Agenda

Create some of the most routinely used plots to explore data using the geom_* functions:

• Scatter Plot
• Bar Plot
• Box Plot
• Histogram
• Line Chart
• Regression Line

Libraries

library(ggplot2)
library(readr)

Data

ecom <- 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)

Point

ggplot(mtcars, aes(x = disp, y = mpg)) + 
  geom_point()

Regression Line

• geom_abline()
• geom_smooth()

Regression Line

ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() + 
  geom_abline(intercept = 37.285, slope = -5.344)