Package 'vroom' reference manual

Title:	Read and Write Rectangular Text Data Quickly
Description:	The goal of 'vroom' is to read and write data (like 'csv', 'tsv' and 'fwf') quickly. When reading it uses a quick initial indexing step, then reads the values lazily , so only the data you actually use needs to be read. The writer formats the data in parallel and writes to disk asynchronously from formatting.
Authors:	Jim Hester [aut] , Hadley Wickham [aut] , Jennifer Bryan [aut, cre] , Shelby Bearrows [ctb], https://github.com/mandreyel/ [cph] (mio library), Jukka Jylänki [cph] (grisu3 implementation), Mikkel Jørgensen [cph] (grisu3 implementation), Posit Software, PBC [cph, fnd]
Maintainer:	Jennifer Bryan <[email protected]>
License:	MIT + file LICENSE
Version:	1.6.5.9000
Built:	2024-11-22 02:52:11 UTC
Source:	https://github.com/tidyverse/vroom

Create column specification

Description

cols() includes all columns in the input data, guessing the column types as the default. cols_only() includes only the columns you explicitly specify, skipping the rest.

Usage

cols(..., .default = col_guess(), .delim = NULL)

cols_only(...)

col_logical(...)

col_integer(...)

col_big_integer(...)

col_double(...)

col_character(...)

col_skip(...)

col_number(...)

col_guess(...)

col_factor(levels = NULL, ordered = FALSE, include_na = FALSE, ...)

col_datetime(format = "", ...)

col_date(format = "", ...)

col_time(format = "", ...)
cols(..., .default = col_guess(), .delim = NULL)

cols_only(...)

col_logical(...)

col_integer(...)

col_big_integer(...)

col_double(...)

col_character(...)

col_skip(...)

col_number(...)

col_guess(...)

col_factor(levels = NULL, ordered = FALSE, include_na = FALSE, ...)

col_datetime(format = "", ...)

col_date(format = "", ...)

col_time(format = "", ...)

Arguments

`...`	Either column objects created by `⁠col_()⁠`, or their abbreviated character names (as described in the `col_types` argument of `vroom()`). If you're only overriding a few columns, it's best to refer to columns by name. If not named, the column types must match the column names exactly. In `⁠col_()⁠` functions these are stored in the object.
`.default`	Any named columns not explicitly overridden in `...` will be read with this column type.
`.delim`	The delimiter to use when parsing. If the `delim` argument used in the call to `vroom()` it takes precedence over the one specified in `col_types`.
`levels`	Character vector of the allowed levels. When `levels = NULL` (the default), `levels` are discovered from the unique values of `x`, in the order in which they appear in `x`.
`ordered`	Is it an ordered factor?
`include_na`	If `TRUE` and `x` contains at least one `NA`, then `NA` is included in the levels of the constructed factor.
`format`	A format specification, as described below. If set to "", date times are parsed as ISO8601, dates and times used the date and time formats specified in the `locale()`. Unlike `strptime()`, the format specification must match the complete string.

Details

The available specifications are: (long names in quotes and string abbreviations in brackets)

function	long name	short name	description
`col_logical()`	"logical"	"l"	Logical values containing only `T`, `F`, `TRUE` or `FALSE`.
`col_integer()`	"integer"	"i"	Integer numbers.
`col_big_integer()`	"big_integer"	"I"	Big Integers (64bit), requires the `bit64` package.
`col_double()`	"double", "numeric"	"d"	64-bit double floating point numbers.
`col_character()`	"character"	"c"	Character string data.
`col_factor(levels, ordered)`	"factor"	"f"	A fixed set of values.
`col_date(format = "")`	"date"	"D"	Calendar dates formatted with the locale's `date_format`.
`col_time(format = "")`	"time"	"t"	Times formatted with the locale's `time_format`.
`col_datetime(format = "")`	"datetime", "POSIXct"	"T"	ISO8601 date times.
`col_number()`	"number"	"n"	Human readable numbers containing the `grouping_mark`
`col_skip()`	"skip", "NULL"	"_", "-"	Skip and don't import this column.
`col_guess()`	"guess", "NA"	"?"	Parse using the "best" guessed type based on the input.

