Analysis

Set up

library(tidyverse)

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library(plotly)

Attaching package: 'plotly'

The following object is masked from 'package:ggplot2':

    last_plot

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    filter

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library(rnaturalearth)

library(scales)

Attaching package: 'scales'

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    discard

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library(sf)

Linking to GEOS 3.13.1, GDAL 3.10.2, PROJ 9.5.1; sf_use_s2() is TRUE

Load Data

# Load cleaned data
load("data/hivdata.RData")

Q1: Top 10 Countries with Highest Number of Adults Living with HIV In 2022

# Select relevant indicator
df_adult <- df_hiv |>
  filter(series == "Adults (ages 15+) living with HIV") |> 
  select(country, year, series, hiv)

df_adult |>
  filter(year == 2022) |>
  filter(!is.na(hiv)) |> # Remove invalid values
  arrange(desc(hiv)) |>
  head(10)

# A tibble: 10 × 4
   country       year series                                hiv
   <chr>        <int> <chr>                               <dbl>
 1 South Africa  2022 Adults (ages 15+) living with HIV 7400000
 2 India         2022 Adults (ages 15+) living with HIV 2400000
 3 Mozambique    2022 Adults (ages 15+) living with HIV 2300000
 4 Tanzania      2022 Adults (ages 15+) living with HIV 1600000
 5 Uganda        2022 Adults (ages 15+) living with HIV 1400000
 6 Kenya         2022 Adults (ages 15+) living with HIV 1300000
 7 Zambia        2022 Adults (ages 15+) living with HIV 1300000
 8 Zimbabwe      2022 Adults (ages 15+) living with HIV 1200000
 9 Malawi        2022 Adults (ages 15+) living with HIV  950000
10 Ethiopia      2022 Adults (ages 15+) living with HIV  570000

Data Visualization

# List of Top 10 countries with number of adults living with HIV in 2022
adult_list <- c("South Africa", "India", "Mozambique", "Tanzania", "Uganda", 
                  "Kenya", "Zambia", "Zimbabwe", "Malawi", "Ethiopia")

# specify ten colors
colors <- c("South Africa" = "#67000d","India" = "#a50f15", "Mozambique" = "#cb181d","Tanzania" = "#ef3b2c","Uganda" = "#fb6a4a", "Kenya" = "#fcbba1","Zambia" = "#fc9272","Zimbabwe" = "#fee0d2","Malawi" = "#fde0dd","Ethiopia" = "#fff5f0")

p1 <- df_adult|>
  filter(year == 2022) |>
  filter(country %in% adult_list) |> # Match the countries in adult_list
  ggplot(aes(x = reorder(country, hiv), y = hiv, fill = country)) +
  geom_col() +
  scale_fill_manual(values = colors) + # Apply custom colors
  coord_flip() +
  scale_y_continuous(labels = comma_format()) + # Use commas to separate numbers
  labs(title = "Top 10 Countries with Highest Number of Adults Living with HIV In 2022",
       x = "Country",
       y = "Number of Adults Living with HIV",
       fill = "Country",
       caption = "Source: World Bank | Author: Cindy Xie") +
  theme_bw() +
  theme(plot.title = element_text(hjust = 0.5, size = 16, face = "bold"), 
        axis.title.x = element_text(size = 12),
        axis.title.y = element_text(size = 12))

ggplotly(p1)

library(htmlwidgets)

# convert to interactive plot
p1 <- ggplotly(p1)

# save the interactive plot
saveWidget(p1, file = "out/p1.html")

Q2: Top 10 Countries with Highest HIV Infection Rates In 2022 and Trends in the Past Decade

# Select relevant indicator
df_infect <- df_hiv |>
  filter(series == "Prevalence of HIV, total (% of population ages 15-49)") |> 
  select(country, country_code, year, series, hiv)

df_infect|>
  filter(year == 2022) |>
  filter(!is.na(hiv)) |> # Remove invalid values
  arrange(desc(hiv)) |>
  head(10)

# A tibble: 10 × 5
   country           country_code  year series                               hiv
   <chr>             <chr>        <int> <chr>                              <dbl>
 1 Eswatini          SWZ           2022 Prevalence of HIV, total (% of po…  25.9
 2 Lesotho           LSO           2022 Prevalence of HIV, total (% of po…  19.3
 3 South Africa      ZAF           2022 Prevalence of HIV, total (% of po…  17.8
 4 Botswana          BWA           2022 Prevalence of HIV, total (% of po…  16.4
 5 Mozambique        MOZ           2022 Prevalence of HIV, total (% of po…  11.6
 6 Namibia           NAM           2022 Prevalence of HIV, total (% of po…  11  
 7 Zimbabwe          ZWE           2022 Prevalence of HIV, total (% of po…  11  
 8 Zambia            ZMB           2022 Prevalence of HIV, total (% of po…  10.8
 9 Malawi            MWI           2022 Prevalence of HIV, total (% of po…   7.1
10 Equatorial Guinea GNQ           2022 Prevalence of HIV, total (% of po…   6.7

