rm(list = ls())
library(rvest)
library(tidyverse)
library(stringi)
library(see)
library(extrafont)

# Font selected: 
my_font <- "Roboto Condensed"

# Colors selected: 
my_colors <- c("#3E606F", "#8C3F4D")



link1 <- "https://en.wikipedia.org/wiki/Nuclear_power_by_country?fbclid=IwAR137hPOmbEvbVH1Acp1FlypUmaNdS2ht1jVi9NfuIxlcIg5X-Dd3T_vSVg"


link1 %>% 
  read_html() %>% 
  html_table(fill = TRUE, header = FALSE) %>% 
  .[[2]] -> df_cap


df_cap <- df_cap[-c(1:2), ]
names(df_cap) <- c("Country", "Reactors", "U_C", "Capacity", "Total_Cap", "Shares", "Notes")

df_cap <- df_cap %>% 
  filter(Capacity != "N/A", Country != "World total") %>% 
  select(-Notes, -U_C)

df_cap_clearned <- df_cap %>% 
  mutate_all(function(x) {as.numeric(x)}) %>% 
  mutate(Country = df_cap$Country) %>% 
  mutate(Country = case_when(str_detect(Country, "Korea") ~ "Korea", str_detect(Country, "Slove") ~ "Slovenia", TRUE ~ Country)) %>% 
  mutate(Share_num = str_replace_all(df_cap$Shares, "%", "") %>% as.numeric(), Share_cha = df_cap$Shares, Shares = NULL)





my_theme <- function(...) {
  theme_abyss() + 
    theme(plot.margin = unit(c(1, 2, 1, 1), "cm")) + 
    theme(plot.title = element_text(size = 22, family = my_font)) + 
    theme(plot.subtitle = element_text(size = 13, family = my_font)) + 
    theme(plot.caption  = element_text(size = 12, family = my_font)) + 
    theme(axis.text = element_text(family = my_font, size = 13)) + 
    theme(panel.grid.major.y = element_blank())
}




df_cap_clearned %>% 
  ggplot(aes(reorder(Country, Total_Cap), Total_Cap)) + 
  geom_col(fill = my_colors[2]) + 
  coord_flip() + 
  my_theme() + 
  scale_y_continuous(labels = c("0", "200,000", "400,000", "600,000", "800,000"), expand = c(0.01, 0)) + 
  labs(x = NULL, y = NULL, title = "Figure 1: Nuclear Electricity Power in GWh by Country, 2017",
       subtitle = "Nuclear power plants currently operate in 31 countries. The United States is the largest producer of\nnuclear power, while France has the largest share of electricity generated by nuclear power.",
       caption = "Data Source: International Atomic Energy Agency (IAEA)") 




df_cap_clearned %>% 
  ggplot(aes(reorder(Country, Share_num), Share_num)) + 
  geom_col(fill = my_colors[2]) + 
  coord_flip() + 
  my_theme() + 
  scale_y_continuous(breaks = seq(0, 80, 10), expand = c(0.01, 0), labels = paste0(seq(0, 80, 10), "%")) + 
  labs(x = NULL, y = NULL, 
       title = "Figure 2: Share of Electricity Production (% of Total)",
       subtitle = "Of the 31 countries in which nuclear power plants operate, only France, Slovakia, Ukraine, Belgium,\nand Hungary use them as the source for a majority of the country's electricity supply.\nOther countries have significant amounts of nuclear power generation capacity.",
       caption = "Data Source: International Atomic Energy Agency (IAEA)")




df_cap_clearned %>% 
  ggplot(aes(reorder(Country, Reactors), Reactors)) + 
  geom_col(fill = my_colors[2]) + 
  coord_flip() + 
  my_theme() + 
  scale_y_continuous(expand = c(0.01, 0), limits = c(0, 100)) + 
  labs(x = NULL, y = NULL, 
       title = "Figure 3: Number of Nuclear Reactors by Country, 2018",
       subtitle = "As of April 2018, there are 449 operable power reactors in the world, with a combined capacity of 394 GW.\nAdditionally, there are 58 reactors under construction and 154 reactors planned, with a combined capacity of\n63 GW and 157 GW, respectively.",
       caption = "Data Source: International Atomic Energy Agency (IAEA)")




