Project Part 2

Interactive and static plots of per capita CO2 emissions from 1950 to 2020.

1. Packages I will use to read in and plot the data.

library(tidyverse)
library(echarts4r)

2. Read the data in from part 1.

country_co2  <- read_csv(here::here("country_co2.csv"))

Interactive Graph

• Start with the data

• Group_by country so there will be a “river” for each

• Use mutate to round CO2emissions so only 2 digits will be displayed when you hover over it.

• Use mutate to change year so it will be displayed as end of year instead of beginning of year

• Use e_charts to create an e_charts object with year on the x axis

• Use e_river to build “rivers” that contain per capita CO2 emissions by country. The depth of each river represents the amount of per capita emissions for each country.

• Use e_tooltip to add a tooltip that will display based on the axis values

• Use e_title to add a title, subtitle, and link to subtitle

• Use e_theme to change the theme to roma

country_co2   %>%
  group_by(Country)  %>%
  mutate(CO2emissions = round(CO2emissions, 2),
         Year = paste(Year, "12", "31", sep="-"))  %>% 
  e_charts(x = Year)   %>% 
  e_river(serie = CO2emissions, legend=FALSE)  %>% 
  e_tooltip(trigger = "axis")  %>% 
  e_title(text = "Per Capita CO2 emissions, by Country",
          subtext = "(in billions of tonnes). Source: Our World in Data",
          sublink = "https://ourworldindata.org/explorers/co2?facet=none&country=CHN~USA~IND~GBR~OWID_WRL~AFG~Africa~ALB~ZWE~ZMB~YEM~WLF~VNM~VEN~VUT~UZB~URY~Upper-middle-income+countries~ARE~UKR~UGA~TUV~TCA~TKM~TUR~TUN~TTO~TON~TGO~TLS~THA~TZA~TJK~TWN~SYR~CHE~SWE~SDN~LKA&Gas=CO%E2%82%82&Accounting=Production-based&Fuel=Total&Count=Per+capita",
          left = "center")  %>% 
  e_theme("royal") 

Static Graph

• Start with the data

• Use ggplot to create a new ggplot object. Use aes to indicate that year will be mapped to the x axis; per capita CO2 emissions will be mapped to the y axis; country will be the fill variable

• geom_area will display per capita CO2emissions

• scale_fill_discrete_divergingx is a function in the colorspace package. It sets the color palette to roma and selects a maximum of 12 colors for the different regions

• theme_classic sets the theme

• theme(legend.position = “bottom”) puts the legend at the bottom of the plot

• labs sets the y axis label, fill = NULL indicates that the fill variable will not have the labelled Region

country_co2   %>% 
  ggplot(aes(x = Year, y = CO2emissions, 
             fill = Country)) +
  geom_area() +
  colorspace::scale_fill_discrete_divergingx(palette = "roma", nmax =11) +
  theme_classic() +
  theme(legend.position = "bottom") +
  labs( y = "in billions of tonnes",
       fill = NULL)

ggsave(filename = here::here("_posts/2022-05-17-project-part-2/preview.png"))

These plots show a comparison of per capita CO2 emissions by some of the top countries contributing to climate change, as well as an overall increase in emissions in every country. The range of data is from 1950 to 2020.