Week 7: Code along 6

Introduction

Questions

Variation

Visualizing Distributions

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

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

diamonds %>%
    filter(carat < 3) %>% 
    ggplot(aes(x=carat)) +
    geom_histogram(binwidth = 0.5)

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
    ggplot(aes(x=carat))+
    geom_histogram(binwidth = 0.01)

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

Unusual values

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

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

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

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

Missing values

diamonds %>%
    
    #filter(y< 3 | y > 20) %>%
    
    mutate(y= ifelse(y< 3| y > 20, NA, y)) %>%
    
    # Plot
    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 continious 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 continous 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(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()