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Developing and Analyzing Statistical Models with R

Developing and Analyzing Statistical Models with R

Discover how to manipulate, analyze, and model data using R. Gain insights into data analysis, designing experiments, non-parametric tests, linear models, and advanced predictive modeling.

Intermediate

108 Lessons

23h

Certificate of Completion

Discover how to manipulate, analyze, and model data using R. Gain insights into data analysis, designing experiments, non-parametric tests, linear models, and advanced predictive modeling.

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This course includes

437 Playgrounds
9 Quizzes

This course includes

437 Playgrounds
9 Quizzes
Course Overview
What You'll Learn
Course Content

Course Overview

R is a programming language for organizing and analyzing statistical data. R is a popular language for data mining, used for bioinformatics and statistical analysis - an essential skill in the era of big data. This course serves as a comprehensive introduction to the R programming language. You’ll learn to manipulate and analyze data working with a real-world dataset (published in Ecology in 2013). You’ll start with an introduction to data analysis before developing rich experimental designs to collect rel...Show More

What You'll Learn

Get familiarized with the basics of R via properly structured chapters and assignments.

Hands-on experience in typing along with the instructions will help overcome the "fear of the R prompt."

Learn to analyze data and think critically about data presented in research and the public realm.

Learn how to seek information to help solve problems and errors with the examples provided.

What You'll Learn

Get familiarized with the basics of R via properly structured chapters and assignments.

Show more

Course Content

1.

Course Introduction

Get familiar with practical data analysis using R, focusing on bridging theory and real-world application.
About This Course
Goals and Course Layout
2.

Introduction to R

Look at the basics of R, objects, functions, data representation, and essential data structures.
Basics of R
Objects in R
Functions in R
Data Representation in R
Vectors and Matrices in R
Data Frames and Lists in R
Summary
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
3.

Thoughts on Proper Data Analysis

Examine key principles of experimental design, fair hypothesis evaluation, and selecting appropriate models in statistics.
Experimental Designs
Plan an Experiment
Decide Between Competing Analyses
Summary
4.

Exploratory Data Analysis and Data Summarization

Discover methods for data exploration, error checking, manipulation, and visualization in R using practical exercises and solutions.
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
5.

Introduction to Plotting

Take a closer look at creating professional and customizable data visualizations in R.
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
6.

Basic Statistical Analysis Using R

11 Lessons

Simplify complex topics in statistical analysis with R, including normality testing, linear models, and nonparametric tests.
7.

More Linear Models in R

15 Lessons

Master the steps to analyze linear models, perform ANOVA/ANCOVA, and visualize results in R.
8.

Advanced Statistical Analysis Using R

9 Lessons

Step through advanced statistical models in R to analyze non-normal data and visualize results.
9.

Mixed-effects Model

10 Lessons

Get started with developing mixed-effects models in R, diagnostics, model selection, and analysis.
10.

Advanced Data Wrangling and Plotting

11 Lessons

Work your way through advanced data manipulation and visualization techniques using the tidyverse and ggplot2.
11.

Writing Loops and Functions in R

8 Lessons

Enhance your skills in writing loops and functions in R to streamline data analysis.
12.

Appendix

2 Lessons

Get started with R setup, enhance coding with RStudio, and emphasize annotated code for clarity.
13.

Conclusion

2 Lessons

Focus on the importance of understanding data and creating effective figures.

Course Author

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