README.Rmd

---
output: github_document
---

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```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# sparseMatrixStats <a href='https://github.com/const-ae/sparseMatrixStats'><img src='man/figures/logo.png' align="right" height="209" /></a>

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The goal of `sparseMatrixStats` is to make the API of [matrixStats](https://github.com/HenrikBengtsson/matrixStats) available
for sparse matrices.

## Installation

You can install the release version of *[sparseMatrixStats](https://bioconductor.org/packages/sparseMatrixStats)* from BioConductor:

``` r
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("sparseMatrixStats")
```

Alternatively, you can get the development version of the package from [GitHub](https://github.com/const-ae/sparseMatrixStats) with:

``` r
# install.packages("devtools")
devtools::install_github("const-ae/sparseMatrixStats")
```

If you have trouble with the installation, see the end of the README.

## Example

```{r include=FALSE}
set.seed(1)
```


```{r}
library(sparseMatrixStats)
```


```{r}
mat <- matrix(0, nrow=10, ncol=6)
mat[sample(seq_len(60), 4)] <- 1:4
# Convert dense matrix to sparse matrix
sparse_mat <- as(mat, "dgCMatrix")
sparse_mat
```

The package provides an interface to quickly do common operations on the rows or columns. For example calculate
the variance:

```{r}
apply(mat, 2, var)
matrixStats::colVars(mat)
sparseMatrixStats::colVars(sparse_mat)
```

On this small example data, all methods are basically equally fast, but if we have a much larger dataset, the 
optimizations for the sparse data start to show.

I generate a dataset with 10,000 rows and 50 columns that is 99% empty

```{r}
big_mat <- matrix(0, nrow=1e4, ncol=50)
big_mat[sample(seq_len(1e4 * 50), 5000)] <- rnorm(5000)
# Convert dense matrix to sparse matrix
big_sparse_mat <- as(big_mat, "dgCMatrix")
```

I use the `bench` package to benchmark the performance difference:

```{r}
bench::mark(
  sparseMatrixStats=sparseMatrixStats::colVars(big_sparse_mat),
  matrixStats=matrixStats::colVars(big_mat),
  apply=apply(big_mat, 2, var)
)
```


As you can see `sparseMatrixStats` is ca. 35 times fast than `matrixStats`, which in turn is 7 times faster than the `apply()` version.





# API

The package now supports all functions from the `matrixStats` API for column sparse matrices (`dgCMatrix`). And thanks to the [`MatrixGenerics`](https://bioconductor.org/packages/MatrixGenerics/) it can be easily integrated along-side [`matrixStats`](https://cran.r-project.org/package=matrixStats) and [`DelayedMatrixStats`](https://bioconductor.org/packages/DelayedMatrixStats/).
Note that the `rowXXX()` functions are called by transposing the input and calling the corresponding `colXXX()` function. Special optimized implementations are available for `rowSums2()`, `rowMeans2()`, and `rowVars()`.

```{r, echo=FALSE, results="asis"}
matrixStats_functions <- sort(
  c("colsum", "rowsum", grep("^(col|row)", 
                             getNamespaceExports("matrixStats"), 
                             value = TRUE)))
DelayedMatrixStats_functions <- grep("^(col|row)", getNamespaceExports("DelayedMatrixStats"), value=TRUE)
DelayedArray_functions <- grep("^(col|row)", getNamespaceExports("DelayedArray"), value=TRUE)
sparseMatrixStats_functions <- grep("^(col|row)", getNamespaceExports("sparseMatrixStats"), value=TRUE)

notes <- c("colAnyMissings"="Not implemented because it is deprecated in favor of `colAnyNAs()`",
           "rowAnyMissings"="Not implemented because it is deprecated in favor of `rowAnyNAs()`",
           "colsum"="Base R function",
           "rowsum"="Base R function",
           "colWeightedMedians"="Only equivalent if `interpolate=FALSE`",
           "rowWeightedMedians"="Only equivalent if `interpolate=FALSE`",
           "colWeightedMads"="Sparse version behaves slightly differently, because it always uses `interpolate=FALSE`.",
           "rowWeightedMads"="Sparse version behaves slightly differently, because it always uses `interpolate=FALSE`.")

api_df <- data.frame(
  Method = paste0(matrixStats_functions, "()"),
  matrixStats = ifelse(matrixStats_functions %in% matrixStats_functions, "✔", "❌"),
  sparseMatrixStats = ifelse(matrixStats_functions %in%sparseMatrixStats_functions, "✔", "❌"),
  Notes = ifelse(matrixStats_functions %in% names(notes), notes[matrixStats_functions], ""),
  stringsAsFactors = FALSE
)
knitr::kable(api_df, row.names = FALSE)

```



# Installation Problems

`sparseMatrixStats` uses features from C++14 and as the standard is more than 6 years old, I thought this wouldn't cause problems. In most circumstances this is true, but there are reoccuring reports, that the installation fails for some people and that is of course annoying. The typical error message is:
```
Error: C++14 standard requested but CXX14 is not defined
```

The main reason that the installation fails is that the compiler is too old. Sufficient support for C++14 came in 

* `clang` version 3.4
* `gcc` version 4.9

Accordingly, you must have a compiler available that is at least that new. If you run on the command line

```{bash eval=FALSE, include=TRUE}
$ gcc --version
```

and it says 4.8, you will have to install a newer compiler. At the end of the section, I have collected a few tips to install an appropriate version on different distributions.

