Read a delimited file into a tibble

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"
)

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
#> [1] "/home/runner/work/_temp/Library/vroom/extdata/mtcars.csv"

# Read from a path

# Input sources -------------------------------------------------------------
# Read from a path
vroom(input_file)
#> Rows: 32 Columns: 12
#> ── Column specification ──────────────────────────────────────────────────
#> Delimiter: ","
#> chr  (1): model
#> dbl (11): mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> # A tibble: 32 × 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 Mazd…  21       6  160    110  3.9   2.62  16.5     0     1     4     4
#>  2 Mazd…  21       6  160    110  3.9   2.88  17.0     0     1     4     4
#>  3 Dats…  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1
#>  4 Horn…  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1
#>  5 Horn…  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
#>  6 Vali…  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
#>  7 Dust…  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
#>  8 Merc…  24.4     4  147.    62  3.69  3.19  20       1     0     4     2
#>  9 Merc…  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2
#> 10 Merc…  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4
#> # ℹ 22 more rows
# You can also use paths directly
# vroom("mtcars.csv")

if (FALSE) {
# Including remote paths
vroom("https://github.com/tidyverse/vroom/raw/main/inst/extdata/mtcars.csv")
}

# Or directly from a string with `I()`
vroom(I("x,y\n1,2\n3,4\n"))
#> Rows: 2 Columns: 2
#> ── Column specification ──────────────────────────────────────────────────
#> Delimiter: ","
#> dbl (2): x, y
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> # A tibble: 2 × 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))
#> Rows: 32 Columns: 3
#> ── Column specification ──────────────────────────────────────────────────
#> Delimiter: ","
#> chr (1): model
#> dbl (2): cyl, gear
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> # A tibble: 32 × 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
#> # ℹ 22 more rows
vroom(input_file, col_select = c(1, 3, 11))
#> Rows: 32 Columns: 3
#> ── Column specification ──────────────────────────────────────────────────
#> Delimiter: ","
#> chr (1): model
#> dbl (2): cyl, gear
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> # A tibble: 32 × 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
#> # ℹ 22 more rows

# Or use the selection helpers
vroom(input_file, col_select = starts_with("d"))
#> Rows: 32 Columns: 2
#> ── Column specification ──────────────────────────────────────────────────
#> Delimiter: ","
#> dbl (2): disp, drat
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> # A tibble: 32 × 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
#> # ℹ 22 more rows

# You can also rename specific columns
vroom(input_file, col_select = c(car = model, everything()))
#> Rows: 32 Columns: 12
#> ── Column specification ──────────────────────────────────────────────────
#> Delimiter: ","
#> chr  (1): model
#> dbl (11): mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> # A tibble: 32 × 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 Mazd…  21       6  160    110  3.9   2.62  16.5     0     1     4     4
#>  2 Mazd…  21       6  160    110  3.9   2.88  17.0     0     1     4     4
#>  3 Dats…  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1
#>  4 Horn…  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1
#>  5 Horn…  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
#>  6 Vali…  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
#>  7 Dust…  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
#>  8 Merc…  24.4     4  147.    62  3.69  3.19  20       1     0     4     2
#>  9 Merc…  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2
#> 10 Merc…  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4
#> # ℹ 22 more rows

# 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")
#> # A tibble: 2 × 2
#>       x y    
#>   <dbl> <chr>
#> 1     1 2    
#> 2     3 4    

# Or with a list of column types:
vroom(I("x,y\n1,2\n3,4\n"), col_types = list(col_double(), col_character()))
#> # A tibble: 2 × 2
#>       x y    
#>   <dbl> <chr>
#> 1     1 2    
#> 2     3 4    

