1  Setting up Our plot

Data4All

1.1 Motivation: Exploratory versus Explanatory

Exploratory analysis:

• exploring and understanding the data, conducting the analysis

Explanatory analysis:

• explaining your findings from your analysis in a coherent narrative that leads to a call to action

1.2 Discuss

• What is the message of this visualization?
• What could be applied to your own plots?
• What doesn’t work for you?

Yanika Borg

1.3 Effective Visual Communication

Focus on three techniques:

• Decluttering your plot
• Annotating your graph and data
• Highlight data using Preattentive Attributes

1.4 Paper Doll Approach

• We’re going to take a basic plot and dress it up
• Modify its appearance to make our point more understandable and immediate

1.5 Dressing Up a Base Plot

We’ll start with a base plot that we’ll dress up. Here’s what that looks like.

R

Let’s save our plot into an R object called a ggplot. We’ll use the <- (left arrow) to assign it to the variable called my_plot:

Python

We need a few packages for Python. We need the pandas, seaborn, and matplotlib packages.

We will start by making a plot object with sns.lineplot():

1.6 Dressing up my_plot

Now, when we want to modify our plot, we can use my_plot. More on this in the next notebook. We’re basically going to add commands to modify our plot. I like to think of it as a paper doll approach: we are dressing our plot in different clothes.

R

Python