CDS-101: Introduction to Computational and Data Sciences

Final Project

2018-12-14T23:59:00-05:00

Github Classroom repo for Final Project

Due Dates

List of project questions: December 7, 2018 @ 11:59pm
Final Project Report: December 14, 2018 @ 11:59pm

Instructions

For the final project, you will be assigned into groups that will conduct an exploratory data analysis using the skills you’ve developed over the semester and write a report. Each group will motivate their exploration by formulating interesting questions that can be answered with the dataset, which are then answered by computing summary statistics as well as wrangling the data into a form that allows you to visualize it. The key words are summary statistics and visualize; the answer to each question must involve the presentation and discussion of visual and quantitative information. If desired, groups may also bring in additional data to strengthen the analysis, but please note that any additional data must be documented, which includes describing how you obtained it and how you’ve integrated it with the main dataset to further your analysis.

The Dataset

All groups will be working with the College Scorecard dataset started by The Obama Administration in September 2015. The dataset is included in the starter repository for your group and it is loaded by default (the variable name is college) in the setup block of the R Markdown file where you write your group report. The source link for the dataset is found here: https://collegescorecard.ed.gov/data/.

The data code-book, which tells you what the columns mean, is available at https://collegescorecard.ed.gov/assets/CollegeScorecardDataDictionary.xlsx. You will have to look through the code-book to understand the meaning of the variables, and this should be your starting point before you start running an analysis on the dataset.

For further information about the dataset, consult the documentation pages at https://collegescorecard.ed.gov/data/documentation/.

This is a large dataset that that contains millions of individual cells. As such, there is no one right way to approach this project. There are many different avenues that you can take, so have fun with it!

Grade

Grades for the final project will be based on the correctness and readability of your R code, how well your report is written (the report should be structured, coherent, and follow the standard rules of spelling and grammar), and how well the exploratory questions are answered. You will be graded as an individual even though this is a group project, meaning that any group members that are judged to have not sufficiently contributed to the final product will have their grade penalized.

As stated in the class syllabus, this project is worth 25% of your class grade.

The report

The group report is built around interesting questions you can ask of the dataset. Each group member will construct and answer 1 question about the dataset. So, for example, a group of four members will have 4 questions in total. These questions must be decided upon first before you begin putting your report together.

The report is to have two sections, Preprocessing the dataset and Exploratory data analysis. Preprocessing the dataset is where you the group will work together to organize and prepare into a common format that everyone will start from when answering his or her question. Exploratory data analysis is where you specify and then answer each question.

The subsections below give more detail about what is expected from your questions and what to put in the report.

Choosing your questions

There are some restrictions on what I will consider a valid question for this final project. Here is a short list of criteria that your question must meet:

Your question cannot be a simple lookup query, like “how many students go to this school?”
The question must be about comparing relationships and trends between two or more variables in the dataset, either by comparing two or more columns or by comparing well-defined groups of values within a single column.
Answering the question must involve creating one or more visualizations and the calculation of one or more summary statistics.
Answering the question must require data aggregation (group_by/summarize) or identifying a trend between 2 or more variables.

Your group will submit a preliminary list of questions by the end of the day on December 7 on Blackboard for my review and comment. For each question, you should provide the following:

The question itself written as a complete sentence.
A list of the columns you plan to use to answer the question.
A one or two sentence explanation for why you think it’s a worthwhile question to ask.

I reserve the right to adjust or veto any questions that do not meet the outlined criteria or that cannot be appropriately justified. Submitting these on time to Blackboard will be counted as part of your final project grade.

Preprocessing the dataset section

This dataset is structured and mostly clean, but there is still some data preprocessing that needs to be done before you can begin analysis. At a minimum, there are three clear tasks to complete and document in this first section of the R Markdown file before you continue on to the Exploratory data analysis section:

After your group has decided on the questions you will answer, figure out which columns in the dataset your group needs to answer the questions. Then, extract those columns using select and save the reduced dataset to another variable, for example college_reduced.
The column names are shortened abbreviations, and should be made more human-readable using the rename function. Use the data code-book, https://collegescorecard.ed.gov/assets/CollegeScorecardDataDictionary.xlsx, to help you figure out what the abbreviations mean.
Categorical variables that are not easy to understand, for example the integer categories under the REGION column, should be relabeled using the recode function.

Additional preprocessing steps may be necessary, depending on the questions your group is trying to answer. Regardless of what you do, these steps should be documented in the usual way, where code blocks are accompanied with written explanations.

Each question in the Exploratory data analysis section must be answered starting from the preprocessed dataset you end up with at the end of this section!

Exploratory data analysis section

This section is where each group member is responsible for answering his or her respective question about the dataset. Each question should be clearly stated and each answer should start from the preprocessed dataset obtained in the previous section. The procedure for obtaining the answer needs to be documented in the form of both code blocks and plain text. Calculated summary statistics and visualizations need to be discussed and interpreted for the reader. For example, if you present a distribution, discuss it’s shape, center, and spread, which you should relate to the computed summary statistics. If you present a scatter plot, what is the trend of the points? After analyzing the various outputs, synthesize it and provide a formal answer to your stated question.

Here is a quick checklist to apply to each question and answer:

Is the question clearly stated?
Does the code used to find the answer contain at least one dplyr-based data transformation, one calculation of a summary statistic, and one ggplot2 data visualization?
Are the results obtained from the code properly explained and interpreted for the reader?
Is a final answer clearly given for the question?

