Splitting faceted graphs

analysis/trends_2025.R

@@ -1,5 +1,6 @@
 ### 2025 trends ###
 library(tidyverse)
+library(ggthemes)
 
 # Read csv file
 ped_cycle_df <- read_csv("~/Documents/Repos/COSA-VisionZero/src/ped_cycle_df.csv")
@@ -14,3 +15,51 @@ pedestrians <- df_filtered %>%
 
 cyclists <- df_filtered %>%
   filter(str_detect(Person_Type, "PEDALCYCLIST"))
+
+# Summarize
+ped_summary <- pedestrians %>%
+  group_by(Crash_Year) %>%
+  summarise(
+    total_crashes = n(),
+    total_injuries = sum(Person_Total_Injury_Count, na.rm = TRUE),
+    serious_injuries = sum(Person_Suspected_Serious_Injury_Count, na.rm = TRUE),
+    deaths = sum(Person_Death_Count, na.rm = TRUE)
+  )
+
+cyclist_summary <- cyclists %>%
+  group_by(Crash_Year) %>%
+  summarise(
+    total_crashes = n(),
+    total_injuries = sum(Person_Total_Injury_Count, na.rm = TRUE),
+    serious_injuries = sum(Person_Suspected_Serious_Injury_Count, na.rm = TRUE),
+    deaths = sum(Person_Death_Count, na.rm = TRUE)
+  )
+
+# Monthly summary
+month_levels <- month.name
+df_filtered$Crash_Month <- factor(df_filtered$Crash_Month, levels = month_levels,
+                                  ordered = TRUE)
+
+# Group and summarize
+# Group and summarize
+monthly_summary <- df_filtered %>%
+  filter(Crash_Year %in% c(2023, 2024, 2025)) %>%
+  mutate(Person_Category = case_when(
+    str_detect(Person_Type, "PEDESTRIAN") ~ "Pedestrian",
+    str_detect(Person_Type, "PEDALCYCLIST") ~ "Cyclist",
+    TRUE ~ "Other"
+  )) %>%
+  filter(Person_Category != "Other") %>%
+  group_by(Person_Category, Crash_Year, Crash_Month) %>%
+  summarise(
+    total_crashes = n(),
+    total_injuries = sum(Person_Total_Injury_Count, na.rm = TRUE),
+    serious_injuries = sum(Person_Suspected_Serious_Injury_Count, na.rm = TRUE),
+    deaths = sum(Person_Death_Count, na.rm = TRUE),
+    .groups = "drop"
+  ) %>%
+  pivot_longer(
+    cols = c(total_crashes, total_injuries, serious_injuries, deaths),
+    names_to = "Metric", values_to = "Count"
+  )
+View(monthly_summary)