README.md
In const-ae/sparseMatrixStats: Summary Statistics for Rows and Columns of Sparse Matrices
sparseMatrixStats 
The goal of sparseMatrixStats is to make the API of
matrixStats available
for sparse matrices.
Installation
You can install the release version of
sparseMatrixStats
from BioConductor:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("sparseMatrixStats")
Alternatively, you can get the development version of the package from
GitHub with:
# install.packages("devtools")
devtools::install_github("const-ae/sparseMatrixStats")
If you have trouble with the installation, see the end of the README.
Example
library(sparseMatrixStats)
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#>
#> Attaching package: 'MatrixGenerics'
#> The following objects are masked from 'package:matrixStats':
#>
#> colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#> colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#> colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#> colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#> colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#> colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#> colWeightedMeans, colWeightedMedians, colWeightedSds,
#> colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#> rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#> rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#> rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#> rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#> rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#> rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#> rowWeightedSds, rowWeightedVars
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
#> 10 x 6 sparse Matrix of class "dgCMatrix"
#>
#> [1,] 4 . . . . .
#> [2,] . . . . . .
#> [3,] . . . . . .
#> [4,] 2 . . . . .
#> [5,] . . . . . .
#> [6,] . . . . . .
#> [7,] . . . . . 1
#> [8,] . . . . . .
#> [9,] . . . 3 . .
#> [10,] . . . . . .
The package provides an interface to quickly do common operations on the
rows or columns. For example calculate the variance:
apply(mat, 2, var)
#> [1] 1.822222 0.000000 0.000000 0.900000 0.000000 0.100000
matrixStats::colVars(mat)
#> [1] 1.822222 0.000000 0.000000 0.900000 0.000000 0.100000
sparseMatrixStats::colVars(sparse_mat)
#> [1] 1.822222 0.000000 0.000000 0.900000 0.000000 0.100000
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
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:
bench::mark(
sparseMatrixStats=sparseMatrixStats::colVars(big_sparse_mat),
matrixStats=matrixStats::colVars(big_mat),
apply=apply(big_mat, 2, var)
)
#> # A tibble: 3 x 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 sparseMatrixStats 37.3µs 42.71µs 20836. 2.93KB 14.6
#> 2 matrixStats 1.48ms 1.65ms 584. 156.8KB 2.03
#> 3 apply 10.61ms 11.18ms 88.9 9.54MB 48.2
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 it
can be easily integrated along-side
matrixStats and
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().
| Method | matrixStats | sparseMatrixStats | Notes |
| :------------------- | :---------- | :---------------- | :--------------------------------------------------------------------------------------- |
| colAlls() | ✔ | ✔ | |
| colAnyMissings() | ✔ | ❌ | Not implemented because it is deprecated in favor of colAnyNAs() |
| colAnyNAs() | ✔ | ✔ | |
| colAnys() | ✔ | ✔ | |
| colAvgsPerRowSet() | ✔ | ✔ | |
| colCollapse() | ✔ | ✔ | |
| colCounts() | ✔ | ✔ | |
| colCummaxs() | ✔ | ✔ | |
| colCummins() | ✔ | ✔ | |
| colCumprods() | ✔ | ✔ | |
| colCumsums() | ✔ | ✔ | |
