Editing Data Into Insights (Spring 2021)

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:'''Instructor:''' [https://jeremydfoote.com Jeremy Foote]  
:'''Instructor:''' [https://jeremydfoote.com Jeremy Foote]  
:'''Email:''' jdfoote@purdue.edu
:'''Email:''' jdfoote@purdue.edu
:'''[[User:Jdfoote/OH|Office Hours]]:''' Fridays 10am-noon and by appointment
:'''Office Hours:''' Thursdays; 3:00-5:00pm and by appointment
 


<div style="float:right;">__TOC__</div>
<div style="float:right;">__TOC__</div>
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Students who complete this course will be able to:
Students who complete this course will be able to:
# Understand the role of narrative in interpreting and producing data analyses
# Understand the role of narrative in interpreting and producing data analyses
# Competently import, process, and prepare data for analysis in the [https://www.r-project.org/ R programming language]
# Competently import, process, and prepare data from analysis in the [https://www.r-project.org/ R programming language]
# Critically analyze data visualizations and presentations, and recognize poor or misleading visualizations
# Critically analyze data visualizations and presentations, and recognize poor or misleading visualizations
# Produce beautiful, well-designed data visualizations in R using [https://ggplot2.tidyverse.org/ ggplot2]
# Produce beautiful, well-designed data visualizations in R using [https://ggplot2.tidyverse.org/ ggplot2]
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**  '''Data Visualization: A Practical Introduction''' by Kieran Healy. [https://socviz.co/index.html Web version (free!)] or [https://amzn.to/2vfAixM Print version (Amazon)]
**  '''Data Visualization: A Practical Introduction''' by Kieran Healy. [https://socviz.co/index.html Web version (free!)] or [https://amzn.to/2vfAixM Print version (Amazon)]
** '''R for Data Science''' by Hadley Wickham and Garrett Grolemund. [https://r4ds.had.co.nz/index.html Web version (free!)] or [http://amzn.to/2aHLAQ1 Print version (Amazon)]
** '''R for Data Science''' by Hadley Wickham and Garrett Grolemund. [https://r4ds.had.co.nz/index.html Web version (free!)] or [http://amzn.to/2aHLAQ1 Print version (Amazon)]
** '''Effective Data Storytelling''' by Brent Dykes. [https://purdue-primo-prod.hosted.exlibrisgroup.com/permalink/f/vjfldl/PURDUE_ALMA51860241510001081 Purdue libraries] or [https://smile.amazon.com/dp/1119615712 Print version (Amazon)]
** '''Effective Data Storytelling''' by Brent Dykes. [https://smile.amazon.com/dp/1119615712 Print version (Amazon)]
 
* Other readings: Readings will be linked to from this page. Where necessary, they will be put on Brightspace
 
=== Reading Academic Articles ===


Some of the readings will be academic articles. I do not expect you to read every word of these articles. Rather, you should practice intentional directed skimming. [https://writingcenter.gmu.edu/guides/strategies-for-reading-academic-articles This article] gives a nice overview. The TL;DR is that you should carefully read the abstract, introduction, and conclusion. For the rest of the article, focus on section headings and topic sentences to extract the main ideas.
* Other readings: Other readings will be made available on Brightspace.


= Course logistics =
= Course logistics =
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This course will follow "flipped" classroom model. I expect you to learn most of the content of the course asynchronously. The goal of our time together is not to tell you new things, but to consolidate knowledge and to clear up misconceptions.
This course will follow "flipped" classroom model. I expect you to learn most of the content of the course asynchronously. The goal of our time together is not to tell you new things, but to consolidate knowledge and to clear up misconceptions.


The Tuesday meeting will be a collaborative, discussion-centric session. Typically, about half of each session will be devoted to going over assignments and the other half will be a discussion of the readings and videos from that week. We will take collaborative notes [https://etherpad.wikimedia.org/p/com-495-data-insight using this Etherpad].
The Tuesday meeting will be a collaborative, discussion-centric session. Typically, about half of each session will be devoted to going over assignments and the other half will be a discussion of the readings and videos from that week.
 
