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GPDI517 - Reproducible Scientific Analysis with R

This is a 5-hour workshop designed for graduate students from Natural and Social Sciences who want to improve their R coding ability. Participants need a working knowledge of R (see Requirements). The purpose of the workshop is to teach the basic skills needed for importing, preparing data for analysis and visualizing scientific data in R in a reproducible manner. A key focus of the workshop is to generate different visualizations, such as violin plots, bar and line plots.
In this workshop, the following topics will be covered:
- Reproducibility: Why is it important? Best practices for writing and sharing code
- Importing data from different file types using the readr package
- Manipulating data using the dplyr package
- Writing code in an Rmarkdown document
- Plotting graphs using ggplot2 and plotly packages

Participants need to be familiar with functions in R, data types, dataframes and basic tasks using dataframes (accessing specific rows or columns, renaming columns, etc.). Participants will need to bring their own laptop, with R (one of the latest versions: 3.4.3 or above) and RStudio installed If you have attended GPDI515 (Beginners Guide to R) or GPDI544 (R Bootcamp), you have all necessary requirements, however many online classes will also get to you to this level (e.g., Lessons 1 and 13 here: ). To learn how to install R and RStudio, please use the following resources:
Mac OS:
We will also be using several R packages through the workshop including readr, dplyr, ggplot2 and plotly.
Here is a quick guide on how to install an R package:

Learning Objectives

At the end of this workshop, participants will be able to:
1. Identify core principles of reproducibility
2. Import data into R;
3. Write data to files;
4. Manipulate data more efficiently using dplyr;
5. Write and execute a Rmarkdown script;
6. Generate publication-quality graphs using ggplot.

Leaders Information

This workshop is led by Alexander Albury.

Alex is a PhD student in Psychology working in the Penhune Lab for Motor Learning and Neural Plasticity. He studies how musical complexity and predictability affect how we learn and experience music. Alex is passionate about data science and programming and enjoys learning and sharing new techniques to make science easier and more accessible.

This workshop is not scheduled at this time.
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