Get started with vroom

knitr::opts_knit$set(root.dir = tempdir())
  collapse = TRUE,
  comment = "#>"
options(tibble.print_min = 3)

The vroom package contains one main function vroom() which is used to read all types of delimited files. A delimited file is any file in which the data is separated (delimited) by one or more characters.

The most common type of delimited files are CSV (Comma Separated Values) or TSV (Tab Separated Values) files, typically these files have a .csv and .tsv suffix respectively.


This vignette covers the following topics:

Reading files

To read a CSV, or other type of delimited file with vroom pass the file to vroom(). The delimiter will be automatically guessed if it is a common delimiter; e.g. ("," "\t" " " "|" ":" ";"). If the guessing fails or you are using a less common delimiter specify it with the delim parameter. (e.g. delim = ",").

We have included an example CSV file in the vroom package for use in examples and tests. Access it with vroom_example("mtcars.csv")

# See where the example file is stored on your machine
file <- vroom_example("mtcars.csv")

# Read the file, by default vroom will guess the delimiter automatically.

# You can also specify it explicitly, which is (slightly) faster, and safer if
# you know how the file is delimited.
vroom(file, delim = ",")

Reading multiple files

If you are reading a set of files which all have the same columns (as in, names and types), you can pass the filenames directly to vroom() and it will combine them into one result. vroom's example datasets include several files named like mtcars-i.csv. These files contain subsets of the mtcars data, for cars with different numbers of cylinders. First, we get a character vector of these filepaths.

ve <- grep("mtcars-[0-9].csv", vroom_examples(), value = TRUE)
files <- sapply(ve, vroom_example)

Now we can efficiently read them into one table by passing the filenames directly to vroom.


Often the filename or directory where the files are stored contains information. The id parameter can be used to add an extra column to the result with the full path to each file. (in this case we name the column path).

vroom(files, id = "path")

Reading compressed files

vroom supports reading zip, gz, bz2 and xz compressed files automatically, just pass the filename of the compressed file to vroom.

file <- vroom_example("mtcars.csv.gz")


vroom() decompresses, indexes and writes the decompressed data to a file in the temp directory in a single stream. The temporary file is used to lazily look up the values and will be automatically cleaned up when all values in the object have been fully read, the object is removed, or the R session ends.

Reading individual files from a multi-file zip archive

If you are reading a zip file that contains multiple files with the same format, you can read a subset of the files at once like so:

zip_file <- vroom_example("")
filenames <- unzip(zip_file, list = TRUE)$Name

# imagine we only want to read 2 of the 3 files
vroom(purrr::map(filenames[c(1, 3)], ~ unz(zip_file, .x)))

Reading remote files

vroom can read files directly from the internet as well by passing the URL of the file to vroom.

file <- ""

It can even read gzipped files from the internet (although not the other compressed formats).

file <- ""

Column selection

vroom provides the same interface for column selection and renaming as dplyr::select(). This provides very efficient, flexible and readable selections. For example you can select by:

file <- vroom_example("mtcars.csv.gz")

vroom(file, col_select = c(model, cyl, gear))
vroom(file, col_select = c(1, 3, 11))
vroom(file, col_select = starts_with("d"))
vroom(file, col_select = c(car = model, everything()))

Reading fixed width files

A fixed width file can be a very compact representation of numeric data. Unfortunately, it's also often painful to read because you need to describe the length of every field. vroom aims to make it as easy as possible by providing a number of different ways to describe the field structure. Use vroom_fwf() in conjunction with one of the following helper functions to read the file.

fwf_sample <- vroom_example("fwf-sample.txt")
vroom_fwf(fwf_sample, fwf_empty(fwf_sample, col_names = c("first", "last", "state", "ssn")))
vroom_fwf(fwf_sample, fwf_widths(c(20, 10, 12), c("name", "state", "ssn")))
vroom_fwf(fwf_sample, fwf_positions(c(1, 30), c(20, 42), c("name", "ssn")))
vroom_fwf(fwf_sample, fwf_cols(name = 20, state = 10, ssn = 12))
vroom_fwf(fwf_sample, fwf_cols(name = c(1, 20), ssn = c(30, 42)))

Column types

vroom guesses the data types of columns as they are read, however sometimes the guessing fails and it is necessary to explicitly set the type of one or more columns.

