R Essential Training Part 2: Modeling Data

Learn how to model data in R, one of the most important tools available for data analysis, machine learning, and data science.

Introduction

• Model data with R
• Using the exercise files

1. R for Data Science

• Data science with R: A case study

2. Exploring Data

• Computing frequencies
• Computing descriptive statistics
• Computing correlations
• Creating contingency tables
• Conducting a principal component analysis
• Conducting an item analysis
• Conducting a confirmatory factor analysis

3. Analyzing Data

• Comparing proportions
• Comparing one mean to a population: One-sample t-test
• Comparing paired means: Paired samples t-test
• Comparing two means: Independent samples t-test
• Comparing multiple means: One-factor analysis of variance
• Comparing means with multiple categorical predictors: Factorial analysis of variance

4. Predicting Outcomes

• Predicting outcomes with linear regression
• Predicting outcomes with lasso regression
• Predicting outcomes with quantile regression
• Predicting outcomes with logistic regression
• Predicting outcomes with Poisson or log-linear regression
• Assessing predictions with blocked-entry models

5. Clustering and Classifying Cases

• Grouping cases with hierarchical clustering
• Grouping cases with k-means clustering
• Classifying cases with k-nearest neighbors
• Classifying cases with decision tree analysis
• Creating ensemble models with random forest classification

Conclusion

• Next steps