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 [1] Introduction

  [1] Make your data make sense.srt

  [2] Using the exercise files.srt

 [2] 1. What Is R

  [1] R in context.srt

  [2] Data science with R A case study.srt

 [3] 2. Getting Started

  [10] Piping commands with %%.srt

  [1] Installing R.srt

  [2] Environments for R.srt

  [3] Installing RStudio.srt

  [4] Navigating the RStudio environment.srt

  [5] Entering data.srt

  [6] Data types and structures.srt

  [7] Comments and headers.srt

  [8] Packages for R.srt

  [9] The tidyverse.srt

 [4] 3. Importing Data

  [1] R's built-in datasets.srt

  [2] Exploring sample datasets with pacman.srt

  [3] Importing data from a spreadsheet.srt

  [4] Importing XML data.srt

  [5] Importing JSON data.srt

  [6] Saving data in native R formats.srt

 [5] 4. Visualizing Data with ggplot2

  [1] Introduction to ggplot2.srt

  [2] Using colors in R.srt

  [3] Using color palettes.srt

  [4] Creating bar charts.srt

  [5] Creating histograms.srt

  [6] Creating box plots.srt

  [7] Creating scatterplots.srt

  [8] Creating multiple graphs.srt

  [9] Creating cluster charts.srt

 [6] 5. Wrangling Data

  [10] Filtering cases and subgroups.srt

  [1] Creating tidy data.srt

  [2] Using tibbles.srt

  [3] Using data.table.srt

  [4] Converting data from wide to tall and from tall to wide.srt

  [5] Converting data from tables to rows.srt

  [6] Working with dates and times.srt

  [7] Working with list data.srt

  [8] Working with XML data.srt

  [9] Working with categorical variables.srt

 [7] 6. Recoding Data

  [1] Recoding categorical data.srt

  [2] Recoding quantitative data.srt

  [3] Transforming outliers.srt

  [4] Creating scale scores by counting.srt

  [5] Creating scale scores by averaging.srt

 [8] Conclusion

  [1] Next steps.srt

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