R Statistics Essential Training (2013)

R is the language of big data—a statistical programming language that helps describe, mine, and test relationships between large amounts of data. Author Barton Poulson shows how to use R to model statistical relationships using graphs, calculations, tests, and other analysis tools. Learn how to enter and modify data; create charts, scatter plots, and histograms; examine outliers; calculate correlations; and compute regressions, bivariate associations, and statistics for three or more variables. Challenge exercises with step-by-step solutions allow you to test your skills as you progress.

Introduction 1m 59s
Welcome 58s
Using the exercise files 20s
Using the challenges 41s

1. Getting Started 1h 24m
Installing R on your computer 3m 19s
Using RStudio 4m 36s
Taking a first look at the interface 10m 56s
Installing and managing packages 11m 17s
Using built-in datasets in R 5m 27s
Entering data manually 4m 37s
Importing data 8m 53s
Converting tabular data to row data 13m 6s
Working with color in R 10m 18s
Exploring color with Colorbrewer 6m 36s
Challenge: Creating color palettes in R 1m 56s
Solution: Creating color palettes in R 3m 54s

2. Charts for One Variable 44m 33s
Creating bar charts for categorical variables 8m 32s
Creating pie charts for categorical variables 6m 47s
Creating histograms for quantitative variables 6m 2s
Creating box plots for quantitative variables 7m 12s
Overlaying plots 7m 25s
Saving images 5m 34s
Challenge: Layering plots 39s
Solution: Layering plots 2m 22s

3. Statistics for One Variable 33m 18s
Calculating frequencies 3m 33s
Calculating descriptives 5m 43s
Using a single proportion: Hypothesis test and confidence interval 4m 2s
Using a single mean: Hypothesis test and confidence interval 4m 9s
Using a single categorical variable: One sample chi-square test 5m 9s
Examining robust statistics for univariate analyses 7m 29s
Challenge: Calculating descriptive statistics 36s
Solution: Calculating descriptive statistics 2m 37s

4. Modifying Data 32m 5s
Examining outliers 6m 42s
Transforming variables 9m 26s
Computing composite variables 6m 18s
Coding missing data 6m 4s
Challenge: Transforming skewed data to pull in outliers 42s
Solution: Transforming skewed data to pull in outliers 2m 53s

5. Working with the Data File 16m 4s
Selecting cases 5m 30s
Analyzing by subgroup 3m 14s
Merging files 5m 16s
Challenge: Analyzing guinea pig data subgroups 33s
Solution: Analyzing guinea pig data subgroups 1m 31s

6. Charts for Associations 19m 38s
Creating bar charts of group means 4m 33s
Creating grouped box plots 5m 4s
Creating scatter plots 5m 2s
Challenge: Creating your own grouped box plots 48s
Solution: Creating your own grouped box plots 4m 11s

7. Statistics for Associations 55m 1s
Calculating correlation 3m 55s
Computing a bivariate regression 6m 13s
Comparing means with the t-test 7m 20s
Comparing paired means: Paired t-test 6m 35s
Comparing means with a one-factor analysis of variance (ANOVA) 8m 27s
Comparing proportions 3m 34s
Creating cross tabs for categorical variables 5m 10s
Computing robust statistics for bivariate associations 8m 39s
Challenge: Comparing proportions across several different groups 1m 1s
Solution: Comparing proportions across several different groups 4m 7s

8. Charts for Three or More Variables 23m 18s
Creating clustered bar charts for means 3m 43s
Creating scatter plots for grouped data 2m 49s
Creating scatter plot matrices 6m 10s
Creating 3D scatter plots 5m 13s
Challenge: Creating your own scatter plot matrix 42s
Solution: Creating your own scatter plot matrix 4m 41s

9. Statistics for Three or More Variables 45m 14s
Computing a multiple regression 9m 26s
Comparing means with a two-factor ANOVA 5m 56s
Conducting a cluster analysis 14m 14s
Conducting a principal components/factor analysis 10m 31s
Challenge: Creating a cluster analysis of states in the US 39s
Solution: Creating a cluster analysis of states in the US 4m 28s

Conclusion 3m 40s
Next steps 3m 40s