1 Setting up Our plot

Data4All

Author

Ted Laderas, PhD

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 Effective Visual Communication

Focus on three techniques:

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

1.3 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

1.4 Dressing Up a Base Plot

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

R
Python

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:

R Code

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Run Code

library(ggplot2)

library(dplyr)

tv_shows <- read.csv("data/tv_shows.csv")

my_plot <-

ggplot(tv_shows) +

aes(x = seasonNumber, y= av_rating, group=title,

color = title) +

geom_line()

my_plot

_webr_editor_1 = Object {code: null, options: Object, indicator: Ke}

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():

Python Code

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Run Code

import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt

tv_shows = pd.read_csv("data/tv_shows.csv")

sns.set_theme()

my_plot = sns.lineplot(data=tv_shows,

x="seasonNumber",

y="av_rating",

hue="title")

plt.show()

_pyodide_editor_2 = Object {code: null, options: Object, indicator: Ke}

1.5 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.