Getting started

  1. Clone your repo appex04-[github_name] to create a new project in RStudio Cloud under the STA 199 class space.

  2. Configure git

    library(usethis)
use_git_config(user.name="your name", user.email="your email")

Packages and Data

We’ll make use of the following packages.

library(sf)
library(tidyverse)

There are two data files we will use: world.shp, coronavirus.shp. We’ll read each of these in with function st_read() and save them as world and virus.

world <- st_read("data/world.shp", quiet = TRUE)
virus <- st_read("data/coronavirus.shp", quiet = TRUE)

Tasks

Task 1

Take a look at objects world and virus. How many fields exist for each object? What type of geometry is associated with each sf object?

world
virus

Task 2

Part 1

Use object world to create a world map of the countries. You’ll want to use functions ggplot() and geom_sf().

Part 2

Build on your map from Part 1 so that the countries have a fill color associated with the population estimate. Variable pop_est is in millions. Be sure to label your map.

Task 3

Filter world for the country “China”. Save the result as china.

china <- world %>% 
  filter(name == "China")

Next, we’ll filter object virus for confirmed cases in China and save the result as china_cv. The code is given below.

china_cv <- virus %>% 
  filter(cntry_r == "Mainland China", !is.na(confrmd))

  • cntry_r == "Mainland China" filters rows so we only keep information on China

  • !is.na(confrmd) filters rows for where there is not a missing confirmed case of coronavirus

Task 4

Use the template provided to overlay the point locations of the coronavirus in China with a map of China. Have the size of the points reflect the number of confirmed cases. Refer to slides 31-32 as a guide.

ggplot(data = china) +
  geom_sf(fill = "#DE2910")

Hints:

  • you’ll need another geom_sf() layer
  • set point colors as color = "#FFDE00"
  • inside this second geom_sf() include show.legend = "point" to have the legend show points rather than squares

Submission

Stage, commit and push

  1. Stage your modified files.
  2. Commit your changes with an informative message.
  3. Push your changes to your GitHub repo.
  4. Verify your files were updated on GitHub.

Slide notes

References

  1. Simple Features for R vignette, https://r-spatial.github.io/sf/