link2 <- "https://en.wikipedia.org/wiki/List_of_nuclear_power_stations?fbclid=IwAR0CpWyQi51x2ULBn4FIcF9WDoY77cUkd_4T1pJcIGOjGD_C3dFmIAo1NPM"


link2 %>% 
  read_html() %>% 
  html_table(fill = TRUE, header = FALSE) %>% 
  .[[2]] %>% 
  slice(-1) %>% 
  mutate_all(function(x) {as.character(x)}) %>% 
  mutate_all(function(x) {stri_trans_general(x, "Latin-ASCII")}) -> df_loc


loc <- df_loc$X5

str_split(loc, ";", simplify = TRUE) %>% 
  as.data.frame() %>% 
  mutate_all(function(x) {as.character(x)}) %>% 
  mutate_all(function(x) {stri_trans_general(x, "Latin-ASCII")}) -> m



# Lấy ra dữ liệu Long: 

m %>% 
  mutate(long = str_sub(V1, start = str_count(V1) - 7, end = str_count(V1)) %>% as.numeric()) %>% 
  mutate(long = case_when(str_detect(V1, "-") ~ -1*long, TRUE ~ long)) -> m_trung_gian



str_sub(m$V2, start = 1, end = 12) %>% 
  str_replace_all("[a-z|A-Z]", "") %>% 
  str_replace_all("\\(", "") %>% 
  stri_trans_general("Latin-ASCII") %>% 
  str_squish() %>% 
  str_split("\\.", simplify = TRUE) %>% 
  as.data.frame() -> n


m_trung_gian %>% 
  mutate(lat1 = n$V1 %>% as.character() %>% as.numeric()) %>% 
  mutate(lat2 = n$V2 %>% as.character() %>% str_sub(1, 5) %>% as.numeric() / 100000) %>% 
  mutate(lat2 = case_when(lat1 < 0 ~ -1*lat2, TRUE ~ lat2)) %>% 
  mutate(lat = lat1 + lat2, lat1 = NULL, lat2 = NULL) %>% 
  mutate(Plant_Name = str_replace_all(V2, "[^a-z|A-Z| ]", "")) -> long_lat_full


long_lat_full %>% select(Plant_Name, long, lat) -> df_long_lat

world <- map_data("world")


ggplot() + 
  geom_map(data = world, map = world, aes(x = long, y = lat, map_id = region, group = group), fill = "white", colour = "#7f7f7f", size = 0.2) +
  geom_point(data = df_long_lat, aes(x = lat, y = long), color = "red", alpha = 0.6) + 
  theme_abyss() +
  theme(plot.margin = unit(c(1, 2, 1, 1), "cm")) +
  theme(plot.title = element_text(size = 22, family = my_font)) +
  theme(plot.subtitle = element_text(size = 13, family = my_font)) +
  theme(plot.caption  = element_text(size = 12, family = my_font)) +
  theme(axis.text = element_blank()) +
  theme(axis.line = element_blank()) + 
  theme(panel.grid.major = element_blank()) +
  labs(x = NULL, y = NULL, 
       title = "Figure 4: All Currently Operational Power Nuclear Stations",
       subtitle = "The following map lists all nuclear power stations that are larger than 1,000 MW in current net capacity.",
       caption = "Data Source: International Atomic Energy Agency (IAEA).")

---
title: "World Nuclear Power Status Report 2018" 
subtitle: "R for Pleasure"
author: "Nguyen Chi Dung"
output:
  html_document: 
    code_download: true
    code_folding: hide
    highlight: zenburn
    # number_sections: yes
    theme: "flatly"
    toc: TRUE
    toc_float: TRUE
---

```{r setup,include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE, fig.retina=2)
```

# Fact 1

Hầu hết các nhà máy điện hạt nhân nằm ở các quốc gia phát triển: 

![](C:\\Users\\Zbook\Desktop\\pic\\p1.jpg)

# Fact 2

Hoa Kì là quốc gia có sản lượng điện từ năng lượng hạt nhân lớn nhất thế giới (hình 1): 