Examples

cols(a = col_integer())
cols_only(a = col_integer())

# You can also use the standard abbreviations
cols(a = "i")
cols(a = "i", b = "d", c = "_")

# Or long names (like utils::read.csv)
cols(a = "integer", b = "double", c = "skip")

# You can also use multiple sets of column definitions by combining
# them like so:

t1 <- cols(
  column_one = col_integer(),
  column_two = col_number())

t2 <- cols(
 column_three = col_character())

t3 <- t1
t3$cols <- c(t1$cols, t2$cols)
t3
cols(a = col_integer())
cols_only(a = col_integer())

# You can also use the standard abbreviations
cols(a = "i")
cols(a = "i", b = "d", c = "_")

# Or long names (like utils::read.csv)
cols(a = "integer", b = "double", c = "skip")

# You can also use multiple sets of column definitions by combining
# them like so:

t1 <- cols(
  column_one = col_integer(),
  column_two = col_number())

t2 <- cols(
 column_three = col_character())

t3 <- t1
t3$cols <- c(t1$cols, t2$cols)
t3

Examine the column specifications for a data frame

Description

cols_condense() takes a spec object and condenses its definition by setting the default column type to the most frequent type and only listing columns with a different type.

spec() extracts the full column specification from a tibble created by readr.

Usage

cols_condense(x)

spec(x)
cols_condense(x)

spec(x)

Arguments

`x`	The data frame object to extract from

Value

A col_spec object.

Examples

df <- vroom(vroom_example("mtcars.csv"))
s <- spec(df)
s

cols_condense(s)
df <- vroom(vroom_example("mtcars.csv"))
s <- spec(df)
s

cols_condense(s)

When parsing dates, you often need to know how weekdays of the week and months are represented as text. This pair of functions allows you to either create your own, or retrieve from a standard list. The standard list is derived from ICU (⁠https://site.icu-project.org⁠) via the stringi package.

Usage

date_names(mon, mon_ab = mon, day, day_ab = day, am_pm = c("AM", "PM"))

date_names_lang(language)

date_names_langs()
date_names(mon, mon_ab = mon, day, day_ab = day, am_pm = c("AM", "PM"))

date_names_lang(language)

date_names_langs()

Arguments

`mon`, `mon_ab`	Full and abbreviated month names.
`day`, `day_ab`	Full and abbreviated week day names. Starts with Sunday.
`am_pm`	Names used for AM and PM.
`language`	A BCP 47 locale, made up of a language and a region, e.g. `"en_US"` for American English. See `date_names_langs()` for a complete list of available locales.

Examples

date_names_lang("en")
date_names_lang("ko")
date_names_lang("fr")
date_names_lang("en")
date_names_lang("ko")
date_names_lang("fr")

Generate a random tibble

Description

This is useful for benchmarking, but also for bug reports when you cannot share the real dataset.

Usage

gen_tbl(
  rows,
  cols = NULL,
  col_types = NULL,
  locale = default_locale(),
  missing = 0
)
gen_tbl(
  rows,
  cols = NULL,
  col_types = NULL,
  locale = default_locale(),
  missing = 0
)

Arguments

`rows`	Number of rows to generate
`cols`	Number of columns to generate, if `NULL` this is derived from `col_types`.
`col_types`	One of `NULL`, a `cols()` specification, or a string. 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)`.
`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.
`missing`	The percentage (from 0 to 1) of missing data to use

Details

There is also a family of functions to generate individual vectors of each type.

Examples

# random 10 x 5 table with random column types
rand_tbl <- gen_tbl(10, 5)
rand_tbl

# all double 25 x 4 table
dbl_tbl <- gen_tbl(25, 4, col_types = "dddd")
dbl_tbl

# Use the dots in long form column types to change the random function and options
types <- rep(times = 4, list(col_double(f = stats::runif, min = -10, max = 25)))
types
dbl_tbl2 <- gen_tbl(25, 4, col_types = types)
dbl_tbl2
# random 10 x 5 table with random column types
rand_tbl <- gen_tbl(10, 5)
rand_tbl

# all double 25 x 4 table
dbl_tbl <- gen_tbl(25, 4, col_types = "dddd")
dbl_tbl

# Use the dots in long form column types to change the random function and options
types <- rep(times = 4, list(col_double(f = stats::runif, min = -10, max = 25)))
types
dbl_tbl2 <- gen_tbl(25, 4, col_types = types)
dbl_tbl2

Generate individual vectors of the types supported by vroom

Description

Generate individual vectors of the types supported by vroom

Usage

gen_character(n, min = 5, max = 25, values = c(letters, LETTERS, 0:9), ...)

gen_double(n, f = stats::rnorm, ...)

gen_number(n, f = stats::rnorm, ...)

gen_integer(n, min = 1L, max = .Machine$integer.max, prob = NULL, ...)

