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Storytelling with Data Visualization

Autodesk - technical meeting

Anabelle Laurent

November 30, 2021

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Why is Data Visualization important? 📊

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Why is Data Visualization important? 📊

  • Universal way to communicate information

  • Provides clear and effective message

  • Find patterns, trends, spot extreme values

  • Make data memorable

  • Maintain the audience's interest

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What make a good visualization? 🤔

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What make a good visualization? 🤔

  • Reveals a trend or relationship between variables

  • Always have at minimum a caption, axis, scales and symbols

  • Distinct and legible symbols (i.e., use contrast)

  • Caption should convey as much information as possible

  • No noise: keep information at minimum

  • the correct graph type based on the kind of data to be presented

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Disclaimer

This workshop does not provide code but all the plots were made using R Studio (see last slides for more details)

Artwork by @allison_horst

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Visualizing distribution

Artwork by @allison_horst

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Visualizing distribution : histograms

For plotting the distribution of a single quantitative variable

Try different bin widths for best visual appearance.

  • Small bin width -> peaky and busy histogram

  • Large bin width -> features might disappear

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Visualizing distribution : density plot

Try different bandwidths for best visual appearance

  • Small bandwidth -> peaky and busy density

  • Large bandwidth -> smooth feature and might look like a gaussian

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Visualizing multiple distributions

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Visualizing multiple distributions

  • The peaks of the density plot are where there is the highest concentration of points

  • For several distributions, density plots work better than histograms.

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Visualizing multiple distributions

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Visualizing multiple distributions: ridgeline plot

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Visualizing multiple distributions: ridgeline plot

Ridgeline plot shows the distribution of a numeric value for several groups (at least 5-6 groups) or when they overlap each other.

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Visualizing distributions: boxplot

A boxplot can summarize the distribution of a numeric variable for several groups

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Visualizing distributions: boxplot

Boxplot does not tell about the number of observations.

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Visualizing distributions: boxplot with jitter

Boxplots with jitter tell about:

  • the distribution of the data

  • if the groups are balanced or unbalanced in terms of observations.

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Visualizing distributions: boxplot with jitter

No overlapping facilitates the visual appearence of the plot

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Visualizing distributions: violin plot

  • Violins are equivalent to density estimate

  • They are useful to represent bimodal data.

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Visualizing associations among quantitative variables

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Relationship between 2 numeric variables: scatterplot

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Relationship between 2 numeric variables: scatterplot + linear fit

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Relationship between 2 numeric variables: scatterplot + quadratic fit

⚠️ Linear fit is widely used but it is not always the best fit, try quadratic fit too.

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Relationship between 2 numeric variables: scatterplot

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Multi-panel plots

Split a single plot using one variable with many levels

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Multi-panel plots

Split a single plot using the combinations of two discrete variables.

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Multi-panel plots

⚠️ different scales can lead to misinterpretation

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Bubble plot

A bubble plot is a scatterplot with 3 numerical variables

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Tell a story with your data 📖

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Tell a story with your data

Before data visualization you must:

  • Know your audience

  • Know the level of data detail expected

  • Give enough context

  • Ask yourself: What do I want my audience know/remember with the data I am presenting?

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Tell a story with your data

Don't be repetitive but be consistent (theme, color scheme, font size etc.)

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Tell a story with your data

Guide your audience by point out specific values

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Tell a story with your data

Guide your audience by pointing out specific values

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Tell a story with your data

Customize your plot using highlighting

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Tell a story with your data

Customize your plot using highlighting + text

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Interactive graphics with ggplotly

010000200003000040000500004050607080
AfricaAmericasAsiaEuropeOceaniaLife expectancy vs GDP per capita in 2007 GDP per capita (US$) Life expectancy (years) Populationcontinent
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Data visulization using interactive web-app

One case-study ISOFAST web-app

Problem Statement:

  • Make most use of the cumulative experiment data collected since 2006
  • Need data-driven insights (overview at the network level and not only farm level)

Development of ISOFAST

  • Audience: farmers, local agronomists, researchers
  • Easy-to-navigate user interface
  • Effective data visualizations
  • Economic analysis for adaptive decision making
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R library used for this presentation

library(ggplot2)
library(dplyr)
library(tidyr)
library(gapminder)
library(gghighlight)
library(ggrepel)
library(dygraphs)
library(plotly)
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Resources to go deeper into Data Viz

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Accurate

Artwork by @allison_horst

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Thank you for your attention

✉️ my email: alaurent@iastate.edu

Slides created via the R package xaringan.

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Why is Data Visualization important? 📊

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