Complete Guide to R Wrangling Visualizing and Modeling Data

• Complete Guide to R Wrangling Visualizing and Modeling Data
• 1. Introduction/1. Make your data make sense.mp4 4.3 MB
• 1. Introduction/2. Using the exercise files.mp4 1.7 MB
• 10. Analyzing Data/1. Comparing proportions.mp4 24.3 MB
• 10. Analyzing Data/2. Comparing one mean to a population- One-sample t-test.mp4 16.1 MB
• 10. Analyzing Data/3. Comparing paired means- Paired samples t-test.mp4 23.2 MB
• 10. Analyzing Data/4. Comparing two means- Independent samples t-test.mp4 21 MB
• 10. Analyzing Data/5. Comparing multiple means- One-factor analysis of variance.mp4 29.9 MB
• 10. Analyzing Data/6. Comparing means with multiple categorical predictors- Factorial analysis of variance.mp4 21.8 MB
• 11. Predicting Outcomes/1. Predicting outcomes with linear regression.mp4 27.7 MB
• 11. Predicting Outcomes/2. Predicting outcomes with lasso regression.mp4 24.7 MB
• 11. Predicting Outcomes/3. Predicting outcomes with quantile regression.mp4 18.3 MB
• 11. Predicting Outcomes/4. Predicting outcomes with logistic regression.mp4 31.3 MB
• 11. Predicting Outcomes/5. Predicting outcomes with Poisson or log-linear regression.mp4 9.3 MB
• 11. Predicting Outcomes/6. Assessing predictions with blocked-entry models.mp4 30.6 MB
• 12. Clustering and Classifying Cases/1. Grouping cases with hierarchical clustering.mp4 29.2 MB
• 12. Clustering and Classifying Cases/2. Grouping cases with k-means clustering.mp4 22.9 MB
• 12. Clustering and Classifying Cases/3. Classifying cases with k-nearest neighbors.mp4 30.6 MB
• 12. Clustering and Classifying Cases/4. Classifying cases with decision tree analysis.mp4 24.4 MB
• 12. Clustering and Classifying Cases/5. Creating ensemble models with random forest classification.mp4 24 MB
• 13. Conclusion/1. Next steps.mp4 5.7 MB
• 2. What Is R/1. R in context.mp4 13.5 MB
• 2. What Is R/2. Data science with R- A case study.mp4 30.6 MB
• 3. Getting Started/1. Installing R.mp4 5.1 MB
• 3. Getting Started/10. Piping commands with %%.mp4 7.9 MB
• 3. Getting Started/2. Environments for R.mp4 11.2 MB
• 3. Getting Started/3. Installing RStudio.mp4 3.3 MB
• 3. Getting Started/4. Navigating the RStudio environment.mp4 17.4 MB
• 3. Getting Started/5. Entering data.mp4 17 MB
• 3. Getting Started/6. Data types and structures.mp4 28.7 MB
• 3. Getting Started/7. Comments and headers.mp4 13.1 MB
• 3. Getting Started/8. Packages for R.mp4 17 MB
• 3. Getting Started/9. The tidyverse.mp4 9.3 MB
• 4. Importing Data/1. Rs built-in datasets.mp4 16.3 MB
• 4. Importing Data/2. Exploring sample datasets with pacman.mp4 21.3 MB
• 4. Importing Data/3. Importing data from a spreadsheet.mp4 18 MB
• 4. Importing Data/4. Importing XML data.mp4 19.3 MB
• 4. Importing Data/5. Importing JSON data.mp4 21.3 MB
• 4. Importing Data/6. Saving data in native R formats.mp4 19.8 MB
• 5. Visualizing Data with ggplot2/1. Introduction to ggplot2.mp4 14.8 MB
• 5. Visualizing Data with ggplot2/2. Using colors in R.mp4 16 MB
• 5. Visualizing Data with ggplot2/3. Using color palettes.mp4 26.5 MB
• 5. Visualizing Data with ggplot2/4. Creating bar charts.mp4 24.2 MB
• 5. Visualizing Data with ggplot2/5. Creating histograms.mp4 13.1 MB
• 5. Visualizing Data with ggplot2/6. Creating box plots.mp4 12.4 MB
• 5. Visualizing Data with ggplot2/7. Creating scatterplots.mp4 14.1 MB
• 5. Visualizing Data with ggplot2/8. Creating multiple graphs.mp4 10 MB
• 5. Visualizing Data with ggplot2/9. Creating cluster charts.mp4 25.2 MB
• 6. Wrangling Data/1. Creating tidy data.mp4 34.3 MB
• 6. Wrangling Data/10. Filtering cases and subgroups.mp4 22 MB
• 6. Wrangling Data/2. Using tibbles.mp4 13.3 MB
• 6. Wrangling Data/3. Using data.table.mp4 15.8 MB
• 6. Wrangling Data/4. Converting data from wide to tall and from tall to wide.mp4 11.4 MB
• 6. Wrangling Data/5. Converting data from tables to rows.mp4 14.9 MB
• 6. Wrangling Data/6. Working with dates and times.mp4 18.8 MB
• 6. Wrangling Data/7. Working with list data.mp4 14.9 MB
• 6. Wrangling Data/8. Working with XML data.mp4 17.2 MB
• 6. Wrangling Data/9. Working with categorical variables.mp4 15.5 MB
• 7. Recoding Data/1. Recoding categorical data.mp4 23.9 MB
• 7. Recoding Data/2. Recoding quantitative data.mp4 22.1 MB
• 7. Recoding Data/3. Transforming outliers.mp4 21.5 MB
• 7. Recoding Data/4. Creating scale scores by counting.mp4 14.4 MB
• 7. Recoding Data/5. Creating scale scores by averaging.mp4 8.5 MB
• 8. An R for Data Science Case Study/1. Data science with R- A case study.mp4 57.4 MB
• 9. Exploring Data/1. Computing frequencies.mp4 14.4 MB
• 9. Exploring Data/2. Computing descriptive statistics.mp4 25.3 MB
• 9. Exploring Data/3. Computing correlations.mp4 16.6 MB
• 9. Exploring Data/4. Creating contingency tables.mp4 16.8 MB
• 9. Exploring Data/5. Conducting a principal component analysis.mp4 37.8 MB
• 9. Exploring Data/6. Conducting an item analysis.mp4 52.1 MB
• 9. Exploring Data/7. Conducting a confirmatory factor analysis.mp4 18.1 MB
• Ex_Files_Complete_Guide_to_R.zip 6.2 MB

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