Case 5
Amy Nguyen
2023-03-30
1: (10 points) Are there missing values in the data? If so, can you show how data is missing? (open-ended question)
colSums(is.na(country))## country year infant_mortality life_expectancy
## 0 0 1453 0
## fertility population gdp continent
## 187 185 2972 0
## region
## 0missing_rows <- which(apply(is.na(country), 1, any))
head(country[missing_rows, ])## country year infant_mortality life_expectancy fertility
## 1 Albania 1960 115.4 62.87 6.19
## 3 Angola 1960 208.0 35.98 7.32
## 4 Antigua and Barbuda 1960 NA 62.97 4.43
## 6 Armenia 1960 NA 66.86 4.55
## 7 Aruba 1960 NA 65.66 4.82
## 10 Azerbaijan 1960 NA 61.33 5.57
## population gdp continent region
## 1 1636054 NA Europe Southern Europe
## 3 5270844 NA Africa Middle Africa
## 4 54681 NA Americas Caribbean
## 6 1867396 NA Asia Western Asia
## 7 54208 NA Americas Caribbean
## 10 3897889 NA Asia Western AsiaBy combining the is.na() and colSums() functions, we can see that there are missing values in the data, specifically in the columns/ variables of fertility, population, gdp, and infant_mortality. In total, there are 4797 missing values. To show how data is missing, I use which() function to locate the rows with missing values, and then subset the data set to show only those rows.
2: (5 points) How many unique countries are included in the data? How many years of observations are included in the data?
num_countries <- length(unique(country$country))
num_years <- length(unique(country$year))
cat("Number of unique countries:", num_countries, "\n")## Number of unique countries: 185cat("Number of years of observations:", num_years, "\n")## Number of years of observations: 573:(5 points) In the data, create a new variable called GDP_per_capita which equals to GDP/population.
country <- country %>%
mutate( GDP_per_capita = gdp/population)
head(country)## country year infant_mortality life_expectancy fertility
## 1 Albania 1960 115.40 62.87 6.19
## 2 Algeria 1960 148.20 47.50 7.65
## 3 Angola 1960 208.00 35.98 7.32
## 4 Antigua and Barbuda 1960 NA 62.97 4.43
## 5 Argentina 1960 59.87 65.39 3.11
## 6 Armenia 1960 NA 66.86 4.55
## population gdp continent region GDP_per_capita
## 1 1636054 NA Europe Southern Europe NA
## 2 11124892 13828152297 Africa Northern Africa 1242.992
## 3 5270844 NA Africa Middle Africa NA
## 4 54681 NA Americas Caribbean NA
## 5 20619075 108322326649 Americas South America 5253.501
## 6 1867396 NA Asia Western Asia NA4: (80 points) Propose four questions you would like to know about this data (20 points per question). At least one question needs to be related to time series and be answered using time series data visualization. Some example questions can be: Does the developing countries grow slower than the developed countries? Is Africa catching up with world or left behind? Is the world more divided now than it was 50 years ago? Please propose your own questions and do not use the exactly same questions listed above.
Question 1: Is there a relationship between infant mortality rate and fertility rate? How does this relationship differ across continents and regions?
ggplot(country, aes(x = year, y = infant_mortality, color = region, group = region)) +
geom_line() +
labs(x = "Year", y = "Infant Mortality Rate", color = "Region") +
facet_wrap(~continent) +
ggtitle("Infant Mortality Rate by Region and Continent") +
theme_bw()
ggplot(country, aes(x = year, y = fertility, color = region, group = region)) +
geom_line() +
labs(x = "Year", y = "Fertility Rate", color = "Region") +
facet_wrap(~continent) +
ggtitle("Fertility Rate by Region and Continent") +
theme_bw()
The resulting plots show the infant mortality rate and fertility rate for each country in the dataset over time, grouped by continent. We can observe that there is a general downward trend in infant mortality rate and fertility rate over time. However, the extent of these trends varies across continents and regions. For example, Asia had a spike in the fertility rate in the 1980s. Looking at the plots, we can also see that, generally, there is a positive relationship between infant mortality rate and fertility rate.
Question 2: Which region has the highest GDP per capita, and has this changed over time?
data <- country[, c("year", "region", "GDP_per_capita")]
data <- aggregate(GDP_per_capita ~ region + year, data, mean)
ggplot(data, aes(x = year, y = GDP_per_capita, color = region)) +
geom_line() +
labs(x = "Year", y = "GDP per capita", title = "GDP per capita by region over time")
From the plot, we can see that the Australia and New Zealand, Northern America, and Northern Europe have the highest GDP per capita, which has experienced significant increase over time.
Question 3: How has the population of different regions changed over time, and are there any regions experiencing significant population growth or decline?
data1 <- country[, c("year", "region", "population")]
data1 <- aggregate(population ~ region + year, data1, sum)
ggplot(data1, aes(x = year, y = population, color = region)) +
geom_line() +
labs(x = "Year", y = "Population", title = "Population by region over time")
Looking at the plot, we can see that the population has been increasing in generally, especially in Eastern Asia and Southern Africa.
Question 4: Which continent have experienced the greatest change in life expectancy over time?
ggplot(country, aes(x = year, y = life_expectancy, color = continent)) +
geom_line() +
labs(x = "Year", y = "Life expectancy") +
ggtitle("Life Expectancy by Continent over Time")