HDS 5.5.1 and 5.5.2

```{r} #| label: Load Packages #| warning: false

library(tidyverse) library(gapminder)


Begin by loading the **tidyverse** and **gapminder** packages 
in the code chunk above and adding your name as the author.

The `dplyr` *Wrangling Penguins* tutorial (up through Section 7) 
and Chapter 5 of Hello Data Science
have shown you how to subset your data by rows (`filter()`) and columns 
(`select()`), how to `relocate()` and `rename()` columns, and how to redefine 
or create new columns (`mutate()`). It's time to put those tools together to 
manipulate, and visualize with `ggplot`, the `gapminder` data with a series of 
commands connected with the pipe, `|>`. Each code chuck below should start with
the original `gapminder` data frame.

## Wrangling and Plotting the `gapminder` Data

Let's start by making a line plot of `lifeExp` versus `year` colored by
`country` for all the countries in Europe. 
Rename `country` to `europe_country` and `lifeExp` to `lifeExp_yrs`.
Modify this code by filling in the `______` to do so:

```{r}
gapminder |>
  filter(continent == "Europe") |>
  rename(europe_country = country, lifeExp_yrs = lifeExp) |>
  ggplot(mapping = aes(x = year, y = lifeExp_yrs, color = europe_country)) +
    geom_line() +
  labs(title = "Life Expectancy by Year in Europe",
       x = "Year",
       y = "Life Expectancy at Birth (years)",
       color = "Country")

Focusing again on Europe, make a plot containing a series of histograms of gdpPercap for each country in Europe.

{r} gapminder |> filter(continent == "Europe") |> ggplot(mapping = aes(x = gdpPercap)) + geom_histogram() + facet_wrap(~ 100) + labs(title = "GDP per Capita for Countries in Europe", x = "GDP per Capita", y = "Country in Europe")

If gdpPercap is the per capita GDP, then we can calculate the total_GDP for each country by multiplying by the population. Create side-by-side boxplots of the total_GDP by continent:

{r} gapminder |> mutate(total_GDP = gdpPercap * pop) |> ggplot(mapping = aes(x = country, y = total_GDP)) + geom_boxplot() + labs(title = "GDP for each Country", x = "Country", y = "Total GDP")

Let’s compare gdpPercap for the countries in Europe and the Americas. Create a line plot of gdpPercap by year for each of the included countries, colored by continent.

{r} gapminder |> filter(continent == "Europe" | continent == "North America" | continent == "South America") |> ggplot(mapping = aes(x = year, y = gdpPercap, group = continent, color = country)) + geom_line() + labs(title = "GDP per Capita in the Americas and Europe by Country", x = "Year", y = "GDP per Capita", color = "Country")

Create a new variable, pop_mil, that is the population of each country in millions of people. Make side-by-side boxplots of pop_mil by continent for the last year of data available:

{r} gapminder |> rename(pop_mil = pop) |> filter(year == 2007) |> ggplot(mapping = aes(x = continent, y = pop_mil)) + geom_boxplot() + labs(title = "Population in Millions for Each Continent in 2007", x = "Continent", y = "Population")

Make a scatterplot of lifeExp versus gdpPercap for the last year of data available. Color the points by continent:

{r} gapminder |> filter(year == 2007) |> ggplot(mapping = aes(x = lifeExp, y = gdpPercap, color = continent)) + geom_point() + labs(title = "Life Expectancy by GDP per Capita by Continent", x = "Life Expanctancy", y = "GDP per Capita", color = "Continent")

Make a series of scatterplots of lifeExp versus gdpPercap for each year. Color the points by continent:

{r} gapminder |> ggplot(mapping = aes(x = lifeExp, y = gdpPercap, color = continent)) + geom_point() + facet_wrap(~ year) + labs(title = "Life Expectancy by GDP per Capita by Year", x = "Life Expectancy", y = "GDP per Capita", color = "Continent")

HDS 5.5.1 and 5.5.2

Alana Zarneke