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")

Arguments

file

path to a local file.

delim

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

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.

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.

n_max

Maximum number of records 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.

trim_ws

Should leading and trailing whitespace 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 records to use for guessing column types.

altrep_opts

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

num_threads

Number of threads to use when reading and materializing vectors.

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.

.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.

Examples

# Show path to example file input_file <- vroom_example("mtcars.csv") # Read from a path # Input sources ------------------------------------------------------------- # Read from a path vroom(input_file)
#> Observations: 32 #> Variables: 12 #> chr [ 1]: model #> dbl [11]: mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb #> #> Call `spec()` for a copy-pastable column specification #> Specify the column types with `col_types` to quiet this message
#> # A tibble: 32 x 12 #> model mpg cyl disp hp drat wt qsec vs am gear carb #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Mazda RX4 21 6 160 110 3.9 2.62 16.5 0 1 4 4 #> 2 Mazda RX4 … 21 6 160 110 3.9 2.88 17.0 0 1 4 4 #> 3 Datsun 710 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 #> 4 Hornet 4 D… 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 #> 5 Hornet Spo… 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 #> 6 Valiant 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 #> 7 Duster 360 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 #> 8 Merc 240D 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 #> 9 Merc 230 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 #> 10 Merc 280 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 #> # … with 22 more rows
# You can also use literal paths directly # vroom("mtcars.csv")
# NOT RUN { # Including remote paths vroom("https://github.com/r-lib/vroom/raw/master/inst/extdata/mtcars.csv") # }
# Or directly from a string (must contain a trailing newline) vroom("x,y\n1,2\n3,4\n")
#> Observations: 2 #> Variables: 2 #> dbl [2]: x, y #> #> Call `spec()` for a copy-pastable column specification #> Specify the column types with `col_types` to quiet this message
#> # A tibble: 2 x 2 #> x y #> <dbl> <dbl> #> 1 1 2 #> 2 3 4
# Column selection ---------------------------------------------------------- # Pass column names or indexes directly to select them vroom(input_file, col_select = c(model, cyl, gear))
#> Observations: 32 #> Variables: 3 #> chr [1]: model #> dbl [2]: cyl, gear #> #> Call `spec()` for a copy-pastable column specification #> Specify the column types with `col_types` to quiet this message
#> # A tibble: 32 x 3 #> model cyl gear #> <chr> <dbl> <dbl> #> 1 Mazda RX4 6 4 #> 2 Mazda RX4 Wag 6 4 #> 3 Datsun 710 4 4 #> 4 Hornet 4 Drive 6 3 #> 5 Hornet Sportabout 8 3 #> 6 Valiant 6 3 #> 7 Duster 360 8 3 #> 8 Merc 240D 4 4 #> 9 Merc 230 4 4 #> 10 Merc 280 6 4 #> # … with 22 more rows
vroom(input_file, col_select = c(1, 3, 11))
#> Observations: 32 #> Variables: 3 #> chr [1]: model #> dbl [2]: cyl, gear #> #> Call `spec()` for a copy-pastable column specification #> Specify the column types with `col_types` to quiet this message
#> # A tibble: 32 x 3 #> model cyl gear #> <chr> <dbl> <dbl> #> 1 Mazda RX4 6 4 #> 2 Mazda RX4 Wag 6 4 #> 3 Datsun 710 4 4 #> 4 Hornet 4 Drive 6 3 #> 5 Hornet Sportabout 8 3 #> 6 Valiant 6 3 #> 7 Duster 360 8 3 #> 8 Merc 240D 4 4 #> 9 Merc 230 4 4 #> 10 Merc 280 6 4 #> # … with 22 more rows
# Or use the selection helpers vroom(input_file, col_select = starts_with("d"))
#> Observations: 32 #> Variables: 2 #> dbl [2]: disp, drat #> #> Call `spec()` for a copy-pastable column specification #> Specify the column types with `col_types` to quiet this message
#> # A tibble: 32 x 2 #> disp drat #> <dbl> <dbl> #> 1 160 3.9 #> 2 160 3.9 #> 3 108 3.85 #> 4 258 3.08 #> 5 360 3.15 #> 6 225 2.76 #> 7 360 3.21 #> 8 147. 3.69 #> 9 141. 3.92 #> 10 168. 3.92 #> # … with 22 more rows
# You can also rename specific columns vroom(input_file, col_select = list(car = model, everything()))
#> Observations: 32 #> Variables: 12 #> chr [ 1]: model #> dbl [11]: mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb #> #> Call `spec()` for a copy-pastable column specification #> Specify the column types with `col_types` to quiet this message
#> # A tibble: 32 x 12 #> car mpg cyl disp hp drat wt qsec vs am gear carb #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Mazda RX4 21 6 160 110 3.9 2.62 16.5 0 1 4 4 #> 2 Mazda RX4 … 21 6 160 110 3.9 2.88 17.0 0 1 4 4 #> 3 Datsun 710 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 #> 4 Hornet 4 D… 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 #> 5 Hornet Spo… 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 #> 6 Valiant 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 #> 7 Duster 360 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 #> 8 Merc 240D 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 #> 9 Merc 230 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 #> 10 Merc 280 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 #> # … with 22 more rows
# 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")
#> # A tibble: 2 x 2 #> x y #> <dbl> <chr> #> 1 1 2 #> 2 3 4
# Or with a list of column types: vroom("x,y\n1,2\n3,4\n", col_types = list(col_double(), col_character()))
#> # A tibble: 2 x 2 #> x y #> <dbl> <chr> #> 1 1 2 #> 2 3 4
# File types ---------------------------------------------------------------- # csv vroom("a,b\n1.0,2.0\n", delim = ",")
#> Observations: 1 #> Variables: 2 #> dbl [2]: a, b #> #> Call `spec()` for a copy-pastable column specification #> Specify the column types with `col_types` to quiet this message
#> # A tibble: 1 x 2 #> a b #> <dbl> <dbl> #> 1 1 2
# tsv vroom("a\tb\n1.0\t2.0\n")
#> Observations: 1 #> Variables: 2 #> dbl [2]: a, b #> #> Call `spec()` for a copy-pastable column specification #> Specify the column types with `col_types` to quiet this message
#> # A tibble: 1 x 2 #> a b #> <dbl> <dbl> #> 1 1 2
# Other delimiters vroom("a|b\n1.0|2.0\n", delim = "|")
#> Observations: 1 #> Variables: 2 #> dbl [2]: a, b #> #> Call `spec()` for a copy-pastable column specification #> Specify the column types with `col_types` to quiet this message
#> # A tibble: 1 x 2 #> a b #> <dbl> <dbl> #> 1 1 2