walkerke · June 27, 2025 23:05 · Jun 27, 2025 · Jun 27, 2025 · Jun 27, 2025 · Jun 27, 2025
diff --git a/pyramids-2024.R b/pyramids-2024.R
@@ -24,13 +24,10 @@ ggplot(pyramid_data, aes(x = value_pct, y = AGEGROUP, fill = SEX)) +
   geom_vline(xintercept = 0, color = "gray40", linewidth = 0.5) +
   scale_x_continuous(
     labels = function(x) paste0(abs(x), "%"),
-    # breaks = seq(-2, 2, 0.5),
     expand = expansion(mult = c(0.05, 0.05))
   ) +
   scale_y_discrete(
-    labels = function(x) str_remove_all(x, "Age\\s|\\syears") %>%
-      str_replace("Under 5", "0-4") %>%
-      str_replace("85 and older", "85+")
+    labels = function(x) str_remove_all(x, "Age\\s|\\syears") 
   ) +
   scale_fill_manual(
     values = c("Female" = "#A23B72", "Male" = "#2E86AB"),

diff --git a/pyramids-2024.R b/pyramids-2024.R
@@ -1,5 +1,5 @@
 library(tidyverse)
-library(tidycensus) # `pak::pak("walkerke/tidycensus")` pending CRAN update
+library(tidycensus)
 
 # Get the data
 pyramid_data <- get_estimates(
@@ -11,18 +11,20 @@ pyramid_data <- get_estimates(
   year = 2024
 ) %>%
   filter(AGEGROUP != "All ages", SEX != "Both sexes", NAME %in% c("Maine", "Utah")) %>%
+  group_by(NAME) %>%
   mutate(
     value_pct = value / sum(value) * 100,
     value_pct = ifelse(SEX == "Male", -value_pct, value_pct)
-  )
+  ) %>%
+  ungroup()
 
-# Build the visualization
+# Create enhanced population pyramids
 ggplot(pyramid_data, aes(x = value_pct, y = AGEGROUP, fill = SEX)) +
   geom_col(alpha = 0.9, width = 0.85) +
   geom_vline(xintercept = 0, color = "gray40", linewidth = 0.5) +
   scale_x_continuous(
     labels = function(x) paste0(abs(x), "%"),
-    breaks = seq(-2, 2, 0.5),
+    # breaks = seq(-2, 2, 0.5),
     expand = expansion(mult = c(0.05, 0.05))
   ) +
   scale_y_discrete(
@@ -71,7 +73,7 @@ ggplot(pyramid_data, aes(x = value_pct, y = AGEGROUP, fill = SEX)) +
     # Panel styling
     panel.spacing = unit(1.5, "lines"),
 
-    # Legend styling 
+    # Legend styling - moved to bottom with horizontal layout
     legend.position = "bottom",
     legend.direction = "horizontal",
     legend.title = element_text(size = 10, margin = margin(r = 3)),  # Reduced spacing
@@ -83,4 +85,4 @@ ggplot(pyramid_data, aes(x = value_pct, y = AGEGROUP, fill = SEX)) +
 
     # Overall plot margins
     plot.margin = margin(20, 20, 10, 20)
-  )
+  )
diff --git a/pyramids-2024.R b/pyramids-2024.R
@@ -1,5 +1,5 @@
 library(tidyverse)
-library(tidycensus)
+library(tidycensus) # `pak::pak("walkerke/tidycensus")` pending CRAN update
 
 # Get the data
 pyramid_data <- get_estimates(

diff --git a/pyramids-2024.R b/pyramids-2024.R
@@ -0,0 +1,86 @@
+library(tidyverse)
+library(tidycensus)
+
+# Get the data
+pyramid_data <- get_estimates(
+  geography = "state",
+  product = "characteristics",
+  breakdown = c("AGEGROUP", "SEX"),
+  breakdown_labels = TRUE,
+  vintage = 2024,
+  year = 2024
+) %>%
+  filter(AGEGROUP != "All ages", SEX != "Both sexes", NAME %in% c("Maine", "Utah")) %>%
+  mutate(
+    value_pct = value / sum(value) * 100,
+    value_pct = ifelse(SEX == "Male", -value_pct, value_pct)
+  )
+
+# Build the visualization
+ggplot(pyramid_data, aes(x = value_pct, y = AGEGROUP, fill = SEX)) +
+  geom_col(alpha = 0.9, width = 0.85) +
+  geom_vline(xintercept = 0, color = "gray40", linewidth = 0.5) +
+  scale_x_continuous(
+    labels = function(x) paste0(abs(x), "%"),
+    breaks = seq(-2, 2, 0.5),
+    expand = expansion(mult = c(0.05, 0.05))
+  ) +
+  scale_y_discrete(
+    labels = function(x) str_remove_all(x, "Age\\s|\\syears") %>%
+      str_replace("Under 5", "0-4") %>%
+      str_replace("85 and older", "85+")
+  ) +
+  scale_fill_manual(
+    values = c("Female" = "#A23B72", "Male" = "#2E86AB"),
+    breaks = c("Male", "Female")  # This controls the legend order
+  ) +
+  facet_wrap(~NAME, scales = "free_x") +
+  labs(
+    title = "The Oldest (Maine) and Youngest (Utah) States in the U.S.",
+    subtitle = "Population distribution by age and sex, 2024",
+    x = "Percentage of State Population",
+    y = "Age Group",
+    caption = "Source: U.S. Census Bureau, Vintage 2024 Population Estimates | tidycensus R package"
+  ) +
+  theme_minimal(base_size = 11) +
+  theme(
+    # Title and text styling
+    plot.title = element_text(face = "bold", size = 18, hjust = 0.5, 
+                              margin = margin(b = 5)),
+    plot.subtitle = element_text(color = "gray50", size = 13, hjust = 0.5,
+                                 margin = margin(b = 15)),
+    plot.caption = element_text(color = "gray60", size = 9, hjust = 1,
+                                margin = margin(t = 15)),
+
+    # Facet styling
+    strip.text = element_text(size = 14, face = "bold", color = "gray20"),
+    strip.background = element_rect(fill = "gray95", color = NA),
+
+    # Axis styling
+    axis.title.x = element_text(size = 11, margin = margin(t = 10)),
+    axis.title.y = element_text(size = 11, margin = margin(r = 10)),
+    axis.text.y = element_text(size = 10, color = "gray30"),
+    axis.text.x = element_text(size = 10, color = "gray30"),
+
+    # Grid styling
+    panel.grid.major.y = element_line(color = "gray90", linewidth = 0.3),
+    panel.grid.minor.y = element_blank(),
+    panel.grid.major.x = element_line(color = "gray85", linewidth = 0.3),
+    panel.grid.minor.x = element_blank(),
+
+    # Panel styling
+    panel.spacing = unit(1.5, "lines"),
+
+    # Legend styling 
+    legend.position = "bottom",
+    legend.direction = "horizontal",
+    legend.title = element_text(size = 10, margin = margin(r = 3)),  # Reduced spacing
+    legend.text = element_text(size = 10),
+    legend.key.width = unit(1.5, "cm"),
+    legend.key.height = unit(0.3, "cm"),
+    legend.margin = margin(t = 5, b = 0),
+    legend.spacing.x = unit(0.3, "cm"),
+
+    # Overall plot margins
+    plot.margin = margin(20, 20, 10, 20)
+  )