If you would like to create collaborative summaries of the readings, you can [https://etherpad.wikimedia.org/p/com-495-summaries use this Etherpad].


The Thursday meetings will be more like a lab. Some of these sessions will include synchronous activities but they will often be more of a co-working time, where you can work synchronously on assignments and I can be available to answer questions.
The Thursday meetings will be more like a lab. Some of these sessions will include synchronous activities but they will often be more of a co-working time, where you can work synchronously on assignments and I can be available to answer questions.
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Your first place to look for help should be each other. By asking and answering questions on Discord, you will not only help to build a repository of shared information, but to reinforce our learning community.
Your first place to look for help should be each other. By asking and answering questions on Discord, you will not only help to build a repository of shared information, but to reinforce our learning community.


I will also hold office hours Friday mornings on Discord ([[User:Jdfoote/OH|sign up here]]). If you come with a programming question, I will expect that you have already tried to solve it yourself in multiple ways and that you have discussed it with a classmate (e.g., on Discord). This policy lets me have time to help more students, but it's also a useful strategy. Often [https://en.wikipedia.org/wiki/Rubber_duck_debugging just trying to explain your code] can help you to recognize where you've gone wrong.
I will also hold office hours Thursday afternoons on Discord. If you come with a programming question, I will expect that you have already tried to solve it yourself in multiple ways and that you have discussed it with a classmate (e.g., on Discord). This policy lets me have time to help more students, but it's also a useful strategy. Often [https://en.wikipedia.org/wiki/Rubber_duck_debugging just trying to explain your code] can help you to recognize where you've gone wrong.


I will also keep an eye on Discord during normal business hours. I encourage you to post questions there, and to use it as a space where we can help and instruct each other. In general, you should contact me there. I am also available by email. You can reach me at [mailto:jdfoote@purdue.edu jdfoote@purdue.edu]. I try hard to maintain a boundary between work and home and I typically respond only on weekdays during business hours.
I will also keep an eye on Discord during normal business hours. I encourage you to post questions there, and to use it as a space where we can help and instruct each other. In general, you should contact me there. I am also available by email. You can reach me at [mailto:jdfoote@purdue.edu jdfoote@purdue.edu]. I try hard to maintain a boundary between work and home and I typically respond only on weekdays during business hours.
=== Resources ===
Especially for the programming assignments, I will often create video walkthroughs that will be linked from the schedule. I also created the following general videos that may be helpful:
* Explanation of ggplot (and Chapter 3 in R4DS) [[https://purdue.brightspace.com/d2l/le/content/208726/viewContent/5507580/View Video]]
* Finding and fixing bugs in your code [[https://purdue.brightspace.com/d2l/le/content/208726/viewContent/5708092/View Video]] [[https://jeremydfoote.com/TDIS/week_8/debugging.Rmd R Markdown file]] [[https://jeremydfoote.com/TDIS/week_8/debugging.html HTML file]]


= Assignments =
= Assignments =
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== Discussion Questions ==
== Discussion Questions ==


This course will have two "modes". For much of the class, we will be reading about theories of communication and rhetoric, about principles of data visualization, etc. For these sessions, you will be required to submit 1-2 discussion questions on Discord on Monday by noon. I will then curate some of these questions (and add some of my own) to use to guide our discussion on Tuesday. I will post the questions on the Etherpad at https://etherpad.wikimedia.org/p/com-495-data-insight
This course will have two "modes". For much of the class, we will be reading about theories of communication and rhetoric, about principles of data visualization, etc. For these sessions, you will be required to submit 1-2 discussion questions on Discord on Monday by noon. I will then curate some of these questions (and add some of my own) to use to guide our discussion on Tuesday.
 
Questions should engage with the readings and either connect to other concepts or to the "real world". Here are some good example questions:
 
* The readings this week talked a lot about how data visualizations can be misleading. How can we tell when visualizations are intentionally trying to mislead versus when they are just poorly designed?
* I was confused by the reading on counterfactuals. We obviously can't really know what would have happened in different conditions, so why even try?
* Imagine you were asked to create an ad campaign to recruit students to Purdue. What types of appeals would you use and why?


