R for Data Science

Chapter 6

Introduction

Question

Visulizing Distrobutions

diamonds %>%
    ggplot(aes(x = cut)) +
  geom_bar()

diamonds %>%
    ggplot(mapping = aes(x = carat)) +
    geom_histogram(binwith = 0.5)

## Warning in geom_histogram(binwith = 0.5): Ignoring unknown parameters:
## `binwith`

## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.

diamonds %>%
    filter(carat < 3) %>%

    ggplot(aes(x = carat)) +
    geom_histogram(binwith = 0.5)

## Warning in geom_histogram(binwith = 0.5): Ignoring unknown parameters:
## `binwith`

## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.

diamonds %>%
    ggplot(aes(x = carat, color = cut)) +
    geom_freqpoly()

## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.

Typical Values

diamonds %>%
  # Filter out diamonds > 3 carat
  filter(carat < 3) %>%
  
  # Plot histogram
  ggplot(aes(x = carat)) +
  geom_histogram(binwidth = 0.01)

faithful %>%
    ggplot(aes(eruptions)) +
    geom_histogram(binwith = 0.25)

## Warning in geom_histogram(binwith = 0.25): Ignoring unknown parameters:
## `binwith`

## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.

Unusual Values

diamonds %>%
    ggplot(aes(y)) +
    geom_histogram() 

## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.

diamonds %>%
    ggplot(aes(y)) +
    geom_histogram() +
    coord_cartesian(ylim = c(0, 50))

## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.

Missing Values

diamonds %>%
  # Replace unusual y values (<3 or >20) with NA
  mutate(y = ifelse(y < 3 | y > 20, NA, y)) %>%
  
  # Plot x vs y
  ggplot(aes(x = x, y = y)) +
    geom_point()

## Warning: Removed 9 rows containing missing values or values outside the scale range
## (`geom_point()`).

Covariation

A categorical and continuous variable

diamonds %>%
    ggplot(aes(x = cut, y = price)) +
    geom_boxplot()

Two categorical variables

diamonds %>%
    count(color, cut) %>%
    ggplot(aes(x = color, y = cut, fill = n)) +
    geom_tile()

Two continoues variables

library(hexbin)
diamonds %>%
    ggplot(aes(x = carat, y = price)) + 
    geom_hex()

diamonds %>%
    filter(carat < 3) %>%
    ggplot(aes(x = carat, y = price)) +
    geom_boxplot(aes(group = cut_width(carat, 0.1)))

Patterns and models

library(tidyverse)
library(modelr)

mod <- lm(log(price) ~ log(carat), data = diamonds)

diamonds4 <- diamonds %>%
  modelr::add_residuals(mod) %>%
  mutate(resid = exp(resid))

diamonds4 %>%
  ggplot(aes(carat, resid)) +
  geom_point()

diamonds4 %>%
    ggplot(aes(cut, resid)) +
    geom_boxplot()