2020_08_04_europe_energy.Rmd

---
title: "TidyTemplate"
date: 2020-08-04
output: html_output
---

# TidyTuesday

Join the R4DS Online Learning Community in the weekly #TidyTuesday event!
Every week we post a raw dataset, a chart or article related to that dataset, and ask you to explore the data.
While the dataset will be “tamed”, it will not always be tidy! As such you might need to apply various R for Data Science techniques to wrangle the data into a true tidy format.
The goal of TidyTuesday is to apply your R skills, get feedback, explore other’s work, and connect with the greater #RStats community!
As such we encourage everyone of all skills to participate!

```{r setup, include=FALSE}

knitr::opts_chunk$set(echo = TRUE)

library(tidyverse)
library(tidytuesdayR)
library(scales)
theme_set(theme_light())
```

# Load the weekly Data

Dowload the weekly data and make available in the `tt` object.

```{r Load}
tt <- tt_load("2020-08-04")

energy_types <- tt$energy_types %>%
  pivot_longer(cols = starts_with("2"),
               names_to = "year",
               values_to = "gigawatt_hours") %>%
  mutate(year = as.integer(year)) %>%
  replace_na(list(country_name = "United Kingdom")) %>%
  mutate(country = ifelse(country == "UK", "GB", country),
         country = ifelse(country == "EL", "GR", country),
         country = str_to_lower(country))

energy_types %>%
  count(level, type, sort = TRUE)

energy_types %>%
  count(country_name, sort = TRUE)
```

What will we do today?

* Graph of energy consumption by type over time
* Map
* Not an animation

```{r}
tt$country_totals %>%
  count(type, sort = TRUE)
```

We may look at exports, imports

### Total energy consumption in Europe

```{r}
europe_totals <- energy_types %>%
  filter(level == "Level 1") %>%
  group_by(year, type) %>%
  summarize(total_power = sum(gigawatt_hours)) %>%
  ungroup() %>%
  mutate(type = fct_reorder(type, total_power, sum))

europe_totals %>%
  ggplot(aes(year, total_power, fill = type)) +
  geom_col() +
  scale_y_continuous(labels = comma) +
  labs(x = "Year",
       y = "Total power production (gigawatt-hours)",
       fill = "Type")

europe_totals %>%
  ggplot(aes(year, total_power)) +
  geom_col() +
  scale_y_continuous(labels = comma) +
  labs(x = "Year",
       y = "Total power production (gigawatt-hours)") +
  facet_wrap(~ type, scales = "free_y")
```

```{r}
europe_totals %>%
  filter(year == 2018) %>%
  mutate(type = fct_reorder(type, total_power, sum)) %>%
  ggplot(aes(total_power, type)) +
  geom_col() +
  scale_x_continuous(labels = comma) +
  labs(x = "Total power production in 2018 in Europe (gigawatt-hours)")
```

```{r}
library(tidytext)
# devtools::install_github("rensa/ggflags")

library(ggflags)

data_prepared <- energy_types %>%
  filter(level == "Level 1",
         year == 2018,
         gigawatt_hours > 0,
         type != "Other") %>%
  group_by(type) %>%
  mutate(country_name = fct_lump(country_name, 10, w = gigawatt_hours),
         country = as.character(fct_lump(country, 10, w = gigawatt_hours))) %>%
  mutate(country_name = reorder_within(country_name, gigawatt_hours, type, fun = sum),
         type = fct_reorder(type, -gigawatt_hours, sum))

data_prepared %>%
  filter(country != "Other") %>%
  mutate(country = str_to_lower(country)) %>%
  ggplot(aes(gigawatt_hours, country_name)) +
  geom_col(width = .1) +
  geom_flag(aes(country = country)) +
  scale_y_reordered() +
  facet_wrap(~ type, scales = "free") +
  scale_x_continuous(labels = comma) +
  scale_country(guide = FALSE) +
  theme_minimal() +
  theme(panel.grid.minor.x = element_blank(),
        panel.grid.minor.y = element_blank()) +
  labs(x = "Total power production in 2018 (gigawatt-hours)",
       y = "")
```

Try ggflag!

```{r}
plot_slopegraph <- function(tbl) {
  tbl %>%
    filter(gigawatt_hours > 1) %>%
    ggplot(aes(year, gigawatt_hours)) +
    geom_line(aes(group = country)) +
    geom_flag(aes(country = country)) +
    geom_text(aes(label = ifelse(year == 2017, NA, country_name),
                  hjust = ifelse(year == 2016, 1.2, -.2)),
              check_overlap = TRUE) +
    scale_x_continuous(breaks = c(2016, 2018),
                       limits = c(2015, 2019)) +
    theme(panel.grid = element_blank()) +
    labs(x = "",
         y = "Gigawatt hours produced in this year")
}

energy_types %>%
  filter(type == "Wind") %>%
  plot_slopegraph() +
  scale_y_continuous(labels = comma) +
  # scale_y_log10(labels = comma) +
  labs(title = "Wind power production over time by country")

energy_types %>%
  filter(type == "Solar") %>%
  plot_slopegraph() +
  scale_y_continuous(labels = comma) +
  # scale_y_log10(labels = comma) +
  labs(title = "Solar power production over time by country")

energy_types %>%
  filter(type %in% c("Solar", "Wind", "Conventional thermal", "Hydro")) %>%
  group_by(type) %>%
  filter(fct_lump(country, 10, w = gigawatt_hours) != "Other") %>%
  ungroup() %>%
  plot_slopegraph() +
  scale_y_continuous(labels = comma) +
  facet_wrap(~ type, scales = "free_y") +
  # scale_y_log10(labels = comma) +
  labs(title = "Solar power production over time by country")
```


```{r}
energy_types %>%
  filter(level == "Level 1",
         year == 2018,
         gigawatt_hours > 0) %>%
  ggplot(aes(type, gigawatt_hours)) +
  geom_point() +
  geom_flag(aes(country = country))
```