• Welcome
  • Companion Web site
  • Dedication
  • 1 Introduction
    • 1.1 Why programming?
    • 1.2 Why R?
    • 1.3 Is this book for you?
  • 2 Get Started With R in a Few Easy Steps
    • 2.1 What we’ll cover
    • 2.2 Download R and RStudio
    • 2.3 A brief introduction to RStudio
      • 2.3.1 The console
      • 2.3.2 Other RStudio panes
      • 2.3.3 RStudio Projects
    • 2.4 Try out the console
      • 2.4.1 Create your first object
      • 2.4.2 Data types you’re likely to use often
    • 2.5 Install packages
    • 2.6 Additional infrastructure
    • 2.7 Getting help with packages and functions
    • 2.8 RStudio keyboard shortcuts
    • 2.9 Additional files available online
    • 2.10 Wrap-Up
    • 2.11 Additional resources
  • 3 See How Much You Can Do in a Few Lines of Code
    • 3.1 Packages needed in this chapter
    • 3.2 What we’ll cover
    • 3.3 Simple stock market graphing
    • 3.4 Download and graph a city’s median income
    • 3.5 So many packages!
    • 3.6 Running functions without loading packages
    • 3.7 Comparing one city’s data to the US median
    • 3.8 Run a remote script to make an interactive map
    • 3.9 Bonus map: Mapping income data
    • 3.10 Wrap-Up
    • 3.11 Additional resources
  • 4 Import Data into R
    • 4.1 What we’ll cover
    • 4.2 Packages needed in this chapter
    • 4.3 The magic of rio
      • 4.3.1 Import a file from the Web
    • 4.4 Import data from packages
      • 4.4.1 When the data’s not ideally formatted
    • 4.5 What’s a data frame? And what can you do with one?
      • 4.5.1 When a number’s not really a number
    • 4.6 Easy sample data
      • 4.6.1 Create a data frame manually within R
    • 4.7 Exporting data
    • 4.8 Additional resources
  • 5 Basic Data Exploration
    • 5.1 Project: Weather data
    • 5.2 What we’ll cover
    • 5.3 Packages needed in this chapter
    • 5.4 Download this book’s files
    • 5.5 Data summaries
    • 5.6 Data ‘interviews’
    • 5.7 Slicing and dicing your data set
    • 5.8 More subsetting with dplyr
    • 5.9 Wrap-Up
    • 5.10 Additional resources
  • 6 Beginning data visualization
  • 7 Two or more data sets
  • 8 Analyze data by groups
  • 9 Graphing by Group
  • 10 Write your own R functions
  • 11 Maps in R
  • 12 Putting it all Together: R on Election Day
    • 12.1 Project: Election data
    • 12.2 What we’ll cover
    • 12.3 Packages needed in this chapter
    • 12.4 Election Day preparation
      • 12.4.1 Step 1: Configure data files
      • 12.4.2 Step 2: Load packages
      • 12.4.3 Step 3: Import data.
      • 12.4.4 Step 4: Examine (and wrangle) the data
      • 12.4.5 Step 5: Who won and by how much?
      • 12.4.6 Step 6: Exploratory visualizations
    • 12.5 Visualizing election results
    • 12.6 Graph for a smaller set of results
    • 12.7 plotly
    • 12.8 Other interactive alternatives
      • 12.8.1 taucharts
      • 12.8.2 highcharter
      • 12.8.3 metricsgraphics
    • 12.9 Wrap-up
    • 12.10 (Non-election) inspiration
    • 12.11 Additional resources
  • 13 Date calculations
  • 14 Help! My data’s in the wrong format!
  • 15 Integrate R With Your Storytelling Using R Markdown
  • 16 Simple Web scraping
  • 17 An R project from start to finish
  • 18 Additional resources
  • Appendix A Online: How do I …
  • Appendix B Online: Functions
  • Appendix C Online: Packages

Practical R for Mass Communication and Journalism

12.3 Packages needed in this chapter

pacman::p_load(dplyr, ggplot2, magrittr, tmap, tmaptools, leaflet, stringr, janitor, 
               readr, DT, rio, htmlwidgets, plotly)
pacman::p_load_gh("smach/rmiscutils")
pacman::p_load_gh("hrbrmstr/taucharts")