The Turning Tide of HIV: Africa’s Decline

Data from the World Bank and visualization techniques using {ggstream}, {ggplot2}, and {paletteer} to illustrate changes in HIV incidence across continents.

About the Data

The data used in this visualization come from the World Bank’s DataBank, accessed programmatically through the R package {wbstats}. Two main indicators were combined: SH.HIV.INCD.ZS — the estimated number of new HIV infections per 1,000 uninfected population aged 15–49, and SP.POP.TOTL — the total population. Together, they provide an approximation of the absolute number of new HIV cases per year by country. The World Bank compiles these indicators from sources such as UNAIDS, the World Health Organization (WHO), and national health ministries, ensuring comparability across time and regions. Additional metadata and definitions are available through the World Development Indicators catalogue.

This graphic shows the estimated annual number of new HIV infections from 1990 to 2022, grouped by continent. The x-axis represents time in years, while the y-axis indicates the approximate number of new HIV cases per year. Each colored stream depicts a continent’s contribution to global new infections, with stream thickness reflecting case volume. The numeric labels above the plot show the estimated global total of new HIV infections for each year, highlighting Africa’s dominant yet declining share.

How I made this graphic?

Loading required libraries, data import & creating custom functions

# Data Import and Wrangling Tools
pacman::p_load(
  tidyverse,    # Data Wrangling and Plotting
  scales,       # Nice scales for ggplot2
  fontawesome,  # Icons display in ggplot2
  ggtext,       # Markdown text support ggplot2
  showtext,     # Display fonts in ggplot2
  colorspace,   # Lighten and darken colours
  patchwork,    # Combining plots together
  magick,       # Image processing and editing
  wbstats,      # World Bank data access
  ggstream,     # Stream Plots in R
  scales        # Nice scales with ggplot2
)


# 1. Download, merge, compute continent & approximate absolute new cases, and total prevalence of HIV/AIDS
rawdf1 <- wb_data(
  "SH.HIV.INCD.ZS", 
  start_date = 1960, 
  end_date = 2024
  )

rawdf2 <- wb_data(
  "SP.POP.TOTL", 
  start_date = 1960, 
  end_date = 2024
  )

rawdf3 <- wb_data(
  "SH.DYN.AIDS.ZS", 
  start_date = 1960, 
  end_date = 2024
)

aids_milestones <- tibble::tibble(
  date = c(
    1981.5,  # mid-year
    1983.5,
    1987.0,
    1995.5,
    1996.5,
    2001.5,
    2003.0,
    2008.0,
    2012.5,
    2014.5,
    2017.5,
    2021.5
  ),
  description = c(
    "First AIDS cases reported by CDC (U.S.)",
    "HIV virus identified as cause of AIDS",
    "AZT approved — first antiretroviral drug",
    "UNAIDS established to coordinate global response",
    "First effective triple therapy (HAART) introduced",
    "UN adopts Declaration of Commitment on HIV/AIDS",
    "PEPFAR launched by the U.S. Government",
    "WHO issues '3 by 5' antiretroviral access initiative",
    "Global HIV incidence begins to decline",
    "UNAIDS 90–90–90 targets announced",
    "WHO recommends 'Treat All' strategy for HIV",
    "UNAIDS launches 95–95–95 targets for 2030"
  )
) |> 
  slice(5, 7, 8, 11)

Visualization Parameters

# Font for titles
font_add_google("Roboto",
  family = "title_font"
) 

# Font for the caption
font_add_google("Saira Extra Condensed",
  family = "caption_font"
) 

# Font for plot text
font_add_google("Roboto Condensed",
  family = "body_font"
) 

showtext_auto()

# A base Colour
bg_col <- "white"
seecolor::print_color(bg_col)

# Colour for highlighted text
text_hil <- "grey40"
seecolor::print_color(text_hil)

# Colour for the text
text_col <- "grey30"
seecolor::print_color(text_col)

line_col <- "grey30"

