12.9 Application: Spread of Coronavirus

Mapping isn’t the only application where we might want to show an animation. Here is a brief example using ggplot in a different way to reveal trends over time.

We are going to map the spread of confirmed COVID cases based on data from John Hopkins University.

load("covidlongfmt.RData")

We have variables case_count and Country.Region along with date_fmt. Let’s compare trends in confirmed cases between Italy and France.

italyspain <- subset(covidlongfmt, Country.Region %in% c("Italy", "Spain"))

Let’s divide cases by 1000 to make it easier to visualize.

italyspain$case_count_thousands <- italyspain$case_count /1000

With ggplot, this time instead of using geom_polygon, we will use geom_line. Our x-axis will be time, and our y-axis will be the case count.

Again, to animate a ggplot, we just add an argument to indicate the variable that dictates the transition between different states, in this case, the date variable, date_fmt. The other parts of the plot stay very similar to before.

ggplot()+
  geom_line(data=italyspain, aes(x=date_fmt, y=case_count_thousands, 
                                 colour=Country.Region))+
  
  ## Add labels with exact case count
  geom_text_repel(data=italyspain, aes(x=date_fmt,  
                                       y=case_count_thousands,
                                       colour=Country.Region,
                                 label=case_count_thousands))+
 
  ## axis and title labels
  ylab("Cases (thousands)")+
  ggtitle("Confirmed Cases in Italy and Spain")+
  theme_minimal()+
  
  ## We use transition_reveal to slowly reveal the trend
  transition_reveal(date_fmt)+
  labs(x = "Date: {frame_along}")
anim_save("italyspain.gif")

12.9.1 Mapping Animation with World Map

Just like with the terrorism data, we can make a map with the data, too.

To illustrate the process, let’s create a plot for just one day: 2020-03-01.

covidmarch <- subset(covidlongfmt, date_fmt == "2020-03-01")

We create a world map and add points to indicate the case count, with the size proportionate to the count. We include alpha to make the points transparent.

world <- map_data("world")

ggplot()+
  
  ## create the world map
  geom_polygon(data=world, aes(x=long, y=lat, group=group), colour="black", fill="white")+
  
  ## add points
  geom_point(data=covidmarch, aes(x=Long, y=Lat, size=case_count), alpha=.4, colour="red")+
  scale_size(range = c(-1,10)) +
  
  ## aesthetics
  ggtitle("COVID-19 Confirmed Cases on March 1, 2020")+
  coord_quickmap()+
  theme_void()

## Warning: Removed 2 rows containing missing values (geom_point).

Now, we use the full data and transition through the date variable. We then “render” and save the animation.

world <- map_data("world")

ggplot()+
  
  ## create the world map
  geom_polygon(data=world, aes(x=long, y=lat, group=group), colour="black", fill="white")+
  
  ## add points
  geom_point(data=covidlongfmt, aes(x=Long, y=Lat, size=case_count), alpha=.4, colour="red")+
  scale_size(range = c(-1,15)) +
  
  ## aesthetics
  ggtitle("COVID-19 Confirmed Cases")+
  coord_quickmap()+
  theme_void() +
  
  ## add the transition and a label for the plot
  transition_time(date_fmt)+
  labs(title="Date: {frame_time}")
## save animation as a gif
anim_save("covidplot.gif")

How could we improve this visualization?