52M Data Analysis in R
Introduction
This module introduces you to data analysis in R. The first 4 weeks covers core concepts about scientific computing, types of variable, the role of variables in analysis and how to use RStudio to organise analysis and import, summarise and plot data. In weeks 5 to 8, you will learn about the logic of hypothesis testing, confidence intervals, what is meant by a statistical model, two-sample tests and one-way analysis of variance (ANOVA). You will learn how to write reproducible reports in Quarto in weeks 9 and 10. Finally, there will be a drop-in for your questions in week 11.
This module complement the work you will do in BIO00070M Research, Professional and Team Skills where you will you will learn how to organise reproducible data analyses using a project-oriented workflow and analyses RNA sequence data. It will be important to use the skills and tools you learn in 52M and apply them in 70M.
Module Learning Objectives
The Module Learning outcomes are:
Explain the purpose of data analysis and the rationale for scripting analysis in the biosciences
Recognise when statistics such as t-tests, one-way ANOVA, correlation and regression can be applied, and use R to perform these analyses on data in a variety of formats
Summarise data in single or multiple groups, recognise tidy data formats, and carry out some typical data tidying tasks
Use markdown (through Quarto) to produce reproducible analyses, figures and reports
How 52M is organised
A key feature of 52M is that you really do learn as you go along and you should not need to revise very much. To support this learning, every week is structured in the same way with contact time and well-guided independent study to prepare you for the contact time and consolidate what you have learned.
Each week has:
An overview on the “About” page which gives the Learning Objectives, a topic summary and the instructions for the week. You should read this first.
Some independent study on the “Prepare!” page to prepare you for the workshop. This will be reading from the course book (Computational Analysis for Bioscientists), watching a video, or doing some coding or set up. It is designed to take about 30-45 mins on average. You will most likely learn best if you can find people to study with.
A two-hour workshop using R. This will usually start with me doing a short demonstration of one or more of the examples that were in “Prepare!” but you will spend most of the session going through some exercises. Anything you have not done before is explained and guided but you will also have to use the skills gained in previous workshops. I often remind you to take care of future you by making notes so you can look up your previous work but you can also search the Data Analysis in R site (search is top right). Talking to other people in the workshop about the exercises and working together will really help you understand more. There will be plenty of help from me and my demonstrators.
Some independent study on the “Consolidate!” page to give you more practice. The exercises are usually similar to those in the workshop but with less guidance. Occasionally, there will be reading to do. It is designed to take about 30-45 mins on average but may be quicker if you understood the workshop very well or slower if you need to revisit the workshop.
Learning Data Analysis in R is like learning to speak a new language or play an instrument or a technical sport - you can’t really rush it or cram for it. You need regular practice.
a little bit of engagement and practice is always better than none
if you get behind, just pick up where you left off rather than jumping in. It is fine to work on a previous week’s workshop
Content
Week 1: Understanding file systems
You will learn about operating systems, files and file systems, working directories, absolute and relative paths, what R and RStudio are
Week 2: Introduction to R and project organisation
You will start writing R code in RStudio and will create your first graph! You will learn about data types such as “numerics” and “characters” and some of the different types of objects in R such as “vectors” and “dataframes”. These are the building blocks for the rest of your R journey. You will also learn a workflow and about the layout of RStudio and using RStudio Projects.
Week 3: Types of variable, summarising and plotting data
The type of values our data can take is important in how we analyse and visualise it. This week you will learn the difference between continuous and discrete values and how we summarise and visualise them. The focus will be on plotting and summarising single variables. You will also learn how to read in data in to RStudio from plain text files and Excel files.
Week 4: Summarising data with several variables
This week you will start plotting data sets with more than one variable. This means you need to be able determine which variable is the response and which is the explanatory. You will find out what is meant by “tidy” data and how to perform a simple data tidying task. Finally you will discover how to save your figures and place them in documents.
Week 5: The logic of hypothesis testing and CI
This week we will cover the logic of consider the logic of hypothesis testing and type 1 and type 2 errors. We will also find out what the sampling distribution of the mean and the standard error are, and how to calculate confidence intervals.
Week 6: Introduction to statistical models: Single regression
This week you will be introduced to the idea of a statistical “model” in general and to general linear model in particular. Our first general linear model will be single linear regression which puts a line of best fit through data so the response can be predicted from the explanatory variable. We will consider the two “parameters” estimated by the model (the slope and the intercept) and whether these differ from zero
Week 7: Two-sample tests
This week you will how to use and interpret the general linear model when the x variable is categorical and has two groups. Just as with single linear regression, the model puts a line of best through data and the model parameters, the intercept and the slope, have the same in interpretation The intercept is one of the group means and the slope is the difference between that, mean and the other group mean. You will also learn about the non-parametric equivalents - the tests we use when the assumptions of the general linear model are not met.
Week 8: One-way ANOVA and Kruskal-Wallis
Last week you learnt how to use and interpret the general linear model when the x variable was categorical with two groups. You will now extend that to situations when there are more than two groups. This is often known as the one-way ANOVA (analysis of variance). You will also learn about the Kruskal- Wallis test which can be used when the assumptions of the general linear model are not met.
Week 9: Assessment intro
This week, we will introduce you to the assessment for this module. We will look at a specimen assessment using techniques you have already learned and apply them to analysing a dataset. Your assessment will use a different dataset but you will apply the same principles. We will be covering what your assessment submission should contain using this example.
Week 10: Reproducible Reporting
Following on from last week’s introduction to the assessment. You will be introduced to quarto, which is a way of generating reproducible reports and you will need for your assessment. This will cover how to embed sections and executable chunks of R code within your quarto files.
Week 11: Drop-in
This session contains no set material however, we will cover topics that people have had difficulty with during the course and cover any material you may still be struggling with from the workshops. This will be our last timetabled session to ask questions prior to the asessment release and a good opportunity to ask any outstanding questions. No questions are silly questions!