Introd u ction IN TR OD U C TION TO DATA VISU AL IZATION W ITH G G - - PowerPoint PPT Presentation

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Introd u ction IN TR OD U C TION TO DATA VISU AL IZATION W ITH G G - - PowerPoint PPT Presentation

Nov 10, 2022 152 likes •580 views

Introd u ction IN TR OD U C TION TO DATA VISU AL IZATION W ITH G G P L OT 2 Rick Sca v e a Fo u nder , Sca v e a Academ y Yo u r instr u ctor - Rick Sca v etta - e - mail : o ce @ sca v e a . academ y - T w i er : @ Rick _ Sca

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Introduction

IN TR OD U C TION TO DATA VISU AL IZATION W ITH G G P L OT2

Rick Scavea

Founder, Scavea Academy

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Your instructor - Rick Scavetta

  • e-mail: oce@scavea.academy
  • Twier: @Rick_Scavea
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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Data visualization & data science

A core skill in Data Science.

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Exploratory versus explanatory

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

MASS::mammals

MASS::mammals body brain Arctic fox 3.385 44.50 Owl monkey 0.480 15.50 Mountain beaver 1.350 8.10 Cow 465.000 423.00 Grey wolf 36.330 119.50 Goat 27.660 115.00 Roe deer 14.830 98.20 ... Pig 192.000 180.00 Echidna 3.000 25.00 Brazilian tapir 160.000 169.00 Tenrec 0.900 2.60 Phalanger 1.620 11.40 Tree shrew 0.104 2.50 Red fox 4.235 50.40

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

A scatter plot

ggplot(mammals, aes(x = body, y = brain)) geom_point()

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Explore with a linear model

ggplot(mammals, aes(x = body, y = brain)) geom_point(alpha = 0.6) + stat_smooth( method = "lm", color = "red", se = FALSE )

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Explore: fine-tuning

ggplot(mammals, aes(x = body, y = brain)) geom_point(alpha = 0.6) + coord_fixed() + scale_x_log10() + scale_y_log10() + stat_smooth( method = "lm", color = "#C42126", se = FALSE, size = 1 )

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Publication-ready plot

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Anscombe's plots

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Anscombe's plots

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Anscombe's plots

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Anscombe's plots

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Anscombe's plots

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Anscombe's plots

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Anscombe's plots

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Anscombe's plots

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INTRODUCTION TO DATA VISUALIZATION WITH GGPLOT2

Anscombe's plots

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Let's practice!

IN TR OD U C TION TO DATA VISU AL IZATION W ITH G G P L OT2

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The grammar of graphics

IN TR OD U C TION TO DATA VISU AL IZATION W ITH G G P L OT2

Rick Scavea

Founder, Scavea Academy

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The quick brown fox jumps over the lazy dog

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The quick brown fox jumps over the lazy dog

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Grammar of graphics

Ploing framework Leland Wilkinson, Grammar of Graphics, 1999 2 principles Graphics = distinct layers of grammatical elements Meaningful plots through aesthetic mappings

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The three essential grammatical elements

Element Description Data The data-set being ploed. Aesthetics The scales onto which we map our data. Geometries The visual elements used for our data.

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Course 1: core competency

Element Description Data The data-set being ploed. Aesthetics The scales onto which we map our data. Geometries The visual elements used for our data. Themes All non-data ink.

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The seven grammatical elements

Element Description Data The data-set being ploed. Aesthetics The scales onto which we map our data. Geometries The visual elements used for our data. Themes All non-data ink. Statistics Representations of our data to aid understanding. Coordinates The space on which the data will be ploed. Facets Ploing small multiples.

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Jargon for each element

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Course 2: Tools for EDA

Remaining 3 layers Best practices for Data Viz

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Let's practice!

IN TR OD U C TION TO DATA VISU AL IZATION W ITH G G P L OT2

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ggplot2 layers

IN TR OD U C TION TO DATA VISU AL IZATION W ITH G G P L OT2

Rick Scavea

Founder, Scavea Academy

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ggplot2 package

The grammar of graphics implemented in R Two key concepts:

  • 1. Layer grammatical elements
  • 2. Aesthetic mappings
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Data

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Iris dataset

Fisher, R. A. (1936) The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179–

  • 188. Anderson, Edgar (1935). The irises of the Gaspe Peninsula, Bulletin of the American Iris Society, 59, 2–5.

1 2

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Iris dataset

iris Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2 setosa 3 4.7 3.2 1.3 0.2 setosa ... 50 5.0 3.3 1.4 0.2 setosa 51 7.0 3.2 4.7 1.4 versicolor 52 6.4 3.2 4.5 1.5 versicolor 53 6.9 3.1 4.9 1.5 versicolor ... 100 5.7 2.8 4.1 1.3 versicolor 101 6.3 3.3 6.0 2.5 virginica 102 5.8 2.7 5.1 1.9 virginica 103 7.1 3.0 5.9 2.1 virginica ... 150 5.9 3.0 5.1 1.8 virginica

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Aesthetics

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Iris aesthetics

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Geometries

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Iris geometries

g <- ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width) geom_jitter() g

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Themes

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Iris themes

g <- g + labs(x = "Sepal Length (cm)", y = "Sepal Width (cm)") theme_classic() g

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Let's practice!

IN TR OD U C TION TO DATA VISU AL IZATION W ITH G G P L OT2