1 CO2 emmission (per person)

1.1 Data preparation

Go the following website and download time series data of CO2 emmission by country.

https://www.gapminder.org/data/

Data <- read.csv("co2_emissions_tonnes_per_person.csv")
Data %<>% gather(year, value=emmission, -country, -region) 
Data %<>% mutate(year=year %>% substr(2,5) %>% as.numeric())
Data %<>% arrange(country, year)

Data %>% filter(country %in% c('Japan')) %>% tail(30) %>% 
kable("latex", booktabs=T, caption="Recent co2 emmission in Japan",
format.args=list(big.mark=",")) %>%
kable_styling(font_size=8, latex_options=c("striped","hold_position"))

1.2 Visual presentation of data

1.2.1 Initial plots for 9 different countie in different regions

country.list <- c("Japan", "China", "United States", "United Kingdom",
                  "France", "Germany", "Brazil", "Australia", "Kenya")

Data %>% filter(country %in% country.list) %>% 
  ggplot(aes(year, emmission, color=country)) + geom_point() +
  geom_line() + theme(legend.position="right") + 
  labs(title="co2 emmission in several countries") +
  xlab("Year") + ylab("Emmission (tons)")

Data %>% filter(country %in% country.list) %>% 
  ggplot(aes(year, emmission, fill=country)) + geom_area() + 
  labs(title="co2 emmission in world") +
  xlab("Year") + ylab("Emmission (tons)") + facet_wrap(.~country)

1.2.2 Regional difference in mean

Data %>% group_by(region, year) %>%
summarise(emmission=sum(emmission, na.rm=T)) %>% data.frame() %>% 
  ggplot(aes(year, emmission, fill=region)) + 
  geom_area() + labs(title="co2 emmission in world") +
  xlab("Year") + ylab("Emmission (tons)") + facet_wrap(.~region)

1.2.3 Difference in Asian region

Data %>% filter(region %in% "Asia") %>%
  ggplot(aes(year, emmission, col=country)) + 
  geom_line() + theme(legend.position="bottom") + 
  facet_wrap(.~country, ncol=6)

Data %>% filter(region %in% "Asia") %>%
  ggplot(aes(year, emmission, col=country)) + 
  geom_line() + theme(legend.position="bottom") + 
  xlim(1960,2020) + ylim(0,100) + facet_wrap(.~country, ncol=6)