disk.frame <img src="inst/figures/disk.frame.png" align="right">

NOTICE

{disk.frame} has been soft-deprecated in favor of {arrow}. With the {arrow} 6.0.0 release, it’s now capable of doing larger-than-RAM data analysis quite well see release note. Hence, there is no strong reason to prefer {disk.frame} unless you have very specific feature needs.

For the above reason, I’ve decided to soft-deprecate {disk.frame} which means I will no longer actively develop new features for it but it will remain on CRAN in maintenance mode.

To help with the transition I’ve created a function, disk.frame::disk.frame_to_parquet(df, outdir) to help you convert existing {disk.frame}s to the parquet format so you can use {arrow} with it.

I am working on an reincarnation of {disk.frame} in Julia, so the {disk.frame} will live on!

Thank your for support {disk.frame}. I’ve learnt alot along the way, but time has come to move on!

Introduction

How do I manipulate tabular data that doesn’t fit into Random Access Memory (RAM)?

Use {disk.frame}!

In a nutshell, {disk.frame} makes use of two simple ideas

split up a larger-than-RAM dataset into chunks and store each chunk in a separate file inside a folder and
provide a convenient API to manipulate these chunks

{disk.frame} performs a similar role to distributed systems such as Apache Spark, Python’s Dask, and Julia’s JuliaDB.jl for medium data which are datasets that are too large for RAM but not quite large enough to qualify as big data.

Installation

You can install the released version of {disk.frame} from CRAN with:

install.packages("disk.frame")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("DiskFrame/disk.frame")

On some platforms, such as SageMaker, you may need to explicitly specify a repo like this

install.packages("disk.frame", repo="https://cran.rstudio.com")

Vignettes and articles

Please see these vignettes and articles about {disk.frame}

Quick start: {disk.frame} which replicates the sparklyr vignette for manipulating the nycflights13 flights data.
Ingesting data into {disk.frame} which lists some commons way of creating disk.frames
{disk.frame} can be more epic! shows some ways of loading large CSVs and the importance of srckeep
Group-by the various types of group-bys
Custom one-stage group-by functions how to define custom one-stage group-by functions
Fitting GLMs (including logistic regression) introduces the dfglm function for fitting generalized linear models
Using data.table syntax with disk.frame
disk.frame concepts
Benchmark 1: disk.frame vs Dask vs JuliaDB

Common questions

a) What is `{disk.frame}` and why create it?

{disk.frame} is an R package that provides a framework for manipulating larger-than-RAM structured tabular data on disk efficiently. The reason one would want to manipulate data on disk is that it allows arbitrarily large datasets to be processed by R. In other words, we go from “R can only deal with data that fits in RAM” to “R can deal with any data that fits on disk”. See the next section.

b) How is it different to `data.frame` and `data.table`?

A data.frame in R is an in-memory data structure, which means that R must load the data in its entirety into RAM. A corollary of this is that only data that can fit into RAM can be processed using data.frames. This places significant restrictions on what R can process with minimal hassle.

In contrast, {disk.frame} provides a framework to store and manipulate data on the hard drive. It does this by loading only a small part of the data, called a chunk, into RAM; process the chunk, write out the results and repeat with the next chunk. This chunking strategy is widely applied in other packages to enable processing large amounts of data in R, for example, see chunkded arkdb, and iotools.

Furthermore, there is a row-limit of 2^31 for data.frames in R; hence an alternate approach is needed to apply R to these large datasets. The chunking mechanism in {disk.frame} provides such an avenue to enable data manipulation beyond the 2^31 row limit.

c) How is `{disk.frame}` different to previous “big” data solutions for R?

R has many packages that can deal with larger-than-RAM datasets, including ff and bigmemory. However, ff and bigmemory restrict the user to primitive data types such as double, which means they do not support character (string) and factor types. In contrast, {disk.frame} makes use of data.table::data.table and data.frame directly, so all data types are supported. Also, {disk.frame} strives to provide an API that is as similar to data.frame’s where possible. {disk.frame} supports many dplyr verbs for manipulating disk.frames.

Additionally, {disk.frame} supports parallel data operations using infrastructures provided by the excellent future package to take advantage of multi-core CPUs. Further, {disk.frame} uses state-of-the-art data storage techniques such as fast data compression, and random access to rows and columns provided by the fst package to provide superior data manipulation speeds.

d) How does `{disk.frame}` work?

{disk.frame} works by breaking large datasets into smaller individual chunks and storing the chunks in fst files inside a folder. Each chunk is a fst file containing a data.frame/data.table. One can construct the original large dataset by loading all the chunks into RAM and row-bind all the chunks into one large data.frame. Of course, in practice this isn’t always possible; hence why we store them as smaller individual chunks.