# Define Base Text Size
bts <- 80


# Caption stuff for the plot
sysfonts::font_add(
  family = "Font Awesome 6 Brands",
  regular = here::here("docs", "Font Awesome 6 Brands-Regular-400.otf")
)
github <- "&#xf09b"
github_username <- "aditya-dahiya"
xtwitter <- "&#xe61b"
xtwitter_username <- "@adityadahiyaias"
social_caption_1 <- glue::glue("<span style='font-family:\"Font Awesome 6 Brands\";'>{github};</span> <span style='color: {text_hil}'>{github_username}  </span>")
social_caption_2 <- glue::glue("<span style='font-family:\"Font Awesome 6 Brands\";'>{xtwitter};</span> <span style='color: {text_hil}'>{xtwitter_username}</span>")
plot_caption <- paste0(
  "**Data:**  World Bank's DataBank",
  " |  **Code:** ",
  social_caption_1,
  " |  **Graphics:** ",
  social_caption_2
)
rm(
  github, github_username, xtwitter,
  xtwitter_username, social_caption_1,
  social_caption_2
)

Annotation Text for the Plot

plot_title <- "Global HIV Epidemic Trends\n(1990–2022)"
str_view(plot_title)

plot_subtitle <- "New HIV infections have declined significantly in Africa, the region with the highest burden, while incidence in other continents remains largely stable." |> 
  str_wrap(60)

str_view(plot_subtitle)

inset_text <- "Data for this visualization come from the World Bank’s DataBank, using indicators **SH.HIV.INCD.ZS** (HIV incidence rate per 1,000 uninfected population aged 15–49) and **SP.POP.TOTL** (total population). Together, these yield an approximate estimate of the absolute number of new HIV infections per year. The World Bank compiles these indicators from UNAIDS, WHO, and national health surveys to enable consistent cross-country and time-series analysis of public health trends." |> 
  str_wrap(50) |> 
  str_replace_all("\\n", "<br>")

str_view(inset_text)

Exploratory Data Analysis & Data Wrangling

bg_col

# 1. Download, merge, compute continent & approximate absolute new cases
df <- rawdf1 |> 
  select(iso2c, iso3c, date, country, SH.HIV.INCD.ZS) |> 
  inner_join( 
    rawdf2 |> 
      select(iso2c, iso3c, date, country, SP.POP.TOTL), 
    relationship = "many-to-many" 
    ) |> # compute new cases and continent, drop NA, aggregate by continent-year
  mutate( 
    continent = countrycode::countrycode(iso2c, "iso2c", "continent"), 
    new_cases = SH.HIV.INCD.ZS / 1000 * SP.POP.TOTL ) |> 
  filter(!is.na(new_cases), !is.na(continent)) |> group_by(date, continent) |> 
  summarise(total_cases = sum(new_cases, na.rm = TRUE), .groups = "drop")

# Size Var
size_var_df <- df |> 
  group_by(continent) |> 
  summarise(
    size_var = sum(total_cases)
  )

df <- df |> 
  left_join(
    size_var_df
  )

# 2. Extract stream polygon coordinates from stat_ggstream (used to position labels)
stream_coords <- (
  ggplot(df, aes(x = date, y = total_cases, fill = continent)) +
    geom_stream(
    extra_span = 0.2, 
    bw = 0.75, 
    color = "grey20",
    linewidth = 0.2,
    type = "ridge"
    )
) |>
  ggplot_build() |>
  (\(b) b$data[[1]])()     # anonymous fn to pluck the computed data frame

# 3. Compute top boundary per x (date) and attach global totals
top_y <- stream_coords |>
  group_by(x) |>
  summarise(ymax = max(y, na.rm = TRUE), .groups = "drop") |>
  rename(date = x) |> 
  mutate(date = round(date, 0)) |> 
  left_join(
    df |>
      group_by(date) |>
      summarise(global_cases = sum(total_cases, na.rm = TRUE), .groups = "drop")
  ) |> 
  slice_max(order_by = ymax, n = 1, by = date)

