Stat 450: Homework 5

Do 7.3.4 # 1-4 all

Do 7.4.1 # 1, 2

Do 7.5.1 # 2-6 all

Excercise 7.3.4 # 1-4

1. They are all right skewed with mostly small diamonds and very few large. All three distributions also have bimodality. According to the documentation x is length, y is width, and z is depth.

2.

require("tidyverse")

## Loading required package: tidyverse

## ── Attaching packages ─────────────────────────────────────────────────── tidyverse 1.2.1 ──

## ✔ ggplot2 3.0.0     ✔ purrr   0.2.5
## ✔ tibble  1.4.2     ✔ dplyr   0.7.6
## ✔ tidyr   0.8.1     ✔ stringr 1.3.1
## ✔ readr   1.1.1     ✔ forcats 0.3.0

## ── Conflicts ────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

ggplot(data=diamonds, mapping=aes(x=price)) +
  geom_freqpoly(binwidth=0.1)

ggplot(filter(diamonds), aes(x=price)) +
  geom_histogram(binwidth = 100, center=0)

ggplot(filter(diamonds, price < 2500), aes(x=price)) +
  geom_histogram(binwidth = 20, center= 0)

ggplot(filter(diamonds), aes(x=price)) +
  geom_histogram(binwidth = 100, center= 0)

3.

diamonds %>%
  filter(carat >=0.99, carat<= 1) %>% count(carat)

4. The coord_cartesian() function zooms in on the area specified by the limits, after having calculated and drawn the geoms. Since the histogram bins have already been calculated, it is unaffected. However, the xlim() and ylim() functions influence actions before the calculation of the stats related to the histogram.

7.4.1 # 1, 2

1. When you remove observations from geom_histogram() it gives you a warning that certain rows were taken out. With geom_bar() NA observations are not numerical and immediately are dropped because they have no bin to be placed in.

2. NA removes values from the vector before calculating them.

mean(c(0,1,2,NA), na.rm=TRUE)

## [1] 1

sum(c(0,1,2,NA), na.rm =TRUE)

## [1] 3

7.5.1 # 2-6 all

2. I would say carat is the best predictor of price. According to the boxplot, I would say that there is a negative relationship between cut and carat. This leads me to believe that cheaper diamonds have an overall better cut.

ggplot(diamonds, aes(x = cut, y = carat)) +
  geom_boxplot()

3. The aesthetics are flipped, but the output is exactly the same.

4.

library("lvplot")

ggplot(diamonds, aes(x=cut, y=price)) +
  geom_lv()

5.

ggplot(data = diamonds, mapping = aes(x = price, y = ..density..)) +
  geom_freqpoly(mapping = aes(color = cut), binwidth = 500)

ggplot(data = diamonds, mapping = aes(x = cut, y = price)) +
  geom_violin() +
  coord_flip()

6.

require("ggbeeswarm")

## Loading required package: ggbeeswarm

ggplot(data = mpg) +
  geom_quasirandom(mapping = aes(x = reorder(class, hwy, FUN = median),
                                 y = hwy),
                   method = "tukeyDense")