FL.R

library(dplyr)
library(tidyr)
library(ggplot2)
library(stringr)
library(forcats)
library(showtext)
library(scales)
library(sysfonts)
library(extrafont)
font_add("Sanchez", regular = "./poster/font/Sanchez/Sanchez-Regular.ttf")



burger_king_menu <- read.csv("data/burger-king-menu.csv")
deaths_obesity <- read.csv("data/deaths-due-to-obesity.csv")
quick_service_restaurants_us <- read.csv("data/number-of-quick-service-restaurants-in-the-us-2011_2022.csv")
Nutrition_Value_Dataset <- read.csv("data/Nutrition_Value_Dataset.csv")
obesity <- read.csv("data/obesity-percents.csv")
mc_in_Europe <- read.csv("data/mcdonalds_in_europe.csv", sep = ";")
mcZestaw <- read.csv("data/McZestawBigMac.csv")
zdrowyObiad <- read.csv("data/zdrowyObiad.csv")
spendings <- read.csv("data/spendings_fast_food_USA_2004_2022.csv", sep = ";")
usa_population <- read.csv("data/population_usa.csv")
population <- read.csv("data/world_population.csv")




zdrowy_vs_fastfood <- mcZestaw %>% 
  filter(Danie == "Zestaw") %>% 
  select(-c(porcja,Blonnik..g.,Cukry..g.,Kwasy.tluszczowe.nasycone..g.)) %>% 
  rows_insert(y = zdrowyObiad %>% 
                select(-porcja))





obesity %>% 
  filter(Country == "United States of America",
         Sex == "Both sexes") %>% 
  ggplot(aes(x = Year, y = Obesity_percent, color = Sex)) +
  geom_point()

quick_service_restaurants_us %>% 
  ggplot(aes(x = year, y = number_of_restaurants)) +
  geom_point()

mcZestaw %>% 
  group_by(porcja) %>%
  summarise(across(where(is.numeric), ~ sum(., na.rm = TRUE))) %>% 
  pivot_longer(cols = -porcja, names_to = "Kolumna", values_to = "Suma") %>% 
  ggplot(aes(x = Kolumna, y = Suma, fill = porcja)) +
  geom_col(position = "dodge")

zdrowy_vs_fastfood %>% 
  pivot_longer(cols = -Danie, values_to = "Wartosc", names_to = "Skladnik") %>% 
  ggplot(aes(x = Skladnik, y = Wartosc, fill = Danie)) +
  geom_col(position = "dodge")

spendings %>% 
  left_join(usa_population, by = "Year") %>% 
  mutate(person_year_spending = Spending.billion.dollars. * 1000000000 / Population) %>% 
  ggplot(aes(x = Year, y = person_year_spending)) +
  geom_line()

deaths_obesity %>% 
  filter(Code == "USA") %>% 
  mutate(dead = Deaths / (usa_population %>% 
           filter(Year %in% 1990:2019))$Population) %>% 
  ggplot(aes(x = Year, y = dead)) + 
  geom_point()

# barplot

frequency_of_visiting_fast_food <- read.csv("data/average-fast-food-consumption-per-week-in-2016-2018.csv", sep = ";")

frequency_of_visiting_fast_food_modified <- frequency_of_visiting_fast_food %>% 
  mutate(X2016 = X2016/100, X2017 = X2017/100, X2018 = X2018/100) %>% 
  pivot_longer(cols = c(X2016, X2017, X2018), 
               names_to = "Year", 
               values_to = "PercentageShare") %>%
  mutate(Year = as.factor(gsub("X", "", Year)))


