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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

### 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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