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https://github.com/privefl/bigstatsr
R package for statistical tools with big matrices stored on disk.
https://github.com/privefl/bigstatsr
big-data large-matrices memory-mapped-file parallel-computing r r-package statistical-methods
Last synced: about 2 months ago
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R package for statistical tools with big matrices stored on disk.
- Host: GitHub
- URL: https://github.com/privefl/bigstatsr
- Owner: privefl
- Created: 2016-09-15T06:50:50.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2024-03-24T14:10:50.000Z (6 months ago)
- Last Synced: 2024-07-19T22:47:34.654Z (2 months ago)
- Topics: big-data, large-matrices, memory-mapped-file, parallel-computing, r, r-package, statistical-methods
- Language: R
- Homepage: https://privefl.github.io/bigstatsr/
- Size: 34.9 MB
- Stars: 179
- Watchers: 4
- Forks: 30
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- jimsghstars - privefl/bigstatsr - R package for statistical tools with big matrices stored on disk. (R)
README
[](https://github.com/privefl/bigstatsr/actions)
[](https://app.codecov.io/gh/privefl/bigstatsr?branch=master)
[](https://cran.r-project.org/package=bigstatsr)
[](https://doi.org/10.1093/bioinformatics/bty185)
# bigstatsr
R package {bigstatsr} provides functions for fast statistical analysis of large-scale data encoded as matrices. The package can handle matrices that are too large to fit in memory thanks to memory-mapping to binary files on disk. This is very similar to the format `big.matrix` provided by [R package {bigmemory}](https://github.com/kaneplusplus/bigmemory), which is **no longer used** by this package (see [the corresponding vignette](https://privefl.github.io/bigstatsr/articles/bigstatsr-and-bigmemory.html)).
As inputs, package {bigstatsr} uses [Filebacked Big Matrices (FBM)](https://privefl.github.io/bigstatsr/reference/FBM-class.html).
[**LIST OF FEATURES**](https://privefl.github.io/bigstatsr/reference/index.html)
**Note that most of the algorithms of this package don't handle missing values.**
## Installation
```r
# For the CRAN version
install.packages("bigstatsr")
# For the latest version
remotes::install_github("privefl/bigstatsr")
```
## Small example
```r
library(bigstatsr)
# Create the data on disk
X <- FBM(5e3, 10e3, backingfile = "test")$save()
# If you open a new session you can do
X <- big_attach("test.rds")
# Fill it by chunks with random values
U <- matrix(0, nrow(X), 5); U[] <- rnorm(length(U))
V <- matrix(0, ncol(X), 5); V[] <- rnorm(length(V))
NCORES <- nb_cores()
# X = U V^T + E
big_apply(X, a.FUN = function(X, ind, U, V) {
X[, ind] <- tcrossprod(U, V[ind, ]) + rnorm(nrow(X) * length(ind))
NULL ## you don't want to return anything here
}, a.combine = 'c', ncores = NCORES, U = U, V = V)
# Check some values
X[1:5, 1:5]
# Compute first 10 PCs
obj.svd <- big_randomSVD(X, fun.scaling = big_scale(),
k = 10, ncores = NCORES)
plot(obj.svd)
# Cleanup
unlink(paste0("test", c(".bk", ".rds")))
```
Learn more with this
[introduction to package {bigstatsr}](https://privefl.github.io/R-presentation/bigstatsr.html).
If you want to use Rcpp code, look at [this tutorial](https://privefl.github.io/R-presentation/Rcpp.html).
## Some use cases
### Parallelization
Package {bigstatsr} uses package {foreach} for its parallelization tasks. Learn more on parallelism with {foreach} with [this tutorial](https://privefl.github.io/blog/a-guide-to-parallelism-in-r/).
- [Permute matrix columns in parallel](https://stackoverflow.com/q/48832010/6103040)
- [Parallelized search until found](https://stackoverflow.com/q/49056271/6103040)
### Large datasets
- [Computing the null space of a big matrix](https://stackoverflow.com/questions/46253537/computing-the-null-space-of-a-bigmatrix-in-r/) (works if one dimension is not too large)
- [Rowwise matrix multiplication](https://stackoverflow.com/q/48879643/6103040)
- [Operating with a big.matrix](https://stackoverflow.com/q/42111876/6103040)
## Bug report / Help
[How to make a great R reproducible example?](https://stackoverflow.com/q/5963269/6103040)
Please open an issue if you find a bug.
If you want help using {bigstatsr}, please open an issue as well or post on Stack Overflow with the tag *bigstatsr*.
I will always redirect you to GitHub issues if you email me, so that others can benefit from our discussion.
## References
- Privé, Florian, et al. "Efficient analysis of large-scale genome-wide data with two R packages: bigstatsr and bigsnpr." Bioinformatics 34.16 (2018): 2781-2787.
- Privé, Florian, Hugues Aschard, and Michael GB Blum. "Efficient implementation of penalized regression for genetic risk prediction." Genetics 212.1 (2019): 65-74.