![](C:\\Users\\Zbook\Desktop\\pic\\p2.jpg)

# Fact 3

Các quốc gia công nghiệp phát triển có tỉ trọng điện từ năng lượng hạt nhân là lớn nhất (hình 2):


![](C:\\Users\\Zbook\Desktop\\pic\\p3.jpg)


# Fact 4

Số lò phản ứng hạt nhân cho sản xuất điện nhiều nhất vẫn là ở các quốc gia công nghiệp phát triển (hình 3):


![](C:\\Users\\Zbook\Desktop\\pic\\p4.jpg)

# R Codes for Collecting Data and Visualization

R codes để thu thập dữ liệu và hình ảnh hóa: 

```{r, eval=FALSE}
rm(list = ls())
library(rvest)
library(tidyverse)
library(stringi)
library(see)
library(extrafont)

# Font selected: 
my_font <- "Roboto Condensed"

# Colors selected: 
my_colors <- c("#3E606F", "#8C3F4D")



link1 <- "https://en.wikipedia.org/wiki/Nuclear_power_by_country?fbclid=IwAR137hPOmbEvbVH1Acp1FlypUmaNdS2ht1jVi9NfuIxlcIg5X-Dd3T_vSVg"


link1 %>% 
  read_html() %>% 
  html_table(fill = TRUE, header = FALSE) %>% 
  .[[2]] -> df_cap


df_cap <- df_cap[-c(1:2), ]
names(df_cap) <- c("Country", "Reactors", "U_C", "Capacity", "Total_Cap", "Shares", "Notes")

df_cap <- df_cap %>% 
  filter(Capacity != "N/A", Country != "World total") %>% 
  select(-Notes, -U_C)

df_cap_clearned <- df_cap %>% 
  mutate_all(function(x) {as.numeric(x)}) %>% 
  mutate(Country = df_cap$Country) %>% 
  mutate(Country = case_when(str_detect(Country, "Korea") ~ "Korea", str_detect(Country, "Slove") ~ "Slovenia", TRUE ~ Country)) %>% 
  mutate(Share_num = str_replace_all(df_cap$Shares, "%", "") %>% as.numeric(), Share_cha = df_cap$Shares, Shares = NULL)





my_theme <- function(...) {
  theme_abyss() + 
    theme(plot.margin = unit(c(1, 2, 1, 1), "cm")) + 
    theme(plot.title = element_text(size = 22, family = my_font)) + 
    theme(plot.subtitle = element_text(size = 13, family = my_font)) + 
    theme(plot.caption  = element_text(size = 12, family = my_font)) + 
    theme(axis.text = element_text(family = my_font, size = 13)) + 
    theme(panel.grid.major.y = element_blank())
}




df_cap_clearned %>% 
  ggplot(aes(reorder(Country, Total_Cap), Total_Cap)) + 
  geom_col(fill = my_colors[2]) + 
  coord_flip() + 
  my_theme() + 
  scale_y_continuous(labels = c("0", "200,000", "400,000", "600,000", "800,000"), expand = c(0.01, 0)) + 
  labs(x = NULL, y = NULL, title = "Figure 1: Nuclear Electricity Power in GWh by Country, 2017",
       subtitle = "Nuclear power plants currently operate in 31 countries. The United States is the largest producer of\nnuclear power, while France has the largest share of electricity generated by nuclear power.",
       caption = "Data Source: International Atomic Energy Agency (IAEA)") 




df_cap_clearned %>% 
  ggplot(aes(reorder(Country, Share_num), Share_num)) + 
  geom_col(fill = my_colors[2]) + 
  coord_flip() + 
  my_theme() + 
  scale_y_continuous(breaks = seq(0, 80, 10), expand = c(0.01, 0), labels = paste0(seq(0, 80, 10), "%")) + 
  labs(x = NULL, y = NULL, 
       title = "Figure 2: Share of Electricity Production (% of Total)",
       subtitle = "Of the 31 countries in which nuclear power plants operate, only France, Slovakia, Ukraine, Belgium,\nand Hungary use them as the source for a majority of the country's electricity supply.\nOther countries have significant amounts of nuclear power generation capacity.",
