4  Reproducible workflows

Required material

Key concepts and skills

4.1 Introduction

Suppose you have cancer and you have to choose between a black box AI surgeon that cannot explain how it works but has a 90% cure rate and a human surgeon with an 80% cure rate. Do you want the AI surgeon to be illegal?

Geoffrey Hinton, 20 February 2020.

The number one thing to keep in mind about machine learning is that performance is evaluated on samples from one dataset, but the model is used in production on samples that may not necessarily follow the same characteristics… So when asking the question, “would you rather use a model that was evaluated as 90% accurate, or a human that was evaluated as 80% accurate”, the answer depends on whether your data is typical per the evaluation process. Humans are adaptable, models are not. If significant uncertainty is involved, go with the human. They may have inferior pattern recognition capabilities (versus models trained on enormous amounts of data), but they understand what they do, they can reason about it, and they can improvise when faced with novelty.

François Chollet, 20 February 2020.

If science is about systematically building and organizing knowledge in terms of testable explanations and predictions, then data science takes this and focuses on data. This means that building, organizing, and sharing knowledge is a critical aspect. Creating knowledge, once, in a way that only you can do it, does not meet this standard. Hence, the need for reproducible data science workflows.

Alexander (2019) talks about how reproducible research means it can be exactly redone, given all the materials used. This underscores the importance of providing the code, data, and environment. The minimum expectation is that another person is independently able to use your code, data, and environment, to get your results, including figures and tables. Ironically, there are different definitions of reproducibility between disciplines. Barba (2018) surveys a variety of disciplines and concludes that the predominant language usage implies the following definitions: Reproducible research is when ‘[a]uthors provide all the necessary data and the computer codes to run the analysis again, re-creating the results.’ A replication is a study ‘that arrives at the same scientific findings as another study, collecting new data (possibly with different methods) and completing new analyses.’

Regardless of what it is specifically called, Gelman (2016) identifies how large an issue this is in various social sciences. The problem with work that is not reproducible, is that it does not contribute to our stock of knowledge about the world. Since Gelman (2016), a great deal of work has been done in many social sciences and the situation has improved a little, but much work remains. And the situation is similar in the life sciences (Heil et al. 2021) and computer science (Pineau et al. 2021).

Some of the examples that Gelman (2016) talks about, which turned out to not reproduce, such as himmicanes and power pose, are not that important in the scheme of things. But at the same time, we saw, and continue to see, similar approaches being used in areas with big impacts. For instance, many governments have created ‘nudge’ units that implement public policy (Sunstein and Reisch 2017). Governments are increasingly using algorithms that they do not make open (Chouldechova et al. 2018). And Herndon, Ash, and Pollin (2014) document how a paper in economics that was used by governments to justify austerity policies following the Global Financial Crisis turned out to not be reproducible.

At a minimum, and with few exceptions, we must release our code, datasets, and environment. Without these, it is difficult to know what a finding speaks to (Miyakawa 2020). More banally, we also do not know if there are mistakes or aspects that were inadvertently overlooked (Merali 2010; Hillel 2017; Silver 2020). Increasingly, we consider a paper to be an advertisement, and for the associated code, data, and environment to be the actual work (Buckheit and Donoho 1995). Steve Jobs, a co-founder of Apple, talked about how the best craftsmen ensure that even the aspects of their work that no one else will ever see are as well-finished and high-quality as the aspects that are public facing (Isaacson 2011). The same is true in data science, where often one of the distinguishing aspects of high-quality work is that the README and code comments are as polished as the abstract of the associated paper.

Workflows exist within a cultural and social context, which imposes an additional reason for the need for them to be reproducible. For instance, Wang and Kosinski (2018) use deep neural networks to train a model to distinguish between gay and heterosexual men (Murphy (2017) provides a summary of the paper, the associated issues, and comments from its authors). To do this, Wang and Kosinski (2018, 248) needed a dataset of photos of folks that were ‘adult, Caucasian, fully visible, and of a gender that matched the one reported on the user’s profile’. They verified this using Amazon Mechanical Turk, an online platform that pays workers a small amount of money to complete specific tasks. The instructions provided to the Mechanical Turk workers for this task specify that Obama, who had a white mother and a black father, should be classified as ‘Black’; and that Latino is an ethnicity, rather than a race (Mattson 2017). The classification task may seem objective, but, perhaps unthinkingly, echoes the views of Americans with a certain class and background.

This is just one specific concern about one part of the Wang and Kosinski (2018) workflow. Broader concerns are raised by others including Gelman, Mattson, and Simpson (2018). The main issue is that statistical models are specific to the data on which they were trained. And the only reason that we can identify likely issues in the model of Wang and Kosinski (2018) is because, despite not releasing the specific dataset that they used, they were nonetheless open about their procedure. For our work to be credible, it needs to be reproducible by others.

