2.1 Data collection

In this tutorial I’ll be retrieve data and metadata from World Bank on world popoulation between 1960 and 2021 WDI package.

We start by loading the population data and countries metadata from World Bank.

#population data
country_pop <- WDI::WDI(indicator='SP.POP.TOTL',
                        start=1960,
                        end=2022)
#metadata
country_meta <- WDI::WDI_data$country

Now let’s have a brief look on these two large tables.

#data table
glimpse(country_pop)

## Rows: 16,492
## Columns: 5
## $ country     <chr> "Africa Eastern and Southern", "Africa Eastern and Souther…
## $ iso2c       <chr> "ZH", "ZH", "ZH", "ZH", "ZH", "ZH", "ZH", "ZH", "ZH", "ZH"…
## $ iso3c       <chr> "AFE", "AFE", "AFE", "AFE", "AFE", "AFE", "AFE", "AFE", "A…
## $ year        <int> 2021, 2020, 2019, 2018, 2017, 2016, 2015, 2014, 2013, 2012…
## $ SP.POP.TOTL <dbl> 694665117, 677243299, 660046272, 643090131, 626392880, 609…

gt::gt(head(country_pop))

country	iso2c	iso3c	year	SP.POP.TOTL
Africa Eastern and Southern	ZH	AFE	2021	694665117
Africa Eastern and Southern	ZH	AFE	2020	677243299
Africa Eastern and Southern	ZH	AFE	2019	660046272
Africa Eastern and Southern	ZH	AFE	2018	643090131
Africa Eastern and Southern	ZH	AFE	2017	626392880
Africa Eastern and Southern	ZH	AFE	2016	609978946

#metadata table
glimpse(country_meta)

## Rows: 299
## Columns: 9
## $ iso3c     <chr> "ABW", "AFE", "AFG", "AFR", "AFW", "AGO", "ALB", "AND", "ARB…
## $ iso2c     <chr> "AW", "ZH", "AF", "A9", "ZI", "AO", "AL", "AD", "1A", "AE", …
## $ country   <chr> "Aruba", "Africa Eastern and Southern", "Afghanistan", "Afri…
## $ region    <chr> "Latin America & Caribbean", "Aggregates", "South Asia", "Ag…
## $ capital   <chr> "Oranjestad", "", "Kabul", "", "", "Luanda", "Tirane", "Ando…
## $ longitude <chr> "-70.0167", "", "69.1761", "", "", "13.242", "19.8172", "1.5…
## $ latitude  <chr> "12.5167", "", "34.5228", "", "", "-8.81155", "41.3317", "42…
## $ income    <chr> "High income", "Aggregates", "Low income", "Aggregates", "Ag…
## $ lending   <chr> "Not classified", "Aggregates", "IDA", "Aggregates", "Aggreg…

gt::gt(head(country_meta))

iso3c	iso2c	country	region	capital	longitude	latitude	income	lending
ABW	AW	Aruba	Latin America & Caribbean	Oranjestad	-70.0167	12.5167	High income	Not classified
AFE	ZH	Africa Eastern and Southern	Aggregates				Aggregates	Aggregates
AFG	AF	Afghanistan	South Asia	Kabul	69.1761	34.5228	Low income	IDA
AFR	A9	Africa	Aggregates				Aggregates	Aggregates
AFW	ZI	Africa Western and Central	Aggregates				Aggregates	Aggregates
AGO	AO	Angola	Sub-Saharan Africa	Luanda	13.242	-8.81155	Lower middle income	IBRD

Looks like a lot of data! Let’s dive a bit deeper and explore the data visually.