How to edit and merge each group member’s content into the final report

Like in the group submissions for the homework assignments, collaboration on the GitHub repository will work best if you create your own versions of the final_project.Rmd file where you make a copy and rename it to include your last name. For example, if your last name is Smith, then you should make a file copy and rename it to final_project_smith.Rmd. Eventually, the group will need to merge in everyone’s answers into the final_project.Rmd document. The following checklist may help with this:

Select an editor to be in charge of merging everyone’s answers into the final document final_project.Rmd.
The editor should ensure that everyone has committed and pushed their answers to GitHub so they can copy and paste in each contribution.
The editor needs to make sure that the final submission reads like a coherent document and that the writing style and code style are uniform across all the answers. In other words, it should read like a single person answered all the questions, not a group of four people.
The editor will be in charge of of committing and pushing the final R Markdown file to GitHub, knitting to PDF, and uploading the PDF file on Blackboard.

Additional guidelines

The following are additional guidelines for your Final Project submission:

Your group’s final project will only be graded if a PDF of your final report is submitted on Blackboard and the final versions of the group’s R Markdown files are pushed to GitHub.
The final R Markdown report file in the group’s GitHub repository must knit to the PDF format without error.
You must use the tidyverse functions in your work, I should not see any “base R” functions used (subset, for example) that were not shown in the lecture slides or the textbook.
Each group member must contribute substantive commits to the group repository on Github that reflect his/her own work.
The report represents your group’s final results and should only contain the methods used to obtain them. Do not include questions in the report that you cannot answer.
Your R code should be clean and readable, which includes what it looks like after knitting. Code blocks should not run off the side of the page when knitted to PDF!
The report’s tone should be professional and should not read like a social media feed or personal blog. Refrain from editorializing about the project as a whole or about a specific question, as this is not an opinion paper. Do not speculate, instead support your claims and explanations using data and analysis. Avoid self-narration or writing about how you felt or what you were thinking as you complete each question, instead write as if you are constructing a step-by-step tutorial for others to use.
Late submissions for the final project will not be accepted, no exceptions.

How to submit

When you are ready to submit, be sure that everyone in your group has committed and pushed their R Markdown files to Github. The editor should do the following in RStudio Server:

If it is not loaded already, open the RStudio project for the group homework assignment.
Click on the Git tab in the upper-right window, then click “Pull” to make sure you have the latest version of all the files.
Press Ctrl+Shift+B to knit the final submission to the PDF format.
Download the PDF file to your computer.
On Blackboard, find the Final Project submission link for your group. Submit the PDF for your group to lock in your submission time.

The editor should confirm that the files for their group’s repository webpage on GitHub are fully up-to-date, and that if I were to knit the PDF file myself, it should be identical to what is uploaded to Blackboard.

Week 16

2018-12-10T08:00:00-05:00

Final Project

Final Project Report (Due: Dec-14)

Week 15

2018-12-09T23:59:00-05:00

R for Data Science

Chapter 23: Model basics

Section 23.4 – Formulas and model families: http://r4ds.had.co.nz/model-basics.html#formulas-and-model-families
Section 23.5 – Missing values: https://r4ds.had.co.nz/model-basics.html#missing-values-5
Section 23.6 – Other model families: https://r4ds.had.co.nz/model-basics.html#other-model-families …

Week 15

2018-12-03T08:00:00-05:00

Final Project

Final Project Report (Due: Dec-14)

Week 14

2018-12-02T23:59:00-05:00

Introductory Statistics with Randomization and Simulation

Click here to download the textbook.

Chapter 5: Introduction to linear regression

From the beginning through to the end of section 5.1.4
Section 5.4.1

R for Data Science

Chapter 22: Introduction to Model section

Full chapter (short): http://r4ds.had.co.nz/model-intro.html

Chapter 23: Model basics

Section 23.1 – Introduction: https://r4ds.had.co.nz/model-basics.html
Section 23.2 – A simple model: https://r4ds.had.co.nz/model-basics.html#a-simple-model
Section 23.3 – Visualizing models: https://r4ds.had.co.nz/model-basics.html#visualising-models

Homework 3

2018-11-27T23:59:00-05:00

Submissions due: November 27, 2018 @ 11:59pm

Github Classroom repo for Homework 3

Instructions

IMPORTANT! DO THIS FIRST!

You must have the most up-to-date version of infer installed on RStudio Server, otherwise the functions shade_p_value() and shade_confidence_interval() will not work. To get the most up-to-date version of infer, run the following code in the Console window of RStudio Server:

remotes::install_github("tidymodels/infer", ref = "develop")

Obtain the Github repository you will use to complete homework 3, which contains a starter RMarkdown file named homework_3.Rmd, which you will use to do your work and write-up when completing the questions below. Be sure to save, commit, and push (upload) frequently to Github so that you have incremental snapshots of your work. When you’re done, follow the How to submit section below to setup a Pull Request, which will be used for feedback.

Remember that the point of us using RMarkdown documents is to combine code and writeups! Each block of R code should have some sort of explanation or justification using full sentences.
Your grade will take into account your code, your explanations, and whether your document looks nice when “knitted” to PDF.

About the dataset

This homework uses the National Survey of Family Growth, Cycle 6 dataset in the file 2002FemPreg.rds, published by the National Center for Health Statistics. Complete descriptions of all the variables can be found in the NSFG Cycle 6: Female Pregnancy File Codebook. Below are selected descriptions of variables that will be used for this homework assignment:

Variable	Description
caseid	integer ID of the respondent
prglngth	integer duration of the pregnancy in weeks
outcome	integer code for the outcome of the pregnancy, with a 1 indicating a live birth
birthord	serial number for live births; the code for a respondent’s first child is 1, and so on. For outcomes other than live birth, this field is blank

Questions

This homework assignment revolves around answering the following question using this dataset:

Do first born children either arrive early or late when compared with non-first-borns?

The questions in this assignment will guide you through the process of answering this question using statistical inference.

Addressing the question “do first born children either arrive early or late when compared with non-first-borns?” means that we should only consider live births in the dataset. Filter the dataset so that it only contains outcomes with live births and assign this result to the variable live_births.

