diff --git a/projectCode/example_data(garlic_mustard).R b/projectCode/example_data(garlic_mustard).R
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+++ b/projectCode/example_data(garlic_mustard).R
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+library(dplyr)
+library(tidyverse)
+library(dplyr)
+library(ggplot2)
+library(lubridate)
+library(tidyr)
+library(maps)
+
+# list files in the Data folder
+# get your file names to be consistent!
+csv_files = list.files("Alliaria_petiolata", full.names = T)
+list(csv_files)
+
+data <- read.csv("Alliaria_petiolata/inat_annot_Alliaria_petiolata_USA_.csv") 
+nrow(data)
+
+range(data$observed_on)
+
+
+# Load necessary libraries
+library(dplyr)
+library(ggplot2)
+
+# Load necessary libraries
+library(dplyr)
+library(ggplot2)
+
+# Load necessary libraries
+library(dplyr)
+library(ggplot2)
+library(lubridate)  # For date handling
+
+
+# Ensure the 'observed_on' column is in date format
+data$observed_on <- as.Date(data$observed_on, format = "%Y-%m-%d")
+
+# Calculate DOY (Day of Year)
+data$DOY <- yday(data$observed_on)
+
+# Filter data for observations since 2018 and exclude 2024
+data_filtered <- data %>% filter(year(observed_on) >= 2018 & year(observed_on) < 2024)
+
+# Extract the year from the observation date
+data_filtered$year <- year(data_filtered$observed_on)
+
+# Group by year and calculate mean latitude and mean DOY of flowers
+grouped_data <- data_filtered %>%
+  filter(flowers == "Y") %>%
+  group_by(year) %>%
+  summarize(mean_latitude = mean(latitude, na.rm = TRUE),
+            mean_DOY_flowers = mean(DOY, na.rm = TRUE))
+
+# Plotting
+ggplot(data_filtered, aes(x = latitude, y = DOY,
+                         color = as.factor(year))) +
+  geom_point() +
+  geom_smooth(method = "lm") +
+  labs(x = "Mean Latitude", y = "Mean DOY of Flowers", title = "Mean Latitude vs Mean DOY of Flowers (2018-2023)") +
+  theme_minimal()
+
+