64 lines
1.8 KiB
R
64 lines
1.8 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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# First, let's create a vector of condition names corresponding to the health index numbers
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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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# Calculate the percentage of trees in each health condition
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percentage <- prop.table(table(df$Tree_Health_Index)) * 100
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# Now, let's create the bar plot
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barplot(percentage,
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names.arg = condition_names,
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main = "Distribution of Tree Health Index",
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xlab = "Health Index",
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ylab = "Percentage of Trees",
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ylim = c(0, max(percentage) + 10),
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col = condition_colors,
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border = "black")
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# Adding a legend
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legend("topright", legend = condition_names, fill = condition_colors)
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# Adding a grid
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#grid(nx = NULL, ny = NULL, col = "lightgray", lty = "dotted")
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# Adding a box around the plot
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box()
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# Add labels with the percentage of trees in each bar
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text(x = barplot(percentage, plot = FALSE), y = percentage, labels = paste0(round(percentage, 1), "%"), pos = 3)
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##
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# Perform a shapiro-wilk normality test
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##
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# Perform Shapiro-Wilk test
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shapiro_test <- shapiro.test(df$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 |