Next, we need to label all births in live_births as either “first” or “other” so that we can easily find the children that are first borns and the ones that are not. There are a couple of different ways that you can label the births:
- Split the dataset into two parts, a first_births dataset and an other_births dataset. Do this by applying a filter to extract the first births, then use mutate() to create a new column called birth_order that labels these rows as “first”. Then repeat this process, except apply a filter to extract all other births and label those as “other” in birth_order. To recombine the datasets into one, use bind_rows().
- Use if_else() with mutate() to create the birth_order column and the “first” and “other” labels. You can also try using case_when() instead of if_else() to accomplish this. If you do it this way, you won’t need to use bind_rows().
After labeling the births, remove the extraneous variables from the data frame leaving just the prglngth and birth_order columns. Assign the resulting data frame to a variable named pregnancy_length.
Take the pregnancy_length dataset and plot a probability mass function (PMF) histogram of the pregnancy length in weeks that shows “first” births and “other” births on the same plot. Choose a reasonable binwidth for the histogram and add coord_cartesian(xlim = combine(27, 46)) to your plot so that the window focuses where most of the data is. After creating the plot, describe the shape, center, and spread of the two distributions. Based on the visualization, do you think the data looks like it supports the statement that “first born children either arrive early or arrive late when compared with non-first-borns”?
Group the variable prglngth into “first” and “other” births and compute the summary statistics (mean, median, standard deviation, inter-quartile range, minimum, maximum) for each group. How do the different summary statistics compare between the two distributions? Does it look like there may be a notable difference between the two distributions? Explain.
Plot the cumulative distribution functions (CDFs) of the pregnancy lengths for “first” and “other” births. The CDF for both distributions should be on the same figure so that we can directly compare them (hint: they should be two different colors and partially transparent). How do the distributions compare? Does it look like there is there a meaningful difference between the two distributions?
If we want to determine whether or not the difference between two distributions is statistically significant, we need to run a hypothesis test. Formalize your analysis by writing down the null hypothesis for the question of whether first babies arrive early or arrive late when compared with non-first-borns. It should be clear from how you write your null hypothesis whether you’re conducting a one-sided or two-sided hypothesis test. You should also restate what the observed value is (this will be a number you compute using the data in prglngth) that you will be comparing against the null distribution.
Use a simulation to generate the null distribution so that you can perform the hypothesis test. Do this using the functions provided in the infer package, specify(), hypothesize(), generate(), and calculate(). To collect enough statistics, you should set the simulation to repeat 10,000 times.

Once you’ve obtained the null distribution, get the p-value for your hypothesis test using get_pvalue(). Assuming a significance level of \(\alpha = 0.05\), state whether we can we reject the null hypothesis.

Finally, visualize the simulated null distribution using visualize() and the meaning of the p-value using shade_p_value().
Use a bootstrap simulation to calculate the 95% confidence interval for your observed value. Do this using the following functions from the infer package, specify(), `generate(), and calculate(). To collect enough statistics, you should set the bootstrap simulation to repeat 10,000 times.

Once you’ve obtained the bootstrap distribution, use get_confidence_interval() to find the upper and lower bounds of the 95% confidence interval. Does the value corresponding to the null result fall within the range of the 95% confidence interval?

Finally, visualize the bootstrap distribution using visualize() and show the 95% confidence interval using shade_confidence_interval().
In addition to hypothesis tests and confidence intervals, we should also consider the effect size, which measures the relative difference between two distributions. The effect size helps us better know how important a given result actually is, not just whether or not we can reject the null hypothesis. One measure of the effect size is called Cohen’s d (https://en.wikipedia.org/wiki/Effect_size#Cohen.27s_d), which we will use to compute the effect size between the pregnancy lengths for “first” and “other” births. The different ranges of d can be interpreted using the following table:

Effect size d

Very small 0.01

Small 0.20

Medium 0.50

Large 0.80

Very large 1.20

Huge 2.00

The following set of functions should also be preloaded for you: cohens_d_bootstrap(), bootstrap_report(), and plot_ci(). These functions will use bootstrap simulations to compute the confidence interval for the Cohen’s d parameter. Run the bootstrap simulation as follows (you can just copy and paste this code):
```
cohens_d_bootstrap(data = pregnancy_length, model = prglngth ~ birth_order)
```
Be sure to assign the results to a variable, for example bootstrap_results.

To print a report for the bootstrap simulation, run:
```
bootstrap_report(bootstrap_results)
```
To visualize the bootstrap distribution and confidence interval, run:
```
plot_ci(bootstrap_results)
```
Using the provided table, report how large the effect size is for the difference in pregnancy lengths for “first” and “other” births.

Effect size	d
Very small	0.01
Small	0.20
Medium	0.50
Large	0.80
Very large	1.20
Huge	2.00

How to submit

When you are ready to submit, be sure to save, commit, and push your final result so that everything is synchronized to GitHub. Then, knit your R Markdown document to PDF, export (download) the file from RStudio Server, and upload it to the Homework 3 posting on Blackboard.

You are to also open a Pull Request on GitHub so that comments can be directly left on your R Markdown source files. After uploading to Blackboard, navigate to your copy of the [GitHub repository][github-classroom] you used for this assignment. You should see your repository, along with the updated files that you synchronized to GitHub. Do the following:

Click the Pull Requests tab near the top of the page.
Click the green button that says “New pull request”.
Click the dropdown menu button labeled “base:”, and select the option grading.
Confirm that the dropdown menu button labeled “compare:” is set to master.
Click the green button that says “Create pull request”.
Give the pull request the following title: Submission: Homework 3, FirstName LastName, replacing FirstName and LastName with your actual first and last name.
In the messagebox, write: My homework submission is ready for grading @jkglasbrenner.
Click “Create pull request” to lock in your submission.

