European Energy Viz

Data source

European Energy

Load library and set theme

#load packages
library(tidyverse)
library(readr)
library(skimr)
library(ggpubr)
library(viridis)
#set theme
theme_set(theme_light())

Import data

#get data
energy_types <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-08-04/energy_types.csv')
country_totals <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-08-04/country_totals.csv')

## [1] 296   7

## [1] "country"      "country_name" "type"         "level"        "2016"        
## [6] "2017"         "2018"

## [1] 185   7

## [1] "country"      "country_name" "type"         "level"        "2016"        
## [6] "2017"         "2018"

Percent of renewable in 2018

#get total energy in 2018
total_energy<-country_totals%>%
  select(-`2017`, -`2016`, -level)%>%
  pivot_wider(names_from=type, values_from=`2018`)%>%
  janitor::clean_names()%>%
  mutate(total_energy_use = total_net_production+imports-exports-energy_absorbed_by_pumping)%>%
  select(country, total_energy_use)

#get percent of renewable
percent_renewable<-energy_types%>%
  select(-`2016`, -`2017`, -`level`)%>%
  pivot_wider(names_from=type, values_from=`2018`)%>%
  janitor::clean_names()%>%
  mutate(
    renewable=hydro+wind+solar+geothermal+pumped_hydro_power, 
    non_renewable = conventional_thermal+nuclear+other,
    percent_renewable = renewable/(renewable+non_renewable)
    )%>%
  left_join(total_energy)%>%
  mutate(
    country_name=ifelse(is.na(country_name), "United Kingdom", country_name),
    other_percent_renewable = renewable/total_energy_use)

#percent renewable summary
summary(percent_renewable$percent_renewable)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.1885  0.3412  0.3831  0.4954  1.0000

Visualization

Percentage of renewable energy - lollipop chart

ggplot(percent_renewable, aes(x=country_name, y=percent_renewable)) +
  geom_segment( aes(x=reorder(country_name,percent_renewable), xend=country_name, y=0, yend=percent_renewable), color="grey") +
  geom_point( color="#69b3a2", size=1) +
  theme_light() +
  theme(
    panel.grid.major.x = element_blank(),
    panel.border = element_blank(),
    axis.ticks.x = element_blank(),
    axis.ticks.y = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.minor.x = element_blank()
  ) +
  xlab("") +
  ylab("") + coord_flip() + 
  labs(title="Percentage of renewable energy", subtitle="European Energy 2018") + 
  scale_y_continuous(labels = scales::percent)

Percentage of renewable energy - circular bar

colnames(percent_renewable)

##  [1] "country"                 "country_name"           
##  [3] "conventional_thermal"    "nuclear"                
##  [5] "hydro"                   "pumped_hydro_power"     
##  [7] "wind"                    "solar"                  
##  [9] "geothermal"              "other"                  
## [11] "renewable"               "non_renewable"          
## [13] "percent_renewable"       "total_energy_use"       
## [15] "other_percent_renewable"

#order data
tmp <- percent_renewable %>% filter(!is.na(percent_renewable)) %>% arrange(desc(percent_renewable)) %>% mutate(country=factor(country,country)) %>% mutate(across(percent_renewable, round, 2))
empty_bar=1

#add lines to temporary set
to_add = matrix(NA, empty_bar, ncol(tmp))
colnames(to_add) = colnames(tmp)
tmp=rbind(tmp, to_add)
tmp$id=seq(1, nrow(tmp))

#get names and y pos of label
label_tmp=tmp
number_of_bar=nrow(label_tmp)
angle= 90 - 360 * (label_tmp$id-0.5) /number_of_bar     
label_tmp$hjust<-ifelse( angle < -90, 1, 0)
label_tmp$angle<-ifelse(angle < -90, angle+180, angle)
label_tmp$country <- gsub("Bosnia & Herzegovina", "B&H", label_tmp$country)
label_tmp$country <- paste(label_tmp$country, " (", label_tmp$percent_renewable,")", sep="")

#plot 
ggplot(tmp, aes(x=as.factor(id), y=percent_renewable, fill = percent_renewable)) +      
  geom_bar(aes(x=as.factor(id), y=percent_renewable, fill = percent_renewable),stat = "identity") +
  ylim(-1,1) +
  theme_minimal() +
  theme(
    axis.text = element_blank(),
    axis.title = element_blank(),
    panel.grid = element_blank(),
    plot.margin = unit(rep(-0.2,4), "cm") 
  ) +
  coord_polar(start = 0) + 
  geom_text(data=label_tmp, aes(x=id, y=percent_renewable, label=country ), color="black", fontface="bold",alpha=0.6, size=2.5, angle= label_tmp$angle, hjust=label_tmp$hjust, inherit.aes = FALSE ) +
  geom_text( aes(x=1, y=1, label=""), color="black", inherit.aes = FALSE) + scale_fill_viridis() + 
  labs(fill="Proportion of renewable energy")

## Warning: Removed 1 rows containing missing values (position_stack).

## Warning: Removed 1 rows containing missing values (geom_text).

Conventional thermal energy production in 2018

percent_renewable %>%
  filter(!is.na(conventional_thermal)) %>%
  arrange(conventional_thermal) %>%
  mutate(country_name=factor(country_name, country_name)) %>%
  ggplot( aes(x=country_name, y=conventional_thermal) ) +
    geom_segment( aes(x=country_name ,xend=country_name, y=0, yend=conventional_thermal), color="grey") +
    geom_point(size=1.5, color="#dda15e") +
    coord_flip() +
    theme_light() +
    theme(
    panel.grid.major.x = element_blank(),
    panel.border = element_blank(),
    axis.ticks.x = element_blank(),
    axis.ticks.y = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.minor.x = element_blank()
  ) +
  xlab("") +
  ylab("") +  
  labs(title="Conventional thermal energy production in GWh", subtitle="European Energy 2018")

Five largest producers by clean energy type

#get top 5 country names and values by energy type
dfe = energy_types
colnames(dfe)[7] <- "y2018"
dfe2 = dfe %>% 
  select(-`2016`, -`2017`, -`level`, -'country') %>%  
  mutate(
    country_name=ifelse(is.na(country_name), "United Kingdom", country_name)) %>%
  janitor::clean_names() %>% 
  arrange(desc(y2018)) %>% 
  group_by(type) %>% 
  slice(1:5) 
dfe2

Individual plots

#nuclear energy
vnuclear = 
  percent_renewable %>%
  filter(!is.na(nuclear)) %>%
  arrange(nuclear) %>%
  mutate(country_name=factor(country_name, country_name)) %>% top_n(5, nuclear) %>% slice_max(nuclear, n=5) %>%
  ggplot( aes(x=country_name, y=nuclear) ) +
    geom_segment( aes(x=country_name ,xend=country_name, y=0, yend=nuclear), color="#294c60") +
    geom_point(size=2, color="#294c60") +
    coord_flip() +
    theme_light() +
    theme(
    panel.grid.major.x = element_blank(),
    panel.border = element_blank(),
    axis.ticks.x = element_blank(),
    axis.ticks.y = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.minor.x = element_blank()
  ) +
  xlab("") +
  ylab("") +  
  labs(title="Nuclear")

#hydrothermal
vhydro = percent_renewable %>%
  filter(!is.na(hydro)) %>%
  arrange(hydro) %>%
  mutate(country_name=factor(country_name, country_name)) %>% top_n(5, hydro) %>% slice_max(nuclear, n=5) %>%
  ggplot( aes(x=country_name, y=hydro) ) +
    geom_segment( aes(x=country_name ,xend=country_name, y=0, yend=hydro), color="#7e96a7") +
    geom_point(size=2, color="#7e96a7") +
    coord_flip() +
    theme_light() +
    theme(
    panel.grid.major.x = element_blank(),
    panel.border = element_blank(),
    axis.ticks.x = element_blank(),
    axis.ticks.y = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.minor.x = element_blank()
  ) +
  xlab("") +
  ylab("") +  
  labs(title="Hydrothermal")

#wind
vwind = percent_renewable %>%
  filter(!is.na(wind)) %>%
  arrange(wind) %>%
  mutate(country_name=factor(country_name, country_name)) %>% top_n(5, wind) %>% slice_max(wind, n=5) %>%
  ggplot( aes(x=country_name, y=wind) ) +
    geom_segment( aes(x=country_name ,xend=country_name, y=0, yend=wind), color="#7e96a7") +
    geom_point(size=2, color="#7e96a7") +
    coord_flip() +
    theme_light() +
    theme(
    panel.grid.major.x = element_blank(),
    panel.border = element_blank(),
    axis.ticks.x = element_blank(),
    axis.ticks.y = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.minor.x = element_blank()
  ) +
  xlab("") +
  ylab("") +  
  labs(title="Wind")

#geothermal
vgeot = percent_renewable %>%
  filter(!is.na(geothermal)) %>%
  arrange(geothermal) %>%
  mutate(country_name=factor(country_name, country_name)) %>% top_n(5,geothermal) %>% slice_max(geothermal, n=5) %>%
  ggplot( aes(x=country_name, y=geothermal) ) +
    geom_segment( aes(x=country_name ,xend=country_name, y=0, yend=geothermal), color="#7e96a7") +
    geom_point(size=2, color="#7e96a7") +
    coord_flip() +
    theme_light() +
    theme(
    panel.grid.major.x = element_blank(),
    panel.border = element_blank(),
    axis.ticks.x = element_blank(),
    axis.ticks.y = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.minor.x = element_blank()
  ) +
  xlab("") +
  ylab("") +  
  labs(title="Geothermal")

#pumped_hydro_power
vphp = percent_renewable %>%
  filter(!is.na(pumped_hydro_power)) %>%
  arrange(pumped_hydro_power) %>%
  mutate(country_name=factor(country_name, country_name)) %>% top_n(5,pumped_hydro_power) %>% slice_max(pumped_hydro_power, n=5) %>%
  ggplot( aes(x=country_name, y=pumped_hydro_power) ) +
    geom_segment( aes(x=country_name ,xend=country_name, y=0, yend=pumped_hydro_power), color="#7e96a7") +
    geom_point(size=2, color="#7e96a7") +
    coord_flip() +
    theme_light() +
    theme(
    panel.grid.major.x = element_blank(),
    panel.border = element_blank(),
    axis.ticks.x = element_blank(),
    axis.ticks.y = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.minor.x = element_blank()
  ) +
  xlab("") +
  ylab("") +  
  labs(title="Pumped hydro power")

#solar
vsol = percent_renewable %>%
  filter(!is.na(solar)) %>%
  arrange(solar) %>%
  mutate(country_name=factor(country_name, country_name)) %>% top_n(5,solar) %>% slice_max(solar, n=5) %>%
  ggplot( aes(x=country_name, y=solar) ) +
    geom_segment( aes(x=country_name ,xend=country_name, y=0, yend=solar), color="#7e96a7") +
    geom_point(size=2, color="#7e96a7") +
    coord_flip() +
    theme_light() +
    theme(
    panel.grid.major.x = element_blank(),
    panel.border = element_blank(),
    axis.ticks.x = element_blank(),
    axis.ticks.y = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.minor.x = element_blank()
  ) +
  xlab("") +
  ylab("") +  
  labs(title="Solar")

Combined plot: largest clean energy producers by type

combined = ggarrange(vnuclear, vhydro, vwind, vphp, vsol, vgeot,
          labels = c("", "", "", "", "", ""),
          ncol = 2, nrow = 3)

title <- expression(atop(bold("Largest european clean energy producers in 2018 (nuclear and renewables)")))
annotate_figure(combined,
                top=text_grob(title))