Please visit the git2rdata website at https://ropensci.github.io/git2rdata/. The vignette code on the website link to a rendered version of the vignette. Functions have a link to their help file.
The git2rdata
package is an R package for writing and reading dataframes as plain text files.
A metadata file stores important information.
- Storing metadata allows to maintain the classes of variables.
By default,
git2rdata
optimizes the data for file storage. The optimization is most effective on data containing factors. The optimization makes the data less human readable. The user can turn this off when they prefer a human readable format over smaller files. Details on the implementation are available invignette("plain_text", package = "git2rdata")
. - Storing metadata also allows smaller row based diffs between two consecutive commits.
This is a useful feature when storing data as plain text files under version control.
Details on this part of the implementation are available in
vignette("version_control", package = "git2rdata")
. Although we envisionedgit2rdata
with a git workflow in mind, you can use it in combination with other version control systems like subversion or mercurial. git2rdata
is a useful tool in a reproducible and traceable workflow.vignette("workflow", package = "git2rdata")
gives a toy example.vignette("efficiency", package = "git2rdata")
provides some insight into the efficiency of file storage, git repository size and speed for writing and reading.
- You can store dataframes as plain text files.
- The dataframe you read identical information content as the one you wrote.
- No changes in data type.
- Factors keep their original levels, including their order.
- Date and date-time format are unambiguous, documented in the metadata.
- The data and the metadata are in a standard and open format, making it readable by other software.
git2rdata
checks the data and metadata during the reading.read_vc()
informs the user if there is tampering with the data or metadata.- Git2rdata integrates with the
git2r
package for working with git repository from R.- Another option is using git2rdata solely for writing to disk and handle the plain text files with your favourite version control system outside of R.
- The optimization reduces the required disk space by about 30% for both the working directory and the git history.
- Reading data from a HDD is 30% faster than
read.table()
, writing to a HDD takes about 70% more time thanwrite.table()
. - Git2rdata is useful as a tool in a reproducible and traceable workflow.
See
vignette("workflow", package = "git2rdata")
. - You can detect when a file was last modified in the git history. Use this to check whether an existing analysis is obsolete due to new data. This allows to not rerun up to date analyses, saving resources.
useR!2019 in Toulouse, France
<iframe width="560" height="315" src="https://www.youtube-nocookie.com/embed/sbRPmakBFqo" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>Install from CRAN
install.packages("git2rdata")
Install the development version from GitHub
# installation requires the "remotes" package
# install.package("remotes")
# install with vignettes (recommended)
remotes::install_github(
"ropensci/git2rdata",
build = TRUE,
dependencies = TRUE,
build_opts = c("--no-resave-data", "--no-manual")
)
# install without vignettes
remotes::install_github("ropensci/git2rdata"))
The user stores dataframes with write_vc()
and retrieves them with read_vc()
.
Both functions share the arguments root
and file
.
root
refers to a base location where to store the dataframe.
It can either point to a local directory or a local git repository.
file
is the file name to use and can include a path relative to root
.
Make sure the relative path stays within root
.
# using a local directory
library(git2rdata)
root <- "~/myproject"
write_vc(my_data, file = "rel_path/filename", root = root)
read_vc(file = "rel_path/filename", root = root)
root <- git2r::repository("~/my_git_repo") # git repository
More details on store dataframes as plain text files in vignette("plain_text", package = "git2rdata")
.
# using a git repository
library(git2rdata)
repo <- repository("~/my_git_repo")
pull(repo)
write_vc(my_data, file = "rel_path/filename", root = repo, stage = TRUE)
commit(repo, "My message")
push(repo)
read_vc(file = "rel_path/filename", root = repo)
Please read vignette("version_control", package = "git2rdata")
for more details on using git2rdata in combination with version control.
The recommendation for git repositories is to use files smaller than 100 MiB, a repository size less than 1 GiB and less than 25k files.
The individual file size is the limiting factor.
Storing the airbag dataset (DAAG::nassCDS
) with write_vc()
requires on average 68 (optimized) or 97 (verbose) byte per record.
The file reaches the 100 MiB limit for this data after about 1.5 million (optimized) or 1 million (verbose) observations.
Storing a 90% random subset of the airbag dataset requires 370 kiB (optimized) or 400 kiB (verbose) storage in the git history. Updating the dataset with other 90% random subsets requires on average 60 kiB (optimized) to 100 kiB (verbose) per commit. The git history reaches the limit of 1 GiB after 17k (optimized) to 10k (verbose) commits.
Your mileage might vary.
Please use the output of citation("git2rdata")
R
: The source scripts of the R functions with documentation in Roxygen formatman
: The help files in Rd formatinst/efficiency
: pre-calculated data to speed upvignette("efficiency", package = "git2rdata")
testthat
: R scripts with unit tests using the testthat frameworkvignettes
: source code for the vignettes describing the packageman-roxygen
: templates for documentation in Roxygen formatpkgdown
: source files for thegit2rdata
website.github
: guidelines and templates for contributors
git2rdata
├── .github
├─┬ inst
│ └── efficiency
├── man
├── man-roxygen
├── pkgdown
├── R
├─┬ tests
│ └── testthat
└── vignettes
git2rdata
welcomes contributions.
Please read our Contributing guidelines first.
The git2rdata
project has a Contributor Code of Conduct.
By contributing to this project, you agree to abide by its terms.