vroom (version 1.0.2)

vroom: Read a delimited file into a tibble


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 = "", trim_ws = TRUE,
  escape_double = TRUE, escape_backslash = FALSE,
  locale = default_locale(), guess_max = 100, altrep_opts = "chr",
  num_threads = vroom_threads(), progress = vroom_progress(),
  .name_repair = "unique")



path to a local file.


One of more characters used to delimiter fields within a record. If NULL the delimiter is guessed from the set of c(",", "\t", " ", "|", ":", ";", "\n").


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.


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.


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.


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.


Number of lines to skip before reading data.


Maximum number of records to read.


Character vector of strings to interpret as missing values. Set this option to character() to indicate no missing values.


Single character used to quote strings.


A string used to identify comments. Any text after the comment characters will be silently ignored.


Should leading and trailing whitespace be trimmed from each field before parsing it?


Does the file escape quotes by doubling them? i.e. If this option is TRUE, the value '""' represents a single quote, '"'.


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.


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.


Maximum number of records to use for guessing column types.


Control which column types use Altrep representations, either a character vector of types, TRUE or FALSE. See vroom_altrep_opts() for for full details.


Number of threads to use when reading and materializing vectors.


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.


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.


Run this code
# Show path to example file
input_file <- vroom_example("mtcars.csv")

# Read from a path

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

# }
# Including remote paths
# }
# Or directly from a string (must contain a trailing newline)

# 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 explcitly with a compact specification:
vroom("x,y\n1,2\n3,4\n", col_types = "dc")

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

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

Run the code above in your browser using DataLab