First steps in data analysis with R

Data analysis from zero to hero

This course is aimed at those that already have a theoretical understanding of statistical concepts and want to learn the practical side of data analysis.

What you’ll learn

  • Develop a reliable data analysis framework.
  • Refresh your statistical knowledge in a visual, intuitive way.
  • Visualise your data with publication-ready figures.
  • Become confident with testing general linear models: regression, ANOVA, etc..
  • Learn and apply the principles of hypothesis testing and model selection.
  • Introduction to generalised linear models and to non-linear modelling.

Course Content

  • Introduction –> 7 lectures • 23min.
  • Housework –> 4 lectures • 9min.
  • Comparing two groups with t.test() –> 2 lectures • 17min.
  • Introducing function lm() –> 1 lecture • 9min.
  • Testing model assumptions with diagnostic plots –> 1 lecture • 8min.
  • Comparing more than two groups: an ANOVA scenario –> 1 lecture • 15min.
  • Linear regression –> 2 lectures • 9min.
  • Advanced data visualization with ggplot2 –> 1 lecture • 10min.
  • An ANCOVA scenario: testing the effect of two predictors on a response variable –> 1 lecture • 13min.
  • Testing the effect of two factorial predictors on a response variable –> 1 lecture • 9min.
  • Multiple regression –> 1 lecture • 14min.
  • Polynomial regression –> 2 lectures • 9min.
  • Fitting user-defined non-linear models –> 1 lecture • 9min.
  • Dealing with non-normality of residuals –> 1 lecture • 7min.
  • Data wrangling –> 1 lecture • 7min.
  • Final remarks –> 1 lecture • 4min.

First steps in data analysis with R

Requirements

This course is aimed at those that already have a theoretical understanding of statistical concepts and want to learn the practical side of data analysis.

Learning how to analyse data can be a daunting test. Applying the statistical knowledge learned from books to real-world scenarios can be challenging, and it’s often made harder by seemingly complicated data analysis softwares.

This course will help you to develop a reliable data analysis pipeline, creating a solid basis that will make it easy for you to further your data analysis skills throughout your career.

We will use R, a free, state-of-the-art software environment for modelling, data handling, data analysis, and data visualisation.

We will start from installing R and taking baby steps to become familiar with the R programming language. We will then learn how to load data in R, how to visualise them with publication-level quality graphs, and how to analyse them.

I will provide you with the scripts that I use throughout the course, so that you can easily use them and adapt them to your own research objectives.

We will learn R one small step at a time, starting from absolute zero:

· how to enter data in R

· how to visualise data using function plot() and package ggplot2

· how to fit, interpret, and evaluate general linear models for a variety of study designs, including t test, ANOVA, regression, ANCOVA, and multiple regression scenarios

· how to fit polynomial regression

· an introduction to user-defined non-linear models

· an introduction to generalised linear models for non-normally distributed data (case study: count data)

· optimal data organisation and “data wrangling” – merging, subsetting, and summarising data

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