Introduction

In this post, the Technology Adoption data set is used to illustrate data exploration R and adding information using the {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.

Setup

Loading the R libraries and data set.

# Loading libraries
library(tidytuesdayR)
library(countrycode)
library(tidyverse)
library(ggthemes)

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

    Downloading file 1 of 1: `technology.csv`

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

# Printing a summary of tt$technology
tt$technology
# A tibble: 491,636 × 7
   variable label                      iso3c  year group categ…¹ value
   <chr>    <chr>                      <chr> <dbl> <chr> <chr>   <dbl>
 1 BCG      % children who received a… AFG    1982 Cons… Vaccin…    10
 2 BCG      % children who received a… AFG    1983 Cons… Vaccin…    10
 3 BCG      % children who received a… AFG    1984 Cons… Vaccin…    11
 4 BCG      % children who received a… AFG    1985 Cons… Vaccin…    17
 5 BCG      % children who received a… AFG    1986 Cons… Vaccin…    18
 6 BCG      % children who received a… AFG    1987 Cons… Vaccin…    27
 7 BCG      % children who received a… AFG    1988 Cons… Vaccin…    40
 8 BCG      % children who received a… AFG    1989 Cons… Vaccin…    38
 9 BCG      % children who received a… AFG    1990 Cons… Vaccin…    30
10 BCG      % children who received a… AFG    1991 Cons… Vaccin…    21
# … with 491,626 more rows, and abbreviated variable name ¹​category
# ℹ Use `print(n = ...)` to see more rows
# 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$technology %>% filter(category == "Energy") %>% select(variable, label) %>%
  distinct()
# A tibble: 11 × 2
   variable              label                                        
   <chr>                 <chr>                                        
 1 elec_coal             Electricity from coal (TWH)                  
 2 elec_cons             Electric power consumption (KWH)             
 3 elec_gas              Electricity from gas (TWH)                   
 4 elec_hydro            Electricity from hydro (TWH)                 
 5 elec_nuc              Electricity from nuclear (TWH)               
 6 elec_oil              Electricity from oil (TWH)                   
 7 elec_renew_other      Electricity from other renewables (TWH)      
 8 elec_solar            Electricity from solar (TWH)                 
 9 elec_wind             Electricity from wind (TWH)                  
10 elecprod              Gross output of electric energy (TWH)        
11 electric_gen_capacity Electricity Generating Capacity, 1000 kilowa…
# Setting a seed to make results reproducible
set.seed("20220719")
# Using sample() to select six rows of tt$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
# A tibble: 6 × 7
  variable        label               iso3c  year group categ…¹  value
  <chr>           <chr>               <chr> <dbl> <chr> <chr>    <dbl>
1 Pol3            % children who rec… PRY    1993 Cons… Vaccin… 6.6 e1
2 pct_ag_ara_land % Arable land shar… LBR    1991 Non-… Agricu… 3.08e1
3 fert_total      Aggregate kg of fe… CHE    1988 Prod… Agricu… 1.78e8
4 railp           Thousands of passe… TUR    1948 Cons… Transp… 4.9 e1
5 ag_land         Land agricultural … TUN    2013 Non-… Agricu… 9.94e3
6 tv              Television sets     NIC    1981 Cons… Commun… 1.14e5
# … with abbreviated variable name ¹​category
# 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)
# A tibble: 6 × 3
  iso3c continent country    
  <chr> <chr>     <chr>      
1 PRY   Americas  Paraguay   
2 LBR   Africa    Liberia    
3 CHE   Europe    Switzerland
4 TUR   Asia      Turkey     
5 TUN   Africa    Tunisia    
6 NIC   Americas  Nicaragua  

