0\t2.0\n"))
# Other delimiters
vroom(I("a|b\n1.0|2.0\n"), delim = "|")

# Read datasets across multiple files ---------------------------------------
mtcars_by_cyl <- vroom_example(vroom_examples("mtcars-"))
mtcars_by_cyl

# Pass the filenames directly to vroom, they are efficiently combined
vroom(mtcars_by_cyl)