malaria.Rmd

---
title: "Malaria Atlas"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r}
library(tidyverse)
theme_set(theme_light())

library(malariaAtlas)
```

```{r}
kenya_pr <- tbl_df(malariaAtlas::getPR(ISO = "KEN", species = "BOTH")) %>%
  filter(!is.na(pr))
```

```{r}
kenya_pr %>%
  group_by(year_start) %>%
  summarize(examined = sum(examined),
            positive = sum(positive),
            studies = n()) %>%
  mutate(pr = positive / examined) %>%
  ggplot(aes(year_start, pr)) +
  geom_line()

kenya_pr %>%
  mutate(decade = 10 * (year_start %/% 10)) %>%
  arrange(pr) %>%
  ggplot(aes(longitude, latitude, color = pr)) +
  borders("world", regions = "Kenya") +
  geom_point() +
  scale_color_gradient2(low = "blue", high = "red", midpoint = .5, labels = scales::percent_format()) +
  facet_wrap(~ decade) +
  theme_void() +
  coord_map() +
  labs(color = "Prevalence")
```

### Aggregated across countries

```{r}
malaria_inc <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2018/2018-11-13/malaria_inc.csv")

malaria_inc_processed <- malaria_inc %>%
  setNames(c("country", "code", "year", "incidence")) %>%
  mutate(incidence = incidence / 1000)
```

```{r}
malaria_inc_processed %>%
  filter(country %in% sample(unique(country), 6)) %>%
  ggplot(aes(year, incidence, color = country)) +
  geom_line() +
  scale_y_continuous(labels = scales::percent_format())
```

Looking at 2015 levels and the change from 2015 to 2000

```{r}
malaria_spread <- malaria_inc_processed %>%
  mutate(year = paste0("Y", year)) %>%
  spread(year, incidence)

malaria_spread %>%
  filter(country != "Turkey",
         !is.na(code)) %>%
  mutate(current = Y2015,
         change =  Y2015 - Y2000) %>%
  ggplot(aes(current, change)) +
  geom_point() +
  geom_text(aes(label = code), vjust = 1, hjust = 1)
```

```{r}
world <- map_data("world") %>%
  filter(region != "Antarctica")

malaria_inc_processed %>%
  filter(incidence < 1) %>%
  inner_join(maps::iso3166 %>%
               select(a3, mapname), by = c(code = "a3")) %>%
  inner_join(world, by = c(mapname = "region")) %>%
  ggplot(aes(long, lat, group = group, fill = incidence)) +
  geom_polygon() +
  scale_fill_gradient2(low = "blue", high = "red", midpoint = .20, labels = scales::percent_format()) +
  coord_map() +
  facet_wrap(~ year) +
  theme_void() +
  labs(title = "Malaria incidence over time around the world")
```

### Malaria deaths over time


```{r}
library(tidyverse)

malaria_deaths <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2018/2018-11-13/malaria_deaths.csv")

malaria_deaths_processed <- malaria_deaths %>%
  setNames(c("country", "code", "year", "deaths"))
```

```{r}
malaria_deaths_processed %>%
  filter(country %in% sample(unique(country), 6)) %>%
  ggplot(aes(year, deaths, color = country)) +
  geom_line() +
  labs(y = "Deaths per 100,000")
```

```{r}
library(fuzzyjoin)
library(stringr)

malaria_country_data <- malaria_deaths_processed %>%
  inner_join(maps::iso3166 %>%
               select(a3, mapname), by = c(code = "a3")) %>%
  mutate(mapname = str_remove(mapname, "\\(.*"))

malaria_map_data <- map_data("world") %>%
  filter(region != "Antarctica") %>%
  tbl_df() %>%
  inner_join(malaria_country_data, by = c(region = "mapname"))

malaria_map_data %>%
  ggplot(aes(long, lat, group = group, fill = deaths)) +
  geom_polygon() +
  scale_fill_gradient2(low = "blue", high = "red", midpoint = 100) +
  theme_void() +
  labs(title = "Malaria deaths over time around the world",
       fill = "Deaths per 100,000")
```

```{r}
library(gganimate)
library(countrycode)
library(gifski)

malaria_map_data %>%
  mutate(continent = countrycode(code, "iso3c", "continent")) %>%
  filter(continent == "Africa") %>%
  ggplot(aes(long, lat, group = group, fill = deaths), renderer = gifski_renderer()) +
  geom_polygon() +
  scale_fill_gradient2(low = "blue", high = "red", midpoint = 100) +
  theme_void() +
  labs(title = "Malaria deaths over time in Africa ({ current_frame })",
       fill = "Deaths per 100,000") +
  transition_manual(year)

anim_save("malaria_map.gif")
```