Some of the steps that we can take to make our work more reproducible include:

  1. Ensure the entire workflow is documented. This may involve addressing questions such as:
    • How was the raw dataset obtained and is access likely to be persistent and available to others?
    • What specific steps are being taken to transform the raw data in the data that were analyzed, and how can this be made available to others?
    • What analysis has been done, and how clearly can this be shared?
    • How has the final paper or report been built and to what extent can others follow that process themselves?
  2. Not worrying about perfect reproducibility initially, but instead focusing on trying to improve with each successive project. For instance, each of the following requirements are increasingly more onerous and there is no need to be concerned about not being able to the last, until we can do the first:
    • Can you run your entire workflow again?
    • Can another person run your entire workflow again?
    • Can ‘future-you’ run your entire workflow again?
    • Can ‘future-another-person’ run your entire workflow again?
  3. Including a detailed discussion about the limitations of the dataset and the approach in the final paper or report.

The workflow that we follow is: Plan -> Simulate -> Acquire -> Explore -> Share. But it can be alternatively considered as: ‘Think an awful lot, mostly read and write, sometimes code’.

There are various tools that we can use at the different stages that will improve the reproducibility of this workflow. This includes Quarto, R Projects, and Git and GitHub.

4.2 Quarto

4.2.1 Getting started

Quarto integrates code and natural language in a way that is called ‘literate programming’ (Knuth 1984). It is the successor to R Markdown, which was a variant of Markdown specifically designed to allow R code chunks to be included. Quatro uses a mark-up language similar to HyperText Markup Language (HTML) or LaTeX, in comparison to a ‘What You See Is What You Get’ (WYSIWYG) language, such as Microsoft Word. This means that all the aspects are consistent, for instance, all top-level heading will look the same. But it means that we use symbols to designate how we would like certain aspects to appear. And it is only when we build the mark-up that we get to see what it looks like. A visual editor option can also be used which hides the need for the user to do this mark-up themselves.

Shoulders of giants

Fernando Pérez is an associate professor, in statistics, at the University of California, Berkeley and a Faculty Scientist, Data Science and Technology Division, at Lawrence Berkeley National Laboratory. After taking a PhD in in particle physics from the University of Colorado, Boulder, in X he did Y ??. During his PhD he created iPython, which enables Python to be used interactively, and now underpins Project Jupyter, which inspired programs such as R Markdown and is an alternative to Quarto. In 2017 he was awarded the ACM Software System Award.

One advantage of literate programming is that we get a ‘live’ document in which code executes and then forms part of the document. Another advantage of Quarto is that very similar code can compile into a variety of documents, including HTML pages and PDFs. Quarto also has default options set up for including title, author, and date sections. One disadvantage is that it can take a while for a document to compile because all the code needs to run. Tierney (2022) provides an especially useful and detailed Quarto usage guide.

We need to download Quarto from here. Or if using R Studio Cloud then it is already built in.

We can create a new Quarto document within R Studio (‘File’ -> ‘New File’ -> ‘Quarto Document…’).

4.2.2 Essential commands

Like R Markdown, Quatro uses a variation of Markdown as its underlying syntax. Essential markdown commands include those for emphasis, headers, lists, links, and images. A reminder of these is included in R Studio (‘Help’ -> ‘Markdown Quick Reference’). It is your choice as to whether you want to use the visual or source editor. But either way, it is good to understand these essentials because it will not always be possible to use a visual editor, for instance if you are quickly looking at a Quarto document in GitHub.

  • Emphasis: *italic*, **bold**
  • Headers (these go on their own line with a blank line before and after):
         # First level header
         ## Second level header
         ### Third level header
  • Unordered list, with sub-lists:
    * Item 1
    * Item 2
        + Item 2a
        + Item 2b
  • Ordered list, with sub-lists:
    1. Item 1
    2. Item 2
    3. Item 3
        + Item 3a
        + Item 3b
  • URLs can be added by linking text: [the address of this book](https://www.tellingstorieswithdata.com) results in the address of this book.
  • A paragraph is created by leaving a blank line.
A paragraph about some idea, nicely spaced from the following paragraph.

Another paragraph about another idea, nicely spaced from the earlier paragraph.

Once we have added some aspects, then we may want to see the actual document. To build the document click ‘Render’.

4.2.3 R chunks

We can include code for R and many other languages in code chunks within a Quarto document. Then when we render the document, the code will run and be included in the document.

To create an R chunk, we start with three backticks and then within curly braces we tell Quarto that this is an R chunk. Anything inside this chunk will be considered R code and run as such. For instance, we could load the tidyverse and AER and make a graph of the number of times a survey respondent visited the doctor in the past two weeks.


data("DoctorVisits", package = "AER")

DoctorVisits |>
  ggplot(aes(x = illness)) +
  geom_histogram(stat = "count")

The output of that code is Figure 4.1.

There are various evaluation options that are available in chunks. We include these, each on a new line, by opening the line with the chunk-specific comment delimiter ‘#|’ and then the option. Helpful options include:

  • echo: This controls whether the code itself is included in the document. For instance, echo: false would mean the code will be run and its output will show, but the code itself would not be included in the document.
  • include: This controls whether the output of the code is included in the document. For instance, `include: false would run the code, but would not result in any output, and the code itself would not be included in the document.
  • eval: This controls whether the code should be included in the document. For instance, eval: false would mean that the code is not run, and hence there would not be any output to include, but the code itself would be included in the document.
  • warning: This controls whether warnings should be included in the document. For instance, warning: false would mean that warnings are not included.
  • message: This controls whether messages should be included in the document. For instance, message: false would mean that messages are not included in the docuemnt.

