Listen to our Learning to Code podcast

Posted by j.laird on 12 November 2021 - 9:00am
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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:

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:

Continuing after RSE Camp:


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