ex2.3.Rmd

---
title: "Exercises for 2.3"
output: html_notebook
---

# Q1

How would you describe the relationship between *cty* and *hwy*?
Do you have any concerns about drawing conclusions from that plot?

```{r}
library(ggplot2)
ggplot(mpg, aes(cty, hwy)) + geom_point()
```

The relationship is strong positive correlation.

# Q2

What does `ggplot(mpg, aes(model, manufacturer)) + geom_point()` show?
Is it useful? How could you modify the data to make it more informative?

```{r}
ggplot(mpg, aes(model, manufacturer)) + geom_point()
```

There's no clear pattern in above plot.

# Q3

1. `ggplot(mpg, aes(cty, hwy)) + geom_point()`: scatter plot of *cty* vs *hwy*.

1. `ggplot(diamonds, aes(carat, price)) + geom_point()`: scatter plot of *carat* vs *price*,
   strong positive correlation.

1. `ggplot(economics, aes(date, unemploy)) + gemo_line()`: there's no relation between date and
   unemploy rate (but there's actually).

1. `ggplot(mpg, aes(cty)) + geom_histogram()`: group all models by its *cty* value.

```{r}
ggplot(mpg, aes(cty, hwy)) + geom_point()
ggplot(diamonds, aes(carat, price)) + geom_point()
ggplot(economics, aes(date, unemploy)) + geom_line()
ggplot(mpg, aes(cty)) + geom_histogram()
```