For instance, we could include the output, but not the code, and suppress any warnings.

#| echo: false
#| warning: false


data("DoctorVisits", package = "AER")

DoctorVisits %>%
  ggplot(aes(x = visits)) +
  geom_histogram(stat = "count")

It is important to leave a blank line on either side of an R chunk, otherwise it may not run properly. It is also important that lower case is used for logical values, i.e. ‘false’ not ‘FALSE’.

Most people did not visit a doctor in the past week.

#| echo: false
#| warning: false


data("DoctorVisits", package = "AER")

DoctorVisits %>%
  ggplot(aes(x = visits)) +
  geom_histogram(stat = "count")

There were some people that visited a doctor once, and then very few people that visited two or more times.

It is also important that the Quarto document itself loads any datasets that are needed. It is not enough that they are in the environment. This is because the Quarto document evaluates the code in the document when it is built, not necessarily the environment.

4.2.4 Top matter

Top matter consists of defining aspects such as the title, author, and date. It is contained within three dashes at the top of a Quarto document. For instance, the following would specify a title, date that automatically updated to the date the document was rendered, and an author.

title: "My document"
author: "Rohan Alexander"
date: format(Sys.time(), '%d %B %Y')
format: html

An abstract is a short summary of the paper, and we could add that to the top matter as well.

title: "My document"
author: "Rohan Alexander"
date: format(Sys.time(), '%d %B %Y')
abstract: "This is my abstract."
format: html

By default, Quarto will create an HTML document, but we can change the output format to produce a PDF. This uses LaTeX in the background and may require the installation of supporting packages. In particular it is common to need to first install tinytex (Xie 2019).

title: "My document"
author: "Rohan Alexander"
date: format(Sys.time(), '%d %B %Y')
abstract: "This is my abstract."
format: pdf

We can include references by specifying a BibTeX file in the top matter and then calling it within the text, as needed.

title: "My document"
author: "Rohan Alexander"
date: format(Sys.time(), '%d %B %Y')
format: pdf
abstract: "This is my abstract."
bibliography: bibliography.bib

We would need to make a separate file called ‘bibliography.bib’ and save it next to the Quarto file. In the BibTeX file we need an entry for the item that is to be referenced. For instance, the citation for R can be obtained with citation() and this can be added to the ‘bibliography.bib’ file. Similarly, the citation for a package can be found by including the package name, for instance citation('tidyverse'). It can be helpful to use Google Scholar, or doi2bib, to get citations for books or articles.

    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2021},
    url = {https://www.R-project.org/},
    title = {Welcome to the {tidyverse}},
    author = {Hadley Wickham and Mara Averick and Jennifer Bryan and Winston Chang and Lucy D'Agostino McGowan and Romain François and Garrett Grolemund and Alex Hayes and Lionel Henry and Jim Hester and Max Kuhn and Thomas Lin Pedersen and Evan Miller and Stephan Milton Bache and Kirill Müller and Jeroen Ooms and David Robinson and Dana Paige Seidel and Vitalie Spinu and Kohske Takahashi and Davis Vaughan and Claus Wilke and Kara Woo and Hiroaki Yutani},
    year = {2019},
    journal = {Journal of Open Source Software},
    volume = {4},
    number = {43},
    pages = {1686},
    doi = {10.21105/joss.01686},

We need to create a unique key that we use to refer to this item in the text. This can be anything, provided it is unique, but meaningful ones can be easier to remember, for instance ‘citeR’.

    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2021},
    url = {https://www.R-project.org/},
    title = {Welcome to the {tidyverse}},
    author = {Hadley Wickham and Mara Averick and Jennifer Bryan and Winston Chang and Lucy D'Agostino McGowan and Romain François and Garrett Grolemund and Alex Hayes and Lionel Henry and Jim Hester and Max Kuhn and Thomas Lin Pedersen and Evan Miller and Stephan Milton Bache and Kirill Müller and Jeroen Ooms and David Robinson and Dana Paige Seidel and Vitalie Spinu and Kohske Takahashi and Davis Vaughan and Claus Wilke and Kara Woo and Hiroaki Yutani},
    year = {2019},
    journal = {Journal of Open Source Software},
    volume = {4},
    number = {43},
    pages = {1686},
    doi = {10.21105/joss.01686},

To cite R in the Quarto document we include @citeR, which would put the brackets around the year, like this: R Core Team (2021), or [@citeR], which would put the brackets around the whole thing, like this: (R Core Team 2021).

The reference list at the end of the paper is automatically built based on calling the BibTeX file and including the references in the paper. At the end of the Quarto document, including a heading ‘# References’ and the actual citations will be included after that. When the Quarto file is rendered, Quarto sees these in the content, goes to BibTeX to get the reference details that it needs, builds the reference list, and then adds it to the end of the rendered document.