The base plot

mypal <- paletteer::paletteer_d(
  "fishualize::Scarus_tricolor", 
  direction = -1
  ) |> 
  as.character() |> 
    substr(1, 7)

g <- ggplot(
  data = df, 
  mapping = aes(
    x = date, 
    y = total_cases
    )
  ) +
  geom_stream(
    mapping = aes(
      fill = continent
    ),
    extra_span = 0.2, 
    bw = 0.75, 
    color = bg_col,
    linewidth = 0.2,
    type = "ridge",
    alpha = 0.5
    ) +
  geom_stream_label(
    mapping = aes(
      fill = continent,
      label = continent,
      colour = continent,
      size = size_var
    ),
    extra_span = 0.2, 
    bw = 0.75, 
    type = "ridge"
  ) +
  geom_text(
    data = top_y,
    mapping = aes(
      x = date, 
      y = ymax * 1.05, 
      label = paste0(round(global_cases / 1e6, 2), " M")
      ),
    size = bts / 5, 
    color = text_col, 
    vjust = 0,
    nudge_y = -2e5,
    family = "caption_font"
  ) +
  # Adding text annotations
  geom_vline(
    data = aids_milestones,
    aes(xintercept = date),
    linetype = "longdash", 
    linewidth = 0.8,
    color = text_col, 
    alpha = 0.8
  ) +
  
  geom_text(
    data = aids_milestones,
    aes(x = date, y = 0.8e6, label = description),
    angle = 90, hjust = 0, vjust = -0.3,
    size = bts / 3, 
    family = "caption_font", 
    color = text_col
  ) +
  
  # Text annotations
  annotate(
    geom = "label",
    x = 2024, y = 7.1e6,
    label = plot_title,
    hjust = 1, vjust = 1,
    size = bts,
    family = "body_font",
    fontface = "bold",
    colour = text_hil,
    lineheight = 0.3,
    fill = alpha(bg_col, 0.8),
    border.colour = NA,
    label.padding = unit(2, "mm")
  ) +
  annotate(
    geom = "label",
    x = 2024, y = 5.8e6,
    label = plot_subtitle,
    hjust = 1, vjust = 1,
    size = bts / 2,
    family = "caption_font",
    colour = text_hil,
    lineheight = 0.3,
    fill = alpha(bg_col, 0.8),
    border.colour = NA,
    label.padding = unit(2, "mm")
  ) +
  annotate(
    geom = "richtext",
    x = 2024, y = 4.8e6,
    label = inset_text,
    hjust = 1, vjust = 1,
    size = bts / 5,
    family = "body_font",
    colour = text_col,
    lineheight = 0.25,
    label.size = NA,
    label.color = NA,
    fill = alpha(bg_col, 0.8),
    label.padding = unit(2, "mm")
  ) +
  scale_size_continuous(range = c(bts/2, bts * 1.5)) +
  scale_fill_manual(values = mypal) +
  scale_colour_manual(values =  darken(mypal, 0.3)) +
  scale_y_continuous(
    labels = label_number(scale_cut = cut_short_scale()),
    expand = expansion(c(0, 0.05))
    ) +
  scale_x_continuous(
    expand = expansion(0),
    breaks = seq(1990, 2022, 5)
  ) +
  labs(
    x = NULL, 
    y = "Estimated number of new HIV infections (per year)",
    caption = plot_caption
  ) +
  coord_cartesian(clip = "off") +
  theme_minimal(
    base_family = "body_font",
    base_size = bts
  ) +
  theme(
    legend.position = "none",

    # Overall
    text = element_text(
      margin = margin(0,0,0,0, "mm"),
      colour = text_col,
      lineheight = 0.3
    ),
    
    panel.background = element_rect(
      fill = NA,
      colour = NA
    ),
    panel.grid.major = element_line(
      linewidth = 0.4,
      linetype = 3,
      colour = alpha("black", 0.8)
    ), 
    panel.grid.minor = element_line(
      linewidth = 0.2,
      linetype = 3,
      colour = alpha("black", 0.6)
    ),
    axis.line = element_line(
      linewidth = 0.5,
      colour = text_col,
      arrow = arrow()
    ),
    axis.text.x.bottom = element_text(
      margin = margin(5,1,1,1, "mm" ),
      angle = 90,
      size = 1.25 * bts
    ),
    axis.text.y.left = element_text(
      margin = margin(2,2,2,2, "mm"),
      size = 1.5 * bts
    ),
    
