Dataset terminology

• Each row is an observation
• Each column is a variable

starwars

## # A tibble: 87 x 14
##   name  height  mass hair_~1 skin_~2 eye_c~3 birth~4 sex   gender
##   <chr>  <int> <dbl> <chr>   <chr>   <chr>     <dbl> <chr> <chr> 
## 1 Luke~    172    77 blond   fair    blue       19   male  mascu~
## 2 C-3PO    167    75 <NA>    gold    yellow    112   none  mascu~
## 3 R2-D2     96    32 <NA>    white,~ red        33   none  mascu~
## 4 Dart~    202   136 none    white   yellow     41.9 male  mascu~
## 5 Leia~    150    49 brown   light   brown      19   fema~ femin~
## 6 Owen~    178   120 brown,~ light   blue       52   male  mascu~
## # ... with 81 more rows, 5 more variables: homeworld <chr>,
## #   species <chr>, films <list>, vehicles <list>,
## #   starships <list>, and abbreviated variable names
## #   1: hair_color, 2: skin_color, 3: eye_color, 4: birth_year

Luke Skywalker

What's in the Star Wars data?

Take a glimpse at the data:

glimpse(starwars)

## Rows: 87
## Columns: 14
## $ name       <chr> "Luke Skywalker", "C-3PO", "R2-D2", "Darth V~
## $ height     <int> 172, 167, 96, 202, 150, 178, 165, 97, 183, 1~
## $ mass       <dbl> 77.0, 75.0, 32.0, 136.0, 49.0, 120.0, 75.0, ~
## $ hair_color <chr> "blond", NA, NA, "none", "brown", "brown, gr~
## $ skin_color <chr> "fair", "gold", "white, blue", "white", "lig~
## $ eye_color  <chr> "blue", "yellow", "red", "yellow", "brown", ~
## $ birth_year <dbl> 19.0, 112.0, 33.0, 41.9, 19.0, 52.0, 47.0, N~
## $ sex        <chr> "male", "none", "none", "male", "female", "m~
## $ gender     <chr> "masculine", "masculine", "masculine", "masc~
## $ homeworld  <chr> "Tatooine", "Tatooine", "Naboo", "Tatooine",~
## $ species    <chr> "Human", "Droid", "Droid", "Human", "Human",~
## $ films      <list> <"The Empire Strikes Back", "Revenge of the~
## $ vehicles   <list> <"Snowspeeder", "Imperial Speeder Bike">, <~
## $ starships  <list> <"X-wing", "Imperial shuttle">, <>, <>, "TI~

?starwars

What is EDA?

• Exploratory data analysis (EDA) is an approach to analysing data sets to summarize its main characteristics
• Often, this is visual — this is what we'll focus on first
• But we might also calculate summary statistics and perform data wrangling/manipulation/transformation at (or before) this stage of the analysis — this is what we'll focus on next

Mass vs. height

Jabba!

Data visualization

"The simple graph has brought more information to the data analyst's mind than any other device." — John Tukey

• Data visualization is the creation and study of the visual representation of data
• Many tools for visualizing data — R is one of them
• Many approaches/systems within R for making data visualizations — ggplot2 is one of them, and that's what we're going to use

ggplot2 $\in$ tidyverse

Grammar of Graphics

Source: BloggoType

Mass vs. height

ggplot(data = starwars, mapping = aes(x = height, y = mass)) +
  geom_point() +
  labs(title = "Mass vs. height of Starwars characters",
       x = "Height (cm)", y = "Weight (kg)")

ggplot(data = starwars, mapping = aes(x = height, y = mass)) +
  geom_point() +
  labs(title = "Mass vs. height of Starwars characters",
       x = "Height (cm)", y = "Weight (kg)")

## Warning: Removed 28 rows containing missing values
## (`geom_point()`).

Hello ggplot2!

ggplot(data = [dataset], 
       mapping = aes(x = [x-variable], y = [y-variable])) +
   geom_xxx() +
   other options

library(tidyverse)

Age at first kiss

Facebook visits