Halving CO2 emissions

Introduction

Visitors to Gapminder.org are welcomed with a question about common misconceptions. Here is one of them.

Once you make your selection, you are directed to a page that explains the right answer and shows a visualization of the distribution of responses from various countries.

Our goal is to create a version of this visualization.

Packages

We will use the tidyverse and scales packages for data wrangling and visualization.

library(tidyverse)
library(scales)

Data

The data we’re going to use is in a CSV file called co2-emissions.csv at https://data-science-with-r.github.io/data/co2-emissions.csv.

co2_emissions <- read_csv("https://data-science-with-r.github.io/data/co2-emissions.csv")
Rows: 8 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): country
dbl (3): Very wrong, Wrong, Correct

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

And let’s take a look at the data.

co2_emissions
# A tibble: 8 × 4
  country        `Very wrong` Wrong Correct
  <chr>                 <dbl> <dbl>   <dbl>
1 Türkiye                  61    23      16
2 Columbia                 64    16      20
3 Sweden                   36    35      29
4 Germany                  26    41      33
5 United Kingdom           29    37      34
6 United States            31    32      37
7 Kenya                    46     8      46
8 Pakistan                 37    13      50

Analysis

  • Pivot the co2_emissions data frame longer such that each row represents a country / answer type combination and answer_type and percentage for that country are columns in the data frame.
# add code here
  • Create a stacked bar plot of response type by countries.
# add code here
  • In the original plot, the levels of answer_type are in the order Very wrong, Wrong, and Correct. Update the previous plot reorder the levels in this order.
# add code here
  • Update the colors of the plot to match the original plot:
    • Very wrong: #96283A
    • Wrong: #DB4C67
    • Correct: #C8F0BF
# add code here
  • Move the legend to the bottom and make sure the levels appear in the same order they appear in the plot.
# add code here
  • Reorder countries to match the original plot – in increasing order of corect percentages.
# add code here
  • Update labels and other elements of the plot to get it closer to the original plot.
# add code here