A. Summarize and plot the median life expectancy (MLE) in 1952 for each of the 5 countries with the top MLE’s and for each of the 5 countries with the bottom MLE’s. You should have one summary table and one plot.

filter1952 <- gapminder %>% filter(year==1952) %>% arrange(lifeExp)
Comb1952 <- rbind(head(filter1952,5),tail(filter1952,5))
LE1952 <- Comb1952 %>% select("Country" = country, "Life Expectancy" = lifeExp)
formattable(LE1952, align = "l")
Country Life Expectancy
Afghanistan 28.801
Gambia 30.000
Angola 30.015
Sierra Leone 30.331
Mozambique 31.286
Denmark 70.780
Sweden 71.860
Netherlands 72.130
Iceland 72.490
Norway 72.670 
ggplot(LE1952, aes(x= reorder(Country, `Life Expectancy`), y=`Life Expectancy`)) + 
   geom_col(fill='steelblue') + 
   theme(axis.text.x =element_text(angle=45, hjust=1), plot.title = element_text(hjust = 0.5), plot.subtitle=element_text(hjust = 0.5)) +
   labs(title="Lowest and Highest Life Expectancies (MLE)", subtitle="in 1952", x="Country", y="Life Expectancy")

Comparing the highest and lowest median life expectancies (MLE) in 1952 reveals that the top five countries have MLEs which are over twice as high as the lowest five countries with a gap of about 40 years. Four of the five countries with the lowest MLEs are African countries, with the exception of Afghanistan, a South-Asian country. Conversely, all of the top five countries are European, with four of them being Scandinavian countries. The disparity between the highest and lowest countries can likely be attributed to their level of military, political, and economic stability, or lack thereof. It is possible, however, that people who make it to adulthood live longer than the data might suggest for the lowest five countries, as the data could be affected by infant mortality and deaths due to military conflict.

B. Summarize and plot the median life expectancy (MLE) in 2007 for each of the 5 countries with the top MLE’s and for each of the 5 countries with the bottom MLE’s. You should have one summary table and one plot.

filter2007 <- gapminder %>% filter(year==2007) %>% arrange(lifeExp)
Comb2007 <- rbind(head(filter2007,5),tail(filter2007,5))
LE2007 = Comb2007 %>% select("Country" = country, "Life Expectancy" = lifeExp)
formattable(LE2007, align = "l")
Country Life Expectancy
Swaziland 39.613
Mozambique 42.082
Zambia 42.384
Sierra Leone 42.568
Lesotho 42.592
Australia 81.235
Switzerland 81.701
Iceland 81.757
Hong Kong, China 82.208
Japan 82.603 
ggplot(LE2007, aes(x= reorder(Country, `Life Expectancy`), y=`Life Expectancy`)) + 
   geom_col(fill='steelblue') + 
   theme(axis.text.x =element_text(angle=45, hjust=1), plot.title = element_text(hjust = 0.5), plot.subtitle=element_text(hjust = 0.5)) +
   labs(title="Lowest and Highest Life Expectancies (MLE)", subtitle="in 2007", x="Country", y="Life Expectancy")

As of 2007, all five countries with the lowest MLE are African countries, with most of them being South-African countries. These life expectancies are not likely to be much affected by conflict, but rather due to low availability of resources, as there have not been recent large scale or organized conflicts in the region. The top five highest countries have become more diverse in recent history, with 2 European countries, 2 Asian countries, and Australia enjoying the highest MLEs. These countries all have easy access to life-improving and life-saving resources, such as food, clean water, shelter, and access to health care. Comparing trends, it appears the gap between the highest and lowest countries has remained stable with about 40 years between them; however, the MLE of both the bottom five and top five countries has increased by about 10 years.

C. Summarize and plot the median life expectancy in each year for the largest 5 countries in terms of 2007 population. You should have one summary table and one plot.

