🏎💨vroom

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The fastest delimited reader for R, 1.23 GB/sec.

But that’s impossible! How can it be so fast?

vroom doesn’t stop to actually read all of your data, it simply indexes where each record is located so it can be read later. The vectors returned use the Altrep framework to lazily load the data on-demand when it is accessed, so you only pay for what you use. This lazy access is done automatically, so no changes to your R data-manipulation code are needed.

vroom also uses multiple threads for indexing, materializing non-character columns, and when writing to further improve performance.

package version time (sec) speedup throughput
vroom 1.5.1 1.36 53.30 1.23 GB/sec
data.table 1.14.0 5.83 12.40 281.65 MB/sec
readr 1.4.0 37.30 1.94 44.02 MB/sec
read.delim 4.1.0 72.31 1.00 22.71 MB/sec

Features

vroom has nearly all of the parsing features of readr for delimited and fixed width files, including

* these are additional features not in readr.

** requires num_threads = 1.

Installation

Install vroom from CRAN with:

install.packages("vroom")

Alternatively, if you need the development version from GitHub install it with:

# install.packages("devtools")
devtools::install_dev("vroom")

Usage

See getting started to jump start your use of vroom!

vroom uses the same interface as readr to specify column types.

vroom::vroom("mtcars.tsv",
  col_types = list(cyl = "i", gear = "f",hp = "i", disp = "_",
                   drat = "_", vs = "l", am = "l", carb = "i")
)
#> # A tibble: 32 × 10
#>   model           mpg   cyl    hp    wt  qsec vs    am    gear   carb
#>   <chr>         <dbl> <int> <int> <dbl> <dbl> <lgl> <lgl> <fct> <int>
#> 1 Mazda RX4      21       6   110  2.62  16.5 FALSE TRUE  4         4
#> 2 Mazda RX4 Wag  21       6   110  2.88  17.0 FALSE TRUE  4         4
#> 3 Datsun 710     22.8     4    93  2.32  18.6 TRUE  TRUE  4         1
#> # … with 29 more rows

Reading multiple files

vroom natively supports reading from multiple files (or even multiple connections!).

First we generate some files to read by splitting the nycflights dataset by airline.

library(nycflights13)
purrr::iwalk(
  split(flights, flights$carrier),
  ~ { .x$carrier[[1]]; vroom::vroom_write(.x, glue::glue("flights_{.y}.tsv"), delim = "\t") }
)

Then we can efficiently read them into one tibble by passing the filenames directly to vroom.

files <- fs::dir_ls(glob = "flights*tsv")
files
#> flights_9E.tsv flights_AA.tsv flights_AS.tsv flights_B6.tsv flights_DL.tsv 
#> flights_EV.tsv flights_F9.tsv flights_FL.tsv flights_HA.tsv flights_MQ.tsv 
#> flights_OO.tsv flights_UA.tsv flights_US.tsv flights_VX.tsv flights_WN.tsv 
#> flights_YV.tsv
vroom::vroom(files)
#> Rows: 336776 Columns: 19
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: "\t"
#> chr   (4): carrier, tailnum, origin, dest
#> dbl  (14): year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, ...
#> dttm  (1): time_hour
#> 
#> 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: 336,776 × 19
#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#>   <dbl> <dbl> <dbl>    <dbl>          <dbl>     <dbl>    <dbl>          <dbl>
#> 1  2013     1     1      810            810         0     1048           1037
#> 2  2013     1     1     1451           1500        -9     1634           1636
#> 3  2013     1     1     1452           1455        -3     1637           1639
#> # … with 336,773 more rows, and 11 more variables: arr_delay <dbl>,
#> #   carrier <chr>, flight <dbl>, tailnum <chr>, origin <chr>, dest <chr>,
#> #   air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

Learning more

Benchmarks

The speed quoted above is from a real 1.53G dataset with 14,388,451 rows and 11 columns, see the benchmark article for full details of the dataset and bench/ for the code used to retrieve the data and perform the benchmarks.

Environment variables

In addition to the arguments to the vroom() function, you can control the behavior of vroom with a few environment variables. Generally these will not need to be set by most users.

There are also a family of variables to control use of the Altrep framework. 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.

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

RStudio caveats

RStudio’s environment pane calls object.size() when it refreshes the pane, which for Altrep objects can be extremely slow. RStudio 1.2.1335+ includes the fixes (RStudio#4210, RStudio#4292) for this issue, so it is recommended you use at least that version.

Thanks