---
title: "Homework assignment #4"
author: "Your name here"
date: "Date here"
output: html_document
---

# Reflection on readings

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# Hans Rosling redux

```{r load-packages-data, warning=FALSE, message=FALSE}
library(tidyverse)
library(scales)
library(gapminder)

# Load the gapminder data from the gapminder package with data()
data(gapminder)

# Look at the variables in the data frame
glimpse(gapminder)

# Create a data frame with only rows from 1997
gapminder_1997 <- gapminder %>%
  filter(year == 1997)
```


## Univariate analysis

Plot a histogram of life expectancy in 1997 (`lifeExp`). Choose an appropriate number of bins. (You get some code to help start you off):

```{r lifeexp-hist}
ggplot(gapminder_1997, aes(x = lifeExp)) +
  geom_histogram()
```

Plot a density plot of life expectancy in 1997. Fill it with some color so it doesn't look sad and empty.

```{r lifeexp-density}

```

Plot a histogram of GDP per capita in 1997 (`gdpPercap`).

```{r gdp-hist}

```

Plot a density plot of GDP per capita in 1997.

```{r gdp-density}

```

## Univariate analysis by groups

Plot multiple violin plots of GDP per capita in 1997 by continent. Fill the continents with different colors. Add points at 50% transparency (hint: `alpha = 0.5` in a `geom_point()` layer would help). (You get some code to help start you off):

```{r gdp-continent}
ggplot(gapminder_1997, aes(x = continent, y = gdpPercap)) +
  geom_boxplot()
```

Plot multiple boxplots (hint: `geom_boxplot()`) of GDP per capita in 1997 by continent. Fill the continents with different colors.

```{r gdp-continent-box}

```

Plot multiple violin plots of life expectancy in 1997 by continent, also with filled continents and semi-transparent points (hint: do basically what you did above, but with `lifeExp` instead of `gdpPercap`)

```{r lifeexp-continent}

```

Plot overlapping density plots of life expectancy in 1997 across continents. Oceania has very few observations, so omit it from the data (I create a filtered data frame for you below). Fill each continent with a color and make each density plot 50% transparent

```{r continent-densities}
gapminder_1997_sans_oceania <- gapminder_1997 %>%
  filter(continent != "Oceania")

ggplot(gapminder_1997_sans_oceania, aes(x = lifeExp, fill = continent)) + 
  geom_density(alpha = 0.5)
```

## Bivariate analysis

Plot health (`lifeExp`) vs. wealth (`gdpPercap`) in 1997. Color each point by continent. (You get some code to help start you off):

```{r health-wealth-basic}
ggplot(gapminder_1997, aes(x = gdpPercap, y = lifeExp)) +
  geom_point()
```

Make that same plot, but add `coord_trans(x = "log10")` as a layer.

```{r health-wealth-transformed}

```

What's different?

Plot health vs. wealth again (without a logged x-axis), and add a `geom_smooth()` layer.

```{r health-wealth-smooth}

```

By default, R will choose `method = "loess"` to plot the line. What is "loess"? (hint: see pages 240-41 in Cairo). Change the smoothing method to `method = "lm"` (`lm` here stands for "linear model")

```{r health-wealth-lm}

```

What's different?

Plot health vs. wealth *with* a logged x-axis *and* with a loess smooth.

```{r health-wealth-log-loess}

```

Plot health vs. wealth *with* a logged x-axis *and* a linear smooth (`lm`). 

```{r health-wealth-log-lm}

```


## Fancy stuff

Here's a fancy, production-quality version of the health-wealth plot. Explain what each of these layers are doing:

- `ggplot(gapminder_1997, aes(...))`: 
- `geom_point()`: 
- `guides()`: 
- `labs()`: 
- `scale_x_continuous()`: 
- `coord_trans()`: 
- `theme_light()`: 
- `theme()`: 

```{r health-wealth-fancy}
ggplot(gapminder_1997, aes(x = gdpPercap, y = lifeExp, 
                           size = pop, color = continent)) +
  geom_point() + 
  guides(size = FALSE, color = guide_legend(title = NULL)) +
  labs(x = "GDP per capita", y = "Life expectancy") +
  scale_x_continuous(labels = dollar) +
  coord_trans(x = "log10") + 
  theme_light() + 
  theme(legend.position = "bottom",
        panel.grid.minor.x = element_blank())
```


## Multiple years

Look at the relationship between health and wealth in 1992, 1997, 2002, and 2007 all in one plot. To do this, you can't use the `gapminder_1997` data frame anymore, since that's just 1997—you'll need to create a new data frame. 

Color each point by continent and resize each point by population. Place each of the four years in a separate facet (hint: look at the documentation for `facet_wrap()`). (You get some code to help start you off):

```{r health-wealth-post-1992}
gapminder_after_1992 <- gapminder %>%
  filter(year >= 1992)

ggplot(gapminder_after_1992, aes(x = gdpPercap, y = lifeExp)) +
  geom_point() + 
  coord_trans(x = "log10")
```

Create a similar plot to show the relationship between health and wealth in 1952, 1957, 1962, and 1967. Again, you won't be able to use either the `gapminder_1997` or the `gapminder_after_1992` data frames—you'll have to create a new data frame. And I won't give you code for that.

```{r health-wealth-early-cold-war}

```
