Gapminder Data Report

About

This is a dataset of 142 countries, with values for life expectancy, GDP per capita, and population, every five years, from 1952 to 2007. Learn more here and watch this data come alive in this TED talk by Han’s Rosling.

gapminder %>% 
  head(10) %>% 
knitr::kable(align = "c")

country	continent	year	lifeExp	pop	gdpPercap
Afghanistan	Asia	1952	28.801	8425333	779.4453
Afghanistan	Asia	1957	30.332	9240934	820.8530
Afghanistan	Asia	1962	31.997	10267083	853.1007
Afghanistan	Asia	1967	34.020	11537966	836.1971
Afghanistan	Asia	1972	36.088	13079460	739.9811
Afghanistan	Asia	1977	38.438	14880372	786.1134
Afghanistan	Asia	1982	39.854	12881816	978.0114
Afghanistan	Asia	1987	40.822	13867957	852.3959
Afghanistan	Asia	1992	41.674	16317921	649.3414
Afghanistan	Asia	1997	41.763	22227415	635.3414

Quick Look

The dataset has 1704 rows and 6 columns.

We are using the glue package here to summarize number of countries in each continent using R code. The chunk itself is invisible here because we set echo = FALSE but you can use the download code button above to see the full code.

• Africa has 624 countries
  
• Americas has 300 countries
  
• Asia has 396 countries
  
• Europe has 360 countries
  
• Oceania has 24 countries

The table below shows how life expectancy varies with continent.

gapminder %>% 
  filter(year == 2007) %>% 
  group_by(continent) %>% 
  summarize(median_life_exp = round(median(lifeExp), 1)) %>% 
  knitr::kable(align = "c", 
               col.names = c("Continent", 
                             "Median Life Expectancy"),
               caption = "Life Expetancy by Continent")

Life Expetancy by Continent

Continent	Median Life Expectancy
Africa	52.9
Americas	72.9
Asia	72.4
Europe	78.6
Oceania	80.7

GDP vs life expectancy?

In this section we will explore to the question “Do people in wealthy countries live longer”. For this report we will do the analysis for the year 2007. If you want to generate similar report for different years try the parameters feature of RMarkdown.

gapminder %>% 
  filter(year == 2007) %>% 
  ggplot(aes(x = gdpPercap, 
             y = lifeExp, 
             color= continent, 
             size = pop/1000000)) + # population in millions
  geom_point()+
  labs(x = "GDP Per Capita",
       y = "Life Expectancy",
       title = "Relationship between GDP and Life Expectancy",
       subtitle = "Data Source: Gapminder", 
       size = "Population in millions")