Data Visualization

# Get global country data
world <- ne_countries(scale = "medium", returnclass = "sf")

# Extract African countries (filtered by the continent)
africa <- world |>
  filter(continent == "Africa") |>
  select(iso_a3, geometry)

# Merge HIV infection rates data with Africa map data
df_merge <- africa |>
  left_join(df_infect, by = c("iso_a3" = "country_code"))

# Map of HIV infection rates in African countries in 2022
p2 <- df_merge |>
  filter(year == 2022) |>
  ggplot(aes()) +
  geom_sf(aes(geometry = geometry, fill = hiv)) +
  scale_fill_gradient(low = "#ffffcc", 
                      high = "#f03b20",
                      na.value = "grey90", 
                      name = "HIV Infection Rates(%)", 
                      guide = guide_colorbar(
                        barwidth = 10,
                        barheight = 0.5,
                        title.position = "top",
                        title.hjust = 0.5
                      )) +
  labs(title = "HIV Infection Rates in African Countries in 2022",
       caption = "Source: World Bank | By: Cindy Xie") +
  theme_void() +
  theme(legend.position = "bottom",
        legend.title = element_text(size = 10),
        plot.title = element_text(hjust = 0.5, size = 16, face = "bold"))

# save the plot as a file
ggsave("out/p2.png", p2, width = 11, height = 6, dpi = 300)

p2

# List of Top 10 countries with highest HIV infection rates in 2022
infect_list <- c("Eswatini", "Lesotho", "South Africa", "Botswana", "Mozambique", 
                  "Namibia", "Zimbabwe", "Zambia", "Malawi", "Equatorial Guinea")

## specify ten colors
colors <- c("Eswatini" = "#9e0142","Lesotho" = "#d53e4f", "South Africa" = "#f46d43","Botswana" = "#fdae61","Mozambique" = "#fee08b","Namibia" = "#e6f598","Zimbabwe" = "#abdda4","Zambia" = "#66c2a5","Malawi" = "#3288bd","Equatorial Guinea" = "#5e4fa2")

p3 <- df_infect |>
  filter(country %in% infect_list) |> # Match the countries in infect_list
  ggplot(aes(x = year, y = hiv, color = country)) +
  geom_line(linewidth = 0.7) + # Adjust line thickness
  scale_color_manual(values = colors) +  # Apply custom colors
  labs(title = "Trends in HIV Infection Rates in Top 10 Countries",
       x = "Year",
       y = "Prevalence of HIV, total (% of population ages 15-49)",
       caption = "Source: World Bank | Author: Cindy Xie") +
  theme_bw() +
  facet_wrap(~country) +
  theme(legend.position = "none") +
  theme(plot.title = element_text(hjust = 0.5, size = 16, face = "bold"), 
        axis.title.x = element_text(size = 12),
        axis.title.y = element_text(size = 12))


# save the plot as a file
ggsave("out/p3.png", p3, width = 11, height = 6, dpi = 300)

p3

Q3: Top 10 Countries with Highest Number of AIDS Deaths In 2022

# Select relevant indicator
df_death <- df_hiv |>
  filter(series == "AIDS estimated deaths (UNAIDS estimates)") |> 
  select(country, year, series, hiv)

df_death |>
  filter(year == 2022) |>
  filter(!is.na(hiv)) |> # Remove invalid values
  arrange(desc(hiv)) |>
  head(10)

# A tibble: 10 × 4
   country       year series                                     hiv
   <chr>        <int> <chr>                                    <dbl>
 1 Mozambique    2022 AIDS estimated deaths (UNAIDS estimates) 48000
 2 South Africa  2022 AIDS estimated deaths (UNAIDS estimates) 45000
 3 India         2022 AIDS estimated deaths (UNAIDS estimates) 40000
 4 Indonesia     2022 AIDS estimated deaths (UNAIDS estimates) 26000
 5 Tanzania      2022 AIDS estimated deaths (UNAIDS estimates) 22000
 6 Zimbabwe      2022 AIDS estimated deaths (UNAIDS estimates) 20000
 7 Zambia        2022 AIDS estimated deaths (UNAIDS estimates) 19000
 8 Kenya         2022 AIDS estimated deaths (UNAIDS estimates) 18000
 9 Uganda        2022 AIDS estimated deaths (UNAIDS estimates) 17000
10 Angola        2022 AIDS estimated deaths (UNAIDS estimates) 13000