During other weeks, we will be more focused on learning practical skills (mostly data manipulation and visualization in R). On those weeks, discussions will center around identifying places where folks are still confused and students will be randomly selected to share their responses to homework questions.
During other weeks, we will be more focused on learning practical skills (mostly data manipulation and visualization in R). On those weeks, discussions will center around identifying places where folks are still confused and students will be randomly selected to share their responses to homework questions.
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* Exceed requirements, but in fairly straightforward ways - e.g., an additional post in discussion every week.
* Exceed requirements, but in fairly straightforward ways - e.g., an additional post in discussion every week.
* Compose complete and sufficiently detailed reflections.
* Compose complete and sufficiently detailed reflections.
* Complete nearly all of the homework assignments, typically at a fairly high level
* Complete many of the homework assignments.


C: This reflects meeting the minimum expectations of the course. Students reaching this level of achievement
C: This reflects meeting the minimum expectations of the course. Students reaching this level of achievement
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* Be collegial and continue discussion, through asking simple or limited questions.
* Be collegial and continue discussion, through asking simple or limited questions.
* Compose reflections with straightforward and easily manageable goals and/or avoid discussions of challenges.
* Compose reflections with straightforward and easily manageable goals and/or avoid discussions of challenges.
* Not complete homework assignments or turn many in in a hasty or incomplete manner.
* Not complete homework assignments or turn some in in a hasty or incomplete manner.


D/F: These are reserved for cases in which students do not complete work or participate. Students may also be impeding the ability of others to learn.
D/F: These are reserved for cases in which students do not complete work or participate. Students may also be impeding the ability of others to learn.
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'''Assignment Due:'''  
'''Assignment Due:'''  
* [[/Discord signup|Sign up for Discord]] and introduce yourself
* None
* Take [https://forms.gle/spJzcKBCsERVLHNSA this very brief survey]


'''Readings (before class):'''  
'''Readings (before class):'''  
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'''Assignment Due:'''  
'''Assignment Due:'''  
* Read the entire syllabus (this document)
* Read the entire syllabus (this document)
* Sign up for [https://discord.gg/WvzkwY4fDK Discord] and introduce yourself
* Take [https://forms.gle/spJzcKBCsERVLHNSA this very brief survey]


== Week 2: Storytelling and Narratives  ==
== Week 2: Storytelling and Narratives  ==
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'''Assignment Due:'''  
'''Assignment Due:'''  
* [[#Discussion Questions|Discussion questions]]
* Summary and discussion questions




'''Readings (before class):'''  
'''Readings (before class):'''  
* Zak, P. (2013). [https://greatergood.berkeley.edu/article/item/how_stories_change_brain How stories change the brain]
* Langston, C. [https://www.youtube.com/watch?v=3klMM9BkW5o How to use rhetoric to get what you want] (video)
* Langston, C. [https://www.youtube.com/watch?v=3klMM9BkW5o How to use rhetoric to get what you want] (video)
* Leighfield, L. [https://boords.com/ethos-pathos-logos-aristotle-modes-of-persuasion Ethos, Pathos & Logos: Aristotle’s Modes of Persuasion]
* Leighfield, L. [https://boords.com/ethos-pathos-logos-aristotle-modes-of-persuasion Ethos, Pathos & Logos: Aristotle’s Modes of Persuasion]
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* [http://www.openculture.com/2014/02/kurt-vonnegut-masters-thesis-rejected-by-u-chicago.html Kurt Vonnegut's Shapes of Stories]
* [http://www.openculture.com/2014/02/kurt-vonnegut-masters-thesis-rejected-by-u-chicago.html Kurt Vonnegut's Shapes of Stories]
* Lafrance, A. [https://www.theatlantic.com/technology/archive/2016/07/the-six-main-arcs-in-storytelling-identified-by-a-computer/490733/ The Six Main Arcs in Storytelling, as Identified by an A.I.]
* Lafrance, A. [https://www.theatlantic.com/technology/archive/2016/07/the-six-main-arcs-in-storytelling-identified-by-a-computer/490733/ The Six Main Arcs in Storytelling, as Identified by an A.I.]
* (Optional) A Rulebook for Arguments (link on Brightspace)




