Create summary statistics for rural & urban classification.

analysis.Rmd

@@ -103,7 +103,9 @@ This figure shows the distribution of alternative fueling station locations by u
 ```{r}
 charger_by_UR20_summary <- dataset %>%
   group_by(UR20) %>%
-  summarise(`Total Chargers` = sum(`Total Chargers`))
+  summarise(`Total Chargers` = sum(`Total Chargers`),
+            `Total Level 2 Chargers` = sum(`EV Level2 EVSE Num`, na.rm = TRUE),
+            `Total DC Fast Chargers` = sum(`EV DC Fast Count`, na.rm = TRUE),)
 ```
 
 A new data frame is created to summarize the Total Chargers based on urban-rural classification.
@@ -122,6 +124,38 @@ ggplot(charger_by_UR20_summary, aes(x = UR20, y = `Total Chargers`)) +
 
 This figure shows the total number of electric vehicle chargers based on urban-rural classification.
 
+### Rural-Classified Alternate Fuel Station Summary Statistics
+```{r}
+rural_charger_summary <- dataset %>%
+  filter(UR20 == 'Rural') %>%
+  summarise(
+    Count = n(),
+    Minimum = min(`Total Chargers`),
+    `First Quartile` = quantile(`Total Chargers`, 0.25),
+    Mean = mean(`Total Chargers`),
+    Median = median(`Total Chargers`),
+    `Third Quartile` = quantile(`Total Chargers`, 0.75),
+    Maximum = max(`Total Chargers`),
+  ) %>%
+  print()
+```
+
+### Urban-Classified Alternate Fuel Station Summary Statistics
+```{r}
+urban_charger_summary <- dataset %>%
+  filter(UR20 == 'Urban') %>%
+  summarise(
+    Count = n(),
+    Minimum = min(`Total Chargers`),
+    `First Quartile` = quantile(`Total Chargers`, 0.25),
+    Mean = mean(`Total Chargers`),
+    Median = median(`Total Chargers`),
+    `Third Quartile` = quantile(`Total Chargers`, 0.75),
+    Maximum = max(`Total Chargers`),
+  ) %>%
+  print()
+```
+
 ```{r}
 charger_by_city_summary <- dataset %>%
   group_by(City) %>%