If you have recent version of `gcc` (>=4.9) or `clang` (>= 3.4) installed, but you still see the error message
```
Error: C++14 standard requested but CXX14 is not defined
```
the problem is that R doesn't yet know about it.

The solution is to either create a `~/.R/Makevars` file and define
```
CXX14 = g++
CXX14FLAGS = -g -O2 $(LTO)
CXX14PICFLAGS = -fpic
CXX14STD = -std=gnu++14
```
or simply call 
```{r eval=FALSE, include=TRUE}
withr::with_makevars(
 new = c(CXX14 = "g++", CXX14FLAGS = "-g -O2 $(LTO)", 
         CXX14PICFLAGS = "-fpic", CXX14STD = "-std=gnu++14"), 
 code = {
   BiocManager::install("sparseMatrixStats")
 })
```



### Update Compiler


#### CentOS / Scientic Linux / RHEL

One of the main culprits causing trouble is CentOS 7. It is popular in scientific computing and is still supported until 2024. It does, however, by default come with a very old version of `gcc` (4.8.5).

To install a more recent compiler, we can use [devtoolset](https://www.softwarecollections.org/en/scls/rhscl/devtoolset-7/). First, we enable the Software Collection Tools and then install for example `gcc` version 7:

```{bash eval=FALSE}
$ yum install centos-release-scl
$ yum install devtoolset-7-gcc*
```

We can now either activate the new compiler for an R session   
```{bash eval=FALSE}
$ scl enable devtoolset-7 R
```
and then call 
```{r eval=FALSE, include=TRUE}
withr::with_makevars(
 new = c(CXX14 = "g++", CXX14FLAGS = "-g -O2 $(LTO)", 
         CXX14PICFLAGS = "-fpic", CXX14STD = "-std=gnu++14"), 
 code = {
   BiocManager::install("sparseMatrixStats")
 })
```

or we refer to the full path of the newly installed g++ from a standard R session

```{r eval=FALSE, include=TRUE}
withr::with_makevars(
 new = c(CXX14 = "/opt/rh/devtoolset-7/root/usr/bin/g++", 
         CXX14FLAGS = "-g -O2 $(LTO)", CXX14PICFLAGS = "-fpic",
         CXX14STD = "-std=gnu++14"), 
 code = {
   BiocManager::install("sparseMatrixStats")
 })
```

Note, that our shenanigans are only necessary once, when we install `sparseMatrixStats`. After the successful installation of the package, we can use R as usual.


#### Debian

All Debian releases later than Jessie (i.e. Stretch, Buster, Bullseye) are recent enough and should install sparseMatrixStats without problems.

I was able to install `sparseMatrixStats` on Debian Jessie (which comes with `gcc` version 4.9.2) by providing the necessary Makefile arguments
```{r eval=FALSE, include=TRUE}
withr::with_makevars(
 new = c(CXX14 = "g++", 
         CXX14FLAGS = "-g -O2 $(LTO)", CXX14PICFLAGS = "-fpic",
         CXX14STD = "-std=gnu++14"), 
 code = {
   BiocManager::install("sparseMatrixStats")
 })
```

Debian Wheezy comes with `gcc` 4.7, which does not support C++14. On the other hand, the last R release that was backported to Wheezy is 3.2.5 (see information on [CRAN](https://cloud.r-project.org/bin/linux/debian/#debian-wheezy-oldoldoldstable)). Thus, if you are still on Wheezy, I would encourage you to update your OS.


#### Ubuntu

Since 16.04, Ubuntu comes with a recent enough compiler.

Ubuntu 14.04 comes with `gcc` 4.8.5, but updating to `gcc-5` is easy:

```{bash eval=FALSE, include=TRUE}
$ sudo add-apt-repository ppa:ubuntu-toolchain-r/test
$ sudo apt-get update
$ sudo apt-get install gcc-5 g++-5
```
After that, you can install `sparseMatrixStats` with a custom Makevars variables that refer to the new compiler
```{r eval=FALSE, include=TRUE}
withr::with_makevars(
 new = c(CXX14 = "g++-5", 
         CXX14FLAGS = "-g -O2 $(LTO)", CXX14PICFLAGS = "-fpic",
         CXX14STD = "-std=gnu++14"), 
 code = {
   BiocManager::install("sparseMatrixStats")
 })
```

#### MacOS

No trouble reported so far. Just do:

```{r eval=FALSE, include=TRUE}
BiocManager::install("sparseMatrixStats")
```

#### Windows

It is important that you have [RTools40](https://cran.r-project.org/bin/windows/Rtools/) installed. After that, you shouldn't have any troubles installing `sparseMatrixStats` directly from Bioconductor:

```{r eval=FALSE, include=TRUE}
BiocManager::install("sparseMatrixStats")
```


### But I still have a problems

1. *Please* make sure to carefully read the full problem section.
2. Make sure that you are using at least R 4.0.0.
3. Make sure your compiler is new enough to support C++14 (ie. `gcc` >= 4.9 and `clang` >= 3.4)

If your problems nonetheless persist, please file an [issue](https://github.com/const-ae/sparseMatrixStats/issues/) including the following information:

* Operating system with exact version (e.g. 'Linux Ubuntu 18.04')
* Compiler and compiler version (e.g. 'gcc version 7.2.5')
* The output of `sessionInfo()`
* Information if you have a `~/.R/Makevars` file and what it contains
* The exact call that you use to install `sparseMatrixStats` including the full error message