Data and setting up your workflow

The goal of this chapter is to provide readers with an overview of the datasets used in the book’s examples. Having an initial understanding of the data helps readers easily navigate between the examples.

Installation of associated packages

To run the examples in the book you need to install the following packages. In addition, to this package list, the associated package corresponds to the geom should be installed. The drone package provides the datasets associated with this geom encyclopedia.

#install.packages(drone)
#install.packages("devtools")
devtools::install_github("thiyangt/drone")
install.packages(tidyverse)
install.packages(patchwork)

Data set use in the geom Encyclopedia

The datasets used in the book coming from country-wise statistics obtained from public reliable websites. Only two datasets are used to explain all geoms so that readers can build intuition by seeing the same data represented in multiple ways. The reasons for using these datasets are:

• The dataset context is familiar and easily understood by individuals from diverse backgrounds.

• Ability to create cross-sectional, time-series, spatial, and spatio-temporal visualizations.

• Ability to visualize relationships between qualitative–qualitative, qualitative–quantitative, and quantitative–quantitative variables.

• Experience addressing common data challenges such as missing values, overplotting, and large-scale datasets.

Why two datasets?

Different visualizations often require datasets to be in specific formats depending on the purpose of analysis or presentation. In this context, one dataset is organized in the long (tidy) format, while another is arranged in the wide format. The happiness_gdp_income dataset has time values spread across multiple columns, making it suitable for wide-format representation. In contrast, the worldbankdata dataset stores time in a single column, following the long-format structure. It is important to note that these datasets are not simply the reverse of each other; each format serves a distinct analytical or visualization purpose.

Dataset 1: happiness_gdp_income

The dataset is obtained from World Population Review report (World Population Review (n.d.)). Happiness score and GDP per Capita are accessed Chotchaeva (2017) via the link. Income level data are accessed from the World Bank Data Catalogue World Bank (2026b) via the link on 11 February 2026.

The variable description is given in Table tbl-WorldHappinessScore.

Table 1: Variable Description for happiness_gdp_income dataset

Variable	Description
Country	Country name
Region	World Bank regional classification
HappinessScore2015	Happiness Score 2015
GDPpercapita2015	GDP per Capita 2015
HappinessScore2016	Happiness Score 2016
GDPpercapita2016	GDP per Capita 2016
HappinessScore2017	Happiness Score 2017
GDPpercapita2017	GDP per Capita 2017
Income2015	Income level categorization in 2015
Income2016	Income level categorization in 2016
Income2017	Income level categorization in 2017

The first few rows of the dataset is shown below

library(drone)
head(happiness_gdp_income)

      Country         Region HappinessScore2015 GDPpercapita2015
1 Switzerland Western Europe              7.587          1.39651
2     Iceland Western Europe              7.561          1.30232
3     Denmark Western Europe              7.527          1.32548
4      Norway Western Europe              7.522          1.45900
5      Canada  North America              7.427          1.32629
6     Finland Western Europe              7.406          1.29025
  HappinessScore2016 GDPpercapita2016 HappinessScore2017 GDPpercapita2017
1              7.509            7.590              7.494         1.564980
2              7.501            7.669              7.504         1.480633
3              7.526            7.592              7.522         1.482383
4              7.498            7.575              7.537         1.616463
5              7.404            7.473              7.316         1.479204
6              7.413            7.475              7.469         1.443572
   Income2015  Income2016  Income2017
1 High income High income High income
2 High income High income High income
3 High income High income High income
4 High income High income High income
5 High income High income High income
6 High income High income High income

happiness_gdp_income: Data Profiling

Table tbl-hp provides a compact summary of the dataset.There are 5 variables. Among the 2 are character variables and 3 are numeric variables. Summary of both character variables and numeric variables are given in the output. For character variables,

n_missing tells how many values are missing for each variable.

complete_rate is the proportion of non-missing values.

n_unique is the number of unique values in the variable.

For numeric variables Table tbl-hp shows mean, sd (standard deviation), p0 (min), percentiles: p25, p50 (median), p75, p100 (max), and a small histogram for each numeric variable.

Table 2: Summary description of the happiness_gdp_income dataset

Data summary

Name	happiness_gdp_income
Number of rows	158
Number of columns	11
_______________________
Column type frequency:
character	2
factor	3
numeric	6
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
Country	0	1	4	24	0	158	0
Region	0	1	12	31	0	10	0

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
Income2015	23	0.85	FALSE	4	Hig: 44, Upp: 36, Low: 31, Low: 24
Income2016	23	0.85	FALSE	4	Hig: 43, Upp: 36, Low: 32, Low: 24
Income2017	23	0.85	FALSE	4	Hig: 42, Low: 35, Upp: 34, Low: 24

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
HappinessScore2015	0	1.00	5.38	1.15	2.84	4.53	5.23	6.24	7.59	▂▇▇▇▆
GDPpercapita2015	0	1.00	0.85	0.40	0.00	0.55	0.91	1.16	1.69	▃▅▆▇▂
HappinessScore2016	7	0.96	5.38	1.15	2.90	4.40	5.31	6.30	7.53	▂▆▇▇▅
GDPpercapita2016	7	0.96	5.48	1.14	3.08	4.46	5.42	6.43	7.67	▃▆▇▇▅
HappinessScore2017	9	0.94	5.36	1.14	2.69	4.50	5.28	6.11	7.54	▂▆▇▇▅
GDPpercapita2017	9	0.94	0.99	0.41	0.00	0.67	1.07	1.32	1.87	▂▅▆▇▂

happiness_gdp_income: Data Quality Analysis

Figure fig-happy2 shows the types of variables we have in the dataset and missing value distribution.

