Brexit plot: How political affiliation translated to Brexit voting

Import the data:

brexit_results <- read_csv("https://raw.githubusercontent.com/kostis-christodoulou/am01/master/data/brexit_results.csv")

# Concatenated colors in new variable

parties_brexit_names <- c("Conservative", "Labour", "Lib Dems", "UKIP")
colors_brexit <- c("#0087dc", "#d50000","#FDBB30","#EFE600")

# Tidy long format

tidyresults <- brexit_results %>% 
  select(1:6) %>% 
  pivot_longer(cols = 2:5,
               names_to = "party",
               values_to = "percentage") 

# Create scatterplot with regression lines

ggplot(tidyresults, aes(x= percentage, y = leave_share, colour = party)) +
  geom_point(alpha = 0.3, size = 1)+
  geom_smooth(method=lm)+
  labs(
    title = "How political affiliation translated to Brexit Voting",
    y = "Leave % in the 2016 Brexit referendum",
    x = "Party % in the UK 2015 general election"
  )+
  theme_bw()+
  ylim(20, 102)+
  theme(legend.position="bottom",
        legend.title = element_blank())+
  theme(text = element_text(size = 7))+
  scale_color_manual(values=colors_brexit,
                    labels=parties_brexit_names)

GDP components over time and among countries

UN_GDP_data  <-  read_excel(here::here("data", "Download-GDPconstant-USD-countries.xls"), # Excel filename
                sheet="Download-GDPconstant-USD-countr", # Sheet name
                skip=2) # Number of rows to skip

Tidy the data, use billions for high values and change to shorter variable names.

# Pivot longer + Renaming indicators

tidy_GDP_data  <- UN_GDP_data %>% 
                      pivot_longer(cols = 4:51, names_to = "Year") %>% 
                      mutate(value=value/(1e9),
                             across('IndicatorName',str_replace,"Exports of goods and services", "Exports"),
                             across('IndicatorName',str_replace,"Imports of goods and services", "Imports"),
                             across('IndicatorName',str_replace,"General government final consumption expenditure", "Government expenditure"),
                             across('IndicatorName',str_replace,"Household consumption expenditure \\(including Non-profit institutions serving households\\)", "Household expenditure"))

# Let us compare GDP components for these 3 countries
country_list <- c("United States","India", "Germany")
indicator_list <- c("Exports","Imports","Government expenditure","Household expenditure","Gross capital formation","Net Exports")

#Creating line chart and setting colors due to different order

tidy_GDP_data %>% 
    mutate(Year=as.integer(Year)) %>% 
    filter(Country %in% country_list, IndicatorName %in% indicator_list) %>% 
    ggplot(aes(Year,value,color=IndicatorName))+
    geom_line(size=0.8)+
    xlim(1970,2017)+
    facet_wrap(~Country)+
    labs(title = "GDP components over time",
       subtitle = "In Constant 2010 USD",
       x=NULL,
       y="Billion US$",
       color= "Components of GDP")+
    #scale_color_discrete(name = "Components of GDP")+
   # theme(legend.title = "Components of GDP")+
    scale_color_manual(values = c("#A3A500",
                                  "#00BF7D",
                                  "#F8766D",
                                  "#2FBEF7",
                                  "#EA7DF4"))+
  theme_bw()+
  theme(panel.grid.minor.x = element_blank())+
  theme(text = element_text(size = 7))

GDP and its breakdown at constant 2010 prices in US Dollars

# Going back to wider to easily calculate columns

tidy_GDP_data %>% 
    mutate(Year=as.integer(Year)) %>% 
    filter(Country %in% country_list, IndicatorName %in% indicator_list) %>% 
    select(Country,IndicatorName,Year,value) %>% 
    pivot_wider(names_from = IndicatorName,values_from = value) %>% 
    clean_names() %>%   
    mutate(net_export=(exports-imports),
           GDP=(household_expenditure+ government_expenditure+gross_capital_formation+net_export),
           "Household expenditure"=household_expenditure/GDP,
           "Government expenditure"=government_expenditure/GDP,
           "Gross capital formation"=gross_capital_formation/GDP,
           "Net Exports"=net_export/GDP)%>% 
    pivot_longer(cols=3:13,names_to = "IndicatorName") %>% 
    filter(IndicatorName %in% indicator_list) %>% 
    ggplot(aes(year,value,color=IndicatorName,group=IndicatorName))+
    geom_line(size=0.8)+
    xlim(1970,2017)+
    facet_wrap(~country)+
    labs(title = "GDP and its breakdown at constant 2010 prices in US Dollars",
       x=NULL,
       y="proportion",
       caption = "Source: United Nations, https://unstats.un.org/unsd/snaama/Downloads")+
     scale_color_discrete(name = NULL)+
    theme_bw() +
    theme(panel.grid.minor.x = element_blank())+
    scale_y_continuous(labels = scales::percent)+
    theme(text = element_text(size = 7))

# Add column for self-made GDP and add column for percentage difference 

GDP_cal<- tidy_GDP_data %>% 
    mutate(Year=as.integer(Year)) %>% 
    filter(Country %in% country_list) %>% 
    pivot_wider(names_from = IndicatorName,values_from = value) %>% 
    clean_names() %>%   
    mutate(net_export=(exports-imports),
           GDP=(household_expenditure+ government_expenditure+gross_capital_formation+net_export),
           gdp_difference=(GDP-gross_domestic_product_gdp)/(GDP))

# Plot it
 
 GDP_cal%>% ggplot(aes(x=year, y=gdp_difference))+
           geom_path()+
          geom_point(size=0.01)+
          facet_wrap(~country)+
          scale_y_continuous(labels = scales::percent)+
          labs(x="Year",
               y="Difference in GDP",
               title = "% difference in given GDP and calculated GDP" )+
          theme_bw()+
          theme(panel.grid.minor.x =element_blank())