📚 Tutorial: The R Infrastructure

The Wayback Machine - https://web.archive.org/web/20260202230646/https://jeroen.github.io/uros2018/

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# The R Infrastructure
## How we build stuff
### Jeroen Ooms
### 2018/09/14

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# Infrastructure is never done...

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# Hello World

About me: PhD Statistics UCLA 2014 (Jan de Leeuw, Mark Hansen). Currently I am postdoc at UC Berkeley with the [rOpenSci](https://ropensci.org/) group.
![team](team.png)

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# The rOpenSci (extended) Family

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# CRAN Packages

[![pkgscreen](packages2.png)](https://cran.r-project.org/web/checks/check_results_jeroen_at_berkeley.edu.html)

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# Also this

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# PART I: Base Infrastructure

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# Base Dependencies

To the R user, the dependency system looks mostly like this:

![diagram](diagram1.png)

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# Base Dependencies

However R itself also depends on other software:

![depends](depends.png)

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# Base Dependencies

So the reality is more like this:

![diagram2](diagram2.png)

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# What are all these libraries used for?

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# BLAS / LAPACK: Linear Algebra

Most statistical methods involve matrix calculations (QR, Cholesky SVD, etc). R uses high performance BLAS / LAPACK routines for linear algebra.
![blas](blas.png)

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# BLAS / LAPACK: Linear Algebra

For example when R calculates
`${\displaystyle {\hat {\boldsymbol {\beta }}}=(\mathbf {X} ^{\mathsf {T}}\mathbf {X} )^{-1}\mathbf {X} ^{\mathsf {T}}\mathbf {y} ,}$`

```r
#  define X matrix and y vector
X <- as.matrix(cbind(1,cars$speed))
y <- as.matrix(cars$dist)
solve(t(X) %*% X) %*% t(X) %*% y
```

```
           [,1]
[1,] -17.579095
[2,]   3.932409
```

```r
# As done by lm()
coef(lm(dist~speed, data = cars))
```

```
(Intercept)       speed 
 -17.579095    3.932409 
```

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# LIBCURL: Networking

R uses libcurl for downloading files over FTP/HTTP/HTTPS. This functionality is used in e.g. `download.file()` and `install.packages()`.
![libcurl](libcurl.png)

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# LIBCURL: Networking

Note that https uses an SSL connection, which requires encryption support.

```r
install.packages("MASS", repos = 'https://cloud.r-project.org')
```
```
trying URL 'https://cloud.r-project.org/bin/macosx/el-capitan/contrib/3.5/MASS_7.3-50.tgz'
Content type 'application/x-gzip' length 1163764 bytes (1.1 MB)
==================================================
downloaded 1.1 MB
```

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# LIBICU: Text and Encoding

ICU (International Components for Unicode) is a used for converting text encoding and string comparision. 
![icu](icu.png)

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# PCRE: Regular Expressions

R exposes several regular expression functions such as `grep` `regexpr`, but also uses regular expressions internally.
![pcre](pcre.png)

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# LIBICU: Text and Encoding

This should yield the same results as in other languages:

```r
validate_ip_address <- function(x){
  ip_addr_rexex <- "\\b(?:\\d{1,3}\\.){3}\\d{1,3}\\b"
  grepl(ip_addr_rexex, x)
}
validate_ip_address(c("127.0.0.1", "1.1.1.1", "1000.1.1.1"))
```

```
[1]  TRUE  TRUE FALSE
```

And results need to be consistent across platforms and locales:

```r
authors <- c("Hadley Wickham", "Gábor Csárdi", "谢益辉")
enc2native("Gábor Csárdi") == authors
```

```
[1] FALSE  TRUE FALSE
```

```r
grep("益", authors, value = TRUE)
```

```
[1] "谢益辉"
```

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# CAIRO: graphical rendering

Cairo is used to render graphics, i.e. to convert the shapes, attributes and text from the R graphics device into a bitmap image.
![cairo](cairo.png)

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# FONTCONFIG: (via cairo) Finding Fonts

Want your plot axis to show sans-serif italic labels? Fontconfig finds the appropriate font that is available on your system.
![freetype](fontconfig.png)

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# FREETYPE: (via cairo) Rendering Text

Freetype then combines the text with font data (font, size, style) to render the actual figures (glyph image) that form the readable characters in your graphic.
![freetype](freetype.png)

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# LIBPNG, LIBJPEG, LIBTIFF

A bitmap is merely a matrix of pixels. Additional libraries are needed to export the bitmap to various image formats that other software will understand.
![libpng](libpng.png)