The available specifications are: (with single letter abbreviations in quotes)

You can tell vroom what columns to use with the col_types() argument in a number of ways.

If you only need to override a single column the most concise way is to use a named vector.

# read the 'hp' columns as an integer
vroom(vroom_example("mtcars.csv"), col_types = c(hp = "i"))

# also skip reading the 'cyl' column
vroom(vroom_example("mtcars.csv"), col_types = c(hp = "i", cyl = "_"))

# also read the gears as a factor
vroom(vroom_example("mtcars.csv"), col_types = c(hp = "i", cyl = "_", gear = "f"))

You can read all the columns with the same type, by using the .default argument. For example reading everything as a character.

vroom(vroom_example("mtcars.csv"), col_types = c(.default = "c"))

However you can also use the col_*() functions in a list.

  col_types = list(hp = col_integer(), cyl = col_skip(), gear = col_factor())

This is most useful when a column type needs additional information, such as for categorical data when you know all of the levels of a factor.

  col_types = list(gear = col_factor(levels = c(gear = c("3", "4", "5"))))

Name repair

Often the names of columns in the original dataset are not ideal to work with. vroom() uses the same .name_repair argument as tibble, so you can use one of the default name repair strategies or provide a custom function. A great approach is to use the janitor::make_clean_names() function as the input. This will automatically clean the names to use whatever case you specify, here I am setting it to use ALLCAPS names.

  .name_repair = ~ janitor::make_clean_names(., case = "all_caps")

Writing delimited files

Use vroom_write() to write delimited files, the default delimiter is tab, to write TSV files. Writing to TSV by default has the following benefits: - Avoids the issue of whether to use ; (common in Europe) or , (common in the US) - Unlikely to require quoting in fields, as very few fields contain tabs - More easily and efficiently ingested by Unix command line tools such as cut, perl and awk.

vroom_write(mtcars, "mtcars.tsv")

Writing CSV delimited files

However you can also use delim = ',' to write CSV files, which are common as inputs to GUI spreadsheet tools like Excel or Google Sheets.

vroom_write(mtcars, "mtcars.csv", delim = ",")

Writing compressed files

For gzip, bzip2 and xz compression the outputs will be automatically compressed if the filename ends in .gz, .bz2 or .xz.

vroom_write(mtcars, "mtcars.tsv.gz")

vroom_write(mtcars, "mtcars.tsv.bz2")

vroom_write(mtcars, "mtcars.tsv.xz")
unlink(c("mtcars.tsv.gz", "mtcars.tsv.bz2", "mtcars.tsv.xz"))

It is also possible to use other compressors by using pipe() with vroom_write() to create a pipe connection to command line utilities, such as

The parallel compression versions can be considerably faster for large output files and generally vroom_write() is fast enough that the compression speed becomes the bottleneck when writing.

vroom_write(mtcars, pipe("pigz > mtcars.tsv.gz"))

Reading and writing from standard input and output

vroom supports reading and writing to the C-level stdin and stdout of the R process by using stdin() and stdout(). E.g. from a shell prompt you can pipe to and from vroom directly.

cat inst/extdata/mtcars.csv | Rscript -e 'vroom::vroom(stdin())'

Rscript -e 'vroom::vroom_write(iris, stdout())' | head

Note this interpretation of stdin() and stdout() differs from that used elsewhere by R, however we believe it better matches most user's expectations for this use case.

Further reading

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vroom documentation built on July 4, 2024, 9:08 a.m.