Posted by j.laird on 12 November 2021 - 9:00am Photo by Icons8 Team on Unsplash

Since September we’ve been running a Learning to Code mentorship programme as part of our Research Software Camp: Beyond the Spreadsheet, focussing on the uses of spreadsheets in research and the next steps into further use of software. Volunteer research software engineers mentored researchers to help them learn a coding language of their choice. 

We spoke to four participants in the programme about their experiences of learning to code in a podcast episode hosted by Code for Thought, the podcast on software, engineering, research and anything in between.

Listen to the podcast here.

You can also read blog posts written by the participants here:

• Don't be afraid! Anyone can learn to code by Emma Karoune.
• Learning to code: my experience as a Psychology graduate by Amirah Khan.
• Reading Human Biomechanics Data into R by Rebecca Hamilton.
• Below-the-line conversations: A computer-facilitated case study of Guardian reader comments by Yenn Lee.

You can also see our other guides and blog posts around learning to code from the Research Software Camp.

Mentee	Mentor	Project
Rebecca Hamilton	Heather Turner	Introduction to R scripts and MatLab for biomechanics dataset integration and analysis
Emma Karoune	Jamie Quinn	Reproducing analysis and data visualisations in R on a project that had used Excel
Amirah Khan	Sadie Bartholomew	Manipulation of data stored in spreadsheets using Python tools
Yenn Lee	Mario Antonioletti	Below-the-line conversations: A computer-facilitated case study of Guardian reader comments

Thank you to the mentors for giving your time and expertise to make this programme possible.

Our Research Software Camps introduce and explore a different topic around research software. They run for two weeks with content including panel discussions, live Q&As, workshops, guides, blogs and more. Sign up to our mailing list to receive updates about future Camps.

Below are some useful resources recommended by the Learning to Code participants.

R:

• RStudio Education beginners page: https://education.rstudio.com/learn/beginner/ - Start Here! I recommend:
  • Install , RStudio, and R packages like the tidyverse 
  • Spend an hour with A Gentle Introduction to Tidy Statistics In R.  
  • RStudio.cloud Primers: you can work on these in the cloud, even before installing R. 
    • To start with: The Basics (inspect, visualise, subset, transform data); 
    • Next steps: Work with Data (extract, subset, transform, summarise data, dplyr); Visualize Data (with ggplot2); Tidy Your Data (reshape data, join data sets, tidyr).
    • Further down the line: Iterate (using purrr), Write Functions,  Report Reproducibly (with R markdown), Build Interactive Web Apps (with Shiny).
• Data Carpentry workshop: https://datacarpentry.org/R-ecology-lesson/index.html: similar to the RStudio primers (basics/next steps), but is one cohesive workshop and also has lesson on R & SQL.  
• SPSS to R notes: https://www.melissagwolf.com/spss-to-r/ conversion of standard statistical analyses from SPSS to R. Probably not what you need right now, but could be helpful to replicate something you’re used to seeing.

Matlab:

• Programming with Matlab: http://swcarpentry.github.io/matlab-novice-inflammation/ - Looks best as a basic introduction to read in data, subset, plot and write Matlab scripts
• Data Science with Matlab: https://www.cdslab.org/matlab/notes/values-variables-types/values/index.html - actually a more comprehensive guide to Matlab programming, maybe something to bookmark if you need to go further.
• Official Matlab tutorials: https://matlabacademy.mathworks.com/ - these look quite practical, especially “MATLAB Onramp” and “MATLAB for Data Processing and Visualization” courses. Not clear if they are free for you - would be good to know!
• Official Matlab doc: https://uk.mathworks.com/help/matlab/data-import-and-analysis.html - again seems more focused on data analysis tasks than most tutorials I could find.
• Matlab help forum: https://uk.mathworks.com/matlabcentral/answers/index 

Continuing after RSE Camp:

• Tidyverse Cookbook: https://rstudio-education.github.io/tidyverse-cookbook 
  • Visualize Data chapter has examples of basic ggplot2 customisation with quick links, e.g. “Change the names within a legend”, “Change the colors used in a plot”
• RStudio cheatsheets: https://www.rstudio.com/resources/cheatsheets/
  • One for each tidyverse package. Very dense information! Use to search for a function of interest, e.g. “geom_ribbon”, then look at the package website for examples, e.g. https://ggplot2.tidyverse.org/reference/geom_ribbon.html - scroll to examples!
• ggplot2 - Essentials: http://www.sthda.com/english/wiki/ggplot2-essentials 
  • Actually a comprehensive reference: has sections on customizing particular types of plots, e.g. “Line plots” as well as sections on types of customization, e.g. “ Add text annotations to a graph”. Bit of a spammy site unfortunately (e.g. pop-up ads) but useful.
• R-Ladies community Slack: https://rladies-community-slack.herokuapp.com/ 
  • Friendly place where you can ask for help if you get stuck, or find out about talks/workshops that may be of interest.
• Cardiff R User Group: https://www.meetup.com/Cardiff-R-User-Group/ 
  • Used to have regular informal pub meetups (turn up and trouble-shoot R issues/share what you’re working on) and occasional events with more formal talks. Since COVID, have had occasional Zoom meetings, mostly working on Tidy Tuesday example data sets (https://github.com/rfordatascience/tidytuesday), plus a Shiny tutorial (tidyverse package for building web apps). New R users very welcome! 
  • Find your own local R group here: https://benubah.github.io/r-community-explorer/rugs.html