Cheatsheets

You are encouraged to review and keep the following cheatsheets handy while working on this assignment:

Week 14

2018-11-26T08:00:00-05:00

Inference and simulation

Homework assignment

Homework 3 (Due: Nov-30)

Readings

Week 14 readings (Due: Dec-02)

Week 13

2018-11-25T23:59:00-05:00

Introductory Statistics with Randomization and Simulation

Click here to download the textbook.

Chapter 2: Foundation for inference

From section 2.4 through to the end of section 2.5

Chapter 4: Inference for numerical data

Section 4.5, skipping subsection 4.5.3

Week 13

2018-11-19T08:00:00-05:00

Inference and simulation

Homework assignment

Homework 3 (Due: Nov-30)

Readings

Week 13 readings (Due: Nov-25)

Week 12

2018-11-18T23:59:00-05:00

Introductory Statistics with Randomization and Simulation

Click here to download the textbook.

Chapter 2: Foundation for inference

From the beginning up to the end of section 2.3

Week 12

2018-11-12T08:00:00-05:00

Inference and simulation

Homework assignment

Homework 3 (Due: Nov-27)

Readings

Week 12 readings (Due: Nov-18)

Slides

Week 11

2018-11-11T23:59:00-05:00

Introduction to computational and data sciences supplemental book

Chapter 4: Representing distributions

Introduction: http://book.cds101.com/representing-distributions.html
Section 4.1 – Probability mass functions: http://book.cds101.com/probability-mass-functions.html
Section 4.2 – Cumulative distribution functions: http://book.cds101.com/cumulative-distribution-functions.html

R for Data Science

Chapter 7: Exploratory data analysis

Full chapter: http://r4ds.had.co.nz/exploratory-data-analysis.html

Homework 2 (Group)

2018-11-09T23:59:00-05:00

Group submissions due: November 9, 2018 @ 11:59pm

Github Classroom repo for Homework 2 (Group)

Instructions

The Homework 2 questions and instructions from last time are available here: http://fall18.cds101.com/assignments/homework-2/

Overview

Your primary task is to use the R Markdown file homework_2_group.Rmd to write up your group’s answers to the homework assignment. Grades for the group submissions will, in addition to correctness, be based on document formatting, visualization quality, writing quality, and code style. This means that your group submission is to be written in the style of a exploratory data report, meaning:

Each exercise must be written up using full sentences such that it is clear what question is being answered.
There needs to be plain text above each code block explaining what you are doing, and the code blocks should be organized.
The R Markdown file must knit without error and generate a PDF file, and the final PDF output must look nice, clean, and be easy to read.

Participation

Credit for group participation will be determined using the following sources:

A CONTRIBUTIONS.md file distributed with your group repository
Commit history on GitHub
Discussion history in your group’s private Slack channel

Each group will need to fill out the CONTRIBUTIONS.md file as part of their submission. This file is where where each group member lists what he or she contributed to the final submission. Read the section Fill out the CONTRIBUTIONS.md file for more details on how this works.

Google Docs

If your group used an external document to coordinate and organize your work, such as a Google Doc, then that can also count as evidence of participation, provided there is a visible writing history and I can identify which student is responsible for each edit. This will require you to share the relevant file with the email address jglasbr2@gmu.edu and give me full privileges on the document so that I can review the document’s editing history. Please note that anonymous edits to Google Docs documents cannot be used as participation evidence, because I have no way to verify the account responsible for the added content. Also, for similar reasons, offline documents traded back and forth via email cannot be accepted as evidence of participation.

How to answer the questions as a group

The following is a checklist you may follow to help you get started with answering the questions as a group:

Share and discuss your individual submissions for Homework 2 with your assigned group and come to a consensus about the correct answer for each question.
As a group, decide who will be responsible for writing the answer to each question.
(Important) Before you start, make a copy of homework_2_group.Rmd and rename the copied file to include your last name. For example, if your last name is Smith, then your file copy should be renamed to homework_2_group_smith.Rmd.
Commit and push your copied file to GitHub. If you need a refresher on how to interact with GitHub, watch the linked video below:
Place your write-ups for your questions in your file. Again, if my last name was Smith, and I agreed to write-up the answers to questions 4, 5, and 6, then I would open up homework_2_group_smith.Rmd and put my answers there. When I’m done, I would save my file, then commit and push my work to GitHub.

How to edit and merge your answers into the group submission

While you will be writing your answers in separate files, eventually the group will need to merge in everyone’s answers into the homework_2_group.Rmd document. The following checklist may help with this:

Select an editor to be in charge of merging everyone’s answers into the final document homework_2_group.Rmd. Because the editor needs to take care of extra work to prepare the document for submission, it is reasonable if he or she does not contribute write-ups for as many of the questions as the rest of the group (for example, if everyone else answers three questions, the editor may only answer two).
The editor should ensure that everyone has committed and pushed their answers to GitHub so they can copy and paste in each contribution.
The editor needs to make sure that the final submission reads like a coherent document and that the writing style and code style are uniform across all the answers. In other words, it should read like a single person answered all the questions, not a group of four people.
The editor will be in charge of of committing and pushing the final R Markdown file to GitHub, knitting to PDF, and uploading the PDF file on Blackboard.

Fill out the CONTRIBUTIONS.md file

After everything is written up and ready for submission, the last thing the group will need to do is fill out the CONTRIBUTIONS.md file. By default, the file looks like this:

# Contributions to group submission

## Editor: FirstName LastName Member 1

*   Questions answered:

## FirstName LastName Member 2

*   Questions answered:

## FirstName LastName Member 3

*   Questions answered:

## FirstName LastName Member 4

*   Questions answered:

At a minimum, you must remove the FirstName LastName Member entries in the template and fill in the names of the people in your group, indicate which group member served as the editor, and state which questions were written up by each member.

