Global Energy Consumption 2018

Data

This plot uses the energy18 data frame of the gcubed package.

The data set contains energy consumption by country(or groups of countries in some cases) and also has a column identifying that country’s region.

The original source of this data is the BP Statistical Report of World Energy 2019.

The first few rows of energy18 look like this:

library(gcubed)
head(energy18)

## # A tibble: 6 x 8
##   Countries   Oil `Natural Gas`  Coal Nuclear Hydroelectric Renewable
##   <chr>     <dbl>         <dbl> <dbl>   <dbl>         <dbl>     <dbl>
## 1 Canada    110            99.5  14.4    22.6          87.6      10.3
## 2 Mexico     82.8          77    11.9     3.1           7.3       4.8
## 3 US        920.          703.  317     192.           65.3     104. 
## 4 Argentina  30.1          41.9   1.2     1.6           9.4       0.9
## 5 Brazil    136.           30.9  15.9     3.5          87.7      23.6
## 6 Chile      18.1           5.5   7.7     0             5.2       3.5
## # … with 1 more variable: Region <chr>

Data wrangling for plot

First get totals for each energy source (natural gas, oil, coal, nuclear, hydroelectric, renewable) for each region:

library(dplyr)

df <- group_by(energy18, Region) %>% 
  summarise(Oil = sum(Oil),
            `Natural Gas` = sum(`Natural Gas`),
            Coal = sum(Coal),
            Nuclear = sum(Nuclear), 
            Hydroelectric = sum(Hydroelectric), 
            Renewable = sum(Renewable))
head(df)

## # A tibble: 6 x 7
##   Region          Oil `Natural Gas`   Coal Nuclear Hydroelectric Renewable
##   <chr>         <dbl>         <dbl>  <dbl>   <dbl>         <dbl>     <dbl>
## 1 Africa         191.          129.  101.      2.5          30.1       7.2
## 2 Asia Pacific  1695.          710. 2841.    125.          389.      225. 
## 3 CIS            194.          499.  135.     46.8          55.4       0.5
## 4 Europe         742.          472.  307.    212.          145.      172. 
## 5 Middle East    412           475.    8.1     1.6           3.4       1.6
## 6 North America 1112.          879.  343.    218.          160.      119.

Now use the gather command of the tidyr package to get all the energy values into the same column while creating an accompanying column to indicate the energy source.

library(tidyr)
df <- gather(df, key = Type, value = Energy, -1)

head(df)

## # A tibble: 6 x 3
##   Region        Type  Energy
##   <chr>         <chr>  <dbl>
## 1 Africa        Oil     191.
## 2 Asia Pacific  Oil    1695.
## 3 CIS           Oil     194.
## 4 Europe        Oil     742.
## 5 Middle East   Oil     412 
## 6 North America Oil    1112.

The df data frame is now suitable for making the plot.

Code

library(ggplot2)
library(scales) #for formatting the axes labels to have commas e.g. 1,000 

df$Type <- factor(df$Type)
energy_plt <- ggplot(df, aes(Type, Energy, fill = Type)) + geom_bar(stat = "identity") + 
  facet_wrap(~Region, ncol = 2, scale = "free") + coord_flip() + 
  scale_x_discrete(limits = rev(levels(df$Type))) + 
  scale_y_continuous(label = comma) + 
  xlab("") + 
  ylab("Million tonnes oil equivalent") +
  theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
        legend.title = element_blank()) + 
  ggtitle("Global Energy Consumption (2018)")  

energy_plt