A heat map animation of daily deaths - r

Question

I have a data frame that has columns Region, Date, and Deaths, and I've imported the package "maps" and its 50 state map.
All of the examples I've seen ask me to merge() the data with the map. However, when I do this merging, I manage to end up with an object of over 4 million rows.
The daily data is in melted8 and then melted9.
Because of the huge size of the merge(), the animate() step takes a long time to run... in fact I cut it short after 10 minutes. I do not know if my ggplot() is correctly made, but it is also huge (240 mb).
Is there a more reasonably-sized object I could give to ggplot(), and am I giving ggplot() the right instructions?
# a sample
melted8[sample(nrow(melted8), 5), ]
region date deaths
<chr> <int> <dbl>
arizona 214 7.2815030
missouri 287 0.0000000
arkansas 160 0.3313668
mississippi 53 0.0000000
new jersey 300 0.7880939
library(ggplot2)
library(gganimate)
library(maps)
us.map <- map_data("state") #50 state map from library(maps)
melted9 <- merge(us.map, melted8, by="region", all.x=T)
d <- ggplot(melted9) +
geom_polygon(aes(long,lat, group = group), color='white', fill=NA, data=us.map) +
geom_polygon(aes(long,lat, group = group, fill = deaths), color = "white") +
scale_fill_gradient(low = "gray65", high = "red") +
labs(title = "Deaths per Day") +
ease_aes("linear")
a <- animate(d, duration = 30, nframes = nrow(melted9)/50, end_pause = 5)
a

Answer 1

You don't have to merge the dataset with the map file, if you use geom_map instead of geom_polygon.
See if this is faster for you:
layer_type.GeomMap <- function(x) 'point' # must run this line first
melted8 %>%
ggplot(aes(fill = deaths, map_id = region)) +
geom_map(map = us.map) +
expand_limits(x = us.map$long, y = us.map$lat) +
coord_fixed() +
scale_fill_gradient(low = "gray65", high = "red") +
theme(legend.position = "bottom") +
labs(title = "Deaths per Day: {closest_state}",
x = "lon", y = "lat") +
transition_states(date)
Dataset used (simulating 7 days of records for each state):
library(dplyr)
set.seed(123)
melted8 <- data.frame(region = unique(us.map$region)) %>%
mutate(date = list(seq(1, 7))) %>%
tidyr::unnest(cols = c(date)) %>%
group_by(region) %>%
mutate(deaths = abs(rnorm(n()))) %>%
ungroup()