Additional information beyond this should be supplied, such as indicating when a group member helped another group member edit an answer or gave helpful comments in a Slack discussion. For example, one group member’s contribution list may read as follows:

## Jane Smith

*   Questions answered: 4, 5, 6
*   Helped with editing on answers 8 and 9
*   Collaborated with group member Jack Williams on answering question 10
*   Pointed out spelling errors and suggested fixes to the document layout in the merged group document

Working with a GitHub repository as a group

You will likely encounter some issues while working in a group-based GitHub repository. In particular, you might find that when you click “Push” in the Git tab of RStudio, that it doesn’t seem to work and instead you get an annoying error message! This will happen when another member of your group has uploaded work before you did. While this can be irritating to deal with, this is actually a good thing, as it is GitHub’s way of protecting your files from accidential overwrites and deletions.

So what should you do to keep things running smoothly? First, always work in your own file, never in another person’s file. If you are not the editor, then you should not edit homework_2_group.Rmd either! Also, do not remove or rename any files that are not your own. Finally, when you are getting ready to work, following the procedure below should help keep the error messages to a minimum:

When you start working, you should begin by going to the Git tab and clicking “Pull” (notice this is not the same as “Push”). This will synchronize any new changes that your group may have made into your files.
Work on your file as normal. When you have completed your work, save your file.
Now we want to commit. But first, go to the Git tab and click “Pull” one more time to check for any other changes. Then, still in the Git tab, click the checkmark next to your updated file, type a message in the messagebox, and click the Commit button.
If the updated file is no longer in the list of files in the Git tab, then your commit was successful.
Click “Push” to upload your changed file.

If the above advice doesn’t work…

If, even after following the advice below, you still encounter error messages when Pulling from and Pushing to GitHub, please post about it in your group’s private Slack channel. Be sure to tag me using @Dr. Glasbrenner.

How to submit

When you are ready to submit, be sure that everyone in your group has committed and pushed their R Markdown files to Github. The editor should do the following in RStudio Server:

If it is not loaded already, open the RStudio project for the group homework assignment.
Click on the Git tab in the upper-right window, then click “Pull” to make sure you have the latest version of all the files.
Press Ctrl+Shift+B to knit the final submission to the PDF format.
Download the PDF file to your computer.
On Blackboard, find the Homework 2 (Group) submission link for your group. Submit the PDF for your group to lock in your submission time.

For the group submissions, you do not need to open up a Pull Request on GitHub.

Cheatsheets

You are encouraged to review and keep the following cheatsheets handy while working on this assignment:

DataCamp exercises (Week 10)

2018-11-05T23:59:00-05:00

Instructions

On Datacamp, watch the tutorial videos and complete the interactive coding challenges from the following lessons, which will let you practice and supplement this week’s content.

Important!

Your progress through these lessons is being tracked and completing them counts towards the Module exercises category of your grade.

From Working with data in the Tidyverse (Due: Nov-05)

Tidy your data
Transform your data

Week 11

2018-11-05T08:00:00-05:00

Data distributions

Homework assignment

Homework 2 (Group) (Due: Nov-09)

Readings

Week 11 readings (Due: Nov-11)

Videos

(Due: Nov-11)

Data distributions - 01 - Quantifying data distributions

Data distributions - 02 - Quantifying data distributions in R

Data distributions - 03 - Probability mass functions

Data distributions - 04 - Cumulative distribution functions

Slides

DataCamp exercises (Week 9)

2018-10-29T23:59:00-04:00

Instructions

On Datacamp, watch the tutorial videos and complete the interactive coding challenges from the following lessons, which will let you practice and supplement this week’s content.

Important!

Your progress through these lessons is being tracked and completing them counts towards the Module exercises category of your grade.

From Working with data in the Tidyverse (Due: Oct-29)

You will be completing the full course, Working with data in the Tidyverse, over the next two weeks. Chapter 3 of the course, “Tidy your data”, provides you with practice using the tidyr package. However, the lessons in chapter 3 assume you’re familiar with the content in chapters 1 and 2, so it’s best to complete those first.

Explore your data
Tame your data

From Working with data in the Tidyverse (Due: Nov-05)

These are the module exercises for week 10. If you complete the first two chapters and want to get started on chapters 3 and 4 now, you may do so.

Tidy your data
Transform your data

Week 9

2018-10-28T23:59:00-04:00

R for Data Science

From chapter 12: from the beginning through to the end of section 12.5

Homework 2

2018-10-26T23:59:00-04:00

Individual submissions due: October 26, 2018 @ 11:59pm

Github Classroom repo for Homework 2

Instructions

Obtain the Github repository you will use to complete homework 2, which contains a starter RMarkdown file named homework_2.Rmd, which you will use to do your work and write-up when completing the questions below. Be sure to save, commit, and push (upload) frequently to Github so that you have incremental snapshots of your work. When you’re done, follow the How to submit section below to setup a Pull Request, which will be used for feedback.

Individual rough draft versus the group draft

Remember that you will be working through the homework assignment individually first (this is what is due by the end of the day on October 26th), and later you will be assigned to a group where you will clean up the draft. As written in the course syllabus:

Homework assignments will completed in two stages. The first is the “rough draft” stage that each student must complete on their own. Grades for these submissions will be primarily based on the correctness of your answers to each question. The second is the “group draft” stage, which will begin approximately 3 days after the rough drafts are due. You will be assigned into groups where you will discuss the questions and assemble a final draft together. You will not know these groups beforehand, and they will change with each assignment. Grades for the group-submitted final drafts will, in addition to correctness, be based on document formatting, visualization quality, writing quality, and code style.

Both the individual rough draft and group report homework submissions are to be completed using the R Markdown format and they must successfully knit to HTML and PDF in a clean RStudio environment. In the individual submissions, full sentences are required when the question is asking for a written response. In the group reports, full sentences with proper grammar and punctuation are to be used throughout the report. The group reports should explain what you are doing with each chunk of code and to interpret the meaning of what you calculate so that a person that is not familiar with the problem could follow your logic.