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# CAIRO, FREETYPE, FONTCONFIG, LIBPNG

Think for a second about the calculations required to determine the color of each pixel in a bitmap image based on a few simple simple shapes.

```r
plot.new()
plot.window(xlim = c(0, 100), ylim = c(0, 100))
polygon(c(10, 40, 80), c(10, 80, 40), col = 'hotpink')
text(40, 90, labels = 'My drawing', col = 'navyblue', cex = 3, family = "Times")
symbols(c(70, 80, 90), c(20, 50, 80), circles = c(10, 20, 10),
        bg = c('yellow', 'orange', 'red'), add = TRUE, lty = 'dashed')
```

![](index_files/figure-html/unnamed-chunk-5-1.png)

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# Why External Libraries

R relies on external libraries to do the heavy lifting. This is great because these libs are:

- Widely used
 
 - Portable (work on all systems)
 
 - Performant
 
 - Thoroughly tested
 
 - Well maintained
 
 - Free

It would be impossible to implement all this functionality ourselves in R.

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# I don't remember installing these things?

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# Static vs Dynamic Linking

The way these libraries are installed depends on your operating system.

On operating systems that install R via a package manager (Linux, Homebrew), R dynamically links to the shared libraries. The package manager automatically installs the dependencies when the user installs R.

<table>
  <tr>
    <th></th>
    <th>Native Compiler</th>
    <th>Native Package Manager</th>
    <th>Linking</th>
  </tr>
  <tr>
    <th>Linux</th>
    <td>yes</td>
    <td>yes</td>
    <td>dynamic</td>
  </tr>
  <tr>
    <th>MacOS</th>
    <td>yes</td>
    <td>no</td>
    <td>static</td>
    <td></td>
  </tr>
  <tr>
    <th>Windows</th>
    <td>no</td>
    <td>no</td>
    <td>static</td>
  </tr>
</table>

On systems that do not have a native package manager (Windows, MacOS), we have to statically link the libs into the R binaries that we ship in the installer.

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# Dynamic Linking

![apt1](apt1.png)

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# Dynamic Linking

![apt2](apt2.png)

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# Static Linking

With static linking, external libraries get embedded into the binaries (`R.dll` in this case).

![rbig](rbig.png)

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# Building R for Windows

Windows does not have a native compiler nor package manager. To build R for Windows:

1. Install build environment with compiler (e.g. rtools)

2. Build required libraries with this compiler

3. Build base R with this compiler and static link to the libs

4. To build R packages, we need same compiler, same R, and same libs.

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# Building R for Windows

The scripts and libs used to build base R for Windows and the installer are open source:
![base1](base1.png)

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# Building R for Windows

The readme explains how you can build R locally. Or you can look at the script.
![base2](base2.png)

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# Building R for Windows

The script runs every night on appveyor and on success, the installers get uploaded to CRAN.
![base3](base3.png)

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# PART II: CRAN and Scalability

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# Building Packages

Just like R itself, many R packages take advantage of external libraries. A few of the older CRAN packages that use external libraries:

<table>
  <tr>
    <th>R Package</th>
    <th>Required libs</th>
    <th>CRAN release</th>
  </tr>
  <tr>
    <td>RMySQL</td>
    <td>libmysqlclient</td>
    <td>2000</td>
  </tr>
  <tr>
    <td>XML</td>
    <td>libxm2</td>
    <td>2000</td>
  </tr>
  <tr>
    <td>RCurl</td>
    <td>libcurl</td>
    <td>2004</td>
  </tr>    
  <tr>
    <td>gmp</td>
    <td>gmp</td>
    <td>2004</td>
  </tr>
  <tr>
    <td>Rmpfr</td>
    <td>libmpfr</td>
    <td>2009</td>
  </tr>  
</table>

On Linux, the user has to install the required libraries manually when installing the R package.

For Windows and MacOS, CRAN (or any other repo) can build so called binary packages that include the __statically linked external library__, just like we did for base R.

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# Building Packages

Statically linked binary packages make installing R packages easy on Windows / MacOS: 
![xml2](xml2.png)

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# Building Packages

But: it is not trivial to make this work.

Somebody has to build (and occasionally update) the library and it's dependencies using the same compiler and flags for static linking with the R package.

Building even a single library can be a lot of work. Unfortunately these libraries are also becoming increasingly complex and interdependent.

![fortran](fortran.jpg)

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# CRAN growing

On top of that, the number of packages on CRAN is growing rapidly:

![cran](cran2.png)

Many of packages require one or more external libraries...

