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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.
Topics include:

Installing R on your computer
Using the built-in datasets
Importing data
Creating bar and pie charts for categorical variables
Creating histograms and box plots for quantitative variables
Calculating frequencies and descriptives
Transforming variables
Coding missing data
Analyzing by subgroups
Creating charts for associations
Calculating correlations
Creating charts and statistics for three or more variables
Creating crosstabs for categorical variables

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### Course Curriculum

 Section 0: Introduction 0.1. Welcome 00:00:00 0.2. Using the exercise files 00:00:00 0.3. Using the challenges 00:20:00 Section 1: Getting Started 1.1. Installing R on your computer 00:35:00 1.2. Using RStudio 00:00:00 1.3. Taking a first lool at the interface 00:00:00 1.4. Instal and managing packages 00:00:00 1.5. Using built-in datasets in R 00:00:00 1.6. Entering data manually 00:00:00 1.7. Importing data 00:00:00 1.8. Converting tabular data to row data 00:00:00 1.9. Working with color in R 00:00:00 1.10. Exploring color with Colorbrewer FREE 00:30:00 1.11. Challenge Creating color palettes in R 00:20:00 1.12. Solution Creating color palettes in R 00:35:00 Section 2: Charts for One Variable 2.1. Creating bar charts for categorical variables 00:00:00 2.2. Creating pie charts for categorical variables 00:00:00 2.3. Creating histograms for quantitative variables 00:00:00 2.4. Creating box plots for quantitative variables 00:00:00 2.5. Overlaying plots 00:00:00 2.6. Saving images 00:00:00 2.7. Challenge Layering plots 00:00:00 2.8. Solution Layering plots 00:00:00 Section 3: Statistics for One Variable 3.1. Calculating frequencies 00:00:00 3.2. Calculating descriptives 00:00:00 3.3. Using a single proportion Hypothesis test and confidence interval 00:00:00 3.4. Using a single mean Hypothesis test and confidence interval 00:00:00 3.5. Using a single categorical variable One sample chi-square test 00:00:00 3.6. Examining robust statistics for univariate analyses 00:00:00 3.7. Challenge Calculating descriptive statistics 00:00:00 3.8. Solution Calculating descriptive statistics 00:00:00 Section 4: Modifying Data 4.1. Examining outliers 00:00:00 4.2.Transforming variables 00:00:00 4. 3. Computing composite variables 00:00:00 4.4. Coding missing data 00:00:00 4.5. Challenge Transforming skewed data to pull in outliers 00:00:00 4.6. Solution Transforming skewed data to pull in outliers 00:00:00 Section 5: Working with the Data File 5.1. Selecting cases 00:00:00 5.2. Analyzing by subgroup 00:00:00 5.3. Merging files 00:00:00 5.4. Challenge Analyzing guinea pig data subgroups 00:00:00 5.5. Solution Analyzing guinea pig data subgroups 00:00:00 Section 6: Charts for Associations 6.1. Creating bar charts of group means 00:00:00 6.2. Creating grouped box plots 00:00:00 6.3. Creating scatter plots 00:00:00 6.4. Challenge Creating your own grouped box plots 00:00:00 6.5. Solution Creating your own grouped box plots 00:00:00 Section 7:Statistics for Associations 7.1. Calculating correlation 00:00:00 7.2. Computing a bivariate regression 00:00:00 7.3. Comparing means with the t-test 00:00:00 7.4. Comparing paired means Paired t-test 00:00:00 7.5. Comparing means with a one-factor analysis of variance (ANOVA).mp4 00:00:00 7.6. Comparing proportions 00:00:00 7.7. Creating cross tabs for categorical variables 00:00:00 7.8. Computing robust statistics for bivariate associations 00:00:00 7.9. Challenge Comparing proportions across several different groups 00:00:00 7.10. Solution Comparing proportions across several different groups 00:00:00 Section 8: Charts for Three or More Variables 8.1. Creating clustered bar charts for means 00:00:00 8.2. Creating scatter plots for grouped data 00:00:00 8.3. Creating scatter plot matrices 00:00:00 8.4. Creating 3D scatter plots 00:00:00 8.5. Challenge Creating your own scatter plot matrix 00:00:00 8. 6. Solution Creating your own scatter plot matrix 00:00:00 Section 9: Statistics for Three or More Variables 9.1. Computing a multiple regression 00:00:00 9.2. Comparing means with a two-factor ANOVA 00:00:00 9.3. Conducting a cluster analysis 00:00:00 9.4. Conducting a principal componentsfactor analysis 00:00:00 9.5. Challenge Creating a cluster analysis of states in the US.mp4 00:00:00 9.6. Solution Creating a cluster analysis of states in the US 00:00:00 Section 10: Conclusion 10.1. Next steps.mp4 00:00:00

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• FREE
• 90 Days
• Course Certificate
20 STUDENTS ENROLLED
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