Satellite images defy economic data of authoritarian regimes

Chart reconstruction

To reproduce the chart, we set the grid, data layer, scales, and appearance parameters such as font, base theme, colors, legends, annotations, and other necessary elements to achieve a visually pleasing and accurate representation.

chart <- ggplot(DF, aes(
  x = order, y = GDP.var.NTL, xend = order,
  yend = GDP.var.R, color = FIW.Status
)) +
  facet_wrap(~ factor(FIW.Status, levels = c("F", "PF", "NF"))) +
  geom_hline(yintercept = 0, linewidth = 0.25) +
  geom_segment() +
  geom_point(aes(xend = order, y = GDP.var.R, size = GDP.in.2015.USD.R / 1e+12),
    shape = 21, fill = "#C7C5BD", color = "white"
  ) +
  geom_point(aes(size = GDP.in.2015.USD.NTL / 1e+12, fill = FIW.Status),
    shape = 21, color = "white"
  ) +
  scale_color_manual(values = c("#FFC259", "#F6423C", "#FFA9A6")) +
  scale_fill_manual(values = c("#FFC259", "#F6423C", "#FFA9A6")) +
  scale_size(range = c(1, 12), breaks = c(0.1, 2, 10), "GDP in 2021, $trn†") +
  scale_y_continuous(
    position = "right",
    limits = c(-50, 450),
    n.breaks = 10,
    expand = expansion(0)
  ) +
  lims(x = c(0.5, 46)) +
  theme_economist_white() +
  theme(
    plot.background = element_rect(fill = "white"),
    panel.spacing.x = unit(0, "cm"),
    strip.background = element_rect(fill = "white"),
    strip.text = element_text(color = "white"),
    panel.grid = element_line(color = "ligthgray", linewidth = 0.10),
    axis.line.x.bottom = element_line(color = "gray", linewidth = 0.10),
  ) +
  theme(
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    axis.text.y = element_text(vjust = -0.5, color = "#2C2E2E"),
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank(),
    legend.background = element_rect(fill = "white"),
    legend.position = c(0, 0.85),
    legend.direction = "horizontal",
    legend.justification = c(0, 1),
    legend.key = element_rect(fill = "transparent"),
    plot.caption = element_text(
      hjust = 0, vjust = -1,
      color = "#454545", size = 8
    )
  ) +
  guides(
    color = FALSE, fill = FALSE,
    size = guide_legend(title.position = "top")
  ) +
  geom_text(
    data = DF %>% filter(label == 1 &
      !(Country.Name %in% c(
        "Japan", "Britain", "United States", "China",
        "Australia", "Ireland", "India", "Russia", "Mexico", "Peru"
      ))),
    aes(label = Country.Name), color = "#454545",
    vjust = 1.5, hjust = -0.1, check_overlap = T
  ) +
  geom_text(data = countriestxt, aes(label = labels), color = "#454545", hjust = 0) +
  geom_text(data = titles, aes(label = labels), fontface = "bold", size = 4.5) +
  geom_segment(data = average, aes(
    x = order, xend = order, y = GDP.var.NTL,
    yend = GDP.var.R
  ), color = "black") +
  geom_point(
    data = average, aes(
      x = order, y = GDP.var.NTL,
      size = GDP.in.2015.USD.NTL, fill = FIW.Status
    ),
    shape = 21, color = "black"
  ) +
  geom_point(
    data = average, aes(x = order, y = GDP.var.R, size = GDP.in.2015.USD.R),
    shape = 21, color = "black", fill = "gray"
  ) +
  geom_text(
    data = avTitles, aes(label = labels), fontface = "bold",
    size = 3.5, color = "black"
  ) +
  geom_label(
    data = sumQuote, aes(label = text), color = "black",
    size = 3, hjust = 0, vjust = 1, lineheight = .8, fill = "white",
    label.size = NA, label.padding = unit(0, "cm")
  ) +
  labs(caption = captiontx) +
  inset_element(pinset, left = 0, bottom = 0.83, right = 0.3, top = 1, align_to = "full")

Alternative visualization

Analyzing the original chart, we can see that it has some advantages. For instance, it allows us to identify each of the 103 countries, or it separates clearly the Freedom categories. Nevertheless, we realize that the X-axis is used to create an order among dumbbells, but it doesn’t add more information. Therefore, we propose an alternative visualization that uses this axis to compare GDP values. In our chart, the Y-axis shows the officially reported GDP growth, while the X-axis illustrates the NTL-based estimate of GDP. As a result, we should expect that each country will be located in the intersection line where Y is equal to X. However, the autocratic countries are far away from this line, as their value of the officially reported GDP.

GitHub

What else can we do with Ggplot2?