Top5pop <- gapminder %>% filter(year==2007) %>% arrange(-pop) %>% head(5) %>% .$country %>% as.vector()

Top5popMLE <- gapminder %>% filter(country %in% Top5pop)

T5PMLE <- Top5popMLE %>% select("Country" = country, "Life Expectancy" = lifeExp, "Year" = year)

PivotT5 = T5PMLE %>% pivot_wider(names_from = Year, values_from = `Life Expectancy`)
formattable(PivotT5, align="l", list(area(col = 2:13)  ~  color_tile("#fc4e4e", "#fff2f2")))
Country 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
Brazil 50.91700 53.28500 55.66500 57.63200 59.50400 61.48900 63.33600 65.20500 67.05700 69.38800 71.00600 72.39000
China 44.00000 50.54896 44.50136 58.38112 63.11888 63.96736 65.52500 67.27400 68.69000 70.42600 72.02800 72.96100
India 37.37300 40.24900 43.60500 47.19300 50.65100 54.20800 56.59600 58.55300 60.22300 61.76500 62.87900 64.69800
Indonesia 37.46800 39.91800 42.51800 45.96400 49.20300 52.70200 56.15900 60.13700 62.68100 66.04100 68.58800 70.65000
United States 68.44000 69.49000 70.21000 70.76000 71.34000 73.38000 74.65000 75.02000 76.09000 76.81000 77.31000 78.24200 
ggplot(T5PMLE, aes(x=Year, y=`Life Expectancy`, color=Country)) + geom_line() + 
   theme(axis.text.x =element_text(angle=45, hjust=1), plot.title = element_text(hjust = 0.5), plot.subtitle=element_text(hjust = 0.5)) +
   labs(title="Life Expectancies of Top 5 Most Populous Countries Since 1952*", caption="*Population as of 2007", x="Country", y="Life Expectancy")

The most populous countries as of 2007 are China, India, United States, Indonesia, and Brazil. All five countries have increased their life expectancies since 1952. Four have steadily increased, while China had a notable decrease in life expectancy between 1957 and 1962. This drop can almost certainly be attributed to the “Great Chinese Famine”, a three year period of famine between 1959 and 1961 as a result of agricultural changes and droughts. However, as of 2007, China has caught up and has the 2nd highest life expectancy out of the five most populous countries. The United States has continuously had the highest MLE of the countries in this group and has slowly increased since 1952. Indonesia has seen the biggest increase in MLE with an increase of roughly 33 years, almost doubling their life expectancy.

D. Summarize and plot the median life expectancy in each year for each continent. You should have one summary table and one plot.

ContinentMLE = gapminder %>% group_by(continent, year) %>% mutate(lifeExp = sum(lifeExp)/n()) %>% subset(select = c(continent, lifeExp, year)) %>% unique()

PivotContinent = ContinentMLE %>% pivot_wider(names_from = year, values_from = lifeExp)
formattable(PivotContinent, align="l", list(area(col = 2:13)  ~  color_tile("#fc4e4e", "#fff2f2")))
continent 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
Asia 46.31439 49.31854 51.56322 54.66364 57.31927 59.61056 62.61794 64.85118 66.53721 68.02052 69.23388 70.72848
Europe 64.40850 66.70307 68.53923 69.73760 70.77503 71.93777 72.80640 73.64217 74.44010 75.50517 76.70060 77.64860
Africa 39.13550 41.26635 43.31944 45.33454 47.45094 49.58042 51.59287 53.34479 53.62958 53.59827 53.32523 54.80604
Americas 53.27984 55.96028 58.39876 60.41092 62.39492 64.39156 66.22884 68.09072 69.56836 71.15048 72.42204 73.60812
Oceania 69.25500 70.29500 71.08500 71.31000 71.91000 72.85500 74.29000 75.32000 76.94500 78.19000 79.74000 80.71950 
ggplot(ContinentMLE, aes(x=year, y=lifeExp, color=continent)) + geom_line() + 
   theme(axis.text.x =element_text(angle=45, hjust=1), plot.title = element_text(hjust = 0.5)) + labs(title = "Life Expectancies per Continent Since 1952", x="Year", y="Life Expectancy", color = "Continent")

All continents have increased life expectancies since 1952 with Oceania consistently having the highest MLE, and Africa having the lowest. Africa is the only continent which has not increased steadily, and has had a plateau period between 1987 and 2002, but had an encouraging upturn in life expectancy between 2002 and 2007.