README.md

visualizing data

447 / 547 Visualizing Data. An introductory course by Richard Layton at Rose-Hulman Institute of Technology.

Introduction

• About the course
• Syllabus
• License

Frequent links

• Portfolio
• Portfolio data requirements
• Data and functions index
• Data sources
• Readings
• Index
• Help

calendar

Moodle video (accessible to Rose-Hulman students only)
Moodle reading (accessible to Rose-Hulman students only)
R for Data Science reading (Wickham and Grolemund, 2017)
reading hardcopy
blog reading

w	d	agenda & assignments	milestones	due
1	M	1.1 Getting started Introduction to visual rhetoric [slides] Syllabus highlights Syllabus About the course Doumont (2009) Designing the graph
	T	1.2 Assessing the structure of a data set Data structure / graph design [exercise] [hints] Structured data excerpts D1 data structure Install software	D1 data search sources	Doumont reading
	W lab	1.3 Software studio Introduction to means [slides] Software studio R basics	Software setup complete
	R	1.4 Graphical repertoire Introduction to repertoire [slides] Portfolio highlights Sign-out Tufte reprints Reading prompts 1
2	M	2.1 Data basics lesson (17 min) slides tutorial exercises	D1 data identified wk1 exercises complete
	T	2.2 Reading discussion D2 data structure Tufte (1997) Decision to launch Challenger	D2 data search sources	Reading prompts 1
	W lab	2.3 Data studio Data studio introduction (14 min) Data studio Data sources Managing files [slides] Interacting with R Return reprints 6.1 Running code 6.2 RStudio diagnostics
	R	2.4 Graph basics Graph basics introduction (11 min) Graph basics [exercises] 3.10 The layered grammar of graphics
3	M	R Markdown basics introduction (18 min) [slides] Commit/pull/push regularly R Markdown tutorial RStudio tips	D2 data identified wk2 exercises complete
	T	Design basics introduction (10 min) D3 data structure Design basics tutorial Robbins (2013a) General design principles	D3 data search sources
	W lab	Portfolio studio introduction (9 min) Portfolio studio tutorial Sample portfolio skeleton Document design Document requirements Data requirements Sample portfolio entries and critiques 27.2 R Markdown basics 27.3 Text formatting with Markdown 27.4 Code chunks
	R	D1 Distributions introduction (13 min) D1 Distributions [requirements] Strip plot tutorial [exercises] Box plot tutorial [exercises]
4	M	4.1 Reshaping data lesson: Virginia deaths (8 min) tutorial: Virginia deaths lesson: WHO tuberculosis cases (11 min) tutorial: WHO tuberculosis cases exercises 12.2 Tidy data 12.4 Separating and uniting 12.7 Non-tidy data	D3 data identified wk3 exercises complete
	T	4.2 Reading discussion Reading discussion introduction Wainer (2014) 15 displays about one thing	D4 data search sources	Reading prompts 2
	W lab	4.3 Presentations, practice, & portfolio studio 3P Studio agenda (2 min) Applying the discussion notes (6 min)		D1 graph & prose Presentation prompts
	R	4.4 D2 Multiway D2 Multiway introduction D2 Multiway [requirements] Multiway dot plot tutorial [exercises]
5	M	Introducing factors Working with factors [exercises] 15.2 Creating factors 15.4 Modifying factor order 15.5 Modifying factor levels	D4 data identified wk4 exercises complete
	T	D5 data structure Discovering stories [reading] [reflection]	D5 data search sources
	W lab	Presentations, practice, & portfolio studio		D2 graph & prose Presentation prompts Reflection on rhetoric
	R	D3 Exploring correlations [requirements] Scatterplot [exercises] 28.2 Label
6	M	Carpentry with joins [exercises] 13.4 Mutating joins	D5 data identified wk5 exercises complete
	T	D6 data structure D4 Graph injuries/fatalities ethically [requirements] Dot plot [exercises] Image magick Dragga and Voss (2001) Cruel pies	D6 data search sources	Reading prompts 3
	W lab	Presentations, practice, & portfolio studio		D3 graph & prose Presentation prompts
	R	Time and dates Time series data Line graph [exercises] 16.2 Creating date/times 16.3 Date-time components 16.4 Time spans
7	M	Graphical lies [reflection] Wainer (2000) How to display data badly	D6 data identified wk6 exercises complete
	T	D7 data structure D5 Redesign a graphical lie [requirements] Correcting graphical lies [slides]	D7 data search sources
	W lab	Presentations, practice, & portfolio studio		D4 graph & prose Presentation prompts Reflection on rhetoric
	R	Misc data carpentry [exercises]
8	M	D6 Multivariate data [requirements] Scatterplot matrix [exercises] Parallel coordinate [exercises] Conditioning plot [exercises]	D7 data identified wk7 exercises complete
	T	Kostelnick (2007) Conundrum of clarity		Reading prompts 4
	W lab	Presentations, practice, & portfolio studio PDF scraping example		D5 graph & prose Presentation prompts
	R	Color [slides] Friendly guide to color (Rost, 2018a) Choosing colors (Rost, 2018b) Scales [slides] Robbins (2013b) Scales 28.4 Scales
9	M	D7 Learn a display [requirements] Examples and resources Revising portfolio entries [slides]	wk8 exercises complete
	T	Graph editing: points Graph editing: lines [exercises]
	W lab	Presentations, practice, & portfolio studio		D6 graph & prose Presentation prompts
	R	Graph editing: smooth fit Graph editing: annotation
10	M	Graph design improvisation ggplot extensions Beware of poor design [slides]	wk9 exercises complete
	T	Spence (2006) Playfair & psychology of graphs		Reading prompts 5
	W lab	Portfolio final editing Presentations, practice, & portfolio studio		D7 graph & prose Presentation prompts
	R	Data tables Rendering multiple files Course evaluations		Portfolio, final push Friday, 5pm
11	M	Finals week, no class, no exam The portfolio after the term Updating the R habitat

