exploratory data analysis with r
TRANSCRIPT
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The ability to take data—to be able to understand it, toprocess it, to extract value from it, to visualize it, tocommunicate it—that’s going to be a hugely importantskill in the next decades, … because now we really dohave essentially free and ubiquitous data. So thecomplimentary scarce factor is the ability to understandthat data and extract value from it.
Hal Varian, Google’s Chief EconomistThe McKinsey Quarterly, Jan 2009
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Job Postings for Data Scientists
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Source: Dice Salary Survey 2017
Top-paying Tech SkillsSkill 2016 Change Skill 2016 Change
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Source: O’Reilly 2015 Data Science Salary Survey
Data Science Tools
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Overview
Introduction to R
Working with Data
Descriptive Statistics
Data Visualization
Beyond R and EDA
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Introduction to R
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What is R?
Open source
Language and environment
Numerical and graphical analysis
Cross platform
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What is R?
Active development
Large user community
Modular and extensible
9000+ extensions
and best of all…
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FREE
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FREE
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Source: http://redmonk.com/sogrady/2016/07/20/language-rankings-6-16/
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Code Demo
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Working with Data
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Working with Data
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Working with Data
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Working with Data
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Working with Data
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Working with Data
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Working with Data
Data munging
Data wrangling
Data cleaning
Data cleansing
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Loading Data in R
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Loading Data in R
CSV
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Loading Data in R
CSV XML
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Loading Data in R
CSV XML
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Loading Data in R
CSV XML
∑
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Cleaning Data
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Cleaning Data
Reshape data
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Cleaning Data
Reshape data
Rename columns
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Cleaning Data
Reshape data
Rename columns
Convert data types
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Cleaning Data
Reshape data
Rename columns
Convert data types
Ensure proper encoding
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Cleaning Data
Reshape data
Rename columns
Convert data types
Ensure proper encoding
Ensure internal consistency
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Cleaning Data
Reshape data
Rename columns
Convert data types
Ensure proper encoding
Ensure internal consistency
Handle errors and outliers
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Cleaning Data
Reshape data
Rename columns
Convert data types
Ensure proper encoding
Ensure internal consistency
Handle errors and outliers
Handle missing values
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Transforming Data
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Transforming Data
Select columns
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Transforming Data
Select columns
Select rows
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Transforming Data
Select columns
Select rows
Group rows
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Transforming Data
Select columns
Select rows
Group rows
Order rows
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Transforming Data
Select columns
Select rows
Group rows
Order rows
Merging data sets
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Exporting Data
File-based data
Web-based data
Databases
Statistical data
CSV XML
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Advice for Working with Data
Often difficult
Time consuming
TIP: Record all steps
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Movies
Title Year RatingRuntime(minutes) Genre
Critic Score
Box Office
The Whole Nine Yards 2000 R 98 Comedy 45% $57.3M
Cirque du Soleil 2000 G 39 Family 45% $13.4M
Gladiator 2000 R 155 Action 76% $187.3M
Dinosaur 2000 PG 82 Family 65% $135.6M
Big Momma's House 2000 PG-13 99 Comedy 30% $0.5M
Open Movies Database
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1. Column with wrong name
2. Rows with missing values
3. Runtime column has units