4.2.5 Cross-references

It can be useful to cross-reference figures, tables, and equations. This makes it easier to refer to them in the text. To do this for a figure we refer to the name of the R chunk that creates or contains the figure. For instance, if we had the following code:

#| label: fig-uniquename
#| fig-cap: Number of illnesses in the past two weeks, based on the 1977--1978 Australian Health Survey
#| echo: true
#| warning: false


data("DoctorVisits", package = "AER")

DoctorVisits |>
  ggplot(aes(x = illness)) +
  geom_histogram(stat = "count")

Then (@fig-uniquename) would produce: (Figure 4.2) as the name of the R chunk is fig-uniquename. We need to add ‘fig’ to the start of the chunk name so that Quarto knows that this is a figure. We then include a ‘fig-cap:’ in the R chunk that specifies a caption.

We can add #| layout-ncol: 2 in an R chunk within a Quarto document to have two graphs appear side-by-side (Figure 4.3). Here Figure 4.3 (a) uses the minimal theme, and Figure 4.3 (b) uses the classic theme. These cross-reference #| label: fig-doctorgraphsidebyside and are created in the text by (@fig-doctorgraphsidebyside), @fig-doctorgraphsidebyside-1, and @fig-doctorgraphsidebyside-2.

DoctorVisits |>
  ggplot(aes(x = illness)) +
  geom_histogram(stat = "count")

DoctorVisits |>
  ggplot(aes(x = visits)) +
  geom_histogram(stat = "count")

We can take a similar approach to cross-reference tables. For instance, (@tbl-docvisittable) will produce: (Table 4.1). In this case we specify ‘tbl’ at the start of the label so that Quarto knows that it is a table. And we specify a caption for the table with ‘tbl-cap:’.

#| label: tbl-docvisittable
#| echo: true
#| tbl-cap: "Number of visits to the doctor in the past two weeks, based on the 1977--1978 Australian Health Survey"

DoctorVisits |> 
  count(visits) |> 
Table 4.1: Number of visits to the doctor in the past two weeks, based on the 1977–1978 Australian Health Survey
visits n
0 4141
1 782
2 174
3 30
4 24
5 9
6 12
7 12
8 5
9 1

Finally, we can also cross-reference equations. To that we need to add a tag {#eq-macroidentity} which we then reference.

Y = C + I + G + (X - M)
$$ {#eq-macroidentity}

For instance, we then use @eq-macroidentity to produce Equation 4.1.

\[ Y = C + I + G + (X - M) \qquad(4.1)\]

When using cross-references, it is important that the labels are relatively simple. In general, try to keep the names simple but unique, avoid punctuation and stick to letters and hyphens. Do not use underscores, because they can cause an error.

4.3 R projects and file structure

Projects are widely used in software development and exist to keeps all the files (data, analysis, report, etc) associated with a particular project together and related to each other. An R project can be created in R Studio ‘File’ -> ‘New Project’, then select ‘Empty project’, name the project and decide where to save it. For instance, a project focused on maternal mortality, may be called ‘maternalmortality’, and might be saved within a folder of other projects. The use of R projects enables ‘reliable, polite behavior across different computers or users and over time.’ (Jenny Bryan and Hester 2020). This is because it removes the context of that folder from its broader existence. So files exist in relation to the base of the R project, not the base of the computer.

Once a project has been created, a new file with the extension ‘.RProj’ will appear in that folder. As an example, of a folder with an R Project, an example Quarto document, and an appropriate file structure is available here. That can be downloaded: ‘Code’ -> ‘Download ZIP’.

The main advantage of using an R Project is that we are more easily able to reference other files in a self-contained way. That means when others want to reproduce our work, they know that all the file references and structure should not need to be changed. It means that files are referenced in relation to where the ‘.Rproj’ file is. For instance, instead of reading a csv from, say, "~/Documents/projects/book/data/" you can read it in from book/data/. It may be that someone else does not have a ‘projects’ folder, and so the former would not work for them, while the latter would.

The use of R projects is required to meet the minimal level of reproducibility. The use of functions such as setwd(), and computer-specific file paths, bind work to a specific computer in a way that is not appropriate. Trisovic et al. (2022) describe the use of absolute paths, rather than relative paths, as a common error that they had to correct in their large-scale study of R code, uploaded to the Harvard Dataverse, that underpins research papers.

There are a variety of ways to set-up a folder. A variant of Wilson et al. (2017) that is often useful is shown in the example file structure. Here we have an ‘inputs’ folder that contains raw data (which should never be modified (Wilson et al. 2017)) and literature related to the project (which cannot be modified). An ‘outputs’ folder contains data that we create using R, as well as the paper that we are writing. And a ‘scripts’ folder is what modifies the raw data and saves it into ‘outputs’. We will do most of our work in ‘scripts’, and the Quarto file for the paper in ‘outputs’. Useful other aspects include a ‘README.md’ which will specify overview details about the project, and a LICENSE. Another helpful variant of this project skeleton is provided by Mineault and The Good Research Code Handbook Community (2021).

4.4 Version control

We implement version control through a combination of Git and GitHub. There are a variety of reasons for this including:

  1. Enhancing the reproducibility of work by making it easier to share code and data;
  2. Making it easier to share work;
  3. Improving workflow by encouraging systematic approaches; and
  4. Making it easier to work in teams.