gen_factor(
  n,
  levels = NULL,
  ordered = FALSE,
  num_levels = gen_integer(1L, 1L, 25L),
  ...
)

gen_time(n, min = 0, max = hms::hms(days = 1), fractional = FALSE, ...)

gen_date(n, min = as.Date("2001-01-01"), max = as.Date("2021-01-01"), ...)

gen_datetime(
  n,
  min = as.POSIXct("2001-01-01"),
  max = as.POSIXct("2021-01-01"),
  tz = "UTC",
  ...
)

gen_logical(n, ...)

gen_name(n)
gen_character(n, min = 5, max = 25, values = c(letters, LETTERS, 0:9), ...)

gen_double(n, f = stats::rnorm, ...)

gen_number(n, f = stats::rnorm, ...)

gen_integer(n, min = 1L, max = .Machine$integer.max, prob = NULL, ...)

gen_factor(
  n,
  levels = NULL,
  ordered = FALSE,
  num_levels = gen_integer(1L, 1L, 25L),
  ...
)

gen_time(n, min = 0, max = hms::hms(days = 1), fractional = FALSE, ...)

gen_date(n, min = as.Date("2001-01-01"), max = as.Date("2021-01-01"), ...)

gen_datetime(
  n,
  min = as.POSIXct("2001-01-01"),
  max = as.POSIXct("2021-01-01"),
  tz = "UTC",
  ...
)

gen_logical(n, ...)

gen_name(n)

Arguments

`n`	The size of the vector to generate
`min`	The minimum range for the vector
`max`	The maximum range for the vector
`values`	The explicit values to use.
`...`	Additional arguments passed to internal generation functions
`f`	The random function to use.
`prob`	a vector of probability weights for obtaining the elements of the vector being sampled.
`levels`	The explicit levels to use, if `NULL` random levels are generated using `gen_name()`.
`ordered`	Should the factors be ordered factors?
`num_levels`	The number of factor levels to generate
`fractional`	Whether to generate times with fractional seconds
`tz`	The timezone to use for dates

Examples

# characters
gen_character(4)

# factors
gen_factor(4)

# logical
gen_logical(4)

# numbers
gen_double(4)
gen_integer(4)

# temporal data
gen_time(4)
gen_date(4)
gen_datetime(4)
# characters
gen_character(4)

# factors
gen_factor(4)

# logical
gen_logical(4)

# numbers
gen_double(4)
gen_integer(4)

# temporal data
gen_time(4)
gen_date(4)
gen_datetime(4)

Guess the type of a vector

Description

Guess the type of a vector

Usage

guess_type(
  x,
  na = c("", "NA"),
  locale = default_locale(),
  guess_integer = FALSE
)
guess_type(
  x,
  na = c("", "NA"),
  locale = default_locale(),
  guess_integer = FALSE
)

Arguments

`x`	Character vector of values to parse.
`na`	Character vector of strings to interpret as missing values. Set this option to `character()` to indicate no missing values.
`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_integer`	If `TRUE`, guess integer types for whole numbers, if `FALSE` guess numeric type for all numbers.

Examples

 # Logical vectors
 guess_type(c("FALSE", "TRUE", "F", "T"))
 # Integers and doubles
 guess_type(c("1","2","3"))
 guess_type(c("1.6","2.6","3.4"))
 # Numbers containing grouping mark
 guess_type("1,234,566")
 # ISO 8601 date times
 guess_type(c("2010-10-10"))
 guess_type(c("2010-10-10 01:02:03"))
 guess_type(c("01:02:03 AM"))
# Logical vectors
 guess_type(c("FALSE", "TRUE", "F", "T"))
 # Integers and doubles
 guess_type(c("1","2","3"))
 guess_type(c("1.6","2.6","3.4"))
 # Numbers containing grouping mark
 guess_type("1,234,566")
 # ISO 8601 date times
 guess_type(c("2010-10-10"))
 guess_type(c("2010-10-10 01:02:03"))
 guess_type(c("01:02:03 AM"))

Create locales

Description

A locale object tries to capture all the defaults that can vary between countries. You set the locale in once, and the details are automatically passed on down to the columns parsers. The defaults have been chosen to match R (i.e. US English) as closely as possible. See vignette("locales") for more details.