4. Revenue in multiple scales
5. Wrong file format
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Code Demo
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Descriptive Statistics
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Descriptive Statistics
Describe data
Provides a summary
aka: Summary statistics
Movie Runtime
Statistic Value (minutes)
Minimum 38
1st Quartile 93
Median 101
Mean 104
3rd Quartile 113
Maximum 219
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Statistical Terms
ID Date Customer Product Quantity
1 2015-08-27 John Pizza 2
2 2015-08-27 John Soda 2
3 2015-08-27 Jill Salad 1
4 2015-08-27 Jill Milk 1
5 2015-08-28 Miko Pizza 3
6 2015-08-28 Miko Soda 2
7 2015-08-28 Sam Pizza 1
8 2015-08-28 Sam Milk 1
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Statistical Terms
Observations
ID Date Customer Product Quantity
1 2015-08-27 John Pizza 2
2 2015-08-27 John Soda 2
3 2015-08-27 Jill Salad 1
4 2015-08-27 Jill Milk 1
5 2015-08-28 Miko Pizza 3
6 2015-08-28 Miko Soda 2
7 2015-08-28 Sam Pizza 1
8 2015-08-28 Sam Milk 1
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Statistical Terms
Observations
Variables
ID Date Customer Product Quantity
1 2015-08-27 John Pizza 2
2 2015-08-27 John Soda 2
3 2015-08-27 Jill Salad 1
4 2015-08-27 Jill Milk 1
5 2015-08-28 Miko Pizza 3
6 2015-08-28 Miko Soda 2
7 2015-08-28 Sam Pizza 1
8 2015-08-28 Sam Milk 1
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Statistical Terms
Observations
Variables
Categorical variables
ID Date Customer Product Quantity
1 2015-08-27 John Pizza 2
2 2015-08-27 John Soda 2
3 2015-08-27 Jill Salad 1
4 2015-08-27 Jill Milk 1
5 2015-08-28 Miko Pizza 3
6 2015-08-28 Miko Soda 2
7 2015-08-28 Sam Pizza 1
8 2015-08-28 Sam Milk 1
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Statistical Terms
Observations
Variables
Categorical variables
Numeric variables
ID Date Customer Product Quantity
1 2015-08-27 John Pizza 2
2 2015-08-27 John Soda 2
3 2015-08-27 Jill Salad 1
4 2015-08-27 Jill Milk 1
5 2015-08-28 Miko Pizza 3
6 2015-08-28 Miko Soda 2
7 2015-08-28 Sam Pizza 1
8 2015-08-28 Sam Milk 1
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Types of Analysis
Type of Variable(s)
Nu
mb
er
of
Var
iab
les
One Categorical
Variable
One NumericVariable
TwoCategoricalVariables
TwoNumericVariables
Categorical & Numeric
Variable
ManyVariables
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Analyzing One Categorical Variable
Type of Variable(s)
Nu
mb
er
of
Var
iab
les
One Categorical
Variable
One NumericVariable
TwoCategoricalVariables
TwoNumericVariables
Categorical & Numeric
Variable
ManyVariables
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Analyzing One Categorical Variable
Frequency
Movies by Genre
Genre Frequency Percentage
Action 612 9%
Adventure 496 7%
Animation 168 2%
Comedy 1281 18%
Drama 1570 22%
Horror 269 4%
… … …
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Analyzing One Categorical Variable
Frequency
Proportion
Movies by Genre
Genre Frequency Percentage
Action 612 9%
Adventure 496 7%
Animation 168 2%
Comedy 1281 18%
Drama 1570 22%
Horror 269 4%
… … …
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Analyzing One Numeric Variable
Type of Variable(s)
Nu
mb
er
of
Var
iab
les
One Categorical
Variable
One NumericVariable
TwoCategoricalVariables
TwoNumericVariables
Categorical & Numeric
Variable
ManyVariables
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Analyzing One Numeric Variable
Central tendency
Dispersion
Shape
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Analyzing Two Categorical Variables
Type of Variable(s)
Nu
mb
er
of
Var
iab
les
One Categorical
Variable
One NumericVariable
TwoCategoricalVariables
TwoNumericVariables
Categorical & Numeric
Variable
ManyVariables
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Analyzing Two Categorical Variables
Joint frequency
Movies by Genre and Rating
Genre G PG PG-13 R Total
Action 2 70 311 229 612
Adventure 44 179 209 64 496
Animation 43 111 8 6 168
Comedy 45 258 472 506 1218
Drama 12 136 586 836 1570
Family 38 181 10 1 230
… … … … … …
Total 230 1207 2686 3058 7181
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Analyzing Two Categorical Variables
Joint frequency
Contingency table
Movies by Genre and Rating
Genre G PG PG-13 R Total
Action 2 70 311 229 612
Adventure 44 179 209 64 496
Animation 43 111 8 6 168
Comedy 45 258 472 506 1218
Drama 12 136 586 836 1570
Family 38 181 10 1 230
… … … … … …
Total 230 1207 2686 3058 7181
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Analyzing Two Categorical Variables
Joint frequency
Contingency table
Marginal frequency
Movies by Genre and Rating
Genre G PG PG-13 R Total
Action 2 70 311 229 612
Adventure 44 179 209 64 496
Animation 43 111 8 6 168
Comedy 45 258 472 506 1218
Drama 12 136 586 836 1570
Family 38 181 10 1 230
… … … … … …
Total 230 1207 2686 3058 7181
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Analyzing Two Categorical Variables
Joint frequency
Contingency table
Marginal frequency
Relative frequency
Movies by Genre and Rating
Genre G PG PG-13 R Total
Action 0.001 0.010 0.043 0.032 0.086