Git is a version control system. The way one often starts doing version control is to have various versions of the one file: ‘first_go.R’, ‘first_go-fixed.R’, ‘first_go-fixed-with-mons-edits.R’. But this soon becomes cumbersome. One often soon turns to dates, for instance: ‘2022-01-01-analysis.R’, ‘2022-01-02-analysis.R’, ‘2022-01-03-analysis.R’, etc. While this keeps a record it can be difficult to search when we need to go back, because it can be difficult to remember the date some change was made. In any case, it quickly gets unwieldy for a project that is being regularly worked on.

Instead of this, we use Git so that we can have one version of the file, say, ‘analysis.R’ and then use Git to keep a record of the changes to that file, and a snapshot of that file at a given point in time. We determine when Git takes that snapshot, and when we take that snapshot. We additionally include a message saying what changed between this snapshot and the last. In that way, there is only ever one version of the file, but the history can be more easily searched.

One complication is that Git was designed for teams of software developers. As such, while it works, it can be a little ungainly for non-developers. But in general it is the case that Git has been usefully adapted for data science, even when the only collaborator one may have is one’s future self (Jenny Bryan 2018).

GitHub, GitLab, and various other companies offer easier-to-use services that build on Git. While there are tradeoffs, we introduce GitHub here because it is the predominant platform (Eghbal 2020, 21). Git and GitHub are built into R Studio Cloud, which provides a nice option if you have issues with local installation. One of the initial challenging aspects of Git is the terminology. Folders are called ‘repos’. Creating a snapshot is called a ‘commit’. One gets used to it eventually, but feeling confused initially is normal. Jenny Bryan (2020) is especially useful for setting up and using Git and GitHub.

4.4.1 Git

We first need to git check whether Git is installed. Open R Studio, go to the Terminal, type the following, and then enter/return.

git --version

If you get a version number, then you are done (Figure 4.4).

Git is pre-installed in R Studio Cloud, it should be pre-installed on Mac, and it may be pre-installed on Windows. If you do not get a version number in response, then you need to install it. To do that you should follow the instructions specific to your operating system in Jenny Bryan (2020, chap. 5).

Given Git is installed we need to tell it our username and email. We need to do this because Git adds this information whenever we take a ‘snapshot’, or to use Git’s language, whenever we make a commit.

Again, within the Terminal, type the following, replacing the details with yours, and then enter/return after each line.

git config --global user.name 'Rohan Alexander'
git config --global user.email 'rohan.alexander@utoronto.ca'
git config --global --list

When this set-up has been done properly, the values that you entered for ‘user.name’ and ‘user.email’ will be returned after the last line (Figure 4.5).

These details–username and email address–will be public. There are various ways to hide the email address if necessary, and GitHub provides instructions about this. Jenny Bryan (2020, chap. 7) provides more detailed instructions about this step, and a trouble-shooting guide.

4.4.2 GitHub

Now that Git is set-up, we need to set-up GitHub. We created an account in Chapter 2, which we use again here. After being signed in we first need to make a new folder, which is called a ‘repo’ in Git. Look for a ‘+’ in the top right, and then select ‘New Repository’ (Figure 4.6).

At this point we can add a sensible name for the repo. Leave it as ‘public’ for now, because it can always be deleted later. And check the box to ‘Initialize this repository with a README’. Change ‘Add .gitignore’ to R. After that, click ‘Create repository’ (Figure 4.7).

This will take us to a screen that is fairly empty, but the details that we need are in the green ‘Clone or Download’ button, which we can copy by clicking the clipboard (Figure 4.8).

Now returning to R Studio, in R Studio Cloud, we create a ‘New Project’ using ‘New Project from Git Repository’. It will ask for the URL that we just copied (Figure 4.9). If you are using a local machine, then this same step is accomplished through the menu: ‘File’ -> ‘New Project…’ -> ‘Version Control’ -> ‘Git’, then paste in the URL, give the folder a meaningful name, check ‘Open in new session’, then ‘Create Project’.

At this point, a new folder has been created locally that we can use. We will want to be able to push it back to GitHub, and for that we will need to use a Personal Access Token (PAT) to link our R Studio Workspace with our GitHub account. We use usethis (Wickham and Bryan 2020) and gitcreds (Csárdi 2020) to enable this. These packages are, respectively, a package that automates repetitive tasks, and a package that authenticates with GitHub. To create a PAT, while signed into GitHub in the browser, run usethis::create_github_token() in your R session. GitHub will open in the browser with various options filled out (Figure 4.10). It can be useful to give the PAT an informative name by replacing ‘Note’, for instance ‘PAT for R Studio’, then ‘Generate token’.

We only have one shot to copy this token, and if we make a mistake then we will need to generate a new one. Do not include the PAT in any R script or Quarto document. Instead run gitcreds::gitcreds_set(), which will then prompt you to add your PAT in the console.

