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

## Assignment

Create box and whisker plots of `SVL.initial` or `Mass.final` for each group. What does this reveal about animals that emerged earlier or later during the window of metamorphosis?

# Assignment

Create box and whisker plots of `SVL.initial` or `Mass.final` for each group. What does this reveal about animals that emerged earlier or later during the window of metamorphosis?

Use what you’ve learned so far to create different box and whisker plots.

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

Exercise: Create Box and Whisker Plots

Assignment