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'''Assignment Due:'''
'''Assignment Due:'''
* [[#Discussion Questions|Discussion questions]]


'''Readings:'''
'''Readings:'''  
* Effective Data Storytelling (EDS) Ch. 1--3 ([https://purdue-primo-prod.hosted.exlibrisgroup.com/permalink/f/vjfldl/PURDUE_ALMA51860241510001081 Purdue libraries copy])
* Matei, S. [https://purdue.brightspace.com/d2l/le/content/208726/viewContent/4750659/View What is a (data) story?]
* [https://purdue.brightspace.com/d2l/le/content/208726/viewContent/5392546/View Counterfactuals and Storytelling lecture ] [4:49]
* (Optional) Levy, J. (2015). [https://www-tandfonline-com.ezproxy.lib.purdue.edu/doi/full/10.1080/09636412.2015.1070602 Counterfactuals, Causal Inference, and Historical Analysis]
* (Optional) [https://towardsdatascience.com/storytelling-for-data-scientists-317c2723aa31 Storytelling for Data Scientists]
* (Optional) [https://towardsdatascience.com/how-to-properly-tell-a-story-with-data-and-common-pitfalls-to-avoid-317d8817e0c9 How to properly tell a story with data — and common pitfalls to avoid]


'''Class Schedule:'''
'''Class Schedule:'''
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* Counterfactual thinking
* Counterfactual thinking
* The role of statistics
* The role of statistics


== Week 4: The ethics of data stories (Part I) ==
== Week 4: The ethics of data stories (Part I) ==
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'''Assignment Due:'''
'''Assignment Due:'''
* Turn in your [[Self Assessment Reflection]] on Brightspace
* Turn in your [[Self Assessment Reflection]] on Brightspace
* [[/Purdue WP Case|Case Study]] (Be prepared to talk about this case, based on the readings and the class so far)
* No Discussion Questions (but feel free to have discussions on Discord!)


'''Readings:'''  
'''Readings:'''  
* Salganik, M. (2017). [https://www.bitbybitbook.com/en/ethics/ethics-intro/ Chapter 6: Ethics] from ''Bit by Bit''.
* Kassner, M. [https://www.techrepublic.com/article/5-ethics-principles-big-data-analysts-must-follow/ 5 ethics principles big data analysts must follow]
* McNulty, K. (2018). [https://drkeithmcnulty.com/2018/07/22/beware-of-storytelling-in-data-and-analytics/ Beware of 'storytelling' in data and analytics]
* (Optional) Steinmann, M., Matei, S. A., & Collmann, J. (2016). A Theoretical Framework for Ethical Reflection in Big Data Research. (On Brightspace)


'''Class Schedule:'''
'''Class Schedule:'''
* Ethical frameworks
* What are ethical data stories?
* When do analysts need to make ethical decisions?
* Transparency, respect, beneficence, honesty


== Week 5: Where does data come from? ==
== Week 5: Where does data come from? ==
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'''Assignment Due:'''  
'''Assignment Due:'''  
* [[#Discussion Questions|Discussion questions]]


'''Readings:'''
'''Readings:'''
* [https://purdue.brightspace.com/d2l/le/content/208726/viewContent/5431820/View Where data comes from lecture] [14:02]
* Pelz, W. [https://courses.lumenlearning.com/suny-hccc-research-methods/chapter/chapter-6-measurement-of-constructs/ Measurement of Constructs] in ''Research Methods for the Social Sciences''.
* [https://uxplanet.org/dirty-data-what-is-it-and-how-to-prevent-it-742accad081e Dirty Data article]
* Salganik, M. [https://www.bitbybitbook.com/en/1st-ed/observing-behavior Observing behavior] in ''Bit by Bit''
* EDS Chapter 5
* Perkel, J. [https://www-nature-com.ezproxy.lib.purdue.edu/articles/d41586-018-05990-5 A toolkit for data transparency takes shape]
* (Optional) Tayi, G. K. and Ballou, D. P. (1998). [https://www.researchgate.net/publication/27297579_Examining_Data_Quality Examining Data Quality]