To use GitHub for a project that we are actively working on we follow a procedure:

  1. The first thing to do is almost always to get any changes with ‘pull’. To do this, open the Git pane in R Studio, and click the blue down arrow. This gets any changes to the folder, as it is on GitHub, into our own version of the folder.
  2. We can then make our changes to our copy of the folder. For instance, we could update the README, and then save it as normal.
  3. Once this is done, we need to ‘add’, ‘commit’, and ‘push’. In the Git pane in R Studio, select the files to be added. This adds them to the staging area. Then click ‘Commit’ (Figure 4.11). A new window will open. Add a commit message which is informative about the change that was made, and then click ‘Commit’ in that new window (Figure 4.12). Finally, click ‘Push’ to send the changes to GitHub.

There are a few common pain-points when it comes to Git and GitHub. We recommend committing and pushing regularly, especially when you are new to version control. This increases the number of snapshots that you could come back to if needed. All commits should have an informative commit message. If you are new to version control, then the expectation of a good commit message is that it contains a short summary of the change, followed by a blank line, and then an explanation of the change including what the change is, and why it is being made. For instance, if your commit adds graphs to a paper, then a commit message could be:

Add graphs

Graphs of unemployment and inflation added into Data section.

There is some evidence of a relationship between overall quality and commit behavior (Sprint and Conci 2019). In an ideal scenario the commit messages act as a kind of journal of the project.

Git and GitHub were designed for software developers, rather than data scientists. GitHub limits the size of the files it will consider to 100MB, and even 50MB will prompt a warning. Data science projects regularly involve datasets that are larger than this. In Chapter 12 we discuss the use of data deposits, which can be especially useful when a project is completed, but when we are actively working on a project it can be useful to ignore the file, at least as far as Git and GitHub are concerned. We do this using a ‘.gitignore’ file, in which we list all of the files that we do not want to track using Git. The starter folder contains an example ‘.gitignore’ file. And it can be helpful to run usethis::git_vaccinate(), which will add a variety of files to a global ‘.gitignore’ file in case you forget to do it on a project-basis.

We used the Git pane in R Studio which removed the need to use the Terminal, but it did not remove the need to go to GitHub and set-up a new project. Having set-up Git and GitHub, we can further improve this aspect of our workflow with usethis (Wickham and Bryan 2020).

First check that Git is set-up with usethis::git_sitrep(). This should print information about the username and email. We can use usethis::use_git_config() to update these details if needed.

  user.name = "Rohan Alexander", 
  user.email = "rohan.alexander@utoronto.ca"

Rather than starting a new project in GitHub, and then adding it locally, we can now use usethis::use_git() to initiate it and commit the files. Having committed, we can use usethis::use_github() to push to GitHub, which will create the folder on GitHub as well.

4.5 Using R in practice

4.5.1 Dealing with errors

When you are programming, eventually your code will break, when I say eventually, I mean like probably 10 or 20 times a day.

Gelfand (2021)

Everyone who uses R, or any programming language for that matter, has trouble find them at some point. This is normal. Programming is hard. At some point code will not run or will throw an error. This happens to everyone. It is common to get frustrated, but to move forward we develop strategies to work through the issues:

  1. If you are getting an error message, then sometimes it will be useful. Try to read it carefully to see if there is anything of use in it.
  2. Try to search, say on Google, for the error message. It can be useful to include ‘tidyverse’ or ‘in R’ in the search to help make the results more appropriate. Sometimes Stack Overflow results can be useful.
  3. Look at the help file for the function, by putting ‘?’ before the function, for instance, ?pivot_wider(). A common issue is to use a slightly incorrect argument name or format, such as accidentally including a string instead of an object name.
  4. Look at where the error is happening and remove or comment out code until the error is resolved, and then slowly add code back again.
  5. Check the class of the object, with class(), for instance, class(data_set$data_column). Ensure that it is what is expected.
  6. Restart R (‘Session’ -> ‘Restart R and Clear Output’) and load everything again.
  7. Restart the computer.
  8. Search for what you are trying to do, rather than the error, being sure to include ‘tidyverse’ or ‘in R’ in the search to help make the results more appropriate. For instance, ‘save PDF of graph in R made using ggplot’. Sometimes there are relevant blog posts or Stack Overflow answers that will help.
  9. Making a small, self-contained, reproducible example ‘reprex’ to see if the issue can be isolated and to enable others to help.

More generally, while this is rarely possible to do, it is almost always helpful to take a break and come back the next day.

4.5.2 Reproducible examples

No one can advise or help you—no one. There is only one thing you should do. Go into yourself.

Rilke (1929)

Asking for help is a skill like any other. We get better at it with practice. It is important to try not to say ‘this doesn’t work’, ‘I tried everything’, ‘your code does not work’, or ‘here is the error message, what do I do?’. In general, it is not possible to help based on these comments, because there are too many possible issues. You need to make it easy for others to help you. This involves a few steps.

  1. Provide a small, self-contained, example of your data, and code, and detail what is going wrong.
  2. Document what you have tried so far, including which Stack Overflow and R Studio Community pages have you looked at, and why are they not quite what you are after?
  3. Be clear about the outcome that you would like.

Begin by creating a minimal REPRoducible EXample–a ‘reprex’. This is code that contains what is needed to reproduce the error, but only what is needed. This means that the code it likely a smaller, simpler, version that nonetheless reproduces the error.

