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Developing and Analyzing Statistical Models with R

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Course Introduction

About This Course Goals and Course Layout

Introduction to R

Data Representation in R

Vectors and Matrices in R

Data Frames and Lists in R

Exercise: Create Factors and a Data Frame

Solution: Create Factors and a Data Frame

Exercise: Create Plots with Numerical Data

Solution: Create Plots with Numerical Data

Exercise: Create Plots with Categorical Data

Solution: Create Plots with Categorical Data

Quiz: Introduction to R

Thoughts on Proper Data Analysis

Experimental Designs Plan an Experiment Decide Between Competing Analyses Summary

Exploratory Data Analysis and Data Summarization

About the Data Data Exploration and Error Checking Fixing Mistakes in the Data Summarizing and Manipulating Data Summary Exercise: Hands-on Data Filtering/Manipulation Solution: Hands-on Data Filtering/Manipulation Exercise: Create Box and Whisker Plots Solution: Create Box and Whisker Plots Quiz: Exploratory Data Analysis

Introduction to Plotting

Introduction to Plotting Different Types of Plotting Techniques Plotting Data Using gplots Plotting Data Using ggplot2 Summary Exercise: Create Plots with Facets, Colors and Descriptive Labels Solution: Create Plots with Facets, Colors and Descriptive Labels Exercise: Create Enhanced Bar Plots Solution: Create Enhanced Bar Plots Quiz: Introduction to Plotting

Basic Statistical Analysis Using R

Basic Statistical Analysis Testing for Normality in Our Data Nonparametric Tests Introducing Linear Models Multiple Comparisons Summary Exercise: Conduct a Series of One-way ANOVAs Solution: Conduct a Series of One-way ANOVAs Exercise: Examine the Effects of Log-transformation on Results Solution: Examine the Effects of Log-transformation on Results Quiz: Basic Statistical Analysis Using R

More Linear Models in R

Introduction to Multi-way ANOVA More Linear Models Linear Regression Plotting the Regression Analysis of Covariance (ANCOVA)The Predict() Function Plotting with ggplot() Instead of qplot()Summary Exercise: Analyze Data with Multiple Predictors Solution: Analyze Data with Multiple Predictors Exercise: Perform Analysis of Covariance (ANCOVA) and Plot Solution: Perform Analysis of Covariance (ANCOVA) and Plot Exercise: Statistical Analysis with Different Interactions Solution: Statistical Analysis with Different Interactions Quiz: More Linear Models

Advanced Statistical Analysis Using R

Generalized Linear Models Understanding and Interpreting the GLM GLMs and ANCOVAs Summary Exercise: Explore the Data with Multiple Predictors Solution: Explore Data with Multiple Predictors Exercise: Appropriately Visualize the Significant Findings Solution: Appropriately Visualize the Significant Findings Quiz: Advanced Analytical Statistics Using R

Mixed-effects Model

Understanding Mixed-effects Models Diagnostic Plots Generalized Linear Mixed Model (GLMM)More About Model Selection Summary Exercise: Rerun the ANCOVA Models Solution: Rerun the ANCOVA Models Exercise: Compare the AIC Scores Solution: Compare the AIC Scores Quiz: Mixed Effects Models

Advanced Data Wrangling and Plotting

The tidyverse Library Basic Data Wrangling Advanced Data Wrangling Making Better Figures with ggplot2 Customizing a Figure in R Faceting in R Combining Data Wrangling and Plotting with ggplot2 Summary Exercise: Create a Bar Graph for Each Tank in the Experiment Solution: Create a Bar Graph for Each Tank in the Experiment Quiz: Advanced Wrangling and Plotting

Writing Loops and Functions in R

For Loops Functions Writing a Simple Function Writing More Complex Functions Summary Exercise: Automate the Repeated Tasks and Simulate the Data Solution: Automate the Repeated Tasks and Simulate the Data Quiz: Writing Loops and Functions in R

Appendix

Setup on the Local Machine Create Well-documented and Annotated Code

Conclusion

Final Thoughts Copyright Information

Solution: Create Plots with Categorical Data

Let's look at the solution to the previous exercise.

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Solution

Solution

The plot() function is an essential and fundamental function in R. Let’s explore what happens when we give it a single vector of numbers.

When we provide plot() with the variable species, it plots a bar for each category with the height of the bar representing the number of observations in each category. In this case, that means three bars that are each ten units high.

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