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index

Index, free clip art from Clickartstockphotos

course management
R & RStudio
data
graphs
portfolio
visual rhetoric and graph design
project management
software
readings

course management

• Introductory slides
• About the course
• Syllabus
• Calendar
• Help links
• License

R & RStudio

• R basics
• Interacting with R
• RStudio tips
• Updating the R habitat
• Color names in R
• R functions in tutorials

data

Data sources
Data links

Basics

• Data basics slides
• Four basic data skills
• Data in base R and in R packages
• Reading raw data files
• Web download using import()
• Data directory write and read
• All R objects have types
• Some R objects have attributes
• All R objects have class
• Datasets in tutorials
• Time and dates
• Time series data
• PDF scraping example

Factors

• Factor type and attributes
• Factor definition
• Creating a factor variable
• Reorder factor levels manually
• Reorder factor levels by a date variable
• Reorder factor levels by a quantitative variable
• Reorder factor levels by frequency of levels
• Recode factor levels
• Remove unused levels
• Reverse factor level order

Data studio

• Classify your data structure
• Use Notepad for CSV files
• Workflow basics
• Data transformation filter(), arrange(), select(), mutate(), group_by(), and dplyr::summarize()
• Data import
• Data links

Time series

• Time series with separate year month day
• Time series with decimal dates
• Edit the date scale
• Facet by a date variable

Data carpentry

• Data in row names
• Keys and values in coordinatized data
• rowrecs_to_blocks()
• blocks_to_rowrecs()
• WHO case study in data reshaping
• Web download using import()
• select with matches()
• unpivot_to_blocks()
• drop_na()
• str_replace()
• separate()
• WHO group_by() and summarize()
• WHO graphs
• Carpentry with joins
• MIDFIELD data
• MIDFIELD joins
• MIDFIELD carpentry
• MIDFIELD design
• Swiss bank data

Data exercises

• Data basics exercises
• 4.4.1 workflow
• 4.4.2 workflow
• 4.4.3 workflow
• 5.2.4 filter()
• 5.3.1 arrange()
• 5.4.1 select()
• 5.5.2 mutate()
• 5.6.7 group_by()
• Data reshaping pivot_to_rowrecs(), unpivot_to_blocks(), rowrecs_to_blocks(), and blocks_to_rowrecs()
• Carpentry with joins

▲ index

graphs

Graph tutorials

• Graph basics
• Strip plot
• Box plot
• Multiway dot plot
• Scatterplot
• Dot plot
• Line graph
• Scatterplot matrix, ggscatmat(), ggpairs(), and non-ggplot packages
• Parallel coordinate
• Conditioning plot

Graph exercises

• Graph basics exercises
• Strip plot exercises
• Box plot exercises
• Multiway dot plot exercises
• Scatterplot exercises
• Dot plot exercises
• Line graph exercises
• Scatterplot matrix exercises
• Parallel coordinate exercises
• Conditioning plot exercises

Graph elements

• Line color by group
• Panels with free y-scales
• symbol color
• symbol size
• symbol shape
• text as symbols
• text as symbols legend
• line color
• line type
• line size
• reference lines vertical
• reference lines horizontal
• reference lines sloped
• linear fit
• loess fit
• identical fit in every panel
• highlight data symbols
• highlight data with labels
• text placed arbitrarily
• text placed arbitrarily in multiple panels

▲ index

portfolio

Portfolio
Document design
Data requirements
Sample README

• D1 distributions
• D2 multiway
• D3 correlations
• D4 injuries or fatalities
• D5 redesign a graphical lie
• D6 multivariate
• D7 self-taught

R Markdown basics

• Rmd basics
• create an Rmd script
• set the Rmd output format
• how to format text
• initialize a report
• initialize knitr
• introductory prose
• using code chunks
• source R scripts
• data table
• include graphics
• spell check

