Open-source software

Programming languages such as R and Python offer free, powerful resources for data processing, visualisation and analysis. Experience in these programs is highly valued in data-intensive disciplines.

Open data

Original data has become a public good in many research fields thanks to cultural and technological advances. On the internet, we can find innumerable data sets from sources such as scientific journals and repositories (e.g., OSF), local and national governments (e.g., London, UK [1, 2]), non-governmental organisations (e.g., data.world), etc. Researchers inside and outside academia nowadays share a lot of their data under attribution licences (e.g., Creative Commons, the UK Open Government Licence, etc.). This allows any external analysts to access these raw data, create (additional) visualisations and analyses, and share these. In society, making data more accessible can demonstrably benefit citizens (despite limitations).

Workshop 1: Introduction to R

This workshop can serve as an introduction to R or a revision. It demonstrates what can be done in R, and provides resources for individual training. Since the duration is limited, online courses are also recommended (see examples and fee waivers for full content).

• Data structures
• Packages: general-purpose examples (e.g., tidyverse) and more specific ones (e.g., for statistics or geography)
• Loading and writing data, in native and foreign formats
• Wide format (also dubbed ‘untidy’) versus tidy format (also dubbed ‘long’ or ‘narrow’). For most processes in R, data needs to be in a tidy format.

Image from Postma and Goedhart (2019; https://doi.org/10.1371/journal.pbio.3000202.g001).

• Combining data sets
• Data summaries
• Plots with ggplot2::ggplot()
• Interactive plots with plotly::ggplotly()
• Statistics
• Linear mixed-effects models (see also a review of practices)
• How functions work
• Debugging. Code errors are known as bugs. They can tiresome, but also interesting sometimes! ? Some tips for the first many years of experience include: reading and investigating error messages, in both source and console windows; controlling letter case and typos; closing parentheses and inverted commas; ensuring to have the necessary packages installed and loaded; following the format required by each function. To debug, break up code into subcomponents and test each of those to find out the source of the error. Once we act on that, the best outcome is seeing the code work, but sometimes different errors overlap, in which case we may see one error disappearing before another one appears. Debugging soon leads to proficient information seeking. The search process often begins on an internet search engine and extends to user communities, package documentation, tutorials, blogs… (see video explanation). Advanced debugging tools are also available.
• Vast availability of free resources on the internet, from Coursera and other MOOC sites, RStudio, University of Glasgow, Carpentries, etc.
• Community: StackOverflow, RStudio Community, Github issues (e.g., for R packages), etc. Using and contributing back.
• RStudio Cloud: a personal RStudio environment on the internet

Workshop 2: R Markdown documents

Set your input and output in stone using R Markdown. The analysis reports may be enriched with website features (HTML/CSS) and published as HTML, PDF or Word documents. Moreover, with R packages such as bookdown, bookdownplus, blogdown and flexdashboard, documents can be formatted as websites, digital papers and books and data dashboards. Other useful packages include rmarkdown, knitr, DT, kableExtra and ggplot2. Further background: presentation by Michael Frank, slides by Ed Berry.

As well as facilitating the reproducibility of analyses and results to third parties, R Markdown is helpful during the creation of a report. In particular, it reduces the chances of errors and the number of repetitive tasks. For instance, any part of the data can be inputted in the text directly from the source, rather than manually copying it (e.g., `r mean(dat[dat$location=='Havana', 'measure'])` (expand). Thus, if and when the analysis needs to be changed or updated, the report can be automatically updated at the click of a button. In another area, the captions for figures and tables can be automatised using cross-reference labels (e.g., Table \@ref(tab:mtcars)). This secures the match between the text and the captions of figures and tables, and it automatically updates the numbering whenever and wherever a new figure or table is introduced.

Image from bookdownplus package (https://bookdownplus.netlify.com/portfolio/).

Workshop 3: Introduction to data dashboards

Data dashboards are web applications used to visualise data in detail through tables and plots. They assist in explaining and accounting for our data processing and analysis. They don’t require any coding from the end user. While most dashboards and web applications present existing data, a few of them serve the purpose of creating or simulating new data (see example).

These all-reproducible dashboards are published as websites, and thus, they can include hyperlinks and downloadable files. Some of the R packages used are knitr, DT, kableExtra, reactable, ggplot2, plotly, rmarkdown, flexdashboard and shiny. The aim of this workshop is to practise creating different forms of dashboards—Flexdashboard and Shiny—the latter of which offers greater features, and to practise also with the hosting platforms fitting each type—such as personal websites, RPubs, Binder, Shinyapps and custom servers.

A great thing about dashboards is that they may be made very simple, but they can also be taken to the next level using some HTML, CSS or Javascript code (on top of the back-end code present in the R packages used), which is addressed in the next workshop.

Workshop 4: Binder environments and improving data dashboards

<!-- Javascript function to enable a hovering tooltip -->
<script>
$(document).ready(function(){
$('[data-toggle="tooltip1"]').tooltip();
});
</script>

```r
# Number of words selected on sidebar
# reactive(cat(paste0('Words selected below: ', nrow(selected_props()))))
```

pdf('List of plots per page', width = 13, height = 5)
print(plot1)
print(plot2)
# ...
print(plot150)
dev.off()

Prerequisites and suggestions for participation in each workshop

Necessary: laptop or computer with R and RStudio installed, or access to RStudio Cloud; familiarity with the content of the preceding workshops through the web links herein.

Suggested: having your own data and R code ready (on a Github repository if participating in Workshop 4); participation in some of the preceding workshops.

Prerequisites and suggestions for participation in datathons

Necessary: basic knowledge of reproducible documents or dashboards.

Suggested: familiarity with the development of reproducible documents or dashboards; an idea about the data you’d like to work with and the kind of document or dashboard you want to create.