Usage

locale(
  date_names = "en",
  date_format = "%AD",
  time_format = "%AT",
  decimal_mark = ".",
  grouping_mark = ",",
  tz = "UTC",
  encoding = "UTF-8"
)

default_locale()
locale(
  date_names = "en",
  date_format = "%AD",
  time_format = "%AT",
  decimal_mark = ".",
  grouping_mark = ",",
  tz = "UTC",
  encoding = "UTF-8"
)

default_locale()

Arguments

`date_names`	Character representations of day and month names. Either the language code as string (passed on to `date_names_lang()`) or an object created by `date_names()`.
`date_format`, `time_format`	Default date and time formats.
`decimal_mark`, `grouping_mark`	Symbols used to indicate the decimal place, and to chunk larger numbers. Decimal mark can only be `⁠,⁠` or `.`.
`tz`	Default tz. This is used both for input (if the time zone isn't present in individual strings), and for output (to control the default display). The default is to use "UTC", a time zone that does not use daylight savings time (DST) and hence is typically most useful for data. The absence of time zones makes it approximately 50x faster to generate UTC times than any other time zone. Use `""` to use the system default time zone, but beware that this will not be reproducible across systems. For a complete list of possible time zones, see `OlsonNames()`. Americans, note that "EST" is a Canadian time zone that does not have DST. It is not Eastern Standard Time. It's better to use "US/Eastern", "US/Central" etc.
`encoding`	Default encoding.

Examples

locale()
locale("fr")

# South American locale
locale("es", decimal_mark = ",")
locale()
locale("fr")

# South American locale
locale("es", decimal_mark = ",")

Retrieve parsing problems

Description

vroom will only fail to parse a file if the file is invalid in a way that is unrecoverable. However there are a number of non-fatal problems that you might want to know about. You can retrieve a data frame of these problems with this function.

Usage

problems(x = .Last.value, lazy = FALSE)
problems(x = .Last.value, lazy = FALSE)

Arguments

`x`	A data frame from `vroom::vroom()`.
`lazy`	If `TRUE`, just the problems found so far are returned. If `FALSE` (the default) the lazy data is first read completely and all problems are returned.

Value

A data frame with one row for each problem and four columns:

row,col - Row and column number that caused the problem, referencing the original input
expected - What vroom expected to find
actual - What it actually found
file - The file with the problem

Read a delimited file into a tibble

Description

Read a delimited file into a tibble

Usage

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"
)
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"
)

Arguments

`file`	Either a path to a file, a connection, or literal data (either a single string or a raw vector). `file` can also be a character vector containing multiple filepaths or a list containing multiple connections. 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. To be recognised as literal data, wrap the input with `I()`.
`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. 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`	Columns to include in the results. You can use the same mini-language as `dplyr::select()` to refer to the columns by name. Use `c()` to use more than one selection expression. Although this usage is less common, `col_select` also accepts a numeric column index. See `?tidyselect::language` for full details on the selection language.
`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. See `vignette("column-types", package = "readr")` for more details.
`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`
`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. The default behaviour is to ensure column names are `"unique"`. Various repair strategies are supported: `"minimal"`: No name repair or checks, beyond basic existence of names. `"unique"` (default value): Make sure names are unique and not empty. `"check_unique"`: no name repair, but check they are `unique`. `"universal"`: Make the names `unique` and syntactic. A function: apply custom name repair (e.g., `name_repair = make.names` for names in the style of base R). A purrr-style anonymous function, see `rlang::as_function()`. This argument is passed on as `repair` to `vctrs::vec_as_names()`. See there for more details on these terms and the strategies used to enforce them.

Examples

# 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/tidyverse/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 = c(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)

# If you need to extract data from the filenames, use `id` to request a
# column that reveals the underlying file path
dat <- vroom(mtcars_by_cyl, id = "source")
dat$source <- basename(dat$source)
dat
# 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/tidyverse/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 = c(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)

# If you need to extract data from the filenames, use `id` to request a
# column that reveals the underlying file path
dat <- vroom(mtcars_by_cyl, id = "source")
dat$source <- basename(dat$source)
dat

Show which column types are using Altrep

Description

vroom_altrep() can be used directly as input to the altrep argument of vroom().

Usage

vroom_altrep(which = NULL)
vroom_altrep(which = NULL)

Arguments

which

A character vector of column types to use Altrep for. Can also take TRUE or FALSE to use Altrep for all possible or none of the types

Details

Alternatively there is also a family of environment variables to control use of the Altrep framework. These can then be set in your .Renviron file, e.g. with usethis::edit_r_environ(). For versions of R where the Altrep framework is unavailable (R < 3.5.0) they are automatically turned off and the variables have no effect. The variables can take one of true, false, TRUE, FALSE, 1, or 0.

VROOM_USE_ALTREP_NUMERICS - If set use Altrep for all numeric types (default false).

There are also individual variables for each type. Currently only VROOM_USE_ALTREP_CHR defaults to true.

