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

Now that we have a data frame, let’s plot a histogram of our numeric variables. We can dress it up by adding color, changing the axes’ limits, and adding the main title.

**Tip:** Look at the help file for the `hist()` function.

# Assignment

Now that we have a data frame, let’s plot a histogram of our numeric variables. We can dress it up by adding color, changing the axes’ limits, and adding the main title.

**Tip:** Look at the help file for the `hist()` function.

Use what we’ve learned so far to design plots for numerical 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: Create Plots with Numerical Data

Assignment