Adventure 0.006 0.025 0.029 0.009 0.069
Animation 0.006 0.015 0.001 0.001 0.023
Comedy 0.006 0.036 0.066 0.070 0.170
Drama 0.002 0.019 0.082 0.116 0.219
Family 0.005 0.025 0.001 0.001 0.033
… … … … … …
Total 0.032 0.168 0.374 0.426 1.000
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Analyzing Two Numeric Variables
Type of Variable(s)
Nu
mb
er
of
Var
iab
les
One Categorical
Variable
One NumericVariable
TwoCategoricalVariables
TwoNumericVariables
Categorical & Numeric
Variable
ManyVariables
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Analyzing Two Numeric Variables
Explanatory vs. outcome
Covariance
Correlation
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Analyzing a Numeric Variable Grouped by a Categorical Variable
Type of Variable(s)
Nu
mb
er
of
Var
iab
les
One Categorical
Variable
One NumericVariable
TwoCategoricalVariables
TwoNumericVariables
Categorical & Numeric
Variable
ManyVariables
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One categorical variable
One numeric variable
Aggregate measures
Analyzing a Numeric Variable Grouped by a Categorical Variable
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Analyzing Many Variables
Type of Variable(s)
Nu
mb
er
of
Var
iab
les
One Categorical
Variable
One NumericVariable
TwoCategoricalVariables
TwoNumericVariables
Categorical & Numeric
Variable
ManyVariables
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Cowboys &
The Musical
Space Invaders:
Extended Edition
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Code Demo
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Cowboys &
The Musical
Space Invaders:
Extended Edition
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Data Visualization
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Data Visualization
Visual data representation
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Data Visualization
Visual data representation
Human pattern recognition
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Data Visualization
Visual data representation
Human pattern recognition
Map dimensions to visual
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Data Visualization
ID Date Customer Product Quantity
1 2015-08-27 John Pizza 2
2 2015-08-27 John Soda 2
3 2015-08-27 Jill Salad 1
4 2015-08-27 Jill Milk 1
5 2015-08-28 Miko Pizza 3
6 2015-08-28 Miko Soda 2
7 2015-08-28 Sam Pizza 1
8 2015-08-28 Sam Milk 1
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Data Visualization
ID Date Customer Product Quantity
1 2015-08-27 John Pizza 2
2 2015-08-27 John Soda 2
3 2015-08-27 Jill Salad 1
4 2015-08-27 Jill Milk 1
5 2015-08-28 Miko Pizza 3
6 2015-08-28 Miko Soda 2
7 2015-08-28 Sam Pizza 1
8 2015-08-28 Sam Milk 1
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Types of Analysis
Type of Variable(s)
Nu
mb
er
of
Var
iab
les
One Categorical
Variable
One NumericVariable
TwoCategoricalVariables
TwoNumericVariables
Categorical & Numeric
Variable
ManyVariables
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Cowboys &
The Musical
Space Invaders:
Extended Edition
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Code Demo
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Feature Length PG
Warlordof the
Rings :
The
An Unexpected Adventure
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Beyond R and EDA
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This is just the tip of the iceberg!This is just the tip of the iceberg!
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Advanced Data Analysis with R
Cluster Analysis
Statistical Modeling
Dimensionality Reduction
Analysis of Variance (ANOVA)
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Source: Nathan Yau (www.flowingdata.com)
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Machine Learning with R
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Photos by Radomił Binek,
Danielle Langlois, and Frank Mayfield
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Code Demo
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Where to Go Next…
R website: http://www.cran.r-project.org
RStudio: https://www.rstudio.com
Revolutions: http://blog.revolutionanalytics.com
Flowing Data: http://flowingdata.com
R-Blogger: http://www.r-bloggers.com
R-Seek: http://rseek.org
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www.pluralsight.com/authors/matthew-renze
Data Science with R
Exploratory Data Analysis with R
Data Visualization with R (3-part)
Data Science: The Big Picture
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www.matthewrenze.com
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Feedback
Very important to me!
One thing you liked?
One thing I could improve?
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Conclusion
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Conclusion
Introduction to R
Working with Data
Descriptive statistics
Data visualization
Beyond R & EDA
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Thank You!
Matthew Renze
Data Science Consultant
Renze Consulting
Twitter: @matthewrenze
Email: [email protected]
Website: www.matthewrenze.com