title: "Reproducible Analytical Pipelines - Master's of Data Science"

author: "Bruno Rodrigues"

date: "`r Sys.Date()`"

bibliography: biblio.bib

site: bookdown::bookdown_site

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<div style="text-align:center;">

<img src="/img/cover.png" title = "Cover image created with Dall-e. The prompt was 'Roman engineer building a pipeline in the style of ancient roman art'. The result doesn't really show that, but I thought it looked nice." width="70%">

</div>

This a course that I taught during the winter semester at the University of Luxembourg in 2022.

It was aimed at second year Master's students.

- 2022/10/25, morning: Introduction and data exploration with R

- 2022/10/25, afternoon: Functional programming

- 2022/10/31, afternoon: Git (optional?)

- 2022/11/07, afternoon: Package development and unit testing

- 2022/11/08, morning: Build automation

- 2022/11/08, afternoon: Building Data products

- 2022/11/21, afternoon: Self-contained pipelines with Docker

- 2022/11/22, morning: CI/CD with Github Actions

This course is my take on setting up code that results in some *data product*.

This code has to be reproducible, documented and production ready.

Not my original idea, but introduced by the UK's

[Analysis Function](https://analysisfunction.civilservice.gov.uk/support/reproducible-analytical-pipelines/).

The basic idea of a reproducible analytical pipeline (RAP) is to have code that always produces the

same result when run, whatever this result might be. This is obviously crucial in research and

science, but this is also the case in businesses that deal with data science/data-driven decision

making etc.

A well documented RAP avoids a lot of headache and is usually re-usable for other projects as well.

In this course each of you will develop a *data product*. A data product is anything that requires

data as an input. This can be a very simple report in PDF or Word format or a complex web app. This

website is actually also a data product, which I made using the R programming language. In this

course we will not focus too much on how to create automated reports or web apps (but I'll give an

introduction to these, don't worry) but our focus will be on how to set up a pipeline that results

in these data products in a reproducible way.

No, being a master in machine learning is not enough to become a data scientist. Actually, the

older I get, the more I think that machine learning is almost optional. What is not optional is

knowing how:

- to write, test, and properly document code;

- to acquire (reading in data can be tricky!) and clean data;

- to work inside the Linux terminal/command line interface;

- to use Git, Docker for Dev(Git)Ops;

- the Internet works (what's a firewall? what's a reverse proxy? what's a domain name? etc,

etc...);

But what about machine learning? Well, depending what you'll end up doing, you might indeed focus a

lot on machine learning and/or statistical modeling. That being said, in practice, it is very often

much more efficient to let some automl algorithm figure out the best hyperparameters of a XGBoost

model and simply use that, at least as a starting point (but good luck improving upon automl...).

What matters, is that the data you're feeding to your model is clean, that your analysis is

sensible, and most importantly, that it could be understood by someone taking over (imagine you

get sick) and rerun with minimal effort in the future. The model here should simply be a piece that

could be replaced by another model without much impact. The model is rarely central... but of

course there are exceptions to this, especially in research, but every other point I've made still

stands. It's just that not only do you have to care about your model a lot, you also have to care

about everything else.

So in this course we're going to learn a bit of all of this. We're going to learn how to write

reusable code, learn some basics of the Linux command line, Git and Docker.

In my absolutely objective opinion R is currently the most interesting and simple language

you can use to create such data products. If you learn R you have access to almost 19'000

packages (as of October 2022) to:

- clean data (see: `{dplyr}`, `{tidyr}`, `{data.table}`...);

- work with medium and big data (see: `{arrow}`, `{sparklyr}`...);

- visualize data (see: `{ggplot2}`, `{plotly}`, `{echarts4r}`...);

- do literate programming (using Rmarkdown or Quarto, you can write books, documents even create a website);

- do functional programming (see: `{purrr}`...);

- call other languages from R (see: `{reticulate}` to call Python from R);

- do machine learning and AI (see: `{tidymodels}`, `{tensorflow}`, `{keras}`...)

- create webapps (see: `{shiny}`...)