Important! Several of these qucstions ask for explanations after obtaining the correct answer. For these, you do not have to write very long responses, just write enough to express the correct answer. When you are assigned into groups, that is when you will need to write everything out in full sentences.

Overview

A photograph of the Titanic leaving Southampton on April 10, 1912.

For this homework assignment, you will be exploring a dataset about the passengers on the Titanic, the British passenger liner that crashed into an iceberg during its maiden voyage and sank early in the morning on April 15, 1912. The tragedy stands out as one of the deadliest commercial maritime disasters during peacetime in history. More than half of the passengers and crew died, due in large part to poor safety standards, such as not having enough lifeboats or not ensuring all lifeboats were filled to capacity during evacuation.

This dataset presents the most up-to-date knowledge about the passengers that were on the Titanic, including whether or not they survived. This dataset is frequently used to introduce using machine learning techniques that take multiple inputs and use them to predict an outcome, in this case whether a passenger is likely to have survived. While we won’t be using a machine learning model in this assignment, there is still a lot of information that can be learned by exploring the dataset using the tidyverse suite.

About the dataset

The following are the variable (column) descriptions for the dataset:¹

Variable	Description
pclass	Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd)
survival	Survival (0 = No; 1 = Yes)
name	Name
sex	Sex
age	Age
sibsp	Number of Siblings/Spouses Aboard
parch	Number of Parents/Children Aboard
ticket	Ticket Number
fare	Passenger Fare (British pound)
cabin	Cabin
embarked	Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)
boat	Lifeboat
body	Body Identification Number
home.dest	Home/Destination

Also note that the following definitions were used for sibsp and parch:

Label	Definition
Sibling	Brother, Sister, Stepbrother, or Stepsister of Passenger Aboard Titanic
Spouse	Husband or Wife of Passenger Aboard Titanic (Mistresses and Fiances Ignored)
Parent	Mother or Father of Passenger Aboard Titanic
Child	Son, Daughter, Stepson, or Stepdaughter of Passenger Aboard Titanic

Questions

When reading in the dataset using read_csv(file = "titanic_dataset.csv"), several of the columns are converted into inconvenient data types. Fix this so that your later analysis does not run into problems. Use the col_types = cols() argument within read_csv(), see this section of R for Data Science for a review, to change the data type defaults for the following columns:
- Convert survived to the logical data type
- Convert pclass to the character data type
- Convert sibsp to the character data type
- Convert parch to the character data type
Compute how many known passengers were on the Titanic. Do not just print the table, use a function to count the passengers.
A famous directive for evacuating the Titanic was “women and children first”. Use your dplyr functions to verify the first part of this statement by counting the number of men and women that survived and that died. Then, using those counts, calculate the fraction of women that survived, \[\dfrac{\text{Number of female survivors}}{\text{Total number of female passengers}}\] and the fraction of men that survived, \[\dfrac{\text{Number of male survivors}}{\text{Total number of male passengers}}\] Do your computations support the idea that women were more likely to survive? Why or why not?
Verify the second part of the “women and children first” directive. This will not be as straightforward as it was in the previous question, as the dataset only contains people’s ages, which can take on many values. By default, there are no columns with labels of child or adult, so you will need to create your own.

Create a new column named child_or_adult that uses the age data to label each passenger. For our purposes, we want to label anyone aged 0 – 9 as a child and anyone age 10 and up are as adults. If the age cell is blank (NA) for a passenger, also label them as an “adult”. Assign this updated dataset to the variable titanic_age_groups.
Hint: You will need to use the if_else() function to complete this task. An example usage of if_else() is the following:
```
titanic %>%
  mutate(
    cheap_or_expensive = if_else(
      condition = fare < 15,
      true = "cheap ticket",
      false = "not cheap",
      missing = "price unknown"
    )
  )
```
The input missing can be used to handle blank entries. In the above example, NA will be replaced with the text "price unknown".
Using the titanic_age_groups dataset you created in the previous question, count the number of children that survived and the number that did not. Do your computations support the idea that children were also more likely to survive? Why or why not?
A passenger’s age group and sex are not the only predictors of survival. For example, social standing and wealth can play a factor in survival. One of the parameters within this dataset acts as a proxy for distinguishing between the upper and lower classes. Which parameter is it? How do you know?
Group your dataset by sex and the variable you determined in question 6 and count the number that survived and the number that did not. Create a bar chart that summarizes the data, where survived is along the horizontal axis and the passenger counts are along the vertical axis. Use the bar chart fill = aesthetic to break the bar charts down by your variable from question 6. Additionally, facet over the sex variable. Interpret this visualization and describe any survival patterns that you notice.
Create two visualizations:
- The first visualization should be a bar chart displaying the fraction of the passengers that survived for different values of parch, \[\dfrac{\text{For a given parch, the number of survivors}}{\text{Total number of passengers}}.\] Doing this requires grouping your data properly, counting the number of passengers in each grouping, and then dividing this by the total number of passengers on the ship.
- The second visualization should be a bar chart displaying the fraction of the passengers that survived for different values of sibsp, \[\dfrac{\text{For a given sibsp, the number of survivors}}{\text{Total number of passengers}}.\] Like above, doing this requires grouping your data properly, counting the number of passengers in each grouping, and then dividing this by the total number of passengers on the ship.
Interpret the patterns that you see in the visualizations.
Based on your analysis, write a list of the factors that affected the chances of survival for each passenger. You should be able to identify 4 different attributes that had a noticeable impact on survival. Justify each attribute that you list by referencing back to a table or visualization you created in a previous question.

