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"
)Arguments
- file
Either a path to a file, a connection, or literal data (either a single string or a raw vector).
filecan also be a character vector containing multiple filepaths or a list containing multiple connections.Files ending in
.gz,.bz2,.xz, or.zipwill be automatically uncompressed. Files starting withhttp://,https://,ftp://, orftps://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
NULLthe delimiter is guessed from the set ofc(",", "\t", " ", "|", ":", ";").- col_names
Either
TRUE,FALSEor 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. IfFALSE, column names will be generated automatically: X1, X2, X3 etc.If
col_namesis 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,...2etc. Duplicate column names will generate a warning and be made unique, seename_repairto control how this is done.- col_types
One of
NULL, acols()specification, or a string.If
NULL, all column types will be imputed fromguess_maxrows on the input interspersed throughout the file. This is convenient (and fast), but not robust. If the imputation fails, you'll need to increase theguess_maxor supply the correct types yourself.Column specifications created by
list()orcols()must contain one column specification for each column. If you only want to read a subset of the columns, usecols_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
readrguessed they were. To remove this message, setshow_col_types = FALSEor setoptions(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. Usec()to use more than one selection expression. Although this usage is less common,col_selectalso accepts a numeric column index. See?tidyselect::languagefor 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
commentis 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
TRUEthen blank rows will not be represented at all. If it isFALSEthen they will be represented byNAvalues 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_doubleas 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,
TRUEorFALSE. Seevroom_altrep()for for full details.- altrep_opts
![[Deprecated]](figures/lifecycle-deprecated.svg)
- 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_progresstoFALSE.- show_col_types
Control showing the column specifications. If
TRUEcolumn specifications are always show, ifFALSEthey are never shown. IfNULL(the default) they are shown only if an explicit specification is not given tocol_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 areunique."universal": Make the namesuniqueand syntactic.A function: apply custom name repair (e.g.,
name_repair = make.namesfor names in the style of base R).A purrr-style anonymous function, see
rlang::as_function().
This argument is passed on as
repairtovctrs::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] "/tmp/Rtmp3C4CBp/temp_libpath7291f870ab24/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 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
#> # ℹ 22 more rows
# You can also use paths directly
# vroom("mtcars.csv")
if (FALSE) { # \dontrun{
# 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 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
#> # 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] "/tmp/Rtmp3C4CBp/temp_libpath7291f870ab24/vroom/extdata/mtcars-4.csv"
#> [2] "/tmp/Rtmp3C4CBp/temp_libpath7291f870ab24/vroom/extdata/mtcars-6.csv"
#> [3] "/tmp/Rtmp3C4CBp/temp_libpath7291f870ab24/vroom/extdata/mtcars-8.csv"
#> [4] "/tmp/Rtmp3C4CBp/temp_libpath7291f870ab24/vroom/extdata/mtcars-multi-cyl.zip"
# Pass the filenames directly to vroom, they are efficiently combined
vroom(mtcars_by_cyl)
#> Multiple files in zip: reading 'mtcars-4.csv'
#> 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 Datsun 710 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 2 Merc 240D 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 3 Merc 230 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 4 Fiat 128 32.4 4 78.7 66 4.08 2.2 19.5 1 1 4 1
#> 5 Honda Civic 30.4 4 75.7 52 4.93 1.62 18.5 1 1 4 2
#> 6 Toyota Cor… 33.9 4 71.1 65 4.22 1.84 19.9 1 1 4 1
#> 7 Toyota Cor… 21.5 4 120. 97 3.7 2.46 20.0 1 0 3 1
#> 8 Fiat X1-9 27.3 4 79 66 4.08 1.94 18.9 1 1 4 1
#> 9 Porsche 91… 26 4 120. 91 4.43 2.14 16.7 0 1 5 2
#> 10 Lotus Euro… 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")
#> Multiple files in zip: reading 'mtcars-4.csv'
#> 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 gear
#> <chr> <chr> <dbl> <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 4
#> 2 mtcars-4.c… Merc… 24.4 4 147. 62 3.69 3.19 20 1 0 4
#> 3 mtcars-4.c… Merc… 22.8 4 141. 95 3.92 3.15 22.9 1 0 4
#> 4 mtcars-4.c… Fiat… 32.4 4 78.7 66 4.08 2.2 19.5 1 1 4
#> 5 mtcars-4.c… Hond… 30.4 4 75.7 52 4.93 1.62 18.5 1 1 4
#> 6 mtcars-4.c… Toyo… 33.9 4 71.1 65 4.22 1.84 19.9 1 1 4
#> 7 mtcars-4.c… Toyo… 21.5 4 120. 97 3.7 2.46 20.0 1 0 3
#> 8 mtcars-4.c… Fiat… 27.3 4 79 66 4.08 1.94 18.9 1 1 4
#> 9 mtcars-4.c… Pors… 26 4 120. 91 4.43 2.14 16.7 0 1 5
#> 10 mtcars-4.c… Lotu… 30.4 4 95.1 113 3.77 1.51 16.9 1 1 5
#> # ℹ 33 more rows
#> # ℹ 1 more variable: carb <dbl>