add histogram and shapiro-wilk test

ironwood_plots.R

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+##
+# Libary imports
+##
+
+library(readODS)
+library(tidyverse)
+library(dplyr)
+
+
+# read a data frame from the ods document
+df <- read_ods("ironwood_data_cleaned.ods", sheet = 1)
+
+
+##
+# asses the tree health
+##
+
+# create a vector with our condition names
+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")
+# Now, let's create the histogram
+hist(df$Tree_Health_Index,
+     breaks = 0:5 - 0.5,  # Setting breaks at midpoints between integers
+     main = "Distribution of Tree Health Index",
+     xlab = "Health Index",
+     ylab = "Number of Trees",
+     col = condition_colors,  # Assigning colors based on the health index values
+     border = "black", # Border color of the bars
+     xlim = c(-0.5, 4.5), # Setting x-axis limits to include all health index values
+     ylim = c(0, max(table(df$Tree_Health_Index))*1.1), # Setting y-axis limits slightly above the maximum frequency
+     axes = FALSE)  # Suppressing axes for customization
+# Add axis labels
+axis(1, at = 0:4, labels = condition_names)
+axis(2)
+# Adding a legend
+legend("topright", legend = condition_names, fill = condition_colors, border.col = "black")
+# Add a title
+title(main = "Distribution of Tree Health Index")
+# Adding a grid
+grid(nx = NULL, ny = NULL, col = "lightgray", lty = "dotted")
+# Adding a box around the plot
+box()
+
+##
+# Perform a shapiro-wilk normality test
+##
+
+# Perform Shapiro-Wilk test
+shapiro_test <- shapiro.test(df$Tree_Health_Index)
+# Print the test results
+print(shapiro_test)
+# Check the p-value
+p_value <- shapiro_test$p.value
+# Interpret the results
+if (p_value < 0.05) {
+  print("The data is not normally distributed (reject the null hypothesis)")
+} else {
+  print("The data is normally distributed (fail to reject the null hypothesis)")
+}
+
+##
+# Tasks:
+##
+
+# 1. find average dbh
+# 2. find