How to submit

When you are ready to submit, be sure to save, commit, and push your final result so that everything is synchronized to Github. Then, navigate to your copy of the [Github repository][github-classroom] you used for this assignment. You should see your repository, along with the updated files that you just synchronized to Github. Confirm that your files are up-to-date, and then do the following steps:

Click the Pull Requests tab near the top of the page.
Click the green button that says “New pull request”.
Click the dropdown menu button labeled “base:”, and select the option grading.
Confirm that the dropdown menu button labeled “compare:” is set to master.
Click the green button that says “Create pull request”.
Give the pull request the following title: Submission: Homework 2, FirstName LastName, replacing FirstName and LastName with your actual first and last name.
In the messagebox, write: My homework submission is ready for grading @jkglasbrenner.
Click “Create pull request” to lock in your submission.

Cheatsheets

You are encouraged to review and keep the following cheatsheets handy while working on this assignment:

http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.html ↩

DataCamp exercises (Week 8)

2018-10-22T23:59:00-04:00

Instructions

On Datacamp, watch the tutorial videos and complete the interactive coding challenges from the following lessons, which will let you practice and supplement this week’s content.

Important!

Your progress through these lessons is being tracked and completing them counts towards the Module exercises category of your grade.

From Introduction to the Tidyverse

Grouping and summarizing

Week 9

2018-10-22T08:00:00-04:00

Data reshaping

Homework assignment

Homework 2 (continued) (Due: Oct-26)

Module exercises

DataCamp exercises (Week 9) (Due: Oct-29)

Readings

Week 9 readings (Due: Oct-28)

Videos

To be posted…

Slides

Week 8

2018-10-21T23:59:00-04:00

R for Data Science

From chapter 5: sections 5.5 through 5.7

Week 8

2018-10-16T08:00:00-04:00

Data transformation

Homework assignment

Homework 2 (Due: Oct-26)

Module exercises

DataCamp exercises (Week 8) (Due: Oct-22)

Readings

Week 8 readings (Due: Oct-21)

Videos

(Due: Oct-21)

Starter files for lecture demos

Below is the download link that you should click to get a copy of the starter files you’ll need to have when following along with the lecture demos.

dplyr demonstration starter files

Data transformation - 09 - The mutate function

Data transformation - 10 - The group_by and summarize functions

Data transformation - 11 - Other helpful dplyr verbs

Slides

Homework 1 (Group)

2018-10-15T23:59:00-04:00

Individual submissions due: October 15, 2018 @ 11:59pm

Github Classroom repo for Homework 1 (Group)

Instructions

The Homework 1 questions and instructions from last time are available here: http://fall18.cds101.com/assignments/homework-1/

Overview

Your primary task is to use the R Markdown file homework_1_group.Rmd to write up your group’s answers to the homework assignment. Grades for the group submissions will, in addition to correctness, be based on document formatting, visualization quality, writing quality, and code style. This means that your group submission is to be written in the style of a exploratory data report, meaning:

Each exercise must be written up using full sentences such that it is clear what question is being answered.
There needs to be plain text above each code block explaining what you are doing, and the code blocks should be organized.
The R Markdown file must knit without error and generate a PDF file, and the final PDF output must look nice, clean, and be easy to read.

Participation

Credit for group participation will be determined using the following sources:

A CONTRIBUTIONS.md file distributed with your group repository
Commit history on GitHub
Discussion history in your group’s private Slack channel

Google Docs

How to answer the questions as a group

The following is a checklist you may follow to help you get started with answering the questions as a group:

Share and discuss your individual submissions for Homework 1 with your assigned group and come to a consensus about the correct answer for each question.
As a group, decide who will be responsible for writing the answer to each question.
(Important) Before you start, make a copy of homework_1_group.Rmd and rename the copied file to include your last name. For example, if your last name is Smith, then your file copy should be renamed to homework_1_group_smith.Rmd.
Commit and push your copied file to GitHub. If you need a refresher on how to interact with GitHub, watch the linked video below:
Place your write-ups for your questions in your file. Again, if my last name was Smith, and I agreed to write-up the answers to questions 4, 5, and 6, then I would open up homework_1_group_smith.Rmd and put my answers there. When I’m done, I would save my file, then commit and push my work to GitHub.

How to edit and merge your answers into the group submission

While you will be writing your answers in separate files, eventually the group will need to merge in everyone’s answers into the homework_1_group.Rmd document. The following checklist may help with this:

Select an editor to be in charge of merging everyone’s answers into the final document homework_1_group.Rmd. Because the editor needs to take care of extra work to prepare the document for submission, it is reasonable if he or she does not contribute write-ups for as many of the questions as the rest of the group (for example, if everyone else answers three questions, the editor may only answer two).
The editor should ensure that everyone has committed and pushed their answers to GitHub so they can copy and paste in each contribution.
The editor needs to make sure that the final submission reads like a coherent document and that the writing style and code style are uniform across all the answers. In other words, it should read like a single person answered all the questions, not a group of four people.
The editor will be in charge of of committing and pushing the final R Markdown file to GitHub, knitting to PDF, and uploading the PDF file on Blackboard.

Fill out the CONTRIBUTIONS.md file

After everything is written up and ready for submission, the last thing the group will need to do is fill out the CONTRIBUTIONS.md file. By default, the file looks like this:

# Contributions to group submission

## Editor: FirstName LastName Member 1

*   Questions answered:

## FirstName LastName Member 2

*   Questions answered:

## FirstName LastName Member 3

*   Questions answered:

## FirstName LastName Member 4

*   Questions answered:

## Jane Smith

*   Questions answered: 4, 5, 6
*   Helped with editing on answers 8 and 9
*   Collaborated with group member Jack Williams on answering question 10
*   Pointed out spelling errors and suggested fixes to the document layout in the merged group document

Working with a GitHub repository as a group

So what should you do to keep things running smoothly? First, always work in your own file, never in another person’s file. If you are not the editor, then you should not edit homework_1_group.Rmd either! Also, do not remove or rename any files that are not your own. Finally, when you are getting ready to work, following the procedure below should help keep the error messages to a minimum:

When you start working, you should begin by going to the Git tab and clicking “Pull” (notice this is not the same as “Push”). This will synchronize any new changes that your group may have made into your files.
Work on your file as normal. When you have completed your work, save your file.
Now we want to commit. But first, go to the Git tab and click “Pull” one more time to check for any other changes. Then, still in the Git tab, click the checkmark next to your updated file, type a message in the messagebox, and click the Commit button.
If the updated file is no longer in the list of files in the Git tab, then your commit was successful.
Click “Push” to upload your changed file.

