Agenda

• build histogram
• specify bins
• modify
  • color
  • fill
  • alpha
  • bin width
  • line type
  • line size
• map aesthetics to variables

Intro

A histogram is a plot that can be used to examine the shape and spread of continuous data. It looks very similar to a bar graph and can be used to detect outliers and skewness in data. The histogram graphically shows the following:

• center (location) of the data
• spread (dispersion) of the data
• skewness
• outliers
• presence of multiple modes

Histograms

To construct a histogram

• the data is split into intervals called bins
• the intervals may or may not be equal sized
• for each bin, the number of data points that fall into it are counted (frequency)
• the Y axis of the histogram represents the frequency and
• the X axis represents the variable

Libraries

library(ggplot2)
library(dplyr)
library(tidyr)

Data

ecom <- readr::read_csv('https://raw.githubusercontent.com/rsquaredacademy/datasets/master/web.csv')

## # A tibble: 1,000 x 11
##       id referrer device bouncers n_visit n_pages duration        country
##    <int>    <chr>  <chr>    <chr>   <int>   <dbl>    <dbl>          <chr>
##  1     1   google laptop     true      10       1      693 Czech Republic
##  2     2    yahoo tablet     true       9       1      459          Yemen
##  3     3   direct laptop     true       0       1      996         Brazil
##  4     4     bing tablet    false       3      18      468          China
##  5     5    yahoo mobile     true       9       1      955         Poland
##  6     6    yahoo laptop    false       5       5      135   South Africa
##  7     7    yahoo mobile     true      10       1       75     Bangladesh
##  8     8   direct mobile     true      10       1      908      Indonesia
##  9     9     bing mobile    false       3      19      209    Netherlands
## 10    10   google mobile     true       6       1      208 Czech Republic
## # ... with 990 more rows, and 3 more variables: purchase <chr>,
## #   order_items <dbl>, order_value <dbl>

Data Dictionary

• id: row id
• referrer: referrer website/search engine
• os: operating system
• browser: browser
• device: device used to visit the website
• n_pages: number of pages visited
• duration: time spent on the website (in seconds)
• repeat: frequency of visits
• country: country of origin
• purchase: whether visitor purchased
• order_value: order value of visitor (in dollars)

Histogram

ggplot(ecom) +
  geom_histogram(aes(n_visit))

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

Specify Bins

ggplot(ecom) +
  geom_histogram(aes(n_visit), bins = 7)

Fill

ggplot(ecom) +
  geom_histogram(aes(n_visit), bins = 7, fill = 'blue')

Alpha

ggplot(ecom) +
  geom_histogram(aes(n_visit), bins = 7, fill = 'blue', alpha = 0.3)

Color

ggplot(ecom) +
  geom_histogram(aes(n_visit), bins = 7, fill = 'white', color = 'blue')

Bins, Color & Fill

ggplot(ecom) +
  geom_histogram(aes(n_visit), bins = 7, fill = 'blue', color = 'white')

Bin Width

ggplot(ecom) +
  geom_histogram(aes(n_visit), binwidth = 2, fill = 'blue', color = 'black')

Line Type

ggplot(ecom) +
  geom_histogram(aes(n_visit), bins = 5, fill = 'white', 
    color = 'blue', linetype = 3)

Line Size

ggplot(ecom) +
  geom_histogram(aes(n_visit), bins = 5, fill = 'white', 
    color = 'blue', size = 1.25)

Map Fill to Variable

ggplot(ecom) +
  geom_histogram(aes(n_visit, fill = device), bins = 7)