{"id":13262,"date":"2022-01-04T11:32:01","date_gmt":"2022-01-04T16:32:01","guid":{"rendered":"https:\/\/rud.is\/b\/?p=13262"},"modified":"2022-01-04T11:32:01","modified_gmt":"2022-01-04T16:32:01","slug":"starting-2022-off-with-a-fairly-complex-ggplot2-recreation-plot","status":"publish","type":"post","link":"https:\/\/rud.is\/b\/2022\/01\/04\/starting-2022-off-with-a-fairly-complex-ggplot2-recreation-plot\/","title":{"rendered":"Starting 2022 Off With A Fairly Complex {ggplot2} Recreation Plot"},"content":{"rendered":"

The New York Times had a [tragic] story on Covid deaths<\/a> today and one of their plots really stuck with me for how well it told that part of the story.<\/p>\n

\"NYT<\/a><\/p>\n

NOTE: The red panel highlights are off a bit as I manually typed the data in (I only did the recreation to keep {ggplot2} muscle memory as I hadn’t doe a major customization like this in quite some time).<\/p>\n

Only one {grid} hack (for the faceted X axis labels) too!<\/p>\n

Hopefully, I’ll have more real-world opportunity to build some detailed, properly-annotated {ggplot2} plots this year.<\/p>\n

Shout out to @ClausWilke for {ggtext} and all the folks who’ve made {ggplot2} such a powerful data visualization tool.<\/p>\n

