Before we Start

Overview

Questions

• “Why are we even visualizing?”
• “What are the metadata of this dataset?”

Objectives

• “Get to know the importance of visualisations”
• “Get to know the data we are going to work with”

Why even visualise data?

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Data can be complex. Data can be confusing. And a good visualisation of data can reduce some of that complexity and confusion.

A good visualisation can reveal patterns in our data.

A really good visualisation can even provide insight that is difficult, or impossible to find without.

A good example is this map, where the English physician John Snow plotted the deaths from Cholera in Soho, London from 19th august to 30th September 1854.

The concentration of deaths indicated that the source of the disease was a common water pump. Removing the handle from the pump brought an end to the outbreak.

We are probably not going to discover patterns of equal importance in this course.

The dataset we are working with

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We are going to study a dataset containing information on prices and other attributes of 53940 diamonds. The dataset is included in the ggplot2 package, that we installed as part of tidyverse

R

library(tidyverse)
head(diamonds)

OUTPUT

# A tibble: 6 × 10
  carat cut       color clarity depth table price     x     y     z
  <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31
4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75
6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48

There are 10 variables in the dataset:

Variable	What is it?
carat	Weight of the diamond in carat (0.200 gram)
cut	Quality of the cut of the diamond (Fair, Good, Very Good, Premium, Ideal)
color	Colour of the diamond from D (best), to J (worst)
clarity	How clear is the diamond. I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best)
depth	Total depth percentage = z / mean(x, y)
table	Width of the top of the diamond relative to its widest point
price	Price in US dollars
x	Length in mm
y	Width in mm
z	depth in mm

Slightly more detailed information can be found in the help for the dataset:

R

?diamonds

What are we not going to spend time on?

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There are often several considerations to take into account when we plot.

Two of those, are not covered here:

• Is the plot suitable for the data we are working with?
• Is the plot looking cool and impressive?

We are not making art. And if a specific type of plot is useful, we do not care if it is actually suitable for the diamond data we are working with.

Key Points

• “This is not an introduction to R”
• “Visualisation is a useful way of representing data”
• “We are going to study diamonds!”