VROOM_USE_ALTREP_CHR
VROOM_USE_ALTREP_FCT
VROOM_USE_ALTREP_INT
VROOM_USE_ALTREP_BIG_INT
VROOM_USE_ALTREP_DBL
VROOM_USE_ALTREP_NUM
VROOM_USE_ALTREP_LGL
VROOM_USE_ALTREP_DTTM
VROOM_USE_ALTREP_DATE
VROOM_USE_ALTREP_TIME

Examples

vroom_altrep()
vroom_altrep(c("chr", "fct", "int"))
vroom_altrep(TRUE)
vroom_altrep(FALSE)
vroom_altrep()
vroom_altrep(c("chr", "fct", "int"))
vroom_altrep(TRUE)
vroom_altrep(FALSE)

Show which column types are using Altrep

Description

This function is deprecated in favor of vroom_altrep().

Usage

vroom_altrep_opts(which = NULL)
vroom_altrep_opts(which = NULL)

Arguments

which

A character vector of column types to use Altrep for. Can also take TRUE or FALSE to use Altrep for all possible or none of the types

Get path to vroom examples

Description

vroom comes bundled with a number of sample files in its 'inst/extdata' directory. Use vroom_examples() to list all the available examples and vroom_example() to retrieve the path to one example.

Usage

vroom_example(path)

vroom_examples(pattern = NULL)
vroom_example(path)

vroom_examples(pattern = NULL)

Arguments

`path`	Name of file.
`pattern`	A regular expression of filenames to match. If `NULL`, all available files are returned.

Examples

# List all available examples
vroom_examples()

# Get path to one example
vroom_example("mtcars.csv")
# List all available examples
vroom_examples()

# Get path to one example
vroom_example("mtcars.csv")

Convert a data frame to a delimited string

Description

This is equivalent to vroom_write(), but instead of writing to disk, it returns a string. It is primarily useful for examples and for testing.

Usage

vroom_format(
  x,
  delim = "\t",
  eol = "\n",
  na = "NA",
  col_names = TRUE,
  escape = c("double", "backslash", "none"),
  quote = c("needed", "all", "none"),
  bom = FALSE,
  num_threads = vroom_threads()
)
vroom_format(
  x,
  delim = "\t",
  eol = "\n",
  na = "NA",
  col_names = TRUE,
  escape = c("double", "backslash", "none"),
  quote = c("needed", "all", "none"),
  bom = FALSE,
  num_threads = vroom_threads()
)

Arguments

`x`	A data frame or tibble to write to disk.
`delim`	Delimiter used to separate values. Defaults to `⁠\t⁠` to write tab separated value (TSV) files.
`eol`	The end of line character to use. Most commonly either `"\n"` for Unix style newlines, or `"\r\n"` for Windows style newlines.
`na`	String used for missing values. Defaults to 'NA'.
`col_names`	If `FALSE`, column names will not be included at the top of the file. If `TRUE`, column names will be included. If not specified, `col_names` will take the opposite value given to `append`.
`escape`	The type of escape to use when quotes are in the data. `double` - quotes are escaped by doubling them. `backslash` - quotes are escaped by a preceding backslash. `none` - quotes are not escaped.
`quote`	How to handle fields which contain characters that need to be quoted. `needed` - Values are only quoted if needed: if they contain a delimiter, quote, or newline. `all` - Quote all fields. `none` - Never quote fields.
`bom`	If `TRUE` add a UTF-8 BOM at the beginning of the file. This is recommended when saving data for consumption by excel, as it will force excel to read the data with the correct encoding (UTF-8)
`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.

Read a fixed width file into a tibble

Description

Read a fixed width file into a tibble

Usage

vroom_fwf(
  file,
  col_positions = fwf_empty(file, skip, n = guess_max),
  col_types = NULL,
  col_select = NULL,
  id = NULL,
  locale = default_locale(),
  na = c("", "NA"),
  comment = "",
  skip_empty_rows = TRUE,
  trim_ws = TRUE,
  skip = 0,
  n_max = Inf,
  guess_max = 100,
  altrep = TRUE,
  altrep_opts = deprecated(),
  num_threads = vroom_threads(),
  progress = vroom_progress(),
  show_col_types = NULL,
  .name_repair = "unique"
)

fwf_empty(file, skip = 0, col_names = NULL, comment = "", n = 100L)

fwf_widths(widths, col_names = NULL)

fwf_positions(start, end = NULL, col_names = NULL)