- domain specific statistics/machine learning (see [CRAN Task Views](https://cran.r-project.org/web/views/) for an exhaustive list);

- and more

It's not just about what the packages provide: installing R and its packages and dependencies is rarely

frustrating, which is not the case with Python

(Python 2 vs Python 3, `pip` vs `conda`, `pyenv` vs `venv`..., dependency hell is a real place full of snakes)

<div style="text-align:center;">

```{r, echo = F}

</div>

That doesn’t mean that R does not have any issues. Quite the contrary, R sometimes behaves in seemingly truly

bizarre ways (as an example, try running `nchar("1000000000")` and then `nchar(1000000000)` and try to make sense

of it). To know more about such bizarre behaviour, I recommend you read *The R Inferno* (linked at the end

of this chapter). So, yes, R is far from perfect, but it sucks less than the alternatives

(again, in my absolutely objective opinion).

I will assume basic programming knowledge, and not much more. If you need to set up R on your computer

you can read the intro to my other book [Modern R with the tidyverse](https://b-rodrigues.github.io/modern_R/index.html#prerequisites).

Follow the pre-requisites there: install R, RStudio and these packages:

```{r, eval = F}

The course will be very, very hands-on. I'll give general hints and steps, and ask you to do stuff.

It will not always be 100% simple and obvious, and you will need to also think a bit by yourself.

I'll help of course, so don't worry. The idea is to put you in the shoes of a real data scientist

that gets asked at 9 in the morning to come up with a solution to a problem by COB. In 99% of the cases,

you will never have encountered that problem ever, as it will be very specific to the company you're working at.

Google and Stackoverflow will be your only friends in these moments.

The beginning of this course will likely be the toughest part, especially if you're not familiar with R.

I will need to bring you up to speed in 6 hours. Only after can we actually start talking about

RAPs. What's important is to never give up and work together with me.

The way grading works in this course is as follows: during lecture hours you will follow along. At

home, you'll be working on setting up your own pipeline. For this, choose a dataset that ideally

would need some cleaning and/or tweaking to be usable. We are going first to learn how to package

this dataset alongside some functions to make it clean. If time allows, I'll leave some time

during lecture hours for you to work on it and ask me and your colleagues for help. At the end of

the semester, I will need to download your code and get it running. The less effort this takes me,

the better your score. Here is a tentative breakdown:

- Code is on github.com and I can pull it: 2 points;

- Data and functions to run pipeline are in a tested, documented package? 3 points;

- I don't need to do anything to load data: 5 points;

- I can download and install your pipeline's dependencies in one command line: 5 points;

- I can run your pipeline in one command line: 5 points

- Extra points: pipeline is dockerized and uses github actions to run? 5 points

The way to fail this class is to write an undocumented script that only runs on your machine and

expect me to debug it to get it to run.

There’s some jargon that is helpful to know when working with R. Here’s a non-exhaustive list to

get you started:

- CRAN: the Comprehensive R Archive Network. This is a curated online repository of packages and R installers. When you type `install.packages("package_name")` in an R console, the package gets downloaded from there;

- Library: the collection of R packages installed on your machine;

- R console: the program where the R interpreter runs;

- Posit/RStudio: Posit (named RStudio in the past) are the makers of the RStudio IDE and of the *tidyverse* collection of packages;

- tidyverse: a collection of packages created by Posit that offer a common language and syntax to perform any task required for data science — from reading in data, to cleaning data, up to machine learning and visualisation;

- base R: refers to a vanilla installation (and vanilla capabilities) of R. Often used to contrast a *tidyverse* specific approach to a problem (for example, using base R’s `lapply()` in constrast to the *tidyverse* `purrr::map()`).

- `package::function()`: Functions can be accessed in several ways in R, either by loading an entire package at the start of a script with `library(dplyr)` or by using `dplyr::select()`.

- Function factory (sometimes adverb): a function that returns a function.

- Variable: the variable of a function (as in `x` in `f(x)`) or the variable from statistical modeling (synonym of feature)

- `<-` vs `=`: in practice, you can use `<-` and `=` interchangeably. I prefer `<-`, but feel free to use `=` if you wish.

- [An Introduction to R (from the R team themselves)](https://cran.r-project.org/doc/manuals/r-release/R-intro.html)

- [What is CRAN?](https://cran.r-project.org/doc/FAQ/R-FAQ.html#What-is-CRAN_003f)

- [The R Inferno](https://www.burns-stat.com/pages/Tutor/R_inferno.pdf)

- [Reproducible Analytical Pipelines (RAP)](https://analysisfunction.civilservice.gov.uk/support/reproducible-analytical-pipelines/)

This course is licensed under the [WTFPL](http://www.wtfpl.net/txt/copying/).

<a href="http://www.wtfpl.net/"><img

src="img/wtfpl-badge-4.png"

width="80" height="15" alt="WTFPL" /></a>