Week 7: Apply it to your data 6

Introduction

Questions

Variation

ggplot(data = mydata) +
  geom_bar(mapping = aes(x = grade))

Visualizing distributions

ggplot(data = mydata) +
  geom_histogram(mapping = aes(x = date))

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

ggplot(data = mydata, mapping = aes(x = date, colour = grade)) +
  geom_freqpoly()

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

Typical values

ggplot(data = mydata, mapping = aes(x = date)) +
  geom_histogram(binwidth = 0.5)

Unusual values

mydata %>%
    ggplot(aes(date)) +
    geom_histogram(binwidth = 1) +
    coord_cartesian(ylim = c(00, 200)) 

Missing Values

mydata %>%
   
    # filter(y < 3 | y > 20) %>%
    mutate(y = ifelse(date < 1994 | date > 2005, NA, date)) %>%
    
    #Plot
    ggplot(aes(x = date, y = price)) +
    geom_point()

Covariation

A categorical and continuous variable

mydata %>%
    
    ggplot(aes(x = grade, y = price)) +
    geom_boxplot()

Two categorical variables

mydata %>%
    
    count(price, grade) %>%
    ggplot(mapping= aes(x = price, y = grade, fill = n)) + 
    geom_tile()

Two continous variables

ggplot(data = mydata) +
  geom_point(mapping = aes(x = grade, y = price), alpha = 2 / 100)

ggplot(data = mydata) +
  geom_hex(mapping = aes(x = grade, y = price))

Patterns and models

ggplot(data = mydata) + 
  geom_point(mapping = aes(x = grade, y = price))