Refactor Location Analysis: add narrative descriptions, clarify map/table context, and improve accessibility.

report.Rmd

@@ -580,6 +580,9 @@ survey_data %>%
 ```
 
 # Location Analysis {.tabset}
+
+[Back to Top](#)
+
 ```{r func-plot_geographic_data, echo=TRUE}
 plot_geographic_data <- function(joined_data, 
                                  title,
@@ -593,54 +596,42 @@ plot_geographic_data <- function(joined_data,
                                  na_fill_color = "lightgrey") {
   
   current_date <- format(Sys.Date(), "%B %d, %Y")
-  
-  # If subtitle is not provided, use the current date as subtitle
   subtitle_text <- ifelse(is.null(subtitle), paste("Date:", current_date), subtitle)
   
-  # Handle missing data by filling with a specified color
   joined_data[is.na(joined_data$total_trees), "total_trees"] <- NA
   
-  # Select the color scale based on the user's input
   if (color_scale == "viridis") {
-    fill_color <- scale_fill_viridis_c(option = fill_option, na.value = na_fill_color)  # Use na.value to fill NA
+    fill_color <- scale_fill_viridis_c(option = fill_option, na.value = na_fill_color)
   } else if (color_scale == "RColorBrewer") {
-    fill_color <- scale_fill_brewer(palette = "Set3")  # Default RColorBrewer palette
+    fill_color <- scale_fill_brewer(palette = "Set3")
   } else {
-    fill_color <- scale_fill_manual(values = color_scale)  # Custom color scale
+    fill_color <- scale_fill_manual(values = color_scale)
   }
   
-  # Create the plot
   plot <- ggplot(data = joined_data) +
     geom_sf(aes(fill = total_trees), color = "white") +
-    fill_color +  # Color scale for the plot
-    theme_options +  # Apply custom theme
+    fill_color +
+    theme_options +
     labs(title = title,
-         subtitle = subtitle_text,  # Subtitle is handled here
+         subtitle = subtitle_text,
          fill = legend) +
-    theme(axis.text = element_blank(), axis.title = element_blank(),
+    theme(axis.text = element_blank(),
+          axis.title = element_blank(),
           legend.position = legend_position)
   
-  # If save_path is provided, save the plot to file
   if (!is.null(save_path)) {
     ggsave(save_path, plot = plot, width = 10, height = 6)
   }
   
-  # Return the plot
   return(plot)
 }
 ```
 
-[Back to Top](#)
-
 ## By Region
 
-This map displays the **total number of trees planted** across each economic region in **New York State**. The counties are color-coded, with darker shades representing areas where more trees have been planted. This allows users to quickly see which counties have had the most extensive tree planting efforts.
+This map shows the **total number of trees planted** in each of New York’s economic development regions. The shading reflects the volume of planting activity, with darker areas representing higher totals.
 
--   **What to look for**:
-    -   **Dark colors**: Indicate regions with a higher number of trees planted.
-    -   **Lighter colors**: Represent regions with fewer trees planted.
-
-The map provides a visual overview of tree planting distribution across New York, making it easier to identify areas with the highest impact or need for further action.
+Use this map to identify which regions are leading in planting activity, and where more outreach or support might be beneficial.
 
 ```{r create-region-choropleth-map, echo=TRUE, message=FALSE, fig.height=6, fig.width=8}
 survey_data_aggregated <- survey_data %>%
@@ -672,19 +663,19 @@ plot_geographic_data(joined_data = survey_data_joined,
                      na_fill_color = "lightgrey")
 ```
 
+### Regional Planting Summary
+
+The table below breaks down the total number of trees planted by region. It also shows each region’s percentage contribution to overall planting activity across New York State.
+
 ```{r create-summary-table-region, echo=TRUE, message=FALSE, fig.height=6, fig.width=8}
 create_summary_table(survey_data, "Region", "Number of Trees Planted (Required)", remove_na = FALSE, table_font_size = 16)
 ```
 
 ## By County
 
-This map displays the **total number of trees planted** across each county in **New York State**. The counties are color-coded, with darker shades representing areas where more trees have been planted. This allows users to quickly see which counties have had the most extensive tree planting efforts.
+This map provides a county-level view of total trees planted. Darker counties indicate higher planting activity.
 
--   **What to look for**:
-    -   **Dark colors**: Indicate counties with a higher number of trees planted.
-    -   **Lighter colors**: Represent counties with fewer trees planted.
-
-The map provides a visual overview of tree planting distribution across New York, making it easier to identify areas with the highest impact or need for further action.
+This visual helps uncover local patterns within regions, and may guide localized support, outreach, or reporting strategies.
 
 ```{r create-county-choropleth-map, echo=TRUE, message=FALSE, fig.height=6, fig.width=8}
 survey_data_aggregated <- survey_data %>%
@@ -693,12 +684,11 @@ survey_data_aggregated <- survey_data %>%
 
 geographic_data <- counties(state = "NY", cb = TRUE, progress = FALSE) %>% 
   st_as_sf() %>%
-  mutate(NAME = str_replace(NAME, "\\.", "")) # Remove period from "St. Lawrence"
+  mutate(NAME = str_replace(NAME, "\\.", ""))
 
 survey_data_joined <- geographic_data %>%
   left_join(survey_data_aggregated, by = c("NAME" = "County"))
 
-# Example of calling the function with enhancements
 plot_geographic_data(joined_data = survey_data_joined,
                      title = "Number of Trees Planted by County in New York", 
                      legend = "Total Trees Planted",
@@ -706,13 +696,16 @@ plot_geographic_data(joined_data = survey_data_joined,
                      subtitle = "Generated: March 13, 2025",
                      theme_options = theme_minimal(),
                      legend_position = "right",
-                     color_scale = "viridis",  # Default viridis scale
-                     na_fill_color = "lightgrey")  # Color for NA values
+                     color_scale = "viridis",
+                     na_fill_color = "lightgrey")
 ```
 
-```{r create-summary-table-county, echo=TRUE, message=FALSE, , fig.height=6, fig.width=8}
-create_summary_table(survey_data, "County", "Number of Trees Planted (Required)", remove_na = FALSE, table_font_size = 16)
+### County-Level Planting Summary
 
+This table provides a detailed breakdown of trees planted by county. Use it alongside the map to validate trends or investigate specific areas.
+
+```{r create-summary-table-county, echo=TRUE, message=FALSE, fig.height=6, fig.width=8}
+create_summary_table(survey_data, "County", "Number of Trees Planted (Required)", remove_na = FALSE, table_font_size = 16)
 ```
 
 # Tree Analysis {.tabset}