Push the knit button!

library(tidyverse) # contains ggplot2, dplyr, tidyr, etc
filter <- dplyr::filter
select <- dplyr::select
library(leaflet)
library(plotly)
library(gganimate)
library(ggthemes)
library(lubridate)

tuberculosis dataset

tb <- read_csv(here::here("data/TB_notifications_2020-07-01.csv")) %>% 
  dplyr::select(country, iso3, year, new_sp_m04:new_sp_fu) %>%
  pivot_longer(cols=new_sp_m04:new_sp_fu, names_to="sexage", values_to="count") %>%
  mutate(sexage = str_replace(sexage, "new_sp_", "")) %>%
  mutate(sex=substr(sexage, 1, 1), 
         age=substr(sexage, 2, length(sexage))) %>%
  dplyr::select(-sexage) %>%
  filter(!(age %in% c("04", "014", "514", "u"))) %>%
  filter(year > 1996, year < 2013) %>%
  mutate(age_group = factor(age, 
                            labels = c("15-24", "25-34", "35-44", 
                                       "45-54", "55-64", "65-"))) %>%
  dplyr::select(country, year, age_group, sex, count)

# Filter Australia
tb_oz <- tb %>% 
  filter(country == "Australia") 

# Aggregate Australian counts by year
tb_oz_yearly <- tb_oz %>%
  group_by(country, year) %>%
  summarise(count = sum(count))

platypus dataset

load(here::here("data/platypus.rda")) 
platypus <- platypus %>%
  mutate(year = year(eventDate)) %>%
  filter(year > 2018)

Exercise 3.1: Leaflet with different colour

platypus %>%
  leaflet() %>%
  addTiles() %>%
  addCircleMarkers(
    radius=1, opacity = 0.5, color = "purple", label = ~eventDate,
    lat = ~Latitude, lng = ~Longitude)

Exercise 3.2: Add colour to plotly highlighting

Remember this code:

tb_action <- highlight_key(tb_oz, ~age_group)
p2 <- ggplot(tb_action, aes(x=year, y=count)) +
  geom_line(aes(group=age_group)) +
  geom_smooth() + 
  facet_wrap(~sex)  

gg <- ggplotly(p2, height = 300, width = 600) %>%
   layout(title = "Click on a line to highlight an age group")

highlight(gg)

Use this code to make the base plot, and then check that highlighting still works:

p2 <- ggplot(tb_action, aes(x=year, y=count)) +
  geom_line(aes(colour=age_group, group=age_group)) +
  facet_wrap(~sex)  
p2

Exercise 3.3: Animate TB counts for Australia on a map

world_map <- map_data("world")

australia <- world_map %>%
  filter(region == "Australia")

tb_yr_map <- australia %>% 
  left_join(tb_oz_yearly, by=c("region" = "country")) 

tb_map <- ggplot(tb_yr_map, 
                 aes(x=long, y=lat, group=group)) + 
    geom_polygon(aes(fill=count), #<<
             color="grey70", size=0.1, na.rm=TRUE) +  #<<
    scale_fill_distiller("Count", palette = "RdPu",
                         direction = 1, 
                         limits = c(0, 500)) +
    guides(col = guide_legend(nrow=1)) +
    theme_map() +
    geom_text(aes(x=135, y=-25, 
                  label = paste0(round(year, 0), ": ",
                                 round(count, 0))),
              colour="white", size=5) +
    theme(legend.position = "none") +
    transition_states(year, 1, 10, wrap=FALSE) #<<
animate(tb_map, nframes=80, fps=10, #<<
        width=2, height=2, units = "in", res = 150)