       caption = "Data Source: International Atomic Energy Agency (IAEA)")




df_cap_clearned %>% 
  ggplot(aes(reorder(Country, Reactors), Reactors)) + 
  geom_col(fill = my_colors[2]) + 
  coord_flip() + 
  my_theme() + 
  scale_y_continuous(expand = c(0.01, 0), limits = c(0, 100)) + 
  labs(x = NULL, y = NULL, 
       title = "Figure 3: Number of Nuclear Reactors by Country, 2018",
       subtitle = "As of April 2018, there are 449 operable power reactors in the world, with a combined capacity of 394 GW.\nAdditionally, there are 58 reactors under construction and 154 reactors planned, with a combined capacity of\n63 GW and 157 GW, respectively.",
       caption = "Data Source: International Atomic Energy Agency (IAEA)")




link2 <- "https://en.wikipedia.org/wiki/List_of_nuclear_power_stations?fbclid=IwAR0CpWyQi51x2ULBn4FIcF9WDoY77cUkd_4T1pJcIGOjGD_C3dFmIAo1NPM"


link2 %>% 
  read_html() %>% 
  html_table(fill = TRUE, header = FALSE) %>% 
  .[[2]] %>% 
  slice(-1) %>% 
  mutate_all(function(x) {as.character(x)}) %>% 
  mutate_all(function(x) {stri_trans_general(x, "Latin-ASCII")}) -> df_loc


loc <- df_loc$X5

str_split(loc, ";", simplify = TRUE) %>% 
  as.data.frame() %>% 
  mutate_all(function(x) {as.character(x)}) %>% 
  mutate_all(function(x) {stri_trans_general(x, "Latin-ASCII")}) -> m



# Lấy ra dữ liệu Long: 

m %>% 
  mutate(long = str_sub(V1, start = str_count(V1) - 7, end = str_count(V1)) %>% as.numeric()) %>% 
  mutate(long = case_when(str_detect(V1, "-") ~ -1*long, TRUE ~ long)) -> m_trung_gian



str_sub(m$V2, start = 1, end = 12) %>% 
  str_replace_all("[a-z|A-Z]", "") %>% 
  str_replace_all("\\(", "") %>% 
  stri_trans_general("Latin-ASCII") %>% 
  str_squish() %>% 
  str_split("\\.", simplify = TRUE) %>% 
  as.data.frame() -> n


m_trung_gian %>% 
  mutate(lat1 = n$V1 %>% as.character() %>% as.numeric()) %>% 
  mutate(lat2 = n$V2 %>% as.character() %>% str_sub(1, 5) %>% as.numeric() / 100000) %>% 
  mutate(lat2 = case_when(lat1 < 0 ~ -1*lat2, TRUE ~ lat2)) %>% 
  mutate(lat = lat1 + lat2, lat1 = NULL, lat2 = NULL) %>% 
  mutate(Plant_Name = str_replace_all(V2, "[^a-z|A-Z| ]", "")) -> long_lat_full


long_lat_full %>% select(Plant_Name, long, lat) -> df_long_lat

world <- map_data("world")


ggplot() + 
  geom_map(data = world, map = world, aes(x = long, y = lat, map_id = region, group = group), fill = "white", colour = "#7f7f7f", size = 0.2) +
  geom_point(data = df_long_lat, aes(x = lat, y = long), color = "red", alpha = 0.6) + 
  theme_abyss() +
  theme(plot.margin = unit(c(1, 2, 1, 1), "cm")) +
  theme(plot.title = element_text(size = 22, family = my_font)) +
  theme(plot.subtitle = element_text(size = 13, family = my_font)) +
  theme(plot.caption  = element_text(size = 12, family = my_font)) +
  theme(axis.text = element_blank()) +
  theme(axis.line = element_blank()) + 
  theme(panel.grid.major = element_blank()) +
  labs(x = NULL, y = NULL, 
       title = "Figure 4: All Currently Operational Power Nuclear Stations",
       subtitle = "The following map lists all nuclear power stations that are larger than 1,000 MW in current net capacity.",
       caption = "Data Source: International Atomic Energy Agency (IAEA).")

```