| colDiffs() | ✔ | ✔ | |
| colIQRDiffs() | ✔ | ✔ | |
| colIQRs() | ✔ | ✔ | |
| colLogSumExps() | ✔ | ✔ | |
| colMadDiffs() | ✔ | ✔ | |
| colMads() | ✔ | ✔ | |
| colMaxs() | ✔ | ✔ | |
| colMeans2() | ✔ | ✔ | |
| colMedians() | ✔ | ✔ | |
| colMins() | ✔ | ✔ | |
| colOrderStats() | ✔ | ✔ | |
| colProds() | ✔ | ✔ | |
| colQuantiles() | ✔ | ✔ | |
| colRanges() | ✔ | ✔ | |
| colRanks() | ✔ | ✔ | |
| colSdDiffs() | ✔ | ✔ | |
| colSds() | ✔ | ✔ | |
| colsum() | ✔ | ❌ | Base R function |
| colSums2() | ✔ | ✔ | |
| colTabulates() | ✔ | ✔ | |
| colVarDiffs() | ✔ | ✔ | |
| colVars() | ✔ | ✔ | |
| colWeightedMads() | ✔ | ✔ | Sparse version behaves slightly differently, because it always uses interpolate=FALSE. |
| colWeightedMeans() | ✔ | ✔ | |
| colWeightedMedians() | ✔ | ✔ | Only equivalent if interpolate=FALSE |
| colWeightedSds() | ✔ | ✔ | |
| colWeightedVars() | ✔ | ✔ | |
| rowAlls() | ✔ | ✔ | |
| rowAnyMissings() | ✔ | ❌ | Not implemented because it is deprecated in favor of rowAnyNAs() |
| rowAnyNAs() | ✔ | ✔ | |
| rowAnys() | ✔ | ✔ | |
| rowAvgsPerColSet() | ✔ | ✔ | |
| rowCollapse() | ✔ | ✔ | |
| rowCounts() | ✔ | ✔ | |
| rowCummaxs() | ✔ | ✔ | |
| rowCummins() | ✔ | ✔ | |
| rowCumprods() | ✔ | ✔ | |
| rowCumsums() | ✔ | ✔ | |
| rowDiffs() | ✔ | ✔ | |
| rowIQRDiffs() | ✔ | ✔ | |
| rowIQRs() | ✔ | ✔ | |
| rowLogSumExps() | ✔ | ✔ | |
| rowMadDiffs() | ✔ | ✔ | |
| rowMads() | ✔ | ✔ | |
| rowMaxs() | ✔ | ✔ | |
| rowMeans2() | ✔ | ✔ | |
| rowMedians() | ✔ | ✔ | |
| rowMins() | ✔ | ✔ | |
| rowOrderStats() | ✔ | ✔ | |
| rowProds() | ✔ | ✔ | |
| rowQuantiles() | ✔ | ✔ | |
| rowRanges() | ✔ | ✔ | |
| rowRanks() | ✔ | ✔ | |
| rowSdDiffs() | ✔ | ✔ | |
| rowSds() | ✔ | ✔ | |
| rowsum() | ✔ | ❌ | Base R function |
| rowSums2() | ✔ | ✔ | |
| rowTabulates() | ✔ | ✔ | |
| rowVarDiffs() | ✔ | ✔ | |
| rowVars() | ✔ | ✔ | |
| rowWeightedMads() | ✔ | ✔ | Sparse version behaves slightly differently, because it always uses interpolate=FALSE. |
| rowWeightedMeans() | ✔ | ✔ | |
| rowWeightedMedians() | ✔ | ✔ | Only equivalent if interpolate=FALSE |
| rowWeightedSds() | ✔ | ✔ | |
| rowWeightedVars() | ✔ | ✔ | |
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 3.4gcc version 4.9
Accordingly, you must have a compiler available that is at least that
new. If you run on the command line
$ 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
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.
First, we enable the Software Collection Tools and then install for
example gcc version 7:
$ yum install centos-release-scl
$ yum install devtoolset-7-gcc*
We can now either activate the new compiler for an R session
$ scl enable devtoolset-7 R
and then call
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
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
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).
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:
$ 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
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:
BiocManager::install("sparseMatrixStats")
Windows
It is important that you have
RTools40 installed.
After that, you shouldn’t have any troubles installing
sparseMatrixStats directly from Bioconductor:
BiocManager::install("sparseMatrixStats")
But I still have a problems
- Please make sure to carefully read the full problem section.
- Make sure that you are using at least R 4.0.0.
- 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 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
const-ae/sparseMatrixStats documentation built on June 15, 2024, 9:36 a.m.
