add R scripts to analyze food

src/main/R/plotFoodResults.R

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+setwd("C:/Users/Ricardo/git/shared-svn/projects/freight/studies/WP51_EmissionsFood/output/BaseCase_Food2024/Analysis")
+
+# Install and load necessary packages
+if (!requireNamespace("tidyverse", quietly = TRUE)) {
+  install.packages("tidyverse")
+}
+if (!requireNamespace("plotly", quietly = TRUE)) {
+  install.packages("plotly")
+}
+if (!requireNamespace("gridExtra", quietly = TRUE)) {
+  install.packages("gridExtra")
+}
+library(tidyverse)
+library(plotly)
+library(gridExtra)
+
+# Read the TSV file
+df <- read.delim("TimeDistance_perVehicleType.tsv")
+df_tours <- read.delim("TimeDistance_perVehicle.tsv")
+
+# Specify the desired order of vehicleTypeId
+desired_order <- c("light8t", "light8t_frozen","medium18t", "medium18t_frozen","heavy26t", "heavy26t_frozen","heavy40t", "heavy40t_frozen")
+
+# Convert 'vehicleTypeId' to a factor with the desired order
+df$vehicleTypeId <- factor(df$vehicleTypeId, levels = desired_order)
+df_tours$vehicleTypeId <- factor(df_tours$vehicleTypeId, levels = desired_order)
+
+# Ensure both plots have the same levels
+common_levels <- intersect(levels(df$vehicleTypeId), levels(df$vehicleTypeId))
+
+# Filter the data frame to include only common levels
+df <- df %>% filter(vehicleTypeId %in% common_levels)
+
+# 1. Bar Plot for Number of Vehicles by Vehicle Type (Interactive)
+bar_plot <- plot_ly(x = ~df$vehicleTypeId, y = df$nuOfVehicles, type = 'bar') %>%
+  layout(xaxis = list(title = 'Vehicle Type'), yaxis = list(title = 'Number of Vehicles'))
+
+# 3. Bar Plot for Total Costs by Vehicle Type (Interactive)
+bar_plot_costs <- plot_ly(x = df$vehicleTypeId, y = df$totalCosts.EUR., type = 'bar', name = 'Total Costs') %>%
+  layout(xaxis = list(title = 'Vehicle Type'), yaxis = list(title = 'Total Costs (EUR)'))
+
+# 4. Box Plot for Traveled Distances by Vehicle Type (Interactive)
+box_plot_distances <- plot_ly(data = df_tours, x = ~vehicleTypeId, y = ~travelDistance.km., 
+                              type = 'box', boxpoints = "all", jitter = 0.0, pointpos = -0.0) %>%
+  layout(xaxis = list(title = 'Vehicle Type'), yaxis = list(title = 'Traveled Distances (km)'))
+
+# Display the plots separately
+print(bar_plot %>% layout(title = 'Number of Vehicles by Vehicle Type'))
+print(bar_plot_costs %>% layout(title = 'Total Costs by Vehicle Type'))
+print(box_plot_distances %>% layout(title = 'Box Plot of Traveled Distances by Vehicle Type'))

