technology-adoption.Rmd

---
title: "Adding continent and country names with {countrycode}, and subsetting a data frame using sample()"
description: |
  Data wrangling and exploration to plot electricity production according to energy source and continent using the
  #TidyTuesday data set for week 29 of 2022
    (19/7/2022): "Technology Adoption"
author:
  - name: Ronan Harrington
    url: https://github.com/rnnh/
date: 2022-07-21
repository_url: https://github.com/rnnh/TidyTuesday/
preview: technology-adoption_files/figure-html5/fig1-1.png
output:
  distill::distill_article:
    self_contained: false
    toc: true
---


````{r knitr, include=FALSE}
knitr::opts_chunk$set(include = TRUE)
knitr::opts_chunk$set(fig.height = 6)
knitr::opts_chunk$set(fig.width = 9)
```

## Introduction

In this post, the [Technology Adoption](https://github.com/rfordatascience/tidytuesday/blob/master/data/2022/2022-07-19/readme.md) data set is used to illustrate data exploration [R](https://www.r-project.org/) and adding information using the [{countrycode}](https://cran.rstudio.com/web/packages/countrycode/) package.
During data exploration, the `tt$technology` data set is filtered to select for the "Energy" category, and the distinct values for "variable" and "label" are printed.
A subset is then created to test adding full country names and corresponding continents based on 3 letter ISO codes in the data set using the `countrycode()` function.
The full data set is then wrangled into two tibbles for fossil fuel and low-carbon electricity production: the distribution for each energy source is plotted according to the corresponding continent.
The full source for this blog post is [available on GitHub](https://github.com/rnnh/TidyTuesday).

## Setup

Loading the [R](https://www.r-project.org/) libraries and
[data set](https://github.com/rfordatascience/tidytuesday/blob/master/data/2022/2022-07-19/readme.md).

```{r setup}
# Loading libraries
library(tidytuesdayR)
library(countrycode)
library(tidyverse)
library(ggthemes)

# Loading data
tt <- tt_load("2022-07-19")
```

## Exploring tt$technology: selecting distinct values after filtering, and testing adding a "continent" variable

```{r explore}
# Printing a summary of tt$technology
tt$technology

# Printing the distinct "variable" and "label" pairs for the "Energy" category
## This will be used as a reference to create the "energy_type" column/variable
tt$technology %>% filter(category == "Energy") %>% select(variable, label) %>%
  distinct()

# Setting a seed to make results reproducible
set.seed("20220719")
# Using sample() to select six rows of tt$technology at random
sample_rows <- sample(x = rownames(tt$technology), size = 6)
# Creating a subset using the random rows
technology_sample <- tt$technology[sample_rows, ]
# Printing a summary of the randomly sampled subset
technology_sample

# Adding continent and country name columns/variables to the sample subset,
# using the countrycode::countrycode() function
technology_sample <- technology_sample %>%
  mutate(continent = countrycode(iso3c, origin = "iso3c",
    destination = "continent"),
    country = countrycode(iso3c, origin = "iso3c", destination = "country.name"))
# Selecting the country ISO code, continent and country name of the sample
# subset, to confirm that countrycode() worked as intended
technology_sample %>% select(iso3c, continent, country)
```

## Wrangling tt$technology into two electricity production tibbles: fossil fuels and low-carbon sources

```{r wrangling}
# Adding the corresponding continent for each country in tt$technology;
# filtering to select for the "Energy" category; adding a more succinct
# "energy_type" variable; and dropping rows with missing values
energy_tbl <- tt$technology %>%
  mutate(continent = countrycode(iso3c, origin = "iso3c",
    destination = "continent")) %>%
  filter(category == "Energy") %>%
  mutate(energy_type = fct_recode(variable,
    "Consumption" = "elec_cons", "Coal" = "elec_coal", "Gas" = "elec_gas",
    "Hydro" = "elec_hydro", "Nuclear" = "elec_nuc", "Oil" = "elec_oil",
    "Other renewables" = "elec_renew_other", "Solar" = "elec_solar",
    "Wind" = "elec_wind", "Output" = "elecprod",
    "Capacity" = "electric_gen_capacity")) %>%
  drop_na()

# Printing a summary of energy_tbl
energy_tbl

# Filtering energy_table for fossil fuel rows
fossil_fuel_tbl <- energy_tbl %>%
  filter(energy_type != "Consumption" & energy_type != "Output" 
    & energy_type != "Capacity") %>% 
  filter(energy_type == "Coal" | energy_type == "Gas" | energy_type == "Oil")

# Printing a summary of the tibble
fossil_fuel_tbl

# Filtering energy_table for low-carbon energy source rows
low_carbon_tbl <- energy_tbl %>%
  filter(energy_type != "Consumption" & energy_type != "Output" 
    & energy_type != "Capacity") %>% 
  filter(energy_type != "Coal" & energy_type != "Gas" & energy_type != "Oil")

# Printing a summary of the tibble
low_carbon_tbl
```

## Plotting distributions of electricity produced from fossil fuels and low-carbon sources

```{r fig1, fig.cap = "Box plots of electricity produced from fossil fuels, faceted by continent."}
# Plotting distributions of electricity produced from fossil fuels
fossil_fuel_tbl %>%
  ggplot(aes(x = fct_reorder(energy_type, value), y = value, fill = energy_type)) +
  geom_boxplot() +
  theme_solarized() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
    legend.position = "none") +
  scale_colour_discrete() +
  scale_y_log10() +
  facet_wrap(~continent, scales = "free") +
  labs(
    title = "Electricity generated from fossil fuels by continent",
    y = "Output in log terawatt-hours: log10(TWh)",
    x = "Source")
```

```{r fig2, fig.cap = "Box plots of electricity produced from low-carbon energy sources, faceted by continent."}
# Plotting distributions of electricity produced from low-carbon sources
low_carbon_tbl %>%
  ggplot(aes(x = fct_reorder(energy_type, value), y = value, fill = energy_type)) +
  geom_boxplot() +
  theme_solarized() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
    legend.position = "none") +
  scale_colour_discrete() +
  scale_y_log10() +
  facet_wrap(~continent, scales = "free") +
  labs(
    title = "Electricity generated from low-carbon sources by continent",
    y = "Output in log terawatt-hours: log10(TWh)",
    x = "Source")
```