'''Class Schedule:'''
'''Class Schedule:'''


== Week 6: Introduction to R ==
== Week 6: Introduction to R ==
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'''Assignment Due:'''  
'''Assignment Due:'''  
* [[/R Lab 1|R Lab 1]]
 
** [https://purdue.brightspace.com/d2l/le/content/208726/viewContent/5457615/View Video to help with lab] [7:39]




'''Readings:'''  
'''Readings:'''  
* [https://purdue.brightspace.com/d2l/le/content/208726/viewContent/5477440/View Why Programming + Intro to R lecture] [12:53]
* [https://source.opennews.org/articles/what-i-learned-recreating-one-chart-using-24-tools/ What I Learned Recreating One Chart Using 24 Tools]. Lisa Charlotte Rost
* [https://source.opennews.org/articles/what-i-learned-recreating-one-chart-using-24-tools/ What I Learned Recreating One Chart Using 24 Tools]. Lisa Charlotte Rost
* [https://r4ds.had.co.nz/introduction.html R4DS Ch. 1]
* [https://r4ds.had.co.nz/introduction.html RFDS Ch. 1]
(Optional)
(Optional)
* [https://rladiessydney.org/courses/ryouwithme/01-basicbasics-0/ Unit 1: Basic Basics (R Ladies Sydney)]
* [https://rladiessydney.org/courses/ryouwithme/01-basicbasics-0/ Unit 1: Basic Basics (R Ladies Sydney)]
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'''Class Schedule:'''
'''Class Schedule:'''
* Why programming?
* Why R?
* Functions
* Variables
* Data frames
* Tidyverse


== Week 7: Making figures in R ==
== Week 7: Making figures in R ==
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'''Assignment Due:'''  
'''Assignment Due:'''  
* [[/R4DS Chapter 3 Exercises|R4DS Chapter 3 Exercises]]
** [https://purdue.brightspace.com/d2l/le/content/208726/viewContent/5507580/View Video overview of how to do assignment + ggplot explanation] [13:33]


'''Readings:'''  
'''Readings:'''  
* [https://r4ds.had.co.nz/data-visualisation.html R4DS Chapter 3]
* [https://socviz.co/gettingstarted.html DV Chapter 2]


'''Class Schedule:'''
'''Class Schedule:'''
* ggplot2
* ggplot2


== Week 8: Manipulating and Aggregating Data ==
 
 
== Week 8: Visualization principles ==
 


March 9
March 9


'''Assignment Due:'''
'''Assignment Due:'''
* Start [[/R4DS Chapter 5 Exercises|R4DS Chapter 5 Exercises]]
** [https://purdue.brightspace.com/d2l/le/content/208726/viewContent/5562641/View Video explanation of homework] [26:45]
* Turn in your [[Self Assessment Reflection]] on Brightspace
* Turn in your [[Self Assessment Reflection]] on Brightspace


'''Readings:'''
'''Readings:'''  
* [https://r4ds.had.co.nz/workflow-basics.html R4DS Chapter 4 - Workflow Basics]
 
* [https://r4ds.had.co.nz/transform.html R4DS Chapter 5 - Data transformation]
'''Class Schedule:'''
 
 


== Week 9: Visualization Principles ==
== Week 9: Visualization Principles II ==


March 16
March 16


'''Assignment Due:'''
'''Assignment Due:'''  
* [[/R4DS Chapter 5 Exercises|R4DS Chapter 5 Exercises]]
* [[#Discussion Questions|Discussion questions]]
 