frequency_of_visiting_fast_food_modified %>% 
  filter(Year == 2018) %>%
  filter(Answer != "Prefer not to say") %>% 
  mutate(pozycja = case_when(Answer == "I don't eat at fast food restaurants" ~ "A",
                             Answer == "Less than once per week" ~ "B",
                             Answer == "One to three times per week" ~ "c",
                             Answer == "Four to six times per week" ~ "D",
                             Answer == "Seven to nine times per week" ~ "E",
                             Answer == "Ten times or more per week" ~ "F",
                             TRUE ~ "Z")) %>% 
  mutate(Answer = fct_reorder(Answer, pozycja)) %>% 
  ggplot(aes(y = Answer, x = PercentageShare, fill = Year)) +
  geom_col(width = 0.7) +
  labs(title = "How many times a week do we eat fast food?", 
       x = "Share of respondents",
       y = element_blank(),
       legend = element_blank()) +
  scale_fill_manual(values = c("#E4D00A")) +
  scale_x_continuous(expand = expansion(c(0,0), c(0.3, 3)),
                     breaks = seq(0, 30, by = 5),
                     labels = percent_format(scale = 1)) +
  scale_y_discrete(labels = rev(c("More than 9",
                              "7 to 9",
                              "4 to 6",
                              "1 to 3",
                              "Less than 1",
                              "0"))) +
  theme_minimal() +
  theme(plot.background = element_rect(fill = '#18191C'),
        plot.margin = margin(t = 10, r = 10, b = 5, l = 10),
        plot.title = element_text(colour = "white",
                             family = "Sanchez",
                             size = 30,
                             margin = margin(t = 10, b = 30),
                             hjust = 0),
        axis.title.x = element_text(colour = "white",
                                    family = "Sanchez",
                                    size = 16,
                                    margin = margin(t = 10, b = 10)),
        axis.text = element_text(colour = "white",
                                 family = "Sanchez",
                                 size = 15,
                                 face = "bold"),
        axis.text.y = element_text(hjust = 1),
        panel.grid.major.y = element_blank(),
        panel.grid.major.x = element_line(),
        panel.grid.minor.x = element_blank(),
        legend.position = "none")        

# Maciek's plot about number of restaurants

library("ggimage")

melted_data_good_with_logo[c(19:27),4] <- paste("logos", "logo300.png", sep = "/")
melted_data_good_with_logo[c(10:18),4] <- paste("logos", "logo200.png", sep = "/")
melted_data_good_with_logo[c(1:9),4] <- paste("logos", "logo10png", sep = "/")
melted_data_good_with_logo[c(28:36),4] <- paste("logos", "logo40.png", sep = "/")
melted_data_good_with_logo[c(37:45),4] <- paste("logos", "logo500.png", sep = "/")
melted_data_good_with_logo[c(46:54),4] <- paste("logos", "logo600.png", sep = "/")

melted_data_good_with_logo[c(37:38),4] <- paste("logos", "nothing.png", sep = "/")
melted_data_good_with_logo[45,4] <- paste("logos", "nothing.png", sep = "/")

melted_data_good_with_logo[c(37:38),4] <- paste("logos", "logo502.png", sep = "/")
melted_data_good_with_logo[45,4] <- paste("logos", "logo502.png", sep = "/")



melted_data_good_with_logo %>%
  filter(variable != "Domino.s.Pizza" & variable != "PizzaHut") %>% # without pizzas
  ggplot(aes(x=Year, y=value)) +
  geom_line(aes(colour = variable),
            linewidth = 2) + 
  geom_point(aes(colour = variable),
             size = 25.5, #22.5,
             shape = 21,
             fill = "white") + 
  geom_image(aes(image = logo), size = .165) + #.15) +
  scale_x_continuous(breaks = seq(min(melted_data_good_with_logo$Year), 
                                  max(melted_data_good_with_logo$Year), 
                                  by = 1)) +
  scale_y_continuous(limits = c(0, 47000)) +
  scale_colour_manual(values = c("Subway" = "#008C15",
                        "McDonald.s" = "#FFC72C",
                        "KFC" = "#F40027",
                        "Burger.King" = "#0033A0")) +
  labs(title = "Number of major fast food restaurants in recent years",
       x = "Year",
       y = "Number of restaurants") +
  theme_minimal() +
  theme(plot.background = element_rect(fill = '#18191C'),
        plot.margin = margin(t = 10, r = 10, b = 5, l = 10),
        plot.title = element_text(colour = "white",
                                  # family = "Sanchez",
                                  size = 30,
                                  margin = margin(t = 10, b = 30),
                                  hjust = 0),
        axis.title = element_text(colour = "white",
                                    # family = "Sanchez",
                                    size = 16,
                                    margin = margin(t = 10, b = 10)),
        axis.text = element_text(colour = "white",
                                 # family = "Sanchez",
                                 size = 15,
                                 face = "bold"),
        axis.text.y = element_text(hjust = 1),
        panel.grid.major.y = element_line(),
        panel.grid.minor.y = element_blank(),
        panel.grid.major.x = element_line(),
        panel.grid.minor.x = element_blank(),
        legend.position = "none")