dataviz-redo.R

library(ggplot2)

# Get hold of the data ----
# |- Save this file
# https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-022-01158-0/MediaObjects/41588_2022_1158_MOESM4_ESM.xlsx

# |- Read in the data
model_data <- readxl::read_xlsx(here::here(
  "data/41588_2022_1158_MOESM4_ESM.xlsx"
))

# Here's my rendition of the "before" plot ----

model_data |>
  ggplot() +
  geom_abline() +
  geom_point(aes(
    x = h2.uncorrected,
    y = h2.region_corrected,
    colour = Category
  )) +
  geom_segment(aes(
    x = h2.uncorrected,
    xend = h2.uncorrected,
    y = h2.region_corrected - h2_se.region_corrected,
    yend = h2.region_corrected + h2_se.region_corrected,
    colour = Category
  )) +
  geom_segment(aes(
    x = h2.uncorrected - h2_se.uncorrected,
    xend = h2.uncorrected + h2_se.uncorrected,
    y = h2.region_corrected,
    yend = h2.region_corrected,
    colour = Category
  )) +
  theme_minimal()

# And here's a suggeted "after" plot ----

model_data |>
  dplyr::arrange(h2.uncorrected) |>
  dplyr::mutate(Trait = factor(Trait, levels = unique(Trait))) |>
  dplyr::group_by(Category) |>
  dplyr::mutate(mean_h2 = mean(h2.uncorrected, na.rm = TRUE)) |>
  dplyr::ungroup() |>
  dplyr::arrange(-mean_h2) |>
  dplyr::mutate(Category = factor(Category, levels = unique(Category))) |>
  ggplot() +
  geom_segment(
    aes(x = -Inf, xend = Inf, y = Trait, yend = Trait),
    position = position_nudge(x = 0, y = -0.5),
    colour = "white"
  ) +
  annotate(
    geom = "rect",
    xmin = 0.06,
    xmax = Inf,
    ymin = -Inf,
    ymax = Inf,
    fill = "#a2c3bc",
    alpha = 0.25
  ) +
  annotate(
    geom = "rect",
    xmin = 0.138,
    xmax = Inf,
    ymin = -Inf,
    ymax = Inf,
    fill = "#a2c3bc",
    alpha = 0.25
  ) +
  geom_segment(
    aes(
      x = h2.uncorrected - h2_se.uncorrected,
      xend = h2.uncorrected + h2_se.uncorrected,
      y = Trait,
      yend = Trait
    ),
    position = position_nudge(x = 0, y = 0.1),
    colour = "#5e656d",
    alpha = 0.5,
    linewidth = 1.5
  ) +
  geom_segment(
    aes(
      x = h2.region_corrected - h2_se.region_corrected,
      xend = h2.region_corrected + h2_se.region_corrected,
      y = Trait,
      yend = Trait
    ),
    position = position_nudge(x = 0, y = -0.1),
    colour = "#d26ea5",
    alpha = 0.5,
    linewidth = 1.5
  ) +
  geom_segment(aes(
    x = h2.uncorrected,
    xend = h2.region_corrected,
    y = Trait,
    yend = Trait,
    colour = dplyr::case_when(
      h2.uncorrected > h2.region_corrected ~ "#5e656d",
      TRUE ~ "#d26ea5"
    )
  )) +
  geom_point(
    aes(
      y = Trait,
      x = h2.region_corrected
    ),
    shape = 21,
    colour = "#c63c83",
    fill = "#d26ea5",
    stroke = 0.8
  ) +
  geom_point(
    aes(y = Trait, x = h2.uncorrected),
    shape = 21,
    size = 1.5,
    fill = "white",
    colour = "#5e656d",
    stroke = 0.8
  ) +
  geom_point(
    aes(
      y = Trait,
      x = h2.region_corrected
    ),
    shape = 21,
    size = 1.5,
    colour = "#c63c83",
    fill = "#d26ea5",
    alpha = 0.2
  ) +
  labs(
    title = "SNP-based heritability (eta<sup>2</sup> and se) of 56 different traits, uncorrected and <span style='color: #c63c83'>corrected for region</span>",
    caption = "Dataviz: Cara R Thompson | Source: https://www.nature.com/articles/s41588-022-01158-0"
  ) +
  scale_colour_identity() +
  scale_x_continuous(limits = c(0, 0.45), expand = c(0, 0)) +
  theme_minimal() +
  theme(
    text = element_text(family = "Atkinson Hyperlegible", colour = "#272b2f"),
    axis.title = element_blank(),
    plot.title = ggtext::element_markdown(
      face = "bold",
      size = rel(1.5),
      hjust = 0.5,
      margin = margin(0, 0, 11, 0)
    ),
    legend.position = "none",
    strip.text.y.right = element_text(
      angle = 0,
      colour = "#272b2f",
      face = "bold"
    ),
    axis.text = element_text(colour = "#272b2f"),
    axis.text.y = element_text(vjust = 0.35),
    plot.caption = element_text(hjust = 1, margin = margin(11, 0, 0, 0)),
    strip.background = element_rect(colour = "#EAF0F4", fill = "#EAF0F4"),
    panel.background = element_rect(colour = "#EAF0F4", fill = "#EAF0F4"),
    panel.grid = element_line(colour = alpha("#a2c3bc", 0.3)),
    plot.background = element_rect(fill = "#fdfbfb", colour = "#fdfbfb"),
    panel.grid.minor.x = element_blank(),
    panel.grid.major.y = element_blank(),
    plot.caption.position = "plot",
    plot.title.position = "plot",
    plot.margin = margin(rep(22, 2), 11, 22)
  ) +
  facet_grid(rows = "Category", scales = "free_y", space = "free")