{disk.frame} makes it easy to manipulate the underlying chunks by implementing dplyr functions/verbs and other convenient functions (e.g. the cmap(a.disk.frame, fn, lazy = F) function which applies the function fn to each chunk of a.disk.frame in parallel). So that {disk.frame} can be manipulated in a similar fashion to in-memory data.frames.

e) How is `{disk.frame}` different from Spark, Dask, and JuliaDB.jl?

Spark is primarily a distributed system that also works on a single machine. Dask is a Python package that is most similar to {disk.frame}, and JuliaDB.jl is a Julia package. All three can distribute work over a cluster of computers. However, {disk.frame} currently cannot distribute data processes over many computers, and is, therefore, single machine focused.

In R, one can access Spark via sparklyr, but that requires a Spark cluster to be set up. On the other hand {disk.frame} requires zero-setup apart from running install.packages("disk.frame") or devtools::install_github("xiaodaigh/disk.frame").

Finally, Spark can only apply functions that are implemented for Spark, whereas {disk.frame} can use any function in R including user-defined functions.

Example usage

Set-up `{disk.frame}`

{disk.frame} works best if it can process multiple data chunks in parallel. The best way to set-up {disk.frame} so that each CPU core runs a background worker is by using

setup_disk.frame()

# this allows large datasets to be transferred between sessions
options(future.globals.maxSize = Inf)

The setup_disk.frame() sets up background workers equal to the number of CPU cores; please note that, by default, hyper-threaded cores are counted as one not two.

Alternatively, one may specify the number of workers using setup_disk.frame(workers = n).

Quick-start

suppressPackageStartupMessages(library(disk.frame))
library(nycflights13)

# this will setup disk.frame's parallel backend with number of workers equal to the number of CPU cores (hyper-threaded cores are counted as one not two)
setup_disk.frame()
#> The number of workers available for disk.frame is 6
# this allows large datasets to be transferred between sessions
options(future.globals.maxSize = Inf)

# convert the flights data.frame to a disk.frame
# optionally, you may specify an outdir, otherwise, the 
flights.df <- as.disk.frame(nycflights13::flights)

Example: dplyr verbs

dplyr verbs

{disk.frame} aims to support as many dplyr verbs as possible. For example

flights.df %>% 
  filter(year == 2013) %>% 
  mutate(origin_dest = paste0(origin, dest)) %>% 
  head(2)
#>    year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay
#> 1: 2013     1   1      517            515         2      830            819        11
#> 2: 2013     1   1      533            529         4      850            830        20
#>    carrier flight tailnum origin dest air_time distance hour minute           time_hour
#> 1:      UA   1545  N14228    EWR  IAH      227     1400    5     15 2013-01-01 05:00:00
#> 2:      UA   1714  N24211    LGA  IAH      227     1416    5     29 2013-01-01 05:00:00
#>    origin_dest
#> 1:      EWRIAH
#> 2:      LGAIAH

Group-by

Starting from {disk.frame} v0.3.0, there is group_by support f

Disk.frame

Install / Use

README

disk.frame <img src="inst/figures/disk.frame.png" align="right">

NOTICE

Introduction

Installation

Vignettes and articles

Common questions

a) What is `{disk.frame}` and why create it?

b) How is it different to `data.frame` and `data.table`?

c) How is `{disk.frame}` different to previous “big” data solutions for R?

d) How does `{disk.frame}` work?

e) How is `{disk.frame}` different from Spark, Dask, and JuliaDB.jl?

Example usage

Set-up `{disk.frame}`

Quick-start

Example: dplyr verbs

dplyr verbs

Group-by

Disk.frame

Install / Use

README

disk.frame <img src="inst/figures/disk.frame.png" align="right">

NOTICE

Introduction

Installation

Vignettes and articles

Common questions

a) What is {disk.frame} and why create it?

b) How is it different to data.frame and data.table?

c) How is {disk.frame} different to previous “big” data solutions for R?

d) How does {disk.frame} work?

e) How is {disk.frame} different from Spark, Dask, and JuliaDB.jl?

Example usage

Set-up {disk.frame}

Quick-start

Example: dplyr verbs

dplyr verbs

Group-by

a) What is `{disk.frame}` and why create it?

b) How is it different to `data.frame` and `data.table`?

c) How is `{disk.frame}` different to previous “big” data solutions for R?

d) How does `{disk.frame}` work?

e) How is `{disk.frame}` different from Spark, Dask, and JuliaDB.jl?

Set-up `{disk.frame}`