'''Readings:'''  
'''Readings:'''  
* [https://datavizm20.classes.andrewheiss.com/content/02-content/ Graphic Design] by Andrew Heiss. Make sure to watch all 4 videos.
* EDS Chapter 7
* Healy, K. [https://socviz.co/lookatdata.html Data Visualization Chapter 1]
* (Optional) Gelman, A. and Unwin, A. (2012). [http://www.stat.columbia.edu/~gelman/research/published/vis14.pdf Infovis and statistical graphics: Differrent goals, different looks].
* (Optional) Williams, R. (2008). [https://purdue-primo-prod.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=PURDUE_ALMA51793773920001081&context=L&vid=PURDUE&lang=en_US&search_scope=everything&adaptor=Local%20Search%20Engine&tab=default_tab&query=any,contains,The%20Non-Designer%27s%20Design%20Book&mode=Basic The Non-Designer's Design Book], Chapters 1-6


'''Class Schedule:'''
'''Class Schedule:'''


March 18 - READING DAY
March 18 - READING DAY


== Week 10: Visualization Principles II and Exploratory Data Analysis ==
 
 
== Week 10: Advanced visualizations in R ==


March 23
March 23


'''Assignment Due:'''  
'''Assignment Due:'''  
* [[Data_Into_Insights_(Spring_2021)/Final_project#Step_1:_Identify_a_dataset|Submit the data source for your final project]]
* [[/Data Source Assignment|Submit the data source for your final project]]
* [[Data_Into_Insights_(Spring_2021)/Visualization Project|Visualization Project]]
* Submit 2 questions for take-home exam


'''Readings:'''  
'''Readings:'''  
* [https://socviz.co/groupfacettx.html#groupfacettx DV Chapter 4: Show the right numbers]
* EDS Chapter 8
* Hullman, J. [https://www-scientificamerican-com.ezproxy.lib.purdue.edu/article/how-to-get-better-at-embracing-unknowns/ How to get better at embracing unknowns]
* Yau, N. [https://flowingdata.com/2018/01/08/visualizing-the-uncertainty-in-data/ Visualizing the uncertainty in data].
* (Optional) Review [https://r4ds.had.co.nz/transform.html R4DS Ch 5]


'''Class Schedule:'''
'''Class Schedule:'''
* Summarize and discuss readings
* Peer feedback on data source + visualization project
* R4DS Chapter 5 (continued)


== Week 11: Text as data ==
== Week 11: Text as data ==
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'''Assignment Due:'''
'''Assignment Due:'''
* [[#Discussion Questions|Discussion questions]] - One discussion question and one or more examples of "bad" visualizations that you found


'''Readings:'''
'''Readings:'''
* Grimmer, J., & Stewart, B. M. (2013). [https://www.cambridge.org/core/services/aop-cambridge-core/content/view/F7AAC8B2909441603FEB25C156448F20/S1047198700013401a.pdf/text-as-data-the-promise-and-pitfalls-of-automatic-content-analysis-methods-for-political-texts.pdf Text as data: The promise and pitfalls of automatic content analysis methods for political texts]. Political Analysis.
* Reagan, A. J., Mitchell, L., Kiley, D., Danforth, C. M., & Dodds, P. S. (2016). [https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-016-0093-1 The emotional arcs of stories are dominated by six basic shapes]. EPJ Data Science.


'''Class Schedule:'''
'''Class Schedule:'''
* Guest lecture by [https://ryanjgallagher.github.io/ Ryan J. Gallagher]
* Guest lecture by [https://ryanjgallagher.github.io/ Ryan J. Gallagher]


== Week 12: Advanced visualizations in R ==
 
== Week 12: Importing and cleaning data ==


April 6
April 6


'''Assignment Due:'''  
'''Assignment Due:'''
* [[Self Assessment Reflection]]
* [[Self Assessment Reflection]]
* [[/Story Time|Story Time Mini-project]]
* [[/Exam|Take-home Exam]]


'''Readings:'''  
'''Readings:'''
* [https://socviz.co/maps.html#maps DV Chapter 7: Maps]
* [https://r4ds.had.co.nz/graphics-for-communication.html R4DS Ch. 28]
 