library(tidyverse)
library(visdat)
vis_dat(happiness_gdp_income) + 
  scale_fill_brewer(palette = "Dark2")

Figure 1: Summary description of the happiness_gdp_income dataset

Figure fig-happy3 shows combination-wise missing values in the dataset happiness_gdp_income. There are 6 observations where only WorldHapinessScore_2023 missing and there are 3 observations where both WorldHapinessScore_2023 and WorldHapinessScore_2024 missing.

library(naniar)
gg_miss_upset(happiness_gdp_income) 

Figure 2: Combinations of missingness across cases: WorldHappinessScore dataset

Dataset 2: worldbankdata

This dataset provides country-level development indicators compiled from the World Bank Data Catalogue (World Bank (2026a)). It includes information on access to clean cooking fuels, access to electricity, income group classification, and regional grouping across multiple years.

The variable description is given in Table tbl-worldbankdata.

Table 3: Variable Description for worldbankdata dataset

Variable	Description
Country	Country name
Code	ISO country code
Region	World Bank regional classification
Year	Year of observation
Cooking	Access to clean fuels and technologies for cooking (percentage of population).
Electricity	Access to electricity (percentage of population).
Income	Income group classification: L = Low income, LM = Lower middle income, UM = Upper middle income, HI = High income.

To view the dataset use the following code.

library(drone)
library(tibble)
data(worldbankdata)
worldbankdata

# A tibble: 7,937 × 7
   Country Code  Region                     Year Cooking Electricity Income
   <fct>   <fct> <fct>                     <dbl>   <dbl>       <dbl> <fct> 
 1 Aruba   ABW   Latin America & Caribbean  1990      NA       100   H     
 2 Aruba   ABW   Latin America & Caribbean  2000      NA        91.7 H     
 3 Aruba   ABW   Latin America & Caribbean  2013      NA       100   H     
 4 Aruba   ABW   Latin America & Caribbean  2014      NA       100   H     
 5 Aruba   ABW   Latin America & Caribbean  2015      NA       100   H     
 6 Aruba   ABW   Latin America & Caribbean  2016      NA       100   H     
 7 Aruba   ABW   Latin America & Caribbean  2017      NA       100   H     
 8 Aruba   ABW   Latin America & Caribbean  2018      NA       100   H     
 9 Aruba   ABW   Latin America & Caribbean  2019      NA       100   H     
10 Aruba   ABW   Latin America & Caribbean  2020      NA       100   H     
# ℹ 7,927 more rows

worldbankdata: Data Profiling

Table tbl-worldbankdatasummary provides a compact summary of the dataset. The dataset contains 7 variables. Among them, 4 variables are factor variables and 3 variables are numeric variables. In addtion, Table tbl-worldbankdatasummary shows a comprehensive overview of your dataset, showing both the distribution of categorical variables and the summary statistics for numeric variables, including missing values and frequency counts.

Table 4: Summary description of the workldbank dataset

Data summary

Name	worldbankdata
Number of rows	7937
Number of columns	7
_______________________
Column type frequency:
factor	4
numeric	3
________________________
Group variables	None

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
Country	0	1.00	FALSE	227	Afg: 36, Alb: 36, Alg: 36, Ame: 36
Code	0	1.00	FALSE	218	CIV: 48, CUW: 47, CZE: 47, FRO: 47
Region	199	0.97	FALSE	7	Eur: 2038, Sub: 1693, Lat: 1512, Eas: 1343
Income	559	0.93	FALSE	4	H: 2177, LM: 1978, L: 1704, UM: 1519

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Year	1	1.00	2004.59	10.41	1987.00	1996.00	2005.00	2014	2022	▇▇▇▇▇
Cooking	6047	0.24	65.48	38.46	0.00	27.30	84.75	100	100	▃▁▁▁▇
Electricity	5693	0.28	84.40	26.45	2.11	79.86	99.80	100	100	▁▁▁▁▇

worldbankdata: Data Quality Analysis

worldbankdata dataset contains some missing values. Figure fig-miss shows the distribution of types of variables and missing value distribution.

library(tidyverse)
library(visdat)
vis_dat(worldbankdata) + 
  scale_fill_brewer(palette = "Dark2")

Figure 3: Data types and missing values distribution

Figure fig-miss2 shows combinations of missingness across cases. This is especially useful for understanding patterns of missingness across variables. For example, there are 5122 rows where both Electricity and Cooking variable missing.

library(naniar)
gg_miss_upset(worldbankdata) 

Figure 4: Combinations of missingness across cases: worldbankdata dataset

Chotchaeva, Karina. 2017. “World Happiness Report.” Zenodo. https://doi.org/10.5281/zenodo.1470906.

World Bank. 2026a. “GNI Per Capita: Operational Guidelines & Analytical Classifications.” https://datacatalog.worldbank.org/.

———. 2026b. “World Development Indicators.” https://datacatalog.worldbank.org/search/dataset/0037712/world-development-indicators.

World Population Review. n.d. “Happiest Countries in the World.” https://worldpopulationreview.com/country-rankings/happiest-countries-in-the-world.