Attention

This repository used to contain an R wrapper for an old version of dat. Meanwhile dat has changed a lot so this no longer works.

rdat

Software is in alpha stage. Not yet ready for use with real world data

The rdat package provides an R wrapper to the Dat project. Dat (git for data) is a framework for data versioning, replication and synchronisation, see dat-data.com.

Installation instructions

Prerequisites: Instructions below require R, git and nodejs (npm).

Installing `dat` stable

Install the latest stable version from npm:

sudo npm install -g dat

See instructions for more details.

Installing `dat` development version

If you have not already installed dat grab it from github:

git clone https://github.com/maxogden/dat ~/dat
cd ~/dat
npm install .
sudo npm link

To update an existing copy of dat

cd ~/dat
git pull
rm -Rf node_modules
npm install .

Installing `rdat`

Then install the R package:

library(devtools)
install_github("ropensci/rdat")

Run through the examples to verify that everything works:

library(rdat)
example(dat)

API

This api is experimental and hasn't been finalized or implemented. Stay tuned for updates

init

When no remote is specified, dat() will init a new repository:

repo <- dat("cars", path = getwd())

insert

Inserts data from a data frame and gets the dat version key

# insert some data
repo$insert(cars[1:20,])
v1 <- repo$status()$version
v1

Inserts more data, get a new version key

# insert more data
repo$insert(cars[21:25,])
v2 <- repo$status()$version
v2

get

Retreive particular versions of the dataset from the key.

data1 <- repo$get(v1)
data2 <- repo$get(v2)

diff

List changes in between versions

diff <- repo$diff(v1, v2)
diff$key

branching

Fork a dataset from a particular version into a new branch.

# create fork
repo$checkout(v1)
repo$insert(cars[40:42,])
repo$forks()
v3 <- repo$status()$version

checkout

Checkout the data at a particular version.

# go back to v2
repo$checkout(v2)
repo$get()

binary data

Save binary data (files) as attachements to the dataset.

# store binary attachements
repo$write(serialize(iris, NULL), "iris")
unserialize(repo$read("iris"))

clone

# Create another repo
dir.create(newdir <- tempfile())
repo2 <- dat("cars", path = newdir, remote = repo$path())
repo2$forks()
repo2$get()

Specifying a remote (path or url) to clone an existing repo. In this case we clone the previous repo into a new location.

push and pull

Lets make yet another clone of our original repository

# Create a third repo
dir.create(newdir <- tempfile())
repo3 <- dat("cars", path = newdir, remote = repo$path())

Add data in repo2 and then push it back to repo1.

# Add some data and push to origin
repo2$insert(cars[31:40,])
repo2$push()

Then pull data back into repo3.

# sync data with origin
repo3$pull()

# Verify that repositories are in sync
mydata2 <- repo2$get()
mydata3 <- repo3$get()
all.equal(mydata2, mydata3)

karthik/rdat