1.9 Workshop: Software we will use

• What will we use?
• R (only viable competitor is Python)²
  • Check out this chapter.
  • We’ll rely on various packages that you install with the code below

install.packages("pacman")
library(pacman)
p_load(readr, tibble, knitr, kableExtra, stargazer, 
                 gridExtra, ggthemes, colorspace, gt, gtrendsR,
                 CGPfunctions, lubridate, ggrepel, ggcorrplot,
                 GGally, here, rgdal, sf, cowplot,
                 gganimate, gapminder, plotly, printr, shiny,
                 rsconnect, countrycode, WDI, leaflet, HistData,
                 tidyverse, purrr, stringr, tidyr, broom,
                 gtrendsR, ggnewscale, sparkline, htmlwidgets, ggmap,
                 rnaturalearth, rnaturalearthdata, rgeos, ggrepel,
                 update = FALSE) # Set TRUE to update all

• Ggplot2³
  • Base R vs. Ggplot2 (e.g., 1 and 2, 3, 4)
  • I was in the “base R camp” but
    • Weirdly I came to ggplot over plotly (through this project)
    • Now a huge fan of ggplot2: Why? (Q: Latex? or R vs. C++)
• Plotly⁴
• Note: Ideally cite the software you use (especially when it is open-source)

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2. Creators: Core contributors and thousands of package authors.↩

3. Creators: https://github.com/tidyverse/ggplot2↩

4. Creators: https://github.com/plotly/plotly.js; https://github.com/ropensci/plotly↩