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ch-17.Rmd
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ch-17.Rmd
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---
title: "Solutions to Chapter 17 Exercises"
author: "Howard Baek"
date: "Last compiled on `r format(Sys.time(), '%B %d, %Y')`"
output: html_document
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r, include=FALSE}
library(tidyverse)
```
### 17.7 Exercises
1.
```{r}
# faceting by cut and grouping by carat.
diamonds %>%
ggplot(aes(price)) +
geom_histogram(aes(color = carat)) +
facet_wrap(~cut, scales = "free_y")
# faceting by carat and grouping by cut.
diamonds %>%
ggplot(aes(price)) +
geom_histogram(aes(color = cut)) +
facet_wrap(~carat, scales = "free_y")
```
- It makes more sense to facet by cut because its a discrete variable. Faceting by carat, a continuous variable, makes too many facets and renders the plot unreadable!
2.
```{r}
diamonds %>%
ggplot(aes(carat, price)) +
geom_point(aes(color = color))
```
```{r}
diamonds %>%
ggplot(aes(carat, price)) +
geom_point(aes(color = color)) +
facet_wrap(~color)
```
- I think its better to use grouping to compare the different colors. The panels all have the same shape, so it's hard to compare the groups across facets. If I use faceting, I'd add that the plot is facetted by diamond colour, from D (best) to J (worst).
3. I think `facet_wrap()` is more useful than `facet_grid()` because the former function is useful if you have a single variable with many levels and want to arrange the plots in a more space efficient manner. In data analysis, its extremely common to have a single variable with many levels that the analyst wants to arrange the for easy comparison. Although `facet_grid()` works on single variables, `facet_wrap()` involves less typing when you have a single variable.
4.
```{r}
mpg2 <- subset(mpg, cyl != 5 & drv %in% c("4", "f") & class != "2seater")
mpg2 %>%
ggplot(aes(displ, hwy)) +
geom_point() +
geom_smooth(data = mpg2 %>% select(-class),
se = FALSE,
method = "loess") +
facet_wrap(~class)
```