• ggplot follows a “grammar of graphics” that is a little different from the rest of R’s coding structure
• Basic components of a ggplot: a dataset (dataframe), aesthetics (aes), geoms, and formatting
  • ggplot takes dataframes as the basic input, not an x vector and y vector
  • geom’s are different types of plot objects that you can add to the plot (e.g. points, lines, bars, etc.)
  • The aes (short for aesthetics) command tells ggplot which variables in the dataset represent the x values, y values, color, size, etc.
    • You can set aes either in the main ggplot call or within a geom
• The official ggplot cheatsheet is great! Use this as a quick reference once you get the general idea

Try running this code for yourself! (Be sure you have downloaded the datasets folder and placed it in your working directory first!)

library(ggplot2)
library(readr)

# Load data from csv
mobilityData = read_csv('datasets/Trips_by_Distance.csv')

# Calculate the percent of the population staying home
mobilityData$PercentHome = 
  100 * mobilityData$`Population Staying at Home` /
  (mobilityData$`Population Staying at Home` + mobilityData$`Population Not Staying at Home`)

ggplot(mobilityData, aes(x = Date, y = PercentHome)) +
  
  geom_point() + 
  
  labs(title="Percent of Michiganders staying home over 2019 - 2020", 
       x="", y="Percent of population staying home")

Let’s also add a rolling average (this will be another geom!), and format our colors a bit more.

Since we’ll have different y axis variables for our different geoms (regular and rolling average), we’ll move the aes command into our geoms.

Let’s also set the colors for our plot using a hex code!

Just in case you haven’t seen hex colors before!

• They’re formatted like this: # RR GG BB (or sometimes # RR GG BB A)

• But the numbering system is hexadecimal so it goes:
  1, 2, 3, 4, 5, 6, 7, 8, 9, A, B, C, D, E, F

• For example: #008800, #00FF00, #9500AB, #00FFFF, #00AAAA

ggplot(mobilityData) +
  
  geom_point(aes(x = Date, y = PercentHome), color = "#2255AA", alpha = 0.25) + 
  
  geom_line(aes(x = Date, y = zoo::rollmean(PercentHome, 7, fill = NA)), 
            color = "#2255AA", size = 1) + 
  
  labs(title="Percent of Michiganders staying home over 2019 - 2020", 
       x="", y="Percent of population staying home")