fwf_cols(...)
vroom_fwf(
  file,
  col_positions = fwf_empty(file, skip, n = guess_max),
  col_types = NULL,
  col_select = NULL,
  id = NULL,
  locale = default_locale(),
  na = c("", "NA"),
  comment = "",
  skip_empty_rows = TRUE,
  trim_ws = TRUE,
  skip = 0,
  n_max = Inf,
  guess_max = 100,
  altrep = TRUE,
  altrep_opts = deprecated(),
  num_threads = vroom_threads(),
  progress = vroom_progress(),
  show_col_types = NULL,
  .name_repair = "unique"
)

fwf_empty(file, skip = 0, col_names = NULL, comment = "", n = 100L)

fwf_widths(widths, col_names = NULL)

fwf_positions(start, end = NULL, col_names = NULL)

fwf_cols(...)

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. To be recognised as literal data, the input must be either wrapped with `I()`, be a string containing at least one new line, or be a vector containing at least one string with a new line. Using a value of `clipboard()` will read from the system clipboard.
`col_positions`	Column positions, as created by `fwf_empty()`, `fwf_widths()` or `fwf_positions()`. To read in only selected fields, use `fwf_positions()`. If the width of the last column is variable (a ragged fwf file), supply the last end position as NA.
`col_types`	One of `NULL`, a `cols()` specification, or a string. See `vignette("readr")` for more details. If `NULL`, all column types will be inferred from `guess_max` rows of the input, interspersed throughout the file. This is convenient (and fast), but not robust. If the guessed types are wrong, you'll need to increase `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`	Columns to include in the results. You can use the same mini-language as `dplyr::select()` to refer to the columns by name. Use `c()` to use more than one selection expression. Although this usage is less common, `col_select` also accepts a numeric column index. See `?tidyselect::language` for full details on the selection language.
`id`	The name of a column in which to store the file path. This is useful when reading multiple input files and there is data in the file paths, such as the data collection date. If `NULL` (the default) no extra column is created.
`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.
`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?
`skip`	Number of lines to skip before reading data.
`n_max`	Maximum number of lines to read.
`guess_max`	Maximum number of lines to use for guessing column types. Will never use more than the number of lines read. See `vignette("column-types", package = "readr")` for more details.
`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`
`num_threads`	The number of processing threads to use for initial parsing and lazy reading of data. If your data contains newlines within fields the parser should automatically detect this and fall back to using one thread only. However if you know your file has newlines within quoted fields it is safest to set `num_threads = 1` explicitly.
`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`	If `FALSE`, do not show the guessed column types. If `TRUE` always show the column types, even if they are supplied. If `NULL` (the default) only show the column types if they are not explicitly supplied by the `col_types` argument.
`.name_repair`	Handling of column names. The default behaviour is to ensure column names are `"unique"`. Various repair strategies are supported: `"minimal"`: No name repair or checks, beyond basic existence of names. `"unique"` (default value): Make sure names are unique and not empty. `"check_unique"`: no name repair, but check they are `unique`. `"universal"`: Make the names `unique` and syntactic. A function: apply custom name repair (e.g., `name_repair = make.names` for names in the style of base R). A purrr-style anonymous function, see `rlang::as_function()`. This argument is passed on as `repair` to `vctrs::vec_as_names()`. See there for more details on these terms and the strategies used to enforce them.
`col_names`	Either NULL, or a character vector column names.
`n`	Number of lines the tokenizer will read to determine file structure. By default it is set to 100.
`widths`	Width of each field. Use NA as width of last field when reading a ragged fwf file.
`start`, `end`	Starting and ending (inclusive) positions of each field. Use NA as last end field when reading a ragged fwf file.
`...`	If the first element is a data frame, then it must have all numeric columns and either one or two rows. The column names are the variable names. The column values are the variable widths if a length one vector, and if length two, variable start and end positions. The elements of `...` are used to construct a data frame with or or two rows as above.

Details

Note: fwf_empty() cannot take a R connection such as a URL as input, as this would result in reading from the connection twice. In these cases it is better to download the file first before reading.

