wk_32_2020.Rmd

---
title: "European energy"
author: "mnaR99"
date: "3/8/2020"
output: html_document
---

```{r}
library(tidyverse)
library(ggforce)
library(geofacet)
library(scales)
library(ggnewscale)
library(cowplot)
library(ggtext)
library(extrafont)
```

```{r load data}
tuesdata <- tidytuesdayR::tt_load(2020, week = 32)

energy_types <- tuesdata$energy_types
```

```{r clean data}
energy_clean <- energy_types %>% 
  filter(level == "Level 1") %>%
  gather(year, n, 5:7, convert = TRUE) %>% 
  select(-level) %>% 
  mutate(
    country_name = case_when(
      country == "EL" ~ "Greece",
      country == "UK" ~ "United Kingdom",
      TRUE ~ country_name
    ),
    gtype = case_when(
      !type %in% c("Nuclear", "Conventional thermal") ~ "Renewable",
      TRUE ~ type
    )
  ) %>% 
  rename(code = country, name = country_name) %>% 
  rows_update(tibble(code = "MT", type = "Other", year = 2018, n = 172), by = c("code", "type", "year"))
```

```{r geofacet - grid}
eu_grid <- geofacet::europe_countries_grid1

eu_grid[42,] <- list(row = 6, col = 10, code = "GE", name = "Georgia")

eu_grid <- eu_grid %>% 
  filter(!code %in% c("IS", "CH", "BY", "RU", "MD")) %>%
  mutate(name = ifelse(str_detect(name, "Macedonia"), "North Macedonia", name))
```

```{r summary}
prop_eu <- energy_clean %>% 
  filter(year == 2018) %>% 
  group_by(name) %>% 
  summarise(
    total = sum(n),
    p_clean = sum(n[gtype != "Conventional thermal"])/total,
  )
```

```{r plot data}
data_plot <- energy_clean %>% 
  filter(year == 2018) %>% 
  mutate(
    code = recode(code, "UK" = "GB", "EL" = "GR")
  ) %>% 
  count(code, name , year, gtype, wt = n) %>% 
  group_by(code) %>% 
  mutate(p = n/sum(n)) %>% 
  ungroup() %>% 
  left_join(prop_eu)
```

```{r pies plot}
pies <- data_plot %>% 
  ggplot() +
    aes(x0 = 0, y0 = 0) +
    stat_pie(
      aes(r0 = 0.7, r = 1, amount = p, fill = gtype),
      color = "white",
      size = 1,
      show.legend = FALSE
    ) +
    scale_fill_manual(
      values = c("#ECBA82", "#2E933C", "#81C14B")
    ) +
    new_scale_fill() +
    geom_circle(
      aes(r = 0.675, fill = p_clean),
      color = NA
    ) +
    scale_fill_fermenter(
      type = "div", 
      direction = 1,
      labels = c("25%", "50%", "75%"),
      guide = guide_colorsteps(
        title = "Clean Energy Share", 
        title.position = "top"
      )
    ) +
    geom_text(
      aes(x = 0, y = 0, label = str_glue("{comma(total, .01)}\nGWh")), 
      color = "white",
      family = "Montserrat"
    ) +
    coord_fixed() +
    facet_geo(~code, grid = eu_grid, label = "name") +
    theme_void(base_family = "Teko") +
    theme(
      strip.text = element_text(
        size = 16,
        margin = margin(t = 10, b = 10)
      ),
      legend.position = "bottom",
      legend.title = element_text(hjust = 0.5, size = 16),
      legend.key.width = grid::unit(15, "mm"),
      legend.key.height = grid::unit(3, "mm"),
      legend.text = element_text(size = 14)
    )
    
piesG <- ggplotGrob(pies + theme(legend.position = "none"))
legendG <- cowplot::get_legend(pies)
```

```{r notes}
notes <- tribble(
  ~x, ~y, ~label,
  0, -4.5, "**Albania** is dependent on hydroelectricity, with a share in its production of 100%. Still, its topography has the potential to supply power from wind, sun, and soil.",
  -3, 0.5, "**France** is the second largest producer of energy in the region, and has the largest share of nuclear electricity (71.3%) in the world.", 
  1, 1.75, "**Germany** is the country that produced the most energy in 2018, 12.6% was nuclear energy and 31.3% renewable energy.",
  -0.5, 3.15, "Much of **Norway**'s electricity is generated from hydropower (95%) due to the natural advantage of its topography with abundant valleys and steep rivers.", 
  4, 0.5, "**Montenegro** leads the energy transition in the region. In 2018 the production of clean energy increased by more than 100%, compared to the previous year."
)
```

```{r plot}
plot <- qplot() +
  geom_curve(
    aes(x = 1.5, y = -3.25, xend = 4, yend = 0.16), 
    size = 2, 
    curvature = 0.3, 
    color = "white", 
    lineend = "round"
  ) +
  annotation_custom(
    piesG,
    xmin = -5, xmax = 5,
    ymin = -5, ymax = 5
  ) +
  annotation_custom(
    legendG,
    xmin = -5, xmax = -2.2,
    ymin = -3.5, ymax = -3
  ) +
  geom_textbox(
    data = notes,
    aes(x, y, label = label),
    family = "Montserrat",
    lineheight = 1.5,
    # halign = 0,
    width = 0.14,
    box.color = NA,
    fill = NA,
  ) +
  geom_textbox(
    aes(
      x = -5, y = 4.75, 
      label = "<span style='font-size:42pt;font-family:Teko'>Clean Energy Producers, 2018</span>
  <br><br>
  <span style='font-size:15pt'>Total net energy produced (GWh) by european countries in 2018, and its distribution by <b style='color:#ECBA82'>conventional thermal</b>, <b style='color:#2E933C'>nuclear</b> and <b style='color:#81C14B'>renewable</b> energy.</span>
  <br><br>
  <i style='font-size:10pt;color:#737373;'>**GWh**: Energy generated or consumed by 1 GW of power for one hour.<br>
**How Much Power is 1 GW?** 110 Million LEDs, on typical performance.</i>"
    ),
    family = "Montserrat",
    width = unit(27, "lines"),
    lineheight = 2,
    hjust = 0,
    vjust = 1,
    fill = NA,
    box.color = NA,
  ) +
  scale_x_continuous(limits = c(-5,5), breaks = -5:5) +
  scale_y_continuous(limits = c(-5,5), breaks = -5:5) +
  labs(caption = "@AtMissing · Eurostat. Supply of electricity") +
  # theme_light() +
  theme_void() +
  theme(
    plot.caption = element_text(
        hjust = 0.5,
        size = 10,
        family = "Montserrat",
        margin = margin(b = 30)
      ),
    plot.background = element_rect(fill = "#f0f0f0", color = NA)
  )
  
ggsave(here::here("plots","wk_32_2020.pdf"), plot, width = 20, height = 20, device = cairo_pdf)
```

```{r png save}
pdftools::pdf_convert(
  pdf = here::here("plots","wk_32_2020.pdf"),
  filenames = here::here("plots","wk_32_2020.png"),
  format = "png", 
  dpi = 400
)
```