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