``` ```

Adding continent and country names with {countrycode}, and subsetting a data frame using sample()

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”

Ronan Harrington https://github.com/rnnh/
2022-07-21

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 Ă— 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
# â„ą 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$technology %>% filter(category == "Energy") %>% select(variable, label) %>%
  distinct()
# A tibble: 11 Ă— 2
   variable              label                                        
   <chr>                 <chr>                                        
 1 elec_coal             Electricity from coal (TWH)                  
 2 elec_cons             Electric power consumption (KWH)             
 3 elec_gas              Electricity from gas (TWH)                   
 4 elec_hydro            Electricity from hydro (TWH)                 
 5 elec_nuc              Electricity from nuclear (TWH)               
 6 elec_oil              Electricity from oil (TWH)                   
 7 elec_renew_other      Electricity from other renewables (TWH)      
 8 elec_solar            Electricity from solar (TWH)                 
 9 elec_wind             Electricity from wind (TWH)                  
10 elecprod              Gross output of electric energy (TWH)        
11 electric_gen_capacity Electricity Generating Capacity, 1000 kilowa…
# Setting a seed to make results reproducible
set.seed("20220719")
# Using sample() to select six rows of tt$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 Ă— 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 Ă— 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 Ă— 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
# â„ą 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 Ă— 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
# â„ą 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 Ă— 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
# â„ą 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")
Box plots of electricity produced from fossil fuels, faceted by continent.

Figure 1: 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")
Box plots of electricity produced from low-carbon energy sources, faceted by continent.

Figure 2: Box plots of electricity produced from low-carbon energy sources, faceted by continent.

Corrections

If you see mistakes or want to suggest changes, please create an issue on the source repository.

Reuse

Text and figures are licensed under Creative Commons Attribution CC BY 4.0. Source code is available at https://github.com/rnnh/TidyTuesday/, unless otherwise noted. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

Citation

For attribution, please cite this work as

Harrington (2022, July 21). Ronan's #TidyTuesday blog: Adding continent and country names with {countrycode}, and subsetting a data frame using sample(). Retrieved from https://tidytuesday.netlify.app/posts/2022-07-21-technology-adoption/

BibTeX citation

@misc{harrington2022adding,
  author = {Harrington, Ronan},
  title = {Ronan's #TidyTuesday blog: Adding continent and country names with {countrycode}, and subsetting a data frame using sample()},
  url = {https://tidytuesday.netlify.app/posts/2022-07-21-technology-adoption/},
  year = {2022}
}