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# The rwinlib Organization

The Github organization 'rwinlib' is an archive of static libraries for libs used by numerous CRAN packages that were built with Rtools on Windows.

![rwinlib](rwinlib.png)

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# Database Drivers

Most databases have specialized clients libraries:

<table>
  <tr>
    <th>R Package</th>
    <th>Required libs</th>
  </tr>
  <tr>
    <td>RMySQL, RMariaDB</td>
    <td>libmariadb + openssl</td>
  </tr>
  <tr>
    <td>RPostGres, RPostgreSQL</td>
    <td>libpq + openssl</td>
  </tr>
  <tr>
    <td>RODBC, odbc</td>
    <td>unixodbc</td>
  <tr>
    <td>redux, rredis, RcppRedis</td>
    <td>hiredis</td>
  </tr>       
  </tr>
  <tr>
    <td>mongolite</td>
    <td>mongo-c-driver + openssl + libsasl</td>
  </tr>    
</table>

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# GDAL: spatial abstraction library

A complex example is GDAL (Geospatial Data Abstraction Library) that can read and write 100+ different spatial data formats (think maps and sattelite images).
![gdal](gdal.png)

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# GDAL: spatial abstraction library

The current rwinlib GDAL2 stack depends on no less than 35 additional driver libraries! It is used to build the R binary packages for `sf`, `rgdal`, and `rgeos` on Windows.
![gdallibs](gdallibs.png)

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# GDAL: spatial abstraction library

Package authors sometimes request extra features from the libraries:
![gdaledzer](gdaledzer.png)

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# GDAL: spatial abstraction library

And now R users on Windows can access open access EU sattelite images in `sf` and `rgdal`!
![tweet](tweet.png)

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# Imaging, Graphics, and Vision

Another example: At rOpenSci we are developing on a suite of packages to expose high quality images libraries in R across various applications and fields:

- Spatial (as seen before)
 - Medical (MRI)
 - Graphics and post processing
 - Vision
 - OCR
 - Animation and Video
 - Rendering pdf, svg

All of these tools use high quality open source libraries. We provide the R interfaces.

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# OCR (TESSERACT)

```r
library(magick)
image_read("https://jeroen.github.io/images/receipt.png") %>% 
  image_resize('50%')
```

```r
library(tesseract)
numbers <- tesseract(options = list(tessedit_char_whitelist = "$.0123456789"))
text <- ocr("https://jeroen.github.io/images/receipt.png", engine = numbers)
cat(text)
```

```
$90.52
$81.52
$9.00
$90.52
```

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# Vision (OPENCV)

OpenCV has built-in filters for detecting human shapes...

![vision1](opencv1.jpg)

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# Vision (OPENCV)

Or faces:

![vision2](opencv2.jpg)

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# Animated Graphics

```r
library(gganimate)
p <- ggplot(airquality, aes(Day, Temp)) + 
  geom_line(size = 2, colour = 'steelblue') + 
  transition_states(Month, 4, 1) + 
  shadow_mark(size = 1, colour = 'grey')
animate(p, fps = 25, width = 800, height = 350)
```

![](index_files/figure-html/unnamed-chunk-8-1.gif)

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# PART III: Ongoing Developments

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# Infrastructural Work

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# RTOOLS 40

We are currently beta-testing a new version of Rtools that includes a full build environment and package manger.

![rtools40](rtools40.png)

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# RTOOLS 40

This will make it easier to build, distribute and install external libraries on Windows.
![rtools-packages](rtools-packages.png)

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# RTOOLS 40

The system will also it possible to automate building libs on AppVeyor. This makes things more transparent, maintainable and reproducible.
![rtoolsav](rtoolsav.png)

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# RTOOLS 40

A beta version of rtools 40 and a version of R that has been configured for rtools40 is available from CRAN: https://cloud.r-project.org/bin/windows/testing/rtools40.html
![rtoolscran](rtoolscran.png)

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# RHUB

R-hub is a service for building and checking R packages. Part of the project is indexing the system requirements (including libraries) for R packages, and expose this via an API:
![sysreqdb](sysreqdb.png)

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# RHUB

R-hub uses this to automatically install the correct software and libraries on each of the supported operating systems, before building the R package.
![rhubbuilders](rhubbuilders.png)

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# VISION

The overal idea is to create an infrastructure that can support the increasingly complex R packages with powerful system libraries, while reducing the maintenance work for the repository maintainers.

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# Artist Impression

Jan	FEB	Mar
	02
2025	2026	2027