src/main/R/plotFoodResults_comparison.R

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+# Set working directory and load necessary packages (assuming it hasn't changed)
+setwd("C:/Users/Ricardo/git/shared-svn/projects/freight/studies/WP51_EmissionsFood/output")
+
+if (!requireNamespace("tidyverse", quietly = TRUE)) {
+  install.packages("tidyverse")
+}
+if (!requireNamespace("plotly", quietly = TRUE)) {
+  install.packages("plotly")
+}
+if (!requireNamespace("gridExtra", quietly = TRUE)) {
+  install.packages("gridExtra")
+}
+library(tidyverse)
+library(plotly)
+library(gridExtra)
+
+# Function to create a bar plot for vehicles
+create_bar_plot_vehicles <- function(df_base, df_policy, title) {
+  plot_ly() %>%
+    add_bars(x = ~df_base$vehicleCategory, y = df_base$nuOfVehicles, name = 'Base Case', type = 'bar') %>%
+    add_bars(x = ~df_policy$vehicleCategory, y = df_policy$nuOfVehicles, name = 'Policy Case', type = 'bar') %>%
+    layout(xaxis = list(title = 'Vehicle Type'), yaxis = list(title = 'Number of Vehicles')) %>%
+    layout(title = title)
+}
+
+# Function to create a bar plot for costs
+create_bar_plot_costs <- function(df_base, df_policy, title) {
+  plot_ly() %>%
+    add_bars(x = ~df_base$vehicleCategory, y = df_base$totalCosts.EUR., name = 'Base Case', type = 'bar') %>%
+    add_bars(x = ~df_policy$vehicleCategory, y = df_policy$totalCosts.EUR., name = 'Policy Case', type = 'bar') %>%
+    layout(xaxis = list(title = 'Vehicle Type'), yaxis = list(title = 'Total Costs (EUR)')) %>%
+    layout(title = title)
+}
+
+# Function to create a bar plot for distances
+create_bar_plot_distances <- function(df_base, df_policy, title) {
+  plot_ly() %>%
+    add_bars(x = ~df_base$vehicleCategory, y = df_base$SumOfTravelDistances.km, name = 'Base Case', type = 'bar') %>%
+    add_bars(x = ~df_policy$vehicleCategory, y = df_policy$SumOfTravelDistances.km, name = 'Policy Case', type = 'bar') %>%
+    layout(xaxis = list(title = 'Vehicle Type'), yaxis = list(title = 'Traveles Distances (km)')) %>%
+    layout(title = title)
+}
+#function to create categories
+create_vehicle_categories <- function(data) {
+  data <- data %>%
+    mutate(vehicleCategory = ifelse(vehicleTypeId %in% c("light8t", "light8t_frozen"), yes = "light8t",
+                                    no = ifelse(vehicleTypeId %in% c("light8t_electro_Mitsubishi", "light8t_electro_Quantron", "light8t_frozen_electro_Mitsubishi", "light8t_frozen_electro_Quantron"), yes = "light8t_electro",
+                                                no = ifelse(vehicleTypeId %in% c("medium18t", "medium18t_frozen"), yes = "medium18t",
+                                                            no = ifelse(vehicleTypeId %in% c("medium18t_electro_Renault", "medium18t_electro_Volvo"), yes = "medium18t_electro", 
+                                                                        no = ifelse(vehicleTypeId %in% c("heavy26t", "heavy26t_frozen"), yes = "heavy26t", 
+                                                                                    no = ifelse(vehicleTypeId %in% c("heavy26t_electro_Daimler", "heavy26t_electro_Renault", "heavy26t_frozen_electro_Daimler", "heavy26t_frozen_electro_Renault"), yes = "heavy26t_electro", 
+                                                                                                no = ifelse(vehicleTypeId %in% c("heavy40t_electro_Daimler", "heavy40t_electro_Scania", "heavy40t_frozen_electro_Daimler", "heavy40t_frozen_electro_Scania"), yes = "heavy40t_electro", 
+                                                                                                            no = ifelse(vehicleTypeId %in% c("heavy40t", "heavy40t_frozen"), yes = "heavy40t", 
+                                                                                                                        no = as.character(vehicleTypeId))))))))))
+  # Add a category for the sum of all vehicle types
+  data <- data %>%
+    mutate(sum = ifelse(vehicleCategory %in% c("light8t", "light8t_electro","medium18t", "medium18t_electro",
+                                                           "heavy26t", "heavy26t_electro", "heavy40t", "heavy40t_electro"), 
+                                    yes = "Sum", no = as.character(vehicleCategory)))
+
+}
+# Read data for the base case
+df <- read.delim("run3_Base_100it/Analysis/TimeDistance_perVehicleType.tsv")
+df_tours <- read.delim("run3_Base_100it/Analysis/TimeDistance_perVehicle.tsv")
+
+# Read data for the policy case
+df_policy <- read.delim("run2_EV_100it/Analysis/TimeDistance_perVehicleType.tsv")
+df_tours_policy <- read.delim("run2_EV_100it/Analysis/TimeDistance_perVehicle.tsv")
+
+#add categories
+df <- create_vehicle_categories(df)
+df_tours <- create_vehicle_categories(df_tours)
+df_policy <- create_vehicle_categories(df_policy)
+df_tours_policy <- create_vehicle_categories(df_tours_policy)
+
+# Specify the desired order of vehicleTypeId
+desired_order <- c("light8t", "light8t_electro","medium18t", "medium18t_electro",
+                   "heavy26t", "heavy26t_electro", "heavy40t", "heavy40t_electro", "Sum")
+
+# Convert 'vehicleTypeId' to a factor with the desired order for both base and policy cases
+df$vehicleCategory <- factor(df$vehicleCategory, levels = desired_order)
+df_tours$vehicleCategory <- factor(df_tours$vehicleCategory, levels = desired_order)
+df_policy$vehicleCategory <- factor(df_policy$vehicleCategory, levels = desired_order)
+df_tours_policy$vehicleCategory <- factor(df_tours_policy$vehicleCategory, levels = desired_order)
+
+# Ensure both plots have the same levels
+common_levels <- intersect(levels(df$vehicleCategory), levels(df$vehicleCategory))
+
+# Filter the data frame to include only common levels
+#df <- df %>% filter(vehicleTypeId %in% common_levels)
+
+# 1. Bar Plot for Number of Vehicles by Vehicle Type (Interactive)
+combined_bar_plot_vehicles <- create_bar_plot_vehicles(df, df_policy, 'Number of Vehicles by Vehicle Type')
+
+
+# 2. Bar Plot for Total Costs by Vehicle Type (Interactive)
+combined_bar_plot_costs <- create_bar_plot_costs(df, df_policy, 'Total Costs by Vehicle Type')
+
+# 3. Bar Plot for Travel Distacnes by Vehicle Type (Interactive)
+combined_bar_plot_distances <- create_bar_plot_distances(df, df_policy, 'Total Traveled Distances by Vehicle Type')
+
+# 4. Box Plot for Traveled Distances by Vehicle Type (Interactive)
+box_plot_distances <- plot_ly(data = df_tours, x = ~vehicleCategory, y = ~travelDistance.km., 
+                              type = 'box', boxpoints = "all", jitter = 0.0, pointpos = -0.0, colors = ) %>%
+  layout(xaxis = list(title = 'Vehicle Type'), yaxis = list(title = 'Traveled Distances (km)'))
+box_plot_distances_policy <- plot_ly(data = df_tours_policy, x = ~vehicleCategory, y = ~travelDistance.km., 
+                              type = 'box', boxpoints = "all", jitter = 0.0, pointpos = -0.0) %>%
+  layout(xaxis = list(title = 'Vehicle Type'), yaxis = list(title = 'Traveled Distances (km)'))
+
+# Display the combined bar plot
+print(combined_bar_plot_vehicles)
+print(combined_bar_plot_costs)
+print(combined_bar_plot_distances)
+print(box_plot_distances %>% layout(title = 'Box Plot of Traveled Distances by Vehicle Type - Base'))
+print(box_plot_distances_policy %>% layout(title = 'Box Plot of Traveled Distances by Vehicle Type - EV'))