Day 02: Slope

library(rio)
library(data.table)
library(ggplot2)
library(ggtext)
library(refugees)
library(gghighlight)
library(paletteer)

# get population data
dat <- refugees::population
setDT(dat)

# sum total number of refugees per year
dat_sum_year <- dat[,
  lapply(.SD, function(x) sum(x, na.rm = TRUE)),
  .SDcols = c("refugees", "asylum_seekers", "returned_refugees", "idps", "returned_idps", "stateless"),
  by = .(year)]

# add column of total
dat_sum_year[, all_pop := refugees + asylum_seekers + returned_refugees + idps + returned_idps + stateless]

# melt to long
dat_sum_year_long <- melt(
  dat_sum_year,
  variable.name = "pop_type",
  value.name = "n",
  id.vars = c("year")
)

# recoding categories
cat <- c(
  "refugees" = "Refugees",
  "asylum_seekers" = "Asylum Seekers",
  "returned_refugees" = "Returned Refugees",
  "idps" = "IDPs",
  "returned_idps" = "Returned IDPs",
  "stateless" = "Stateless",
  "all_pop" = "Total"
)


dat_sum_year_long[, pop_type := factor(pop_type, levels = names(cat), labels = cat)]

# plot
 g <- ggplot(dat_sum_year_long[pop_type != "Total"],
       aes(x = year, y = n, group = pop_type, color = pop_type)) +
  geom_line(size = 1) +
  gghighlight(pop_type %in% c("Refugees", "IDPs"),
              use_direct_label = TRUE,
              unhighlighted_params = list(color = "gray60", size = 0.2)) +
  theme_dark(base_size = 14) +
  scale_color_manual(values = c(
    "Refugees" = "#00B4D8",
    "IDPs" = "#FF3D7F"
  )) +
  scale_y_continuous(
    labels = scales::label_number(scale_cut = scales::cut_short_scale()),
    breaks = scales::breaks_extended(8)
  ) +
  # x scale with dates every 10 years
  scale_x_continuous(
    breaks = seq(1950, 2025, 10),
    limits = c(1950, 2025)
  ) +
  # vline in 2015
  geom_segment(
    x = 2016,
    xend = 2016,
    y = -4e6,
    yend = 7.2e7,
    color = "#FFD700",
    linetype = "dashed",
    size = 0.3
  ) +
  annotate(
    geom = "text",
    x = 2016,
    y = 7.4e7,
    label = "2016 Grand Bargain",
    color = "#FFD700",
  ) +
  geom_segment(
    x = 2005,
    xend = 2005,
    y = -4e6,
    yend = 7.2e7,
    color = "#FFD700",
    linetype = "dashed",
    size = 0.3
  ) +
  annotate(
    geom = "text",
    x = 2005,
    y = 7.4e7,
    label = "2005 reform",
    color = "#FFD700"
  ) +
  labs(
    title = "NULL",
    x = NULL,
    y = NULL
  ) +
  theme(
    plot.title = element_text(color = "#FF6F61", size = 18, hjust = 0,
                              margin = margin(b = 20, l = -50)),
    plot.title.position = "plot",
    panel.background = element_rect(fill = "gray10"),
    plot.background = element_rect(fill = "gray10"),
    panel.grid.major = element_line(color = "gray25"),
    panel.grid.minor = element_blank(),
    axis.text = element_text(color = "white"),
    axis.line = element_line(color = "white")
  ) +
  geom_textbox(
    aes(x = 1955, y = 6.6e7),
    label =
      "<span style='color:#FFD700;font-size:28pt;'>Global Displacement Trends</span><br>
      <span style='color:#FFC300;font-size:18pt;'>Refugees and IDPs (1951-2024)</span><br><br>
      <span style='color:#FFFFFF;font-size:12pt;'>
      Internally Displaced Persons (IDPs) have been forced to flee their homes due to conflict, violence, persecution or disasters, remaining within their own country while refugees cross broders.<br><br>
      <span style='color:#FF3D7F'>IDPs</span> numbers show consistent growth since the mid-1990s. <span style='color:#00B4D8'>Refugees</span> figures exhibit periodic surges and a constant increase since the early-2000s.</i><br><br>
      Data collection and standardization efforts through reforms have improved displacement tracking.</span>",
    box.color = NA,
    fill = NA,
    width = grid::unit(0.5, "npc"),
    lineheight = 1.4,
    hjust = 0, vjust = 1
  )

# save
ggsave(
  "day_02.png",
  height = 9,
  width = 11,
  dpi = 600,
  type = "cairo-png"
)

Final Plot

Notes

This visualization tracks global displacement trends from 1951 to 2024, focusing on refugees and internally displaced persons (IDPs). The dark theme emphasizes the serious nature of the subject matter.

Data source: UNHCR Refugee Data Finder (accessed via the refugees R package)

Tools used:

• refugees (for displacement data)
• data.table (for data manipulation)
• ggplot2 (for base visualization)
• gghighlight (for emphasizing key displacement categories)
• ggtext (for rich text annotations)

The visualization highlights significant policy moments with vertical markers (2005 reform and 2016 Grand Bargain) and uses contrasting colors to distinguish between IDPs (pink) and refugees (blue). The chart reveals the concerning upward trend in both refugee and IDP numbers over recent decades, with IDPs consistently outpacing refugee numbers since the mid-1990s.