Learn how to model data, its statistical analysis, and visualization in R.

Statistical Analysis with R

R is a popular language for data mining, and is used for bioinformatics and statistical analysis—an essential skill in the era of big data.

This module serves as a comprehensive introduction to the R programming language. We’ll learn to manipulate and analyze data, working with a real-world dataset (published in Ecology in 2013). We’ll start with an introduction to data analysis, before developing rich experimental designs to collect reliable data. Then, we’ll plot our data using R and continue with fundamental statistical analysis. Moreover, we’ll build our understanding of the data with more advanced analyses, using R to perform non-parametric tests and linear models. Finally, we’ll explore advanced data wrangling and predictive modeling.

This module will progress from linear modeling to mixed-effects analyses. By the end, we’ll be able to write advanced functions in R for any dataset.

Developing and Analyzing Statistical Models with R

Let's summarize what we've learned so far.

- **Plotting is necessary:** We've learned that whether base graphics or ggplot2 is used, it's possible to make amazing, professional figures directly in R. 

- **Control over little aspect:** Both plotting methods (base graphics and ggplot2) give tremendous control over every little aspect of the figure.

- **ggplot2:** We discussed that ggplot2 makes it extremely easy to explore the data, and it's better in just about every way imaginable compared to base graphics.

Let's summarize the essential points in the chapter we just covered.

Introduction to R

Thoughts on Proper Data Analysis

Exploratory Data Analysis and Data Summarization

Introduction to Plotting

Basic Statistical Analysis Using R

More Linear Models in R

Advanced Statistical Analysis Using R

Mixed-effects Model

Advanced Data Wrangling and Plotting

Writing Loops and Functions in R

Appendix

Conclusion

Summary