Examples

fwf_sample <- vroom_example("fwf-sample.txt")
writeLines(vroom_lines(fwf_sample))

# You can specify column positions in several ways:
# 1. Guess based on position of empty columns
vroom_fwf(fwf_sample, fwf_empty(fwf_sample, col_names = c("first", "last", "state", "ssn")))
# 2. A vector of field widths
vroom_fwf(fwf_sample, fwf_widths(c(20, 10, 12), c("name", "state", "ssn")))
# 3. Paired vectors of start and end positions
vroom_fwf(fwf_sample, fwf_positions(c(1, 30), c(20, 42), c("name", "ssn")))
# 4. Named arguments with start and end positions
vroom_fwf(fwf_sample, fwf_cols(name = c(1, 20), ssn = c(30, 42)))
# 5. Named arguments with column widths
vroom_fwf(fwf_sample, fwf_cols(name = 20, state = 10, ssn = 12))
fwf_sample <- vroom_example("fwf-sample.txt")
writeLines(vroom_lines(fwf_sample))

# You can specify column positions in several ways:
# 1. Guess based on position of empty columns
vroom_fwf(fwf_sample, fwf_empty(fwf_sample, col_names = c("first", "last", "state", "ssn")))
# 2. A vector of field widths
vroom_fwf(fwf_sample, fwf_widths(c(20, 10, 12), c("name", "state", "ssn")))
# 3. Paired vectors of start and end positions
vroom_fwf(fwf_sample, fwf_positions(c(1, 30), c(20, 42), c("name", "ssn")))
# 4. Named arguments with start and end positions
vroom_fwf(fwf_sample, fwf_cols(name = c(1, 20), ssn = c(30, 42)))
# 5. Named arguments with column widths
vroom_fwf(fwf_sample, fwf_cols(name = 20, state = 10, ssn = 12))

Read lines from a file

Description

vroom_lines() is similar to readLines(), however it reads the lines lazily like vroom(), so operations like length(), head(), tail() and sample() can be done much more efficiently without reading all the data into R.

Usage

vroom_lines(
  file,
  n_max = Inf,
  skip = 0,
  na = character(),
  skip_empty_rows = FALSE,
  locale = default_locale(),
  altrep = TRUE,
  altrep_opts = deprecated(),
  num_threads = vroom_threads(),
  progress = vroom_progress()
)
vroom_lines(
  file,
  n_max = Inf,
  skip = 0,
  na = character(),
  skip_empty_rows = FALSE,
  locale = default_locale(),
  altrep = TRUE,
  altrep_opts = deprecated(),
  num_threads = vroom_threads(),
  progress = vroom_progress()
)

Arguments

`file`	Either a path to a file, a connection, or literal data (either a single string or a raw vector). `file` can also be a character vector containing multiple filepaths or a list containing multiple connections. 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. To be recognised as literal data, wrap the input with `I()`.
`n_max`	Maximum number of lines to read.
`skip`	Number of lines to skip before reading data. If `comment` is supplied any commented lines are ignored after skipping.
`na`	Character vector of strings to interpret as missing values. Set this option to `character()` to indicate no missing values.
`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.
`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.
`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`
`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`.

Examples

lines <- vroom_lines(vroom_example("mtcars.csv"))

length(lines)
head(lines, n = 2)
tail(lines, n = 2)
sample(lines, size = 2)
lines <- vroom_lines(vroom_example("mtcars.csv"))

length(lines)
head(lines, n = 2)
tail(lines, n = 2)
sample(lines, size = 2)

Determine whether progress bars should be shown

Description

By default, vroom shows progress bars. However, progress reporting is suppressed if any of the following conditions hold:

The bar is explicitly disabled by setting the environment variable VROOM_SHOW_PROGRESS to "false".
The code is run in a non-interactive session, as determined by rlang::is_interactive().
The code is run in an RStudio notebook chunk, as determined by getOption("rstudio.notebook.executing").

Usage

vroom_progress()
vroom_progress()

Examples

vroom_progress()
vroom_progress()

Structure of objects

Description

Similar to str() but with more information for Altrep objects.

Usage

vroom_str(x)
vroom_str(x)

Arguments

x

a vector

Examples

# when used on non-altrep objects altrep will always be false
vroom_str(mtcars)

mt <- vroom(vroom_example("mtcars.csv"), ",", altrep = c("chr", "dbl"))
vroom_str(mt)
# when used on non-altrep objects altrep will always be false
vroom_str(mtcars)

mt <- vroom(vroom_example("mtcars.csv"), ",", altrep = c("chr", "dbl"))
vroom_str(mt)

Write a data frame to a delimited file

Description

Write a data frame to a delimited file

Usage

vroom_write(
  x,
  file,
  delim = "\t",
  eol = "\n",
  na = "NA",
  col_names = !append,
  append = FALSE,
  quote = c("needed", "all", "none"),
  escape = c("double", "backslash", "none"),
  bom = FALSE,
  num_threads = vroom_threads(),
  progress = vroom_progress(),
  path = deprecated()
)
vroom_write(
  x,
  file,
  delim = "\t",
  eol = "\n",
  na = "NA",
  col_names = !append,
  append = FALSE,
  quote = c("needed", "all", "none"),
  escape = c("double", "backslash", "none"),
  bom = FALSE,
  num_threads = vroom_threads(),
  progress = vroom_progress(),
  path = deprecated()
)