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sparseMatrixStats
The goal of sparseMatrixStats is to make the API of
matrixStats available
for sparse matrices.
Installation
You can install the release version of sparseMatrixStats from BioConductor:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("sparseMatrixStats")
Alternatively, you can get the development version of the package from GitHub with:
# install.packages("devtools")
devtools::install_github("const-ae/sparseMatrixStats")
If you have trouble with the installation, see the end of the README.
Example
library(sparseMatrixStats)
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#>
#> Attaching package: 'MatrixGenerics'
#> The following objects are masked from 'package:matrixStats':
#>
#> colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#> colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#> colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#> colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#> colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#> colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#> colWeightedMeans, colWeightedMedians, colWeightedSds,
#> colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#> rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#> rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#> rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#> rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#> rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#> rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#> rowWeightedSds, rowWeightedVars
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
#> 10 x 6 sparse Matrix of class "dgCMatrix"
#>
#> [1,] 4 . . . . .
#> [2,] . . . . . .
#> [3,] . . . . . .
#> [4,] 2 . . . . .
#> [5,] . . . . . .
#> [6,] . . . . . .
#> [7,] . . . . . 1
#> [8,] . . . . . .
#> [9,] . . . 3 . .
#> [10,] . . . . . .
The package provides an interface to quickly do common operations on the rows or columns. For example calculate the variance:
apply(mat, 2, var)
#> [1] 1.822222 0.000000 0.000000 0.900000 0.000000 0.100000
matrixStats::colVars(mat)
#> [1] 1.822222 0.000000 0.000000 0.900000 0.000000 0.100000
sparseMatrixStats::colVars(sparse_mat)
#> [1] 1.822222 0.000000 0.000000 0.900000 0.000000 0.100000
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
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:
bench::mark(
sparseMatrixStats=sparseMatrixStats::colVars(big_sparse_mat),
matrixStats=matrixStats::colVars(big_mat),
apply=apply(big_mat, 2, var)
)
#> # A tibble: 3 x 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 sparseMatrixStats 37.3µs 42.71µs 20836. 2.93KB 14.6
#> 2 matrixStats 1.48ms 1.65ms 584. 156.8KB 2.03
#> 3 apply 10.61ms 11.18ms 88.9 9.54MB 48.2
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 it
can be easily integrated along-side
matrixStats and
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().
| Method | matrixStats | sparseMatrixStats | Notes |
| :------------------- | :---------- | :---------------- | :--------------------------------------------------------------------------------------- |
| colAlls() | ✔ | ✔ | |
| colAnyMissings() | ✔ | ❌ | Not implemented because it is deprecated in favor of colAnyNAs() |
| colAnyNAs() | ✔ | ✔ | |
| colAnys() | ✔ | ✔ | |
| colAvgsPerRowSet() | ✔ | ✔ | |
| colCollapse() | ✔ | ✔ | |
| colCounts() | ✔ | ✔ | |
| colCummaxs() | ✔ | ✔ | |
| colCummins() | ✔ | ✔ | |
| colCumprods() | ✔ | ✔ | |
| colCumsums() | ✔ | ✔ | |
| colDiffs() | ✔ | ✔ | |
| colIQRDiffs() | ✔ | ✔ | |
| colIQRs() | ✔ | ✔ | |
| colLogSumExps() | ✔ | ✔ | |
| colMadDiffs() | ✔ | ✔ | |