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

# 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
# A tibble: 66,300 × 9
   variable  label     iso3c  year group categ…¹ value conti…² energ…³
   <chr>     <chr>     <chr> <dbl> <chr> <chr>   <dbl> <chr>   <fct>  
 1 elec_coal Electric… ABW    2000 Prod… Energy      0 Americ… Coal   
 2 elec_coal Electric… ABW    2001 Prod… Energy      0 Americ… Coal   
 3 elec_coal Electric… ABW    2002 Prod… Energy      0 Americ… Coal   
 4 elec_coal Electric… ABW    2003 Prod… Energy      0 Americ… Coal   
 5 elec_coal Electric… ABW    2004 Prod… Energy      0 Americ… Coal   
 6 elec_coal Electric… ABW    2005 Prod… Energy      0 Americ… Coal   
 7 elec_coal Electric… ABW    2006 Prod… Energy      0 Americ… Coal   
 8 elec_coal Electric… ABW    2007 Prod… Energy      0 Americ… Coal   
 9 elec_coal Electric… ABW    2008 Prod… Energy      0 Americ… Coal   
10 elec_coal Electric… ABW    2009 Prod… Energy      0 Americ… Coal   
# … with 66,290 more rows, and abbreviated variable names ¹​category,
#   ²​continent, ³​energy_type
# ℹ Use `print(n = ...)` to see more rows
# 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
# A tibble: 13,914 × 9
   variable  label     iso3c  year group categ…¹ value conti…² energ…³
   <chr>     <chr>     <chr> <dbl> <chr> <chr>   <dbl> <chr>   <fct>  
 1 elec_coal Electric… ABW    2000 Prod… Energy      0 Americ… Coal   
 2 elec_coal Electric… ABW    2001 Prod… Energy      0 Americ… Coal   
 3 elec_coal Electric… ABW    2002 Prod… Energy      0 Americ… Coal   
 4 elec_coal Electric… ABW    2003 Prod… Energy      0 Americ… Coal   
 5 elec_coal Electric… ABW    2004 Prod… Energy      0 Americ… Coal   
 6 elec_coal Electric… ABW    2005 Prod… Energy      0 Americ… Coal   
 7 elec_coal Electric… ABW    2006 Prod… Energy      0 Americ… Coal   
 8 elec_coal Electric… ABW    2007 Prod… Energy      0 Americ… Coal   
 9 elec_coal Electric… ABW    2008 Prod… Energy      0 Americ… Coal   
10 elec_coal Electric… ABW    2009 Prod… Energy      0 Americ… Coal   
# … with 13,904 more rows, and abbreviated variable names ¹​category,
#   ²​continent, ³​energy_type
# ℹ Use `print(n = ...)` to see more rows
# 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
# A tibble: 26,890 × 9
   variable   label    iso3c  year group categ…¹ value conti…² energ…³
   <chr>      <chr>    <chr> <dbl> <chr> <chr>   <dbl> <chr>   <fct>  
 1 elec_hydro Electri… ABW    2000 Prod… Energy      0 Americ… Hydro  
 2 elec_hydro Electri… ABW    2001 Prod… Energy      0 Americ… Hydro  
 3 elec_hydro Electri… ABW    2002 Prod… Energy      0 Americ… Hydro  
 4 elec_hydro Electri… ABW    2003 Prod… Energy      0 Americ… Hydro  
 5 elec_hydro Electri… ABW    2004 Prod… Energy      0 Americ… Hydro  
 6 elec_hydro Electri… ABW    2005 Prod… Energy      0 Americ… Hydro  
 7 elec_hydro Electri… ABW    2006 Prod… Energy      0 Americ… Hydro  
 8 elec_hydro Electri… ABW    2007 Prod… Energy      0 Americ… Hydro  
 9 elec_hydro Electri… ABW    2008 Prod… Energy      0 Americ… Hydro  
10 elec_hydro Electri… ABW    2009 Prod… Energy      0 Americ… Hydro  
# … with 26,880 more rows, and abbreviated variable names ¹​category,
#   ²​continent, ³​energy_type
# ℹ Use `print(n = ...)` to see more rows

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

# 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")

(#fig:fig1)Box plots of electricity produced from fossil fuels, 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")

(#fig:fig2)Box plots of electricity produced from low-carbon energy sources, faceted by continent.