Sometimes this process enables one to solve the problem. If it does not, then it gives someone else a fighting chance of being able to help. It is important to recognize that there is almost no chance that you have got a problem that someone has not addressed before. It is more likely that the main difficulty is in trying to communicate what you are trying to do and what is happening, in a way that allows others to recognize both. Developing tenacity is important.

To develop reproducible examples, reprex (Jennifer Bryan et al. 2019) is especially useful. To use it we:

  1. Load the reprex package: library(reprex).
  2. Highlight, and copy, the code that is giving issues.
  3. Run reprex() in the Console.

If the code is self-contained, then it will preview in the Viewer. If it is not, then it will error, and the code needs to be re-written so that it is self-contained.

If you need data to reproduce the error, then you should use data that is built into R. There are a large number of datasets that are built into R and can be seen using library(help = "datasets"). But if possible, you should use a common option such as ‘mtcars’ or ‘faithful’. Combining a reprex with a GitHub Gist that was introduced in Chapter 2, increases the chances that someone is able to help you.

4.5.3 Mentality

(Y)ou are a real, valid, competent user and programmer no matter what IDE you develop in or what tools you use to make your work work for you

(L)et’s break down the gates, there’s enough room for everyone

Sharla Gelfand, 10 March 2020.

If you write code, then you are a programmer regardless of how you do it, what you are using it for, or who you are. But there are a few traits that one tends to notice great programmers have in common.

  • Focused: Often having an aim to ‘learn R’ or something similar tends to be problematic, because there is no real end point to that. It tends to be more efficient to have smaller, more specific goals, such as ‘make a histogram about the 2022 Australian Election with ggplot’. This is something that can be focused on and achieved in a few hours. The issue with goals that are more nebulous, such as ‘I want to learn R’, is that it becomes easy to get lost on tangents, much more difficult to get help. This can be demoralizing and lead to folks quitting too early.
  • Curious: It is almost always useful to have a go. In general, the worst that happens is that you waste your time. You can rarely break something irreparably with code. If you want to know what happens if you pass a ‘vector’ instead of a ‘dataframe’ to ggplot() then try it.
  • Pragmatic: At the same time, it can be useful to stick within reasonable bounds, and make one small change each time. For instance, say you want to run some regressions, and are curious about the possibility of using the tidymodels package (Kuhn and Wickham 2020) instead of lm(). A pragmatic way to proceed is to use one aspect from the tidymodels package initially and then make another change next time.
  • Tenacious: Again, this is a balancing act. There are always unexpected problems and issues with every project. On the one hand, persevering despite these is a good tendency. But on the other hand, sometimes one does need to be prepared to give up on something if it does not seem like a break-through is possible. Here mentors can be useful as they tend to be a better judge of what is reasonable. It is also where appropriate planning is useful.
  • Planned: It is almost always useful to excessively plan what you are going to do. For instance, you may want to make a histogram of the 2019 Canadian Election. You should plan the steps that are needed and even to sketch out how each step might be implemented. For instance, the first step is to get the data. What packages might be useful? Where might the data be? What is the back-up plan if the data do not exist there?
  • Done is better than perfect: We all have various perfectionist tendencies to a certain extent, but it can be useful to initially try to turn them off to a certain extent. In the first instance, try to write code that works, especially in the early days. You can always come back and improve aspects of it. But it is important to actually ship. Ugly code that gets the job done is better than beautiful code that is never finished.

4.5.4 Code comments and style

Code must be commented (Lee 2018). Comments should focus on why certain code was written, (and to a lesser extent, why a common option is not selected).

There is no one way to write code, especially in R. However, there are some general guidelines that will make it easier for you even if you are just working on your own. It is important to recognize that most projects will evolve over time, and one purpose served by code comments are as ‘[m]essages left for your future self (or near-future others) [that] help retrace and justify your decisions’ (Bowers 2011).

Comments in R script files can be added by including the # symbol. We do not have to put a comment at the start of the line, it can be midway through. In general, we do not need to comment what every aspect of your code is doing but we should comment parts that are not obvious. For instance, if we read in some value then we may like to comment where it is coming from.

You should comment why you are doing something (Wickham 2021). What are you trying to achieve?

You must comment to explain weird things. Like if you are removing some specific row, say row 27, then why are you removing that row? It may seem obvious in the moment, but future-you in six months will not remember.

You should break your code into sections. For instance, setting up the workspace, reading in datasets, manipulating and cleaning the dataset, analyzing the datasets, and finally producing tables and figures. Each of these should be separated with comments explaining what is going on, and sometimes into separate files, depending on the length.

Additionally, at the top of each file it is important to note basic information, such as the purpose of the file, and pre-requisites or dependencies, the date, the author and contact information, and finally and red-flags or todos.

At the very least every R script needs a preamble and a clear demarcation of sections.

#### Preamble ####
# Purpose: Brief sentence about what this script does
# Author: Your name
# Data: The date it was written
# Contact: Add your email
# License: Think about how your code may be used
# Pre-requisites: 
# - Maybe you need some data or some other script to have been run?