If the above advice doesn’t work…

How to submit

When you are ready to submit, be sure that everyone in your group has committed and pushed their R Markdown files to Github. The editor should do the following in RStudio Server:

If it is not loaded already, open the RStudio project for the group homework assignment.
Click on the Git tab in the upper-right window, then click “Pull” to make sure you have the latest version of all the files.
Press Ctrl+Shift+B to knit the final submission to the PDF format.
Download the PDF file to your computer.
On Blackboard, find the Homework 1 (Group) submission link for your group. Submit the PDF for your group to lock in your submission time.

For the group submissions, you do not need to open up a Pull Request on GitHub.

Cheatsheets

You are encouraged to review and keep the following cheatsheets handy while working on this assignment:

DataCamp exercises (Week 6)

2018-10-08T23:59:00-04:00

Instructions

On Datacamp, watch the tutorial videos and complete the interactive coding challenges from the following lessons, which will let you practice and supplement this week’s content.

Important!

Your progress through these lessons is being tracked and completing them counts towards the Module exercises category of your grade.

From Introduction to the Tidyverse

Data wrangling

Week 6

2018-10-07T23:59:00-04:00

R for Data Science

All of chapter 4 (short)

From chapter 5: sections 5.1 through 5.4

Homework 1

2018-10-02T23:59:00-04:00

Individual submissions due: October 2, 2018 @ 11:59pm

Github Classroom repo for Homework 1

Instructions

Obtain the Github repository you will use to complete homework 1, which contains a starter RMarkdown file named homework_1.Rmd, which you will use to do your work and write-up when completing the questions below. Be sure to save, commit, and push (upload) frequently to Github so that you have incremental snapshots of your work. When you’re done, follow the How to submit section below to setup a Pull Request, which will be used for feedback.

Individual rough draft versus the group draft

Remember that you will be working through the homework assignment individually first (this is what is due by the end of the day on October 2nd), and later you will be assigned to a group where you will clean up the draft. As written in the course syllabus:

Homework assignments will completed in two stages. The first is the “rough draft” stage that each student must complete on their own. Grades for these submissions will be primarily based on the correctness of your answers to each question. The second is the “group draft” stage, which will begin approximately 3 days after the rough drafts are due. You will be assigned into groups where you will discuss the questions and assemble a final draft together. You will not know these groups beforehand, and they will change with each assignment. Grades for the group-submitted final drafts will, in addition to correctness, be based on document formatting, visualization quality, writing quality, and code style.

Both the individual rough draft and group report homework submissions are to be completed using the R Markdown format and they must successfully knit to HTML and PDF in a clean RStudio environment. In the individual submissions, full sentences are required when the question is asking for a written response. In the group reports, full sentences with proper grammar and punctuation are to be used throughout the report. The group reports should explain what you are doing with each chunk of code and to interpret the meaning of what you calculate so that a person that is not familiar with the problem could follow your logic.

Important! Several of these questions ask for explanations after obtaining the correct answer. For these, you do not have to write very long responses, just write enough to express the correct answer. When you are assigned into groups, that is when you will need to write everything out in full sentences.

The rail trail dataset

For this homework assignment, you will be working though a set of visualization problems based on the rail_trail dataset. The rail_trail dataset was collected by the Pioneer Valley Planning Commission (PVPC) and counts the number of people that walked through a sensor on a rail trail during a ninety day period. A rail trail is a retired or abandoned railway that was converted into a walking trail. The data was collected from April 5, 2005 to November 15, 2005 using a laser sensor placed at a location north of Chestnut Street in Florence, MA.

The dataset contains the following variables:

Variable	Description
`hightemp`	daily high temperature (in degrees Fahrenheit)
`lowtemp`	daily low temperature (in degrees Fahrenheit)
`avgtemp`	average of daily low and daily high temperature (in degrees Fahrenheit)
`season`	indicates whether the season was Spring, Summer, or Fall
`cloudcover`	measure of cloud cover (in oktas)
`precip`	measure of precipitation (in inches)
`volume`	estimated number of trail users that day (number of breaks recorded)
`weekday`	indicator of whether the day was a non-holiday weekday

How to describe your visualizations

When describing the contents of a visualization, follow the ideas discussed in these resources:

Questions

In the rail_trail dataset, how many rows are there? How many columns? Which variables in the dataset are continuous/numerical and which are categorical?
Create a histogram of the variable volume using the following code:
```
ggplot(data = rail_trail) +
  geom_histogram(mapping = aes(x = volume))
```
Describe the shape and center of the distribution. Afterward, try adjusting the size of the histogram bins by adding the binwidth input. To start with, use binwidth = 21. If you need help with where to place binwidth, read the documentation by running ?geom_histogram in your Console window. Then, find a binwidth that’s too narrow and another one that’s too wide to produce a meaningful histogram.
Choosing a proper bin width for a histogram can be tricky, and for that reason it’s preferable to use visualizations that avoid using bin widths when possible. An easy-to-use alternative to the histogram is geom_density, which creates a density plot. Use geom_density to create a density plot of the variable volume.
Create a density plot for each of the remaining numerical variables, and describe the shape and center of each distribution