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R for Reproducible Psychological Research

Overview

  • Credit value: 15 credits at Level 6
  • Convenor: Dr Marie Smith
  • Assessment: two coursework assessments (40% and 60%)

Module description

This module will introduce you to the fundamentals of programming and data science using R Studio, an interactive environment for statistical computing, data visualisation and research. It will focus on learning the tools necessary to be able to analyse and visualise data via a scripted, open workflow as a cornerstone to building transparency and reproducibility in psychological research. The module assumes knowledge of statistical approaches covered in the Introduction to Research Methods and Research Methods 1 modules. You will need access to a PC or Mac with R Studio installed.

Indicative module syllabus

  • Getting started in R Studio, and learning the basics of object-orientated programming
    • Installing R and R Studio
    • Objects and assignments
    • Getting help
    • Workspaces (how to save stuff)
    • Reading data in and out
    • Intro to R packages
  • Working with data in R
    • Basic data types and structures
    • Data visualisation with base R
    • Data vis with ggplot2
    • Reshaping data frames in base R
    • Advanced functions and/r packages
    • Difference between base R and tidyverse packages
  • Application of statistical approaches in R
    • T tests
    • Correlation and regression
    • ANOVA
    • Categorical data
  • Programming in R
    • Scripting
    • Piping
    • Loops
    • Conditional statements
    • Writing functions
    • Debugging
  • R Markdown for reproducibility
    • Intro to R Markdown for project management
    • Basics of R Markdown + R code chunk
    • Knitr and output formats

Learning objectives

By the end of this module, you will be able to:

  • import and export data into/out of R Studio
  • understand R data types and structures
  • manipulate and clean data into an appropriate format for analysis
  • perform descriptive and inferential statistical techniques learnt in IRM and RM1 (e.g. t-test, correlation, ANOVA)
  • produce informative data visualisations using base R and ggplot2
  • understand the basic principles of programming and scripting
  • produce reproducible reports using R Markdown.