| colMads() | ✔ | ✔ | |
| colMaxs() | ✔ | ✔ | |
| colMeans2() | ✔ | ✔ | |
| colMedians() | ✔ | ✔ | |
| colMins() | ✔ | ✔ | |
| colOrderStats() | ✔ | ✔ | |
| colProds() | ✔ | ✔ | |
| colQuantiles() | ✔ | ✔ | |
| colRanges() | ✔ | ✔ | |
| colRanks() | ✔ | ✔ | |
| colSdDiffs() | ✔ | ✔ | |
| colSds() | ✔ | ✔ | |
| colsum() | ✔ | ❌ | Base R function |
| colSums2() | ✔ | ✔ | |
| colTabulates() | ✔ | ✔ | |
| colVarDiffs() | ✔ | ✔ | |
| colVars() | ✔ | ✔ | |
| colWeightedMads() | ✔ | ✔ | Sparse version behaves slightly differently, because it always uses interpolate=FALSE. |
| colWeightedMeans() | ✔ | ✔ | |
| colWeightedMedians() | ✔ | ✔ | Only equivalent if interpolate=FALSE |
| colWeightedSds() | ✔ | ✔ | |
| colWeightedVars() | ✔ | ✔ | |
| rowAlls() | ✔ | ✔ | |
| rowAnyMissings() | ✔ | ❌ | Not implemented because it is deprecated in favor of rowAnyNAs() |
| rowAnyNAs() | ✔ | ✔ | |
| rowAnys() | ✔ | ✔ | |
| rowAvgsPerColSet() | ✔ | ✔ | |
| rowCollapse() | ✔ | ✔ | |
| rowCounts() | ✔ | ✔ | |
| rowCummaxs() | ✔ | ✔ | |
| rowCummins() | ✔ | ✔ | |
| rowCumprods() | ✔ | ✔ | |
| rowCumsums() | ✔ | ✔ | |
| rowDiffs() | ✔ | ✔ | |
| rowIQRDiffs() | ✔ | ✔ | |
| rowIQRs() | ✔ | ✔ | |
| rowLogSumExps() | ✔ | ✔ | |
| rowMadDiffs() | ✔ | ✔ | |
| rowMads() | ✔ | ✔ | |
| rowMaxs() | ✔ | ✔ | |
| rowMeans2() | ✔ | ✔ | |
| rowMedians() | ✔ | ✔ | |
| rowMins() | ✔ | ✔ | |
| rowOrderStats() | ✔ | ✔ | |
| rowProds() | ✔ | ✔ | |
| rowQuantiles() | ✔ | ✔ | |
| rowRanges() | ✔ | ✔ | |
| rowRanks() | ✔ | ✔ | |
| rowSdDiffs() | ✔ | ✔ | |
| rowSds() | ✔ | ✔ | |
| rowsum() | ✔ | ❌ | Base R function |
| rowSums2() | ✔ | ✔ | |
| rowTabulates() | ✔ | ✔ | |
| rowVarDiffs() | ✔ | ✔ | |
| rowVars() | ✔ | ✔ | |
| rowWeightedMads() | ✔ | ✔ | Sparse version behaves slightly differently, because it always uses interpolate=FALSE. |
| rowWeightedMeans() | ✔ | ✔ | |
| rowWeightedMedians() | ✔ | ✔ | Only equivalent if interpolate=FALSE |
| rowWeightedSds() | ✔ | ✔ | |
| rowWeightedVars() | ✔ | ✔ | |
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
clangversion 3.4gccversion 4.9
Accordingly, you must have a compiler available that is at least that new. If you run on the command line
$ 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
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.
First, we enable the Software Collection Tools and then install for
example gcc version 7:
$ yum install centos-release-scl
$ yum install devtoolset-7-gcc*
We can now either activate the new compiler for an R session
$ scl enable devtoolset-7 R
and then call
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
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
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).
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:
$ 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
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:
BiocManager::install("sparseMatrixStats")
Windows
It is important that you have
RTools40 installed.
After that, you shouldn’t have any troubles installing
sparseMatrixStats directly from Bioconductor:
BiocManager::install("sparseMatrixStats")
But I still have a problems
- Please make sure to carefully read the full problem section.
- Make sure that you are using at least R 4.0.0.
- Make sure your compiler is new enough to support C++14 (ie.
gcc>= 4.9 andclang>= 3.4)
If your problems nonetheless persist, please file an issue 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/Makevarsfile and what it contains - The exact call that you use to install
sparseMatrixStatsincluding the full error message
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