add functioning map and fix naming

ironwood_plots.R

@@ -8,14 +8,18 @@ library(dplyr)
 library(leaflet)
 
 ##
-# parse the input data
+# parse the input data and declare global values
 ##
 
 # read a data frame from the ods document
 df <- read_ods("ironwood_data_cleaned.ods", sheet = 1)
-
-# Output the results
-print(df)
+# site base location
+site_lat <- "-33.943917"
+site_lon <- "23.507389"
+# vector of condition names corresponding to the health index numbers
+condition_names <- c("healthy", "light damage", "medium damage", "severe damage", "at point of death")
+# colors for each condition
+condition_colors <- c("green", "yellow", "orange", "red", "black")
 
 
 ##
@@ -23,16 +27,12 @@ print(df)
 # - create an overview of the populations health
 ##
 
-# First, let's create a vector of condition names corresponding to the health index numbers
-condition_names <- c("Healthy", "Light damage", "Medium damage", "Severe damage", "At point of death")
-# Define colors for each condition
-condition_colors <- c("green", "yellow", "orange", "red", "black")
 # Calculate the percentage of trees in each health condition
-percentage <- prop.table(table(df$Tree_Health_Index)) * 100
+percentage <- prop.table(table(df$tree_health_index)) * 100
 # Now, let's create the bar plot
 barplot(percentage,
         names.arg = condition_names,
-        main = "Distribution of Tree Health Index",
+        main = "Overview of Tree Health Index",
         xlab = "Health Index",
         ylab = "Percentage of Trees",
         ylim = c(0, max(percentage) + 10),
@@ -43,7 +43,7 @@ legend("topright", legend = condition_names, fill = condition_colors)
 # Adding a grid
 #grid(nx = NULL, ny = NULL, col = "lightgray", lty = "dotted")
 # Adding a box around the plot
-box()
+#box()
 # Add labels with the percentage of trees in each bar
 text(x = barplot(percentage, plot = FALSE), y = percentage, labels = paste0(round(percentage, 1), "%"), pos = 3)
 
@@ -53,7 +53,7 @@ text(x = barplot(percentage, plot = FALSE), y = percentage, labels = paste0(roun
 ##
 
 # Perform Shapiro-Wilk test
-shapiro_test <- shapiro.test(df$Tree_Health_Index)
+shapiro_test <- shapiro.test(df$tree_health_index)
 # Print the test results
 print(shapiro_test)
 # Check the p-value
@@ -65,15 +65,30 @@ if (p_value < 0.05) {
   print("The data is normally distributed (fail to reject the null hypothesis)")
 }
 
-
 ##
 # 3. try to fit health and location data in one plot
 ##
 
-sites <- data.frame(
-  id = 1:20,
-  lat = 
-)
+# create a map from our base location
+map <- leaflet() %>%
+  setView(lng = site_lon,
+          lat = site_lat,
+          zoom = 16) %>%
+  addProviderTiles("CartoDB.Positron")
+
+# Add markers with varying color and size based on population health and number of trees
+map <- map %>%
+  addCircleMarkers(
+    data = df,
+    lng = ~tree_lon,
+    lat = ~tree_lat,
+    radius = 5,
+    color = ~condition_colors[tree_health_index+1],
+    fillOpacity = 0.7
+  )
+
+# show map
+map
 
 ##
 # ToDo Tasks: