10 Sustainable Development Goals

Libraries used in this chapter.

library(tidyverse)
library(eurostat)
library(leaflet)
library(sf)
library(ggrepel)

10.1 Share of renewable energy

Let’s see at one key dataset about SDGs: the share of renewable energy in gross final energy consumption by sector.

sdg_07_40 <- get_eurostat("sdg_07_40", 
                          stringsAsFactors = FALSE, 
                          time_format = "num")

For example let’s at Austria’s perfomance during recent years:

sdg_07_40 %>% 
  filter(geo == 'AT') %>% 
  filter(nrg_bal == 'REN') %>% 
  ggplot(aes(x = time, y = values)) +
  geom_line() +
  geom_point() +
  labs(title = "Austria",
       subtitle = "share of renewable energy in gross final energy consumption", 
       x = "Time", y = "% share", 
       caption = "Source: Eurostat sdg_07_40") +
  theme_minimal()

Energy balance (nrg_bal) column might contain the following values:

sdg_07_40 %>% distinct(nrg_bal)

## # A tibble: 4 × 1
##   nrg_bal    
##   <chr>      
## 1 REN        
## 2 REN_ELC    
## 3 REN_HEAT_CL
## 4 REN_TRA

variable	meaning
REN	Renewable energy sources
REN_TRA	Renewable energy sources in transport
REN_ELC	Renewable energy sources in electricity
REN_HEAT_CL	Renewable energy sources in heating and cooling

Now let us look at the three sectors:

sdg_07_40 %>%
  filter(geo == 'AT') %>% 
  filter(nrg_bal != 'REN') %>% 
  ggplot(aes(x = time, y = values, colour = nrg_bal)) +
  geom_line() +
  labs(title = "Austria",
       subtitle = "share of renewable energy in gross final energy consumption", 
       x = "Time", y = "% share", 
       caption = "Source: Eurostat sdg_07_40",
       colour = "sector") +
  theme_minimal()

Or, with bit editing of legend text:

sdg_07_40 %>%
  filter(geo == 'AT') %>% 
  filter(nrg_bal != 'REN') %>% 
  ggplot(aes(x = time, y = values, colour = nrg_bal)) +
  geom_line() +
  labs(title = "Austria",
       subtitle = "share of renewable energy in gross final energy consumption by sector", 
       x = "Time", y = "% share", 
       caption = "Source: Eurostat sdg_07_40",
       colour = "Sector") +
  scale_colour_brewer(palette = "Set1",
                      labels = c("Electricity", "Heat/Cool", "Transport")) +
  theme_minimal()

Plotting the data in separate columns:

sdg_07_40 %>%
  filter(geo == 'AT') %>% 
  filter(nrg_bal != 'REN') %>% 
  ggplot(aes(x = time, y = values, fill = nrg_bal)) +
  geom_col(position = "dodge") +
  labs(title = "Austria",
       subtitle = "share of renewable energy in gross final energy consumption by sector", 
       x = "Time", y = "% share", 
       caption = "Source: Eurostat sdg_07_40",
       fill = "Sector") +
  scale_fill_brewer(palette = "Set1",
                      labels = c("Electricity", "Heat/Cool", "Transport")) +
  theme_minimal()

This is a ungly plot. You may have seen it (or something similir) elsewhere, but it is better to avoid it. A simple panel of three individual plots in cleaner and preferable:

sdg_07_40 %>%
  filter(geo == 'AT') %>% 
  filter(nrg_bal != 'REN') %>% 
  ggplot(aes(x = time, y = values)) +
  geom_line(color = "darkgreen") +
  labs(title = "Austria",
       subtitle = "share of renewable energy in gross final energy consumption by sector", 
       x = "Time", y = "% share", 
       caption = "Source: Eurostat sdg_07_40") +
  theme_minimal() +
  facet_wrap(~nrg_bal, scales = "free")

What if we want to compare two countries, let’s say Austria vs its big neighor:

sdg_07_40 %>%
  filter(geo %in% c('AT', 'DE')) %>% 
  filter(nrg_bal != 'REN') %>% 
  ggplot(aes(x = time, y = values)) +
  geom_line() +
  labs(title = "Austria vs Germany",
       subtitle = "share of renewable energy in gross final energy consumption by sector", 
       x = "Time", y = "% share", 
       caption = "Source: Eurostat sdg_07_40") +
  theme_minimal() +
  facet_grid(nrg_bal ~ geo, scales = "free")

10.2 Employment in high technology sector

sdg_09_20 <- get_eurostat("sdg_09_20", 
                          stringsAsFactors = FALSE, 
                          time_format = "num")

sdg_09_20 %>% 
  inner_join(EU_countries) %>% 
  filter(nace_r2 == 'C_HTC_MH') %>% 
  filter(geo %in% c('LT', 'LV', 'EE')) %>% 
  mutate(LBL = if_else(time == max(time), as.character(Country), NA_character_)) %>%
  ggplot(aes(x = time, y = values, colour = Country)) +
  geom_line(size = 1.2) +
  geom_point(size = 3) +
  labs(x = "Time", y = "% of employment", 
       title = "Baltic countries",
       subtitle = "Employment in high- and medium-high technology manufacturing and knowledge-intensive services") +
  scale_x_continuous(breaks = seq(2006, 2020, by = 2)) +
  scale_y_continuous(limits = c(0, 5), breaks = seq(0, 5, by = 1)) + 
  scale_color_brewer(type = "qual", palette = 6) +
  geom_label_repel(aes(label = LBL), nudge_x = 0, nudge_y = 0.25, size = 5, na.rm = TRUE) +
  theme_minimal(base_size = 16) +
  theme(legend.position = "none")