progress

docs/materials/positconf2024-pattern-fill.qmd

@@ -20,7 +20,7 @@ Install the required packages:
 install.packages(
   c(
     "tidyverse", "cowplot", "sf", "ragg",
-    "palmerpenguins", "magick"
+    "palmerpenguins", "magick", "ggpattern"
   )
 )
 ```
@@ -462,6 +462,6 @@ Unfortunately this is not quite what we wanted. The images are centered within t
 
 ## Exercises
 
-1.  Some exercise here...
+1.  Create a version of the pet ownership isotype plot where bars run vertical instead of horizontal.
 
 ## 

docs/search.json

@@ -6,23 +6,23 @@
       "description": "A bootcamp-style course on data visualization",
       "author": [],
       "contents": "\nThis is the home page for a dataviz shortcourse by Claus O. Wilke. The course is an abbreviated version of a semester-long course taught at The University of Texas at Austin. Many of the materials presented are taken from the book Fundamentals of Data Visualization.\n\nReuse\nText and figures are licensed under Creative Commons Attribution CC BY 4.0. Any computer code (R, HTML, CSS, etc.) in slides and worksheets, including in slide and worksheet sources, is also licensed under MIT. Note that figures in slides may be pulled in from external sources and may be licensed under different terms. For such images, image credits are available in the slide notes, accessible via pressing the letter ‘p’.\n\n\n\n",
-      "last_modified": "2024-07-25T18:25:05+02:00"
+      "last_modified": "2024-07-25T18:38:38+02:00"
     },
     {
       "path": "lessons.html",
       "title": "Lessons",
       "description": "",
       "author": [],
       "contents": "\n\nContents\nEffective Visual Communication\nFrom Data to Visualisation 1\nFrom Data to Visualisation 2\nPrinciples of Figure Design\nReuse\n\nEffective Visual Communication\nThis lecture covers three core concepts of visual communication. First, I will discuss how to engage your audience by telling a story. Second, I will provide high-level guidance for designing visualizations that make a clear point. Third, I will describe key design principles that ensure your figures will be readable by people with vision impairments, and specifically impairments of color perception.\n\nMaterials:\n\nSlides\nFrom Data to Visualisation 1\nVisualizing amounts; visualizing distributions; visualizing associations and trends\n\nMaterials:\n\nSlides\nFrom Data to Visualisation 2\nVisualizing uncertainty; visualizing geospatial data\n\nMaterials:\n\nSlides\nPrinciples of Figure Design\nColors; compound figures; infographics\n\nMaterials:\n\nSlides\nReuse\nText and figures are licensed under Creative Commons Attribution CC BY 4.0. Any computer code (R, HTML, CSS, etc.) in slides and worksheets, including in slide and worksheet sources, is also licensed under MIT. Note that figures in slides may be pulled in from external sources and may be licensed under different terms. For such images, image credits are available in the slide notes, accessible via pressing the letter ‘p’.\n\n\n\n",
-      "last_modified": "2024-07-25T18:25:05+02:00"
+      "last_modified": "2024-07-25T18:38:39+02:00"
     },
     {
       "path": "workshops.html",
       "title": "Workshops",
       "description": "",
       "author": [],
       "contents": "\n\nContents\nReuse\n\nMaterials for past and present workshops. Many workshop materials are based on content from my semester-long data visualization course at UT Austin.\n2024-08-12: Effective Data Visualization with ggplot2\nAll workshop materials will be posted here.\n2024-06-24: Visualizing Uncertainty and Trends\nKeynote, June 24 2024: slides\nWorkshop, June 27 2024:\nQuarto notebook\nHTML\nHTML with solutions\n\n2024-06-20: Effective Visual Communication with R\nWorkshop in support of Ukraine. To work through all the examples provided, you will need the following R packages: tidyverse, cowplot, ggiraph, sf, patchwork\nSlides Part 1\nSlides Part 2\nSlides Part 2, without interactivity (Some browsers crash when there are too many interactive examples in one slide set.)\n2024-05-28: Effective Visual Communication\nSlides Part 1 (telling a story, making a point, making figures accessible)\nSlides Part 2 (figure composition and context, text annotations, overplotting)\nReuse\nText and figures are licensed under Creative Commons Attribution CC BY 4.0. Any computer code (R, HTML, CSS, etc.) in slides and worksheets, including in slide and worksheet sources, is also licensed under MIT. Note that figures in slides may be pulled in from external sources and may be licensed under different terms.\n\n\n\n",
-      "last_modified": "2024-07-25T18:25:06+02:00"
+      "last_modified": "2024-07-25T18:38:40+02:00"
     }
   ],
   "collections": []

materials/positconf2024-legends.qmd

@@ -0,0 +1,108 @@
+---
+title: "Effective Data Visualization with ggplot2"
+subtitle: "Sprucing up your legends"
+format: html
+editor: visual
+---
+
+## Required packages
+
+Install the required packages:
+
+```{r}
+#| eval = FALSE
+
+# Run this command to install the required packages.
+# You need to do this only once.
+install.packages(
+  c(
+    "tidyverse", 
+  )
+)
+```
+
+## 1. Legend order
+
+By default, ggplot places legend entries in alphabetical order, and that is rarely what we want. See this example:
+
+```{r}
+#| message = FALSE,
+#| warning = FALSE
+
+library(tidyverse)
+
+tech_stocks <- read_csv("https://wilkelab.org/dataviz_shortcourse/datasets/tech_stocks.csv") |>
+  mutate(date = ymd(date)) |>
+  select(company, date, price_indexed)
+
+ggplot(tech_stocks) +
+  aes(x = date, y = price_indexed) +
+  geom_line(aes(color = company), na.rm = TRUE) +
+  scale_x_date(
+    limits = c(
+      ymd("2012-06-01"),
+      ymd("2017-05-31")
+    ),
+    expand = c(0, 0)
+  ) + 
+  scale_y_continuous(
+    limits = c(0, 560),
+    expand = c(0, 0)
+  )
+```
+
+The visual order of the lines is Facebook, Alphabet, Microsoft, Apple, and we should match it by reodering the categorical variable.
+
+```{r}
+tech_stocks |>
+  mutate(
+    company = fct_relevel(
+      company,
+      "Facebook",  "Alphabet", "Microsoft", "Apple"
+    )
+  ) |>
+  ggplot() +
+  aes(x = date, y = price_indexed) +
+  geom_line(aes(color = company), na.rm = TRUE) +
+  scale_x_date(
+    limits = c(
+      ymd("2012-06-01"),
+      ymd("2017-05-31")
+    ),
+    expand = c(0, 0)
+  ) + 
+  scale_y_continuous(
+    limits = c(0, 560),
+    expand = c(0, 0)
+  )
+```
+
+This is improved, but we can do better. We can get rid of the legend entirely, by adding a secondary axis.
+
+```{r}
+tech_stocks_last <- tech_stocks |>
+  filter(date == max(date))
+
+ggplot(tech_stocks) +
+  aes(x = date, y = price_indexed) +
+  geom_line(aes(color = company), na.rm = TRUE) +
+  scale_x_date(
+    limits = c(
+      ymd("2012-06-01"),
+      ymd("2017-05-31")
+    ),
+    expand = c(0, 0)
+  ) + 
+  scale_y_continuous(
+    limits = c(0, 560),
+    expand = c(0, 0),
+    sec.axis = dup_axis(
+      breaks = tech_stocks_last$price_indexed,
+      labels = tech_stocks_last$company,
+      name = NULL
+    )
+  ) +
+  guides(color = "none")
+```
+
+## 2. Legend placement