#### Workspace setup ####
# do not keep the install.packages line - comment out if need be
# Load packages

# Read in the raw data. 
raw_data <- readr::read_csv("inputs/data/raw_data.csv")

#### Next section ####

4.6 Exercises and tutorial

4.6.1 Exercises

  1. According to Alexander (2019) research is reproducible if (pick one)?
    1. It is published in peer-reviewed journals.
    2. All of the materials used in the study are provided.
    3. It can be reproduced exactly without the authors providing materials.
    4. It can be reproduced exactly, given all the materials used in the study.
  2. According to the timeline of Gelman (2016), a) when did Paul Meehl identify various issues; and b) when did null hypothesis significance testing (NHST) become controversial (pick one)?
    1. 1970s-1980s; 1990s-2000.
    2. 1960s-1970s; 1980s-1990.
    3. 1970s-1980s; 1980s-1990.
    4. 1960s-1970s; 1990s-2000.
  3. Which of the following are components of the project layout recommended by Wilson et al. (2017) (select all that apply)?
    1. requirements.txt
    2. doc
    3. data
    4. LICENSE
    6. README
    7. src
    8. results
  4. Based on Alexander (2021) please write a paragraph about some of the barriers you overcame, or still face, with regard to sharing code that you wrote.
  5. According to Gelfand (2021), what is the key part of ‘If you need help getting unstuck, the first step is to create a reprex, or reproducible example. The goal of a reprex is to package your problematic code in such a way that other people can run it and feel your pain. Then, hopefully, they can provide a solution and put you out of your misery.’ (pick one)?
    1. package your problematic code
    2. other people can run it and feel your pain
    3. the first step is to create a reprex
    4. they can provide a solution and put you out of your misery
  6. According to Gelfand (2021), what are the three key aspects of a reprex (select all that apply)?
    1. data
    2. only the libraries that are necessary and all the libraries that are necessary
    3. relevant code and only relevant code
  7. According to Wickham (2021) for naming files, how would these files ‘00_get_data.R’, ‘get data.R’ be classified (pick one)?
    1. bad; bad.
    2. good; bad.
    3. bad; good.
    4. good; good.
  8. Which of the following would result in bold text in Quarto (pick one)?
    1. **bold**
    2. ##bold##
    3. *bold*
    4. #bold#
  9. Which option would hide the warnings in a Quarto R chunk (pick one)?
    1. echo: false
    2. eval: false
    3. warning: false
    4. message: false
  10. Which options would run the R code chunk and display the results, but not show the code in a Quarto R chunk (pick one)?
    1. echo: false
    2. include: false
    3. eval: false
    4. warning: false
    5. message: false
  11. Why are R Projects important (select all that apply)?
    1. They help with reproducibility.
    2. They make it easier to share code.
    3. They make your workspace more organized.
    4. They ensure reproducibility.
  12. Please discuss a circumstance in which an R Project would be useful.
  13. Consider this sequence: ‘git pull, git status, ________, git status, git commit -m "My message", git push’. What is the missing step (pick one)?
    1. git add -A.
    2. git status.
    3. git pull.
    4. git push.
  14. Assuming the packages and datasets have been loaded, what is the mistake in this code: DoctorVisits |> select("visits") (pick one)?
    1. "visits"
    2. DoctorVisits
    3. select
    4. |>
  15. What is a reprex and why is it important to be able to make one (select all that apply)?
    1. A reproducible example that enables your error to be reproduced.
    2. A reproducible example that helps others help you.
    3. A reproducible example during the construction of which you may solve your own problem.
    4. A reproducible example that demonstrates you have actually tried to help yourself.
  16. The following code produces an error. Please use reprex (Jennifer Bryan et al. 2019) to build a reproducible example that you could use to get help with it, and submit the reprex using a GitHub Gist. You should simplify many aspects including reducing the number of packages, changing the dataset, and simplying the filter() and mutate().

oecd_gdp <- 



oecd_gdp_most_recent <- 
  oecd_gdp |> 
  filter(TIME == "2021-Q3",
         SUBJECT == "TOT",
         LOCATION %in% c("AUS", "CAN", "CHL", "DEU", "GBR",
                         "IDN", "ESP", "NZL", "USA", "ZAF"),
         MEASURE == "PC_CHGPY") |> 
  mutate(european = if_else(LOCATION %in% c("DEU", "GBR", "ESP"),
                             "Not european"),
         hemisphere = if_else(LOCATION %in% c("CAN", "DEU", "GBR", "ESP", "USA"),
                             "Northern Hemisphere",
                             "Southern Hemisphere"),


oecd_gdp_most_recent |> 
  ggplot(mapping = aes(x = LOCATION, y = Value)) |> 

4.6.2 Tutorial

Please put together a small Quarto file that downloads a dataset using opendatatoronto, cleans it, and makes a graph. Then exchange it with someone else. Ask them to both read the code and to run it, and to then provide you with feedback about both aspects. Write one-to-two pages of single-spaced content about the comments that you received and changes that you could make going forward.

4.6.3 Paper

At about this point, Paper One Appendix A.1 would be appropriate.