library(grid)\nlibrary(gtable)\nlibrary(hrbrthemes)\nlibrary(tidyverse)\n\ngtable_filter_remove <- function (x, name, trim = FALSE) {\n  # https:\/\/stackoverflow.com\/a\/36780639\n  matches <- !(x$layout$name %in% name)\n  x$layout <- x$layout[matches, , drop = FALSE]\n  x$grobs <- x$grobs[matches]\n  if (trim) \n    x <- gtable_trim(x)\n  x\n}\n\nread.csv(text=\"race,age_group,before,after,cause\nWhite,Under 25,1,3,Covid-19 deaths increased as a share of deaths from all cause\nWhite,25-44,3,10,Covid-19 deaths increased as a share of deaths from all cause\nWhite,45-64,8,15,Covid-19 deaths increased as a share of deaths from all cause\nWhite,65-84,13,11,NA\nWhite,85+,14,6,NA\nHispanic,Under 25,3,4,Covid-19 deaths increased as a share of deaths from all cause\nHispanic,25-44,17,21,Covid-19 deaths increased as a share of deaths from all cause\nHispanic,45-64,33,26,NA\nHispanic,65-84,33,17,NA\nHispanic,85+,21,9,NA\nBlack,Under 25,1,3,Covid-19 deaths increased as a share of deaths from all cause\nBlack,25-44,7,13,Covid-19 deaths increased as a share of deaths from all cause\nBlack,45-64,15,17,Covid-19 deaths increased as a share of deaths from all cause\nBlack,65-84,20,12,Covid-19 deaths increased as a share of deaths from all cause\nBlack,85+,17,8,NA\nAsian,Under 25,2,4,Covid-19 deaths increased as a share of deaths from all cause\nAsian,25-44,12,14,Covid-19 deaths increased as a share of deaths from all cause\nAsian,45-64,21,13,NA\nAsian,65-84,23,8,NA\nAsian,85+,17,4,NA\") -> xdf\n\nxdf %>% \n  mutate(\n    before = before\/100,\n    after = after\/100,\n    age_group = fct_inorder(age_group),\n    race = factor(race, levels = rev(c(\"Asian\", \"Black\", \"Hispanic\", \"White\")))\n  ) -> xdf\n\n{\n\n  ggplot( data = xdf) +\n    geom_rect(\n      data = xdf,\n      aes(\n        xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf,\n        fill = cause\n      ),\n      alpha = 1\/6, color = NA\n    ) +\n    geom_rect(\n      data = xdf %>% \n        filter(\n          (race == \"White\" & age_group %in% c(\"65-84\", \"85+\")) |\n            (race == \"Hispanic\" & age_group %in% c(\"45-64\", \"65-84\", \"85+\")) |\n            (race == \"Black\" & age_group %in% c(\"85+\")) |\n            (race == \"Asian\" & age_group %in% c(\"45-64\", \"65-84\", \"85+\"))\n        ),\n      aes(\n        xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf\n      ),\n      fill = \"#999999\", alpha = 1\/6, color = NA\n    ) +\n    geom_segment(\n      aes(-Inf, xend = Inf, -Inf, yend= -Inf),\n      size = 0.25, color = \"black\"\n    ) +\n    geom_segment(\n      data = xdf, aes(\"1\", before, xend=\"2\", yend=after),\n      size = 0.25\n    ) +\n    geom_point(\n      data = xdf, aes(\"1\", before), \n      fill = \"#999999\", color = \"white\", size = 2, stroke = 0.5, shape = 21\n    ) +\n    geom_point(\n      data = xdf, aes(\"2\", after),\n      fill = \"#bb271a\", color = \"white\", size = 2, stroke = 0.5, shape = 21\n    ) +\n    geom_text(\n      data = xdf,\n      aes(\"1\", before+0.05, label = scales::percent(before, 1)),\n      color = \"#999999\", family = font_es_bold, fontface = \"bold\", size = 3\n    ) +\n    geom_text(\n      data = xdf,\n      aes(\"2\", after+0.05, label = scales::percent(after, 1)),\n      color = \"#bb271a\", family = font_es_bold, fontface = \"bold\", size = 3\n    ) +\n    scale_x_discrete(\n      expand = c(0, 0),\n      labels = c(\"<span style='color:#999999'>BEFORE<\/span>\", \"<span style='color:#bb271a'>AFTER<\/a>\")\n    ) +\n    scale_y_percent(\n      limits = c(-0.005, 0.405),\n      breaks = c(-0.005, 0.1, 0.2, 0.3, 0.405),\n      labels = c(\"\", \"\", \"\", \"\", \"40%\\nof deaths from\\nall causes for\\nthis group\")\n    ) +\n    scale_fill_manual(\n      name = NULL,\n      values = c(\"#bb271a\"),\n      na.translate = FALSE\n    ) +\n    coord_cartesian(clip = \"off\") +\n    facet_wrap(\n      facets = race~age_group, \n      scales = \"free_x\",\n      labeller = \\(labels, multi_line = TRUE){\n        labels <- lapply(labels, as.character)\n        labels[[\"race\"]][c(1,2,4,5,6,7,9,10,11,12,14,15,16,17,19,20)] <- \"\"\n        labels[[\"age_group\"]] <- sprintf(\"<span style='font-style:normal;font-weight:normal;'>%s<\/span>\", labels[[\"age_group\"]])\n        labels[[\"race\"]][c(3,8,13,18)] <- sprintf(\"<span style='font-size:12pt;'>**%s**<\/span>\", labels[[\"race\"]][c(3,8,13,18)])\n        labels\n      }\n    ) +\n    labs(\n      x = NULL, y = NULL,\n      title = \"Covid-19 deaths <span style='color:#999999'>before<\/span> and <span style='color:#bb271a'>after<\/span> universal adult vaccine eligibility\",\n      caption = \"Source: Provisional weekly death data from the C.D.C. through Nov. 27. Note: Only the four largest racial and ethnic groups are included. Universal vaccine eligibility was April 19, the date when all adults in the United States were eligible for vaccination.\"\n    ) +\n    theme_ipsum_es(grid=\"Y\", plot_title_size = 16) +\n    theme(\n      plot.title.position = \"plot\",\n      plot.title = ggtext::element_markdown(hjust = 0.5),\n      plot.caption = ggtext::element_textbox_simple(\n        hjust = 0, size = 8.5, family = font_es, color = \"#999999\",\n        margin = margin(t = 14)\n      ),\n      axis.ticks.x.bottom = ell(size = 0.25) ,\n      axis.line.x.bottom = ell(lineend = \"square\", size = 0.25),\n      axis.text.x.bottom = ggtext::element_markdown(size = 8, margin = margin(t = 6)),\n      axis.text.y.left = elt(size = 8, vjust = 1, lineheight = 0.875,  color = \"#999999\"),\n      strip.text.x = ggtext::element_markdown(hjust = 0.5, size = 10, family = font_es),\n      strip.text = ggtext::element_markdown(hjust = 0.5, size = 10, family = font_es),\n      panel.spacing.x = unit(\"40\", \"pt\"),\n      panel.spacing.y = unit(6, \"pt\"),\n      panel.border = elb(),\n      legend.position = \"top\"\n    ) -> gg\n\n  grid.newpage()\n  grid.draw(\n    gtable_filter_remove(\n      x = ggplotGrob(gg),\n      name = c(sprintf(\"axis-b-%d-1\", 2:5), sprintf(\"axis-b-%d-2\", 2:5), sprintf(\"axis-b-%d-3\", 2:5), sprintf(\"axis-b-%d-4\", 2:5))\n    )\n  )\n\n}\n<\/code><\/pre>\n
\"{ggplot2}<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
The New York Times had a [tragic] story on Covid deaths today and one of their plots really stuck with me for how well it told that part of the story. NOTE: The red panel highlights are off a bit as I manually typed the data in (I only did the recreation to keep {ggplot2} […]<\/p>\n","protected":false},"author":1,"featured_media":13264,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"activitypub_content_warning":"","activitypub_content_visibility":"","activitypub_max_image_attachments":3,"activitypub_interaction_policy_quote":"anyone","activitypub_status":"","footnotes":""},"categories":[753,91],"tags":[],"class_list":["post-13262","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ggplot","category-r"],"yoast_head":"\nStarting 2022 Off With A Fairly Complex {ggplot2} Recreation Plot - rud.is<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/rud.is\/b\/2022\/01\/04\/starting-2022-off-with-a-fairly-complex-ggplot2-recreation-plot\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Starting 2022 Off With A Fairly Complex {ggplot2} Recreation Plot - rud.is\" \/>\n<meta property=\"og:description\" content=\"The New York Times had a [tragic] story on Covid deaths today and one of their plots really stuck with me for how well it told that part of the story. 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