# File types ----------------------------------------------------------------
# csv
vroom(I("a,b\n1.0,2.0\n"), delim = ",")
#> Rows: 1 Columns: 2
#> ── Column specification ──────────────────────────────────────────────────
#> Delimiter: ","
#> dbl (2): a, b
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> # A tibble: 1 × 2
#>       a     b
#>   <dbl> <dbl>
#> 1     1     2
# tsv
vroom(I("a\tb\n1.0\t2.0\n"))
#> Rows: 1 Columns: 2
#> ── Column specification ──────────────────────────────────────────────────
#> Delimiter: "\t"
#> dbl (2): a, b
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> # A tibble: 1 × 2
#>       a     b
#>   <dbl> <dbl>
#> 1     1     2
# Other delimiters
vroom(I("a|b\n1.0|2.0\n"), delim = "|")
#> Rows: 1 Columns: 2
#> ── Column specification ──────────────────────────────────────────────────
#> Delimiter: "|"
#> dbl (2): a, b
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> # A tibble: 1 × 2
#>       a     b
#>   <dbl> <dbl>
#> 1     1     2

# Read datasets across multiple files ---------------------------------------
mtcars_by_cyl <- vroom_example(vroom_examples("mtcars-"))
mtcars_by_cyl
#> [1] "/home/runner/work/_temp/Library/vroom/extdata/mtcars-4.csv"        
#> [2] "/home/runner/work/_temp/Library/vroom/extdata/mtcars-6.csv"        
#> [3] "/home/runner/work/_temp/Library/vroom/extdata/mtcars-8.csv"        
#> [4] "/home/runner/work/_temp/Library/vroom/extdata/mtcars-multi-cyl.zip"

# Pass the filenames directly to vroom, they are efficiently combined
vroom(mtcars_by_cyl)
#> Rows: 43 Columns: 12
#> ── Column specification ──────────────────────────────────────────────────
#> Delimiter: ","
#> chr  (1): model
#> dbl (11): mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> # A tibble: 43 × 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 Dats…  22.8     4 108      93  3.85  2.32  18.6     1     1     4     1
#>  2 Merc…  24.4     4 147.     62  3.69  3.19  20       1     0     4     2
#>  3 Merc…  22.8     4 141.     95  3.92  3.15  22.9     1     0     4     2
#>  4 Fiat…  32.4     4  78.7    66  4.08  2.2   19.5     1     1     4     1
#>  5 Hond…  30.4     4  75.7    52  4.93  1.62  18.5     1     1     4     2
#>  6 Toyo…  33.9     4  71.1    65  4.22  1.84  19.9     1     1     4     1
#>  7 Toyo…  21.5     4 120.     97  3.7   2.46  20.0     1     0     3     1
#>  8 Fiat…  27.3     4  79      66  4.08  1.94  18.9     1     1     4     1
#>  9 Pors…  26       4 120.     91  4.43  2.14  16.7     0     1     5     2
#> 10 Lotu…  30.4     4  95.1   113  3.77  1.51  16.9     1     1     5     2
#> # ℹ 33 more rows

# 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")
#> Rows: 43 Columns: 13
#> ── Column specification ──────────────────────────────────────────────────
#> Delimiter: ","
#> chr  (1): model
#> dbl (11): mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
dat$source <- basename(dat$source)
dat
#> # A tibble: 43 × 13
#>    source      model   mpg   cyl  disp    hp  drat    wt  qsec    vs    am
#>    <chr>       <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 mtcars-4.c… Dats…  22.8     4 108      93  3.85  2.32  18.6     1     1
#>  2 mtcars-4.c… Merc…  24.4     4 147.     62  3.69  3.19  20       1     0
#>  3 mtcars-4.c… Merc…  22.8     4 141.     95  3.92  3.15  22.9     1     0
#>  4 mtcars-4.c… Fiat…  32.4     4  78.7    66  4.08  2.2   19.5     1     1
#>  5 mtcars-4.c… Hond…  30.4     4  75.7    52  4.93  1.62  18.5     1     1
#>  6 mtcars-4.c… Toyo…  33.9     4  71.1    65  4.22  1.84  19.9     1     1
#>  7 mtcars-4.c… Toyo…  21.5     4 120.     97  3.7   2.46  20.0     1     0
#>  8 mtcars-4.c… Fiat…  27.3     4  79      66  4.08  1.94  18.9     1     1
#>  9 mtcars-4.c… Pors…  26       4 120.     91  4.43  2.14  16.7     0     1
#> 10 mtcars-4.c… Lotu…  30.4     4  95.1   113  3.77  1.51  16.9     1     1
#> # ℹ 33 more rows
#> # ℹ 2 more variables: gear <dbl>, carb <dbl>