All Countries

Level of Dengue risk around the world

library(denguedatahub)
library(tsibble)
level_of_risk

# A tibble: 293 × 4
   country                  level_of_risk       region last_accessed
   <chr>                    <chr>               <chr>  <date>       
 1 Angola                   Sporadic/Uncertain  Africa 2023-01-16   
 2 Benin                    Sporadic/Uncertain  Africa 2023-01-16   
 3 Burkina Faso             Frequent/Continuous Africa 2023-01-16   
 4 Burundi                  Sporadic/Uncertain  Africa 2023-01-16   
 5 Cameroon                 Sporadic/Uncertain  Africa 2023-01-16   
 6 Cape Verde               Sporadic/Uncertain  Africa 2023-01-16   
 7 Central African Republic Sporadic/Uncertain  Africa 2023-01-16   
 8 Chad                     Sporadic/Uncertain  Africa 2023-01-16   
 9 Comoros                  Sporadic/Uncertain  Africa 2023-01-16   
10 Congo                    Sporadic/Uncertain  Africa 2023-01-16   
# ℹ 283 more rows

Summarize and Visualize level_of_riskdata

library(tidyverse)
tab1 <- level_of_risk |> 
  filter(last_accessed == "2024-09-03") |>
  group_by(region, level_of_risk) |>
  summarise(count = n(), .groups = 'drop')

ggplot(tab1, aes(region, count, fill = level_of_risk)) + 
  geom_bar(stat = "identity", position = position_dodge(preserve = "single")) + 
  theme(legend.position = "bottom") + 
  xlab("Region") + 
  ylab("Number of countries") + 
  scale_fill_brewer(palette = "Dark2") +
  scale_x_discrete(drop = TRUE)

Presence of dengue incidence in 2019

library(denguedatahub)
library(ggplot2)
library(maps)
library(magrittr)
library(viridis)

# Filter for the year 2019
worlddata2019 <- dplyr::filter(world_annual, year == 2019)

# Enhanced plot with caption
ggplot(worlddata2019, aes(x = long, y = lat, group = group, fill = factor(dengue.present))) +
  geom_polygon(color = "black") +
  coord_fixed(1.3) +  # Ensures correct aspect ratio
  scale_fill_viridis_d(name = "Dengue Presence", labels = c("No", "Yes")) +  # Using viridis for better color representation
  labs(
    title = "Global Dengue Distribution in 2019",
    subtitle = "Presence of Dengue Virus Reported by Country",
    x = "Longitude",
    y = "Latitude",
    caption = "Author: Thiyanga S. Talagala, Source: https://denguedatahub.netlify.app/world.html"  # Adding caption
  ) +
  theme_minimal() +
  theme(
    legend.position = "bottom",
    plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5, size = 12),
    axis.text = element_text(size = 10),
    axis.title = element_text(size = 12)
  )

Relationship with longitude and latitude

library(tidyverse)
sum.ll <-  world_annual %>%
  filter(dengue.present == "yes") %>%
  group_by(region, year) %>%
  summarize(mean.longitude = mean(long),
          mean.latitude = mean(lat),
          incidence = sum(incidence))  

sum.ll %>% filter(0 < incidence & incidence < 100000000) %>%
ggplot( aes(x=mean.longitude, y=incidence, col=mean.latitude)) + geom_point(alpha=0.5) +
  scale_color_viridis_c() + theme_bw() + 
  facet_wrap(~year) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

sum.ll %>% filter(0 < incidence & incidence < 100000000) %>%
ggplot( aes(x=mean.latitude, y=incidence, col=mean.longitude)) + geom_point(alpha=0.5) +
  scale_color_viridis_c() +
  theme_bw()+ 
  facet_wrap(~year) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

Relationship with absolute values of longitude and latitude

sum.ll %>% filter(0 < incidence & incidence < 100000000) %>%
ggplot( aes( y=incidence, x=abs(mean.latitude))) + geom_point(alpha=0.5) +
   theme_bw() + 
  facet_wrap(~year) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

sum.ll %>% filter(0 < incidence & incidence < 100000000) %>%
ggplot( aes(x=abs(mean.longitude), y=incidence)) + geom_point(alpha=0.5) +
   theme_bw() + 
  facet_wrap(~year) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

Dynamic visualization

library(gganimate)
sum.ll$year <- as.integer(sum.ll$year)
sum.ll %>% filter(0 < incidence & incidence < 100000000) %>%
ggplot( aes( y=incidence, x=abs(mean.latitude), col=mean.longitude)) + geom_point(alpha=0.5) +
   theme_bw() + 
   scale_color_viridis_c() +
 theme(
   axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + 
   transition_time(year) +
  labs(title = "Year: {frame_time}")