Resources

• Set up README
• Adding links to README
• Setup reading responses
• Document design
• Media
• Fonts
• Headings
• Text color
• Emphasis
• Hyphens and dashes
• Color names in R
• Portfolio final editing
• The portfolio after the term

Citations and references

• BiBTeX
• entry types
• citation keys
• fields
• notes on usage
• articles
• books
• in a book
• in proceedings
• web pages
• software
• summary of entry types

Portfolio studio

• Adding links to README
• Importing images
• Typesetting mathematics
• Create the bib file
• BiBTeX entry types
• YAML bibliography argument
• Add a citation
• Add a references heading
• Format the citations and references
• Reading responses
• Presentation responses

▲ index

visual rhetoric and graph design

• Color names in R
• Design basics
• Discovering stories
• Follow good design practices
• Beware Simpson’s paradox
• Adjust for inflation
• Adjust for population
• Adjust for PPP
• Adjust for lack of context
• Video links discovering stories
• Video links correcting graphical lies

Reading and reflection prompts Copy and paste the Rmd markup into your own Rmd file(s)

• Reading prompts 1 Tufte (1997) Decision to launch Challenger
• Reading prompts 2 Wainer (2014) 15 displays about one thing
• Reading prompts 3 Dragga and Voss (2001) Cruel pies
• Reading prompts 4 Kostelnick (2007) Conundrum of clarity
• Reading prompts 5 Spence (2006) Playfair & psychology of graphs
• Reflection on rhetoric discovering stories
• Reflection on rhetoric correcting graphical lies

project management

• Introductory slides
• Managing files
• Planning the directory structure
• Hyphens and underscores in file names
• Planning a file-naming scheme
• Using relative paths
• Searching files

▲ index

software

Getting started

• Install R and RStudio
• Install git
• Create a library for packages
• Test the initial installation

Software studio

• Setup GitHub
• Create a repo
• Invite collaborator
• Create an Rproject
• Create the Renviron
• Setup directories
• Edit gitignore
• Setup README
• Setup reading responses
• Commits

▲ index

readings

Doumont J-L (2009) Designing the graph. Trees, maps, and theorems: Effective communication for rational minds. Principiae, Kraainem, Belgium, 133–143 http://www.treesmapsandtheorems.com/

Dragga S and Voss D (2001) Cruel pies: The inhumanity of technical illustrations. Technical Communication 48(3), 265–274

Knaflic CN (2012a) Telling multiple stories (part 1). http://tinyurl.com/y4oz8vtv

Knaflic CN (2012b) Telling multiple stories (part 2). http://tinyurl.com/y4jk4jjs

Knaflic CN (2012c) And the winner is... http://tinyurl.com/y462kkbz

Knaflic CN (2013a) Logic in order. http://tinyurl.com/yxf8gspl

Knaflic CN (2013b) The right amount of detail. http://tinyurl.com/y24gn8o4

Knaflic CN (2014) Multifaceted data and story. http://tinyurl.com/yxq8xuf2

Kostelnick C (2007) The visual rhetoric of data displays: The conundrum of clarity. IEEE Transactions on Professional Communication 50(2), 280–294

Robbins N (2013a) General principles for creating effective graphs. Creating More Effective Graphs. Chart House, Wayne, NJ, 154–225 http://www.nbr-graphs.com/resources/recommended-books/

Robbins N (2013b) Scales. Creating More Effective Graphs. Chart House, Wayne, NJ, 226–291 http://www.nbr-graphs.com/resources/recommended-books/

Rost LC (2018a) Your friendly guide to colors in data visualisation. https://blog.datawrapper.de/colorguide/

Rost LC (2018b) What to consider when choosing colors for data visualization. https://blog.datawrapper.de/colors/

Spence I (2006) William Playfair and the psychology of graphs. IEEE Transactions on Professional Communication. American Statistical Association, Alexandria, VA, 2426–2436 http://tinyurl.com/y2njxrbv

Tufte E (1997) The decision to launch the space shuttle Challenger. Visual and statistical thinking: Displays of evidence for making decisions. Graphics Press, Cheshire, CT, 16–31 https://www.edwardtufte.com/tufte/books_textb

Wainer H (2000) Graphical failures: How to display data badly. Visual revelations: Graphical tales of fate and deception from Napoleon Bonaparte To Ross Perot. Psychology Press, Mahwah, NJ, 11–40

Wainer H (2014) Fifteen displays about one thing. Medical illuminations: Using evidence, visualization, and statistical thinking to improve healthcare. Oxford University Press, Oxford, UK, 32–49

Wickham H and Grolemund G (2017) R for Data Science. O’Reilly Media, Inc., Sebastopol, CA https://r4ds.had.co.nz/

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▲ calendar
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