    # Labels and Strip Text
    plot.title = element_text(
      margin = margin(5, 0, 5, 0, "mm"),
      hjust = 0.5,
      vjust = 0.5,
      colour = text_hil,
      size = 1.5 * bts,
      family = "body_font",
      face = "bold",
      lineheight = 0.25
    ),
    plot.subtitle = element_text(
      margin = margin(2, 0, 2, 0, "mm"),
      vjust = 0.5,
      colour = text_hil,
      size = 1.15 * bts,
      hjust = 0.5,
      family = "caption_font",
      lineheight = 0.3
    ),
    plot.caption = element_markdown(
      family = "caption_font",
      hjust = 1,
      margin = margin(5,0,5,0, "mm"),
      colour = text_hil
    ),
    plot.caption.position = "plot",
    plot.title.position = "plot",
    plot.margin = margin(5, 5, 5, 5, "mm")
  )

Adding annotations to the plot

# A QR Code for the infographic
url_graphics <- paste0(
  "https://aditya-dahiya.github.io/projects_presentations/data_vizs/",
  # The file name of the current .qmd file
  "wb_aids_incidence",         
  ".html"
)
# remotes::install_github('coolbutuseless/ggqr')
# library(ggqr)
plot_qr <- ggplot(
  data = NULL, 
  aes(x = 0, y = 0, label = url_graphics)
  ) + 
  ggqr::geom_qr(
    colour = text_hil, 
    fill = bg_col,
    size = 1
    ) +
  annotate(
    geom = "text",
    x = -0.08,
    y = 0,
    label = "Scan for complete\nCode used to make\nthis graphic",
    hjust = 1,
    vjust = 0.5,
    family = "caption_font",
    colour = text_hil,
    size = bts / 6,
    lineheight = 0.35
  ) +
  coord_fixed(clip = "off") +
  theme_void() +
  theme(
    plot.background = element_rect(
      fill = NA, 
      colour = NA
    ),
    panel.background = element_rect(
      fill = NA,
      colour = NA
    ),
    plot.margin = margin(0, 10, 0, 0, "mm")
  )

# Compiling the plots

g_full <- g +
  inset_element(
    p = plot_qr,
    left = 0.92, right = 0.98,
    bottom = 0.48, top = 0.53,
    align_to = "full",
    clip = FALSE
  ) + 
  plot_annotation(
    theme = theme(
      plot.background = element_rect(
        fill = "transparent",
        colour = "transparent"
      )
    )
  )

ggsave(
  filename = here::here(
    "data_vizs",
    "wb_aids_incidence.png"
  ),
  plot = g_full,
  width = 297 * 2,
  height = 210 * 2,
  units = "mm",
  bg = bg_col
)

Savings the graphics

# Saving a thumbnail for the webpage
image_read(here::here("data_vizs", "wb_aids_incidence.png")) |> 
  image_resize(geometry = "400") |> 
  image_write(here::here("data_vizs", 
                         "thumbnails", 
                         "wb_aids_incidence.png"))

Session Info

# Data Import and Wrangling Tools
pacman::p_load(
  tidyverse,    # Data Wrangling and Plotting
  scales,       # Nice scales for ggplot2
  fontawesome,  # Icons display in ggplot2
  ggtext,       # Markdown text support ggplot2
  showtext,     # Display fonts in ggplot2
  colorspace,   # Lighten and darken colours
  patchwork,    # Combining plots together
  magick,       # Image processing and editing
  wbstats,      # World Bank data access
  ggstream,     # Stream Plots in R
  scales        # Nice scales with ggplot2
)

sessioninfo::session_info()$packages |>
  as_tibble() |>
  
  # The attached column is TRUE for packages that were 
  # explicitly loaded with library()
  dplyr::filter(attached == TRUE) |>
  dplyr::select(package,
    version = loadedversion,
    date, source
  ) |>
  dplyr::arrange(package) |>
  janitor::clean_names(
    case = "title"
  ) |>
  gt::gt() |>
  gt::opt_interactive(
    use_search = TRUE
  ) |>
  gtExtras::gt_theme_espn()

Table 1: R Packages and their versions used in the creation of this page and graphics