line chart
timeseries
health
Author

Ana Luisa Bodevan

Published

November 11, 2025

This week challenge dataset in on WHO TB Burden Data. Check the TidyTuesday GitHub repo for the data.

1. SETUP

1.1 Load libraries and data

Code 
library(pacman)
pacman :: p_load(tidytuesdayR, tidyverse, dplyr, janitor, ggtext, showtext, scales, glue)

tuesdata <- tidytuesdayR::tt_load('2025-11-11')

1.2 Set theme

Code 
my_theme <- function(gridline_x = TRUE, gridline_y = TRUE) {
  gridline <- element_line(
    linetype = "dashed",
    linewidth = 0.15,
    color = "#999999"
  )

  gridline_x <- if (isTRUE(gridline_x)) {
    gridline
  } else {
    element_blank()
  }

  gridline_y <- if (isTRUE(gridline_y)) {
    gridline
  } else {
    element_blank()
  }

  # Set base theme  =============================================
  theme_minimal() +
    # Overwrite base theme defaults ============================================
    theme(
      # Text elements ==========================================================
      plot.title = element_text(
        size = 18,
        face = "bold",
        color = "#333333",
        margin = margin(b = 10)
      ),
      plot.subtitle = element_text(
        size = 14,
        color = "#999999",
        margin = margin(b = 10)
      ),
      plot.caption = element_text(
        size = 13,
        color = "#777777",
        margin = margin(t = 15),
        hjust = 0
      ),
      axis.text = element_text(
        size = 11,
        color = "#333333"
      ),
      plot.title.position = "plot",
      plot.caption.position = "plot",

      # Line elements ==========================================================
      panel.grid.minor = element_blank(),
      panel.grid.major.x = gridline_x,
      panel.grid.major.y = gridline_y,
      axis.ticks.x = element_line(
        linetype = "solid",
        linewidth = 0.25,
        color = "#999999"
      )
    )
}  #### thanks r for the rest of us

2. DATA WRANGLING

For this one, we will be exploring the correlation between HIV status and Tuberculosis mortality.

2.1 Check correlation

Code 
tuesdata$who_tb_data |>
  ggplot(aes(x = c_newinc_100k, y = e_mort_tbhiv_100k)) +
  geom_point()

2.2 Get mean mortality

Code 
df <-
  tuesdata$who_tb_data |>
  group_by(year) |>
  summarise(mean_positive = mean(e_mort_tbhiv_100k, na.rm = T),
            mean_negative = mean(e_mort_exc_tbhiv_100k, na.rm = T)) |>
  ungroup() |>
  pivot_longer(cols = c(mean_positive, mean_negative))

df <- df |>
  pivot_wider(names_from = name, values_from = value)

3. PLOT

3.1 Base plot

Code 
ggplot(df, aes(x = year)) +
  geom_ribbon(aes(ymin = mean_positive, ymax = mean_negative),
              fill = "gray80", alpha = 0.5) +
  geom_line(aes(y = mean_positive, color = "HIV-positive"), linewidth = 1.2) +
  geom_line(aes(y = mean_negative, color = "HIV-negative"), linewidth = 1.2) +
  my_theme(gridline_x = FALSE, gridline_y = FALSE)

3.2 Plot

Code 
font_add_google("Ubuntu", "ubuntu")
showtext_auto()
showtext_opts(dpi = 150)

ggplot(df, aes(x = year)) +

  geom_ribbon(aes(ymin = mean_positive, ymax = mean_negative),
              fill = "gray50", alpha = 0.5) +

  geom_line(aes(y = mean_positive, color = "HIV-positive"), linewidth = 1.2) +
  geom_line(aes(y = mean_negative, color = "HIV-negative"), linewidth = 1.2) +

  scale_color_manual(values = c("HIV-positive" = "#ff7f50", "HIV-negative" = "#4682b4"))+

  labs(title = "WHO TB Mortality by HIV Status",
      subtitle = "Comparison in TB mortality between HIV status",
      caption = "#TidyTuesday via {getTBinR}\nanabodevan.github.io",
      color = "HIV Status",
      y = "Mortality (100k)",
      x = "Year") +

  my_theme(gridline_x = FALSE, gridline_y = FALSE) +

  theme(
    # Title + subtitle styling
    plot.title = element_text(
      size = 20, hjust = 0, family = "ubuntu"
    ),
    plot.subtitle = element_text(
      size = 14,
      family = "ubuntu",
      color = "gray20",
      lineheight = 1.5,     # increases distance between lines
      margin = margin(t = 5, b = 10)  # extra spacing below subtitle
    ),
    plot.caption = element_text(size = 8, hjust = 0, family = "ubuntu", lineheight = 1.5),

    axis.title.x = element_text(
      margin = margin(t = 15),
      family = "ubuntu",
      face = "bold"
    ),
    axis.title.y = element_text(margin = margin(r = 15),
      family = "ubuntu",
      face = "bold"),

    legend.title = element_text(face = "bold", size = 11),
    legend.text = element_text(size = 10),
    legend.position = "right",

    plot.margin = margin(20,40,20,20))