Lecture 1: Tasks Solutions

Your Turn #1

Question 2

4*8

[1] 32

Question 3

x = 5 # equivalently x <- 5
x

[1] 5

Question 4

x_3 <- x^5
x_3

[1] 3125

Your Turn #2

Question 1

co2_emissions <- read.csv("https://www.dropbox.com/scl/fi/3jefa7xz8fyvqh76e0k9l/co2_emissions_historical.csv?rlkey=tnmt8l607igvm5fany7xzutdu&dl=1")

Question 2

class(co2_emissions) # check class of object

[1] "data.frame"

Question 3

names(co2_emissions) # obtain variable names

[1] "entity"                          "code"                           
[3] "year"                            "annual_co2_emissions_per_capita"

Question 4

co2_emissions

Your Turn #3

Question 1

library(tidyverse)

co2_emissions |> 
  filter(entity %in% c("United States", "China", 
                       "United Kingdom", "France",
                       "India")) |> 
  ggplot(aes(x = year,
             y = annual_co2_emissions_per_capita,
             color = entity)) + # one line per country
  geom_line()

Question 2

co2_emissions |> 
  filter(entity %in% c("United States", "China", 
                       "United Kingdom", "France",
                       "India", "Japan")) |> 
  ggplot(aes(x = year,
             y = annual_co2_emissions_per_capita,
             color = entity)) + # one line per country
  geom_line()

Question 3

co2_emissions |> 
  filter(entity %in% c("United States", "China", 
                       "United Kingdom", "France",
                       "India", "Japan")) |> 
  ggplot(aes(x = year,
             y = annual_co2_emissions_per_capita,
             color = entity)) + # one line per country
  geom_line() +
  geom_point()

Question 4

co2_emissions_new <- read.csv("https://www.dropbox.com/scl/fi/hkjwayxaq46oqataxpe5p/co2_emissions_historical_with_total.csv?rlkey=uzmt8e1bampskzc7bpp1vimbz&dl=1")

The additional variable contains total annual CO2 emissions.

Question 5

co2_emissions_new |> 
  filter(entity %in% c("United States", "China", 
                       "United Kingdom", "France",
                       "India")) |> 
  ggplot(aes(x = year,
             y = annual_co2_emissions,
             color = entity)) + # one line per country
  geom_line()

Your Turn #4

Question 1

co2_emissions[co2_emissions$entity == "United States" & co2_emissions$year == 1995,]

Question 2

co2_emissions[10:20,]

Question 3

co2_emissions_gdp <- read.csv("https://www.dropbox.com/scl/fi/bbvlq2rwab5nxg617pn5l/co2_emissions_historical_with_gdp.csv?rlkey=mobgeqdhmjr8owvkchctsvqu7&dl=1")

Question 4

names(co2_emissions_gdp)

[1] "entity"                          "code"                           
[3] "year"                            "annual_co2_emissions_per_capita"
[5] "annual_co2_emissions"            "gdp_constant_2015_us"           

co2_emissions_gdp$co2_emissions_per_gdp <- co2_emissions_gdp$annual_co2_emissions / co2_emissions_gdp$gdp_constant_2015_us

names(co2_emissions_gdp)

[1] "entity"                          "code"                           
[3] "year"                            "annual_co2_emissions_per_capita"
[5] "annual_co2_emissions"            "gdp_constant_2015_us"           
[7] "co2_emissions_per_gdp"