Arguments

`x`	A data frame or tibble to write to disk.
`file`	File or connection to write to.
`delim`	Delimiter used to separate values. Defaults to `⁠\t⁠` to write tab separated value (TSV) files.
`eol`	The end of line character to use. Most commonly either `"\n"` for Unix style newlines, or `"\r\n"` for Windows style newlines.
`na`	String used for missing values. Defaults to 'NA'.
`col_names`	If `FALSE`, column names will not be included at the top of the file. If `TRUE`, column names will be included. If not specified, `col_names` will take the opposite value given to `append`.
`append`	If `FALSE`, will overwrite existing file. If `TRUE`, will append to existing file. In both cases, if the file does not exist a new file is created.
`quote`	How to handle fields which contain characters that need to be quoted. `needed` - Values are only quoted if needed: if they contain a delimiter, quote, or newline. `all` - Quote all fields. `none` - Never quote fields.
`escape`	The type of escape to use when quotes are in the data. `double` - quotes are escaped by doubling them. `backslash` - quotes are escaped by a preceding backslash. `none` - quotes are not escaped.
`bom`	If `TRUE` add a UTF-8 BOM at the beginning of the file. This is recommended when saving data for consumption by excel, as it will force excel to read the data with the correct encoding (UTF-8)
`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 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`.
`path`	is no longer supported, use `file` instead.

Examples

# If you only specify a file name, vroom_write() will write
# the file to your current working directory.
out_file <- tempfile(fileext = "csv")
vroom_write(mtcars, out_file, ",")

# You can also use a literal filename
# vroom_write(mtcars, "mtcars.tsv")

# If you add an extension to the file name, write_()* will
# automatically compress the output.
# vroom_write(mtcars, "mtcars.tsv.gz")
# vroom_write(mtcars, "mtcars.tsv.bz2")
# vroom_write(mtcars, "mtcars.tsv.xz")
# If you only specify a file name, vroom_write() will write
# the file to your current working directory.
out_file <- tempfile(fileext = "csv")
vroom_write(mtcars, out_file, ",")

# You can also use a literal filename
# vroom_write(mtcars, "mtcars.tsv")

# If you add an extension to the file name, write_()* will
# automatically compress the output.
# vroom_write(mtcars, "mtcars.tsv.gz")
# vroom_write(mtcars, "mtcars.tsv.bz2")
# vroom_write(mtcars, "mtcars.tsv.xz")

Write lines to a file

Description

Write lines to a file

Usage

vroom_write_lines(
  x,
  file,
  eol = "\n",
  na = "NA",
  append = FALSE,
  num_threads = vroom_threads()
)
vroom_write_lines(
  x,
  file,
  eol = "\n",
  na = "NA",
  append = FALSE,
  num_threads = vroom_threads()
)

Arguments

`x`	A character vector.
`file`	File or connection to write to.
`eol`	The end of line character to use. Most commonly either `"\n"` for Unix style newlines, or `"\r\n"` for Windows style newlines.
`na`	String used for missing values. Defaults to 'NA'.
`append`	If `FALSE`, will overwrite existing file. If `TRUE`, will append to existing file. In both cases, if the file does not exist a new file is created.
`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.

Package 'vroom'

Help Index

Create column specification

Description

Usage

Arguments

Details

Examples

Examine the column specifications for a data frame

Description

Usage

Arguments

Value

Examples

Create or retrieve date names

Description

Usage

Arguments

Examples

Generate a random tibble

Description

Usage

Arguments

Details

See Also

Examples

Generate individual vectors of the types supported by vroom

Description

Usage

Arguments

Examples

Guess the type of a vector

Description

Usage

Arguments

Examples

Create locales

Description

Usage

Arguments

Examples

Retrieve parsing problems

Description

Usage

Arguments

Value

Read a delimited file into a tibble

Description

Usage

Arguments

Examples

Show which column types are using Altrep

Description

Usage

Arguments

Details

Examples

Show which column types are using Altrep

Description

Usage

Arguments

Get path to vroom examples

Description

Usage

Arguments

Examples

Convert a data frame to a delimited string

Description

Usage

Arguments

Read a fixed width file into a tibble

Description

Usage

Arguments

Details

Examples

Read lines from a file

Description

Usage

Arguments