| wlddev | R Documentation |
This dataset contains 5 indicators from the World Bank's World Development Indicators (WDI) database: (1) GDP per capita, (2) Life expectancy at birth, (3) GINI index, (4) Net ODA and official aid received and (5) Population. The panel data is balanced and covers 216 present and historic countries from 1960-2020 (World Bank aggregates and regional entities are excluded).
Apart from the indicators the data contains a number of identifiers (character country name, factor ISO3 country code, World Bank region and income level, numeric year and decade) and 2 generated variables: A logical variable indicating whether the country is an OECD member, and a fictitious variable stating the date the data was recorded. These variables were added so that all common data-types are represented in this dataset, making it an ideal test-dataset for certain collapse functions.
data("wlddev")
A data frame with 13176 observations on the following 13 variables. All variables are labeled e.g. have a 'label' attribute.
countrychr Country Name
iso3cfct Country Code
datedate Date Recorded (Fictitious)
yearint Year
decadeint Decade
regionfct World Bank Region
incomefct World Bank Income Level
OECDlog Is OECD Member Country?
PCGDPnum GDP per capita (constant 2010 US$)
LIFEEXnum Life expectancy at birth, total (years)
GINInum GINI index (World Bank estimate)
ODAnum Net official development assistance and official aid received (constant 2018 US$)
POPnum Population, total
https://data.worldbank.org/, accessed via the WDI package. The codes for the series are c("NY.GDP.PCAP.KD", "SP.DYN.LE00.IN", "SI.POV.GINI", "DT.ODA.ALLD.KD", "SP.POP.TOTL").
GGDC10S, Collapse Overview
data(wlddev)
# Panel-summarizing the 5 series
qsu(wlddev, pid = ~iso3c, cols = 9:13, vlabels = TRUE)
# By Region
qsu(wlddev, by = ~region, cols = 9:13, vlabels = TRUE)
# Panel-summary by region
qsu(wlddev, by = ~region, pid = ~iso3c, cols = 9:13, vlabels = TRUE)
# Pairwise correlations: Ovarall
print(pwcor(get_vars(wlddev, 9:13), N = TRUE, P = TRUE), show = "lower.tri")
# Pairwise correlations: Between Countries
print(pwcor(fmean(get_vars(wlddev, 9:13), wlddev$iso3c), N = TRUE, P = TRUE), show = "lower.tri")
# Pairwise correlations: Within Countries
print(pwcor(fwithin(get_vars(wlddev, 9:13), wlddev$iso3c), N = TRUE, P = TRUE), show = "lower.tri")
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