Merge branch 'drafts'

readings.qmd

@@ -26,18 +26,18 @@ Here are the readings for each week. Please have the readings done _before_ the
 * @Wilke2019 chapters 4, 6, 17 (color, amounts, proportional ink)
 * @W.C.G2023 chapters 1 & 9 (okay to skim as a code reference) (data visualization, layers)
 
-## Week 4, Feb 21: text, annotation, uncertainty, distribution
+## Week 4, Feb 21: text & annotation
 
-* @Wilke2019 chapters 7, 9, 10 (histograms, multiple distributions, proportions)
-* Listen to Data Stories, @B.S2019
 * Pick one (or both) of these styleguides to browse through. Take note of how they use color, text, annotations, and iconography, and what suggestions they have on other elements (labeling axes, direct labeling of points, style of titles, placement and rotation of labels, etc). How do these rules carry over between chart types or for different audiences? How strongly do they create a recognizable visual style or brand?
   * @Urban2023
   * @WHO2023 (it's not super clear but you have to click through to each section)
 
-## Week 5, Feb 28: making good decisions
+## Week 5, Feb 28: uncertainty, distributions, making good decisions
 
-* @Cairo2019 chapter 1 (on Blackboard)
-* @Wilke2019 chapters 13, 14 & 20 (time series, trends, redundancy)
-* @Smith2023
-* Bonus: listen to @B.S2018a, post on Discord or tell us in class what you learned, and you'll get 1 participation point
+* Listen to Data Stories, @B.S2019
+* @Wilke2019 chapters 7, 9, 10, 13, 14, 20 (okay to skim)
+* Pick one. If you post on Discord or tell us in class what you learned, you'll get 1 participation point:
+  * @Cairo2019 chapter 1 (on Blackboard)
+  * @Smith2023
+  * Listen to @B.S2018a
 

weeks/07_annotations.qmd

@@ -56,9 +56,9 @@ acs |>
   geom_text(aes(label = lbl, hjust = just, y = y), color = "white", fontface = "bold") +
   scale_y_barcontinuous() +
   coord_flip() +
-  labs(title = "title",
-       subtitle = "subtitle",
-       caption = "caption",
+  labs(title = "Baltimore city has a higher rate of cost burden than the state or nation",
+       subtitle = "Share of households that are cost burdened, Maryland, 2022",
+       caption = "Source: US Census Bureau American Community Survey, 2022 5-year estimates",
        fill = "fill") +
   theme(panel.grid.major.y = element_blank(),
         panel.grid.major.x = element_line()) 
@@ -69,12 +69,18 @@ acs |>
 
 ### Brainstorm
 
-| Text | Purpose | Could be better? |
-| ---- | ------- | ----------------- |
-|      |         |                   |
-|      |         |                   |
-|      |         |                   |
-|      |         |                   |
+| Text                       | Purpose                                     | Could be better?                                                            |
+| -------------------------- | ------------------------------------------- | --------------------------------------------------------------------------- |
+| Title                      | Takeaway, what you're looking at in context |                                                                             |
+| Subtitle                   | Specifics of what's being measured          | Depending on context, maybe put cost burden definition here                 |
+| Independent axis           | Locations                                   |                                                                             |
+| Independent axis title     | What's on the axis                          | Not necessary; we know what these names are                                 |
+| Legend title               | What colors mean                            |                                                                             |
+| Legend labels              | Location types                              | Drop the legend, put any additional info in subtitle                        |
+| Dependent axis title       | Meaning of variable being measured          | Can remove since it's in the subtitle, but some styleguides may say keep it |
+| Caption                    | Source                                      | Could put definition of cost burden here                                    |
+| Dependent axis labels      | Specify meaning of breaks along axis        | Can drop because redundant                                                                            |
+| Direct data labels on bars | Values of each data point                   |                                                                             |
 
 :::
 
@@ -107,26 +113,29 @@ unemploy_balt <- unemployment |>
 
 ggplot(unemploy_balt, aes(x = date, y = adjusted_rate, color = name, group = name)) +
   geom_line(linewidth = 0.8) +
-    scale_x_date(date_breaks = "5 years", labels = NULL) +
-  # scale_x_date(date_breaks = "5 years", date_labels = "%Y") +
-  scale_y_continuous(breaks = seq(0, 0.12, by = 0.03), labels = NULL) +
+    # scale_x_date(date_breaks = "5 years", labels = NULL) +
+  scale_x_date(date_breaks = "5 years", date_labels = "%Y") +
+  scale_y_continuous(breaks = seq(0, 0.12, by = 0.03), labels = scales::label_percent()) +
   scale_color_manual(values = c(Maryland = "gray50", "Baltimore City" = qual_pal[5])) +
   theme(panel.grid.major.x = element_line(),
         legend.justification = c(0, 1),
         legend.position = c(0.02, 0.98),
         axis.title.y = element_text(hjust = 0.98)) +
   labs(x = NULL, y = "Unemployment rate", color = NULL,
-       title = "Monthly unemployment rate, seasonally adjusted",
-       subtitle = "subtitle",
-       caption = "caption")
+       title = "Monthly unemployment rate, seasonally adjusted, 2000-2023",
+       subtitle = "",
+       caption = "Source: Bureau of Labor Statistics Local Area Unemployment")
 ```
 
 Next, what annotations would be helpful for contextualizing this trend?
 
 :::{.callout-tip}
 ### Brainstorm: contextualizing information
 
--
+- Timespan--years on axis
+- Source
+- Units of measurement
+- Historical events
 
 :::