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 |
vroom has nearly all of the parsing features of readr for delimited and fixed width files, including
dplyr::select()** these are additional features not in readr.
** requires num_threads = 1.
Install vroom from CRAN with:
Alternatively, if you need the development version from GitHub install it with:
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
#> # ℹ 29 more rowsvroom natively supports reading from multiple files (or even multiple connections!).
First we generate some files to read by splitting the nycflights dataset by airline. For the sake of the example, we’ll just take the first 2 lines of each file.
library(nycflights13)
purrr::iwalk(
split(flights, flights$carrier),
~ { .x$carrier[[1]]; vroom::vroom_write(head(.x, 2), glue::glue("flights_{.y}.tsv"), delim = "\t") }
)Then we can efficiently read them into one tibble by passing the filenames directly to vroom. The id argument can be used to request a column that reveals the filename that each row originated from.
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, id = "source")
#> Rows: 32 Columns: 20
#> ── 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: 32 × 20
#> source year month day dep_time sched_dep_time dep_delay arr_time
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 flights_9E.tsv 2013 1 1 810 810 0 1048
#> 2 flights_9E.tsv 2013 1 1 1451 1500 -9 1634
#> 3 flights_AA.tsv 2013 1 1 542 540 2 923
#> # ℹ 29 more rows
#> # ℹ 12 more variables: sched_arr_time <dbl>, 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>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.
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.
VROOM_TEMP_PATH - Path to the directory used to store temporary files when reading from a R connection. If unset defaults to the R session’s temporary directory (tempdir()).VROOM_THREADS - The number of processor threads to use when indexing and parsing. If unset defaults to parallel::detectCores().VROOM_SHOW_PROGRESS - Whether to show the progress bar when indexing. Regardless of this setting the progress bar is disabled in non-interactive settings, R notebooks, when running tests with testthat and when knitting documents.VROOM_CONNECTION_SIZE - The size (in bytes) of the connection buffer when reading from connections (default is 128 KiB).VROOM_WRITE_BUFFER_LINES - The number of lines to use for each buffer when writing files (default: 1000).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.
VROOM_USE_ALTREP_NUMERICS - If set use Altrep for all numeric types (default false).There are also individual variables for each type. Currently only VROOM_USE_ALTREP_CHR defaults to true.
VROOM_USE_ALTREP_CHRVROOM_USE_ALTREP_FCTVROOM_USE_ALTREP_INTVROOM_USE_ALTREP_BIG_INTVROOM_USE_ALTREP_DBLVROOM_USE_ALTREP_NUMVROOM_USE_ALTREP_LGLVROOM_USE_ALTREP_DTTMVROOM_USE_ALTREP_DATEVROOM_USE_ALTREP_TIMERStudio’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.
data.table::fread() is blazing fast and great motivation to see how fast we could go faster!