'''Class Schedule:'''
* Maps
* [https://jeremydfoote.com/Communication-and-Social-Networks/week_6/ggraph_walkthrough.html Networks]
* Annotations


== Week 13: Importing and cleaning data ==
== Week 13: Manipulating and aggregating data ==


April 13
April 13
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* Synchronous session moved to April 15
* Synchronous session moved to April 15


April 15
April 15


'''Assignment Due:'''
'''Assignment Due:'''
* [[Data Into Insights (Spring 2021)/Final project#Step_2:_Explore_the_data_and_write_a_proposal|Proposal for final project]]
* [[/Final project proposal|Proposal for final project]]
* [[/R4DS Chapter 12|R4DS Chapter 12 (12.2 and 12.3)]]
 


'''Readings:'''
'''Readings:'''
* [https://r4ds.had.co.nz/data-import.html R4DS Chapters 11--12]
* (Optional) Wickham, H. (2014). [http://vita.had.co.nz/papers/tidy-data.pdf Tidy Data]. Journal of statistical software, 59(10), 1-23.
* (Optional) Huntington-Klein, N. [https://www.youtube.com/watch?v=CnY5Y5ANnjE&t=785s Data Wrangling with R and the Tidyverse]


'''Class schedule:'''
'''Course schedule:'''
* Provide peer feedback on final project proposal
* Provide peer feedback on final project proposal


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'''Assignment Due:'''  
'''Assignment Due:'''  
* [[#Discussion Questions|One discussion question]]
* [[/Final project proposal|New version of final project proposal]] (edited following peer feedback)
* [[Data_Into_Insights_(Spring_2021)/Final_project#Step_2:_Explore_the_data_and_write_a_proposal|New version of final project proposal]] (edited following peer feedback)
* [[/R4DS Chapter 12|R4DS Chapter 12 (12.4-12.6)]]


'''Readings:'''
'''Readings:'''
* Kim, Y. et al. (2017). [http://users.eecs.northwestern.edu/~jhullman/explaining_the_gap.pdf Explaining the Gap: Visualizing One’s Predictions Improves Recall and Comprehension of Data].
* Knaflic, C. N. (2019). [https://purdue.alma.exlibrisgroup.com/view/uresolver/01PURDUE_PUWL/openurl?ctx_enc=info:ofi/enc:UTF-8&ctx_id=10_1&ctx_tim=2020-06-13T12%3A39%3A32IST&ctx_ver=Z39.88-2004&url_ctx_fmt=info:ofi/fmt:kev:mtx:ctx&url_ver=Z39.88-2004&rfr_id=info:sid/primo.exlibrisgroup.com-PURDUE_ALMA&req_id=_c20e3fe9e4a9a31b0162ece2023b8d45&rft_dat=ie=01PURDUE_PUWL:51807454010001081,language=eng,view=PURDUE&svc_dat=viewit&u.ignore_date_coverage=true&req.skin=PUWL&Force_direct=true&is_new_ui=true Storytelling with Data] Chapter 6
* EDS Chapter 9


== Week 15: Ethics of data stories (Part II) ==
== Week 15: Ethics of data stories (Part II) ==
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'''Assignment Due:'''  
'''Assignment Due:'''  
* 1 [[#Discussion Questions|Discussion question]]
* [[/Final project rough draft|Final project rough draft]] for peer feedback
* [[Data_Into_Insights_(Spring_2021)/Final_project#Step_3:_Write_a_rough_draft|Final project rough draft]] for peer feedback


'''Readings:'''
'''Readings:'''
* Re-read McNulty, K. (2018). [https://drkeithmcnulty.com/2018/07/22/beware-of-storytelling-in-data-and-analytics/ Beware of 'storytelling' in data and analytics] and reflect on how you see this differently now that you know more about data storytelling
'''Topics:'''
* What does an ethical data story look like?


April 29
April 29
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'''Assignment Due:'''
'''Assignment Due:'''
* Peer feedback (via email or Discord)
* Peer feedback (via email or Discord)


== Week 16: Finals week  ==
== Week 16: Finals week  ==
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