Countries GDP Analysis

Q 1a): For US what years is there available data?

• A: 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, 2002, 2007

gapminder %>% 
  select(country, year ) %>%
  filter( country == "United States" ) %>%
  group_by(year) %>%
  summarize(n = n()) %>%
  arrange(year)

Q 1b): For all of the countries in Americas, what years is avilable?

• A: 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, 2002, 2007

gapminder %>% 
  select(continent, year ) %>%
  filter( continent == "Americas" ) %>%
  group_by(year) %>%
  summarize(n = n()) %>%
  arrange(year)

Q 2a): For all of the countries in Americas, what years is avilable?

• A: 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, 2002, 2007

gapminder %>% 
  select( continent, country, year, lifeExp ) %>%
  filter( continent == "Americas", country == "United States", lifeExp > 70 )

Q 2b): For all of the countries in Americas, what years is avilable?

• A: 2007

gapminder %>% 
  select( continent, country, year, lifeExp ) %>%
  filter( continent == "Americas", country == "Honduras", lifeExp > 70 )

Q 2c): For each country in the Americas, compute the average age of the measurenments since the county’s life expectancy exceeded 70?

• Answer:

gapminder %>% 
  select( continent, country, year, lifeExp ) %>%
  filter( continent == "Americas", lifeExp > 70 ) %>%
  group_by(country) %>%
  summarize(Ave_Age = mean(lifeExp))

Q 3a): In 2007, which countries had a population over 1Billion?

• A: China and India

gapminder %>% 
  filter( year == 2007, pop > 1000000000 ) %>%
  group_by(country)

Q 3b): In 2007, which country reached 1Billion people first in what year?

• A: China reachd a billion people first in 1982

gapminder %>% 
  filter( pop > 1000000000 ) %>%
  group_by(country, year) %>%
  summarise(n = n()) %>%
  arrange(year)

Q 4a): In 2007, which 3 countries had lowest GDPs?

• A: Congo, Dem. Rep., Liberia, Burundi

gapminder %>% 
  filter( year == 2007 ) %>%
  group_by(country) %>%
  arrange(gdpPercap)

Q 4b): In 1962, which 3 countries had lowest GDPs?

• A: Burundi, Eritrea, Myanmar

gapminder %>% 
  filter( year == 1962 ) %>%
  group_by(country) %>%
  arrange(gdpPercap)

Q 4c): Which continent are the majority of the countires from each of the two years, 1962 and 2007?

• A: Major Continent of 1962: Africa; Major Continent of 2007: Africa

gapminder %>% 
  filter( year == 1962 ) %>%
  group_by(country, continent) %>%
  arrange(gdpPercap)

gapminder %>% 
  filter( year == 2007 ) %>%
  group_by(country, continent) %>%
  arrange(gdpPercap)

Q 5a): Scatterplot of the x=gdp and y = lifeExp

gapminder %>% 
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point()

Q 5b): Scatterplot of the x=log(gdp) and y = log(lifeExp)

gapminder %>% 
  ggplot(aes(x = log(gdpPercap), y = log(lifeExp))) +
  geom_point()

Q 5c): Add color to the scatterplot to see each continent

gapminder %>% 
  ggplot(aes(x = gdpPercap, y = lifeExp, color = continent)) +
  geom_point()

gapminder %>% 
  ggplot(aes(x = log(gdpPercap), y = log(lifeExp), color = continent)) +
  geom_point()

Q 5d)

Based on the graph: * Lower GDP per capital but higher life Expectancy: Asia * Higher GDP per capital but higher life Expectancy: Europe

gapminder %>% 
  summarise(n = n(), mean_GDP = mean(gdpPercap), mean_Life = mean(lifeExp)) %>%
  arrange(mean_GDP, mean_Life)

# Higher GDP, Higher LifeExp
gapminder %>% 
  filter( gdpPercap > 7215.327, lifeExp > 59.47444) %>%
  group_by(continent) %>%
  summarise(n =n()) %>%
  arrange(n)

# Lower GDP, Higher LifeExp
gapminder %>% 
  filter( gdpPercap < 7215.327, lifeExp > 59.47444) %>%
  group_by(continent) %>%
  summarise(n =n()) %>%
  arrange(n)

gapminder %>% 
  group_by(continent) %>%
  summarise(n = n(), mean_GDP = mean(gdpPercap), mean_Life = mean(lifeExp)) %>%
  arrange(mean_GDP, mean_Life)