Data Visualization

# List of Top 10 countries with highest number of AIDS estimated deaths in 2022
death_list <- c("Mozambique", "South Africa", "India", "Indonesia", "Tanzania", 
                  "Zimbabwe", "Zambia", "Kenya", "Uganda", "Angola")

# specify ten colors
colors <- c("Mozambique" = "#800026","South Africa" = "#bd0026", "India" = "#e31a1c","Indonesia" = "#fc4e2a","Tanzania" = "#fd8d3c","Zimbabwe" = "#feb24c","Zambia" = "#fed976","Kenya" = "#ffeda0","Uganda" = "#fff7bc","Angola" = "#ffffcc")

p4 <- df_death|>
  filter(year == 2022) |>
  filter(country %in% death_list) |> # Match the countries in death_list
  ggplot(aes(x = reorder(country, hiv), y = hiv, fill = country)) +
  geom_col() +
  scale_fill_manual(values = colors) + # Apply custom colors
  coord_flip() +
  labs(title = "AIDS Estimated Deaths In 2022",
       x = "Country",
       y = "Number of AIDS estimated deaths",
       fill = "Country",
       caption = "Source: World Bank | Author: Cindy Xie") +
  geom_text(aes(label = round(hiv)), vjust = 0.3, hjust = 1, size = 3) + # Add text labels to charts and adjust their size
  theme_bw() +
  theme(plot.title = element_text(hjust = 0.5, size = 16, face = "bold"), 
        axis.title.x = element_text(size = 12),
        axis.title.y = element_text(size = 12))

# save the plot as a file
ggsave("out/p4.png", p4, width = 11, height = 6, dpi = 300)

p4

Q4: Top 10 Countries with Highest Number of HIV/AIDS Orphans In 2022 and Trends in the past decade

# Select relevant indicator
df_orphan <- df_hiv |>
  filter(series == "Children orphaned by HIV/AIDS") |> 
  select(country, year, series, hiv)

df_orphan |>
  filter(year == 2022) |>
  filter(!is.na(hiv)) |> # Remove invalid values
  arrange(desc(hiv)) |>
  head(10)

# A tibble: 10 × 4
   country           year series                            hiv
   <chr>            <int> <chr>                           <dbl>
 1 Mozambique        2022 Children orphaned by HIV/AIDS 1300000
 2 Tanzania          2022 Children orphaned by HIV/AIDS  890000
 3 South Africa      2022 Children orphaned by HIV/AIDS  720000
 4 Uganda            2022 Children orphaned by HIV/AIDS  660000
 5 Kenya             2022 Children orphaned by HIV/AIDS  590000
 6 Zambia            2022 Children orphaned by HIV/AIDS  580000
 7 Zimbabwe          2022 Children orphaned by HIV/AIDS  490000
 8 Congo, Dem. Rep.  2022 Children orphaned by HIV/AIDS  470000
 9 Malawi            2022 Children orphaned by HIV/AIDS  470000
10 Cameroon          2022 Children orphaned by HIV/AIDS  350000

Data Visualization

# List of Top 10 countries with highest number of HIV/AIDS orphans in 2022
orphan_list <- c("Mozambique", "Tanzania", "South Africa", "Uganda", "Kenya", 
                  "Zambia", "Zimbabwe", "Congo, Dem. Rep.", "Malawi", "Cameroon")

# specify ten colors
colors <- c("Mozambique" = "#a50026","Tanzania" = "#d73027", "South Africa" = "#f46d43","Uganda" = "#fdae61","Kenya" = "#fee090","Zambia" = "#e0f3f8","Zimbabwe" = "#abd9e9","Congo, Dem. Rep." = "#74add1","Malawi" = "#4575b4","Cameroon" = "#313695")


p5 <- df_orphan |>
  filter(country %in% orphan_list) |> # Match the countries in orphan_list
  ggplot(aes(x = year, y = hiv, color = country)) +
  geom_line(linewidth = 0.9) + # Adjust line thickness
  scale_color_manual(values = colors) + # Apply custom colors
  labs(title = "Trends in the Number of HIV/AIDS Orphaned Children in Top 10 Countries",
       x = "Year",
       y = "Children orphaned by HIV/AIDS",
       caption = "Source: World Bank | Author: Cindy Xie") +
  theme_bw() +
  theme(plot.title = element_text(hjust = 0.5, size = 16, face = "bold"), 
        axis.title.x = element_text(size = 12),
        axis.title.y = element_text(size = 12))

# save the plot as a file
ggsave("out/p5.png", p5, width = 11, height = 6, dpi = 300)

p5