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

Use the `filter()` function to subset the data to just those froglets that emerged during or after the first 30 days of the period of metamorphosis.
>**Tip:** Use the `Age.FromEmergence` variable for this exercise.



# Assignment

Use the `filter()` function to subset the data to just those froglets that emerged during or after the first 30 days of the period of metamorphosis.
>**Tip:** Use the `Age.FromEmergence` variable for this exercise.



Use what we’ve learned so far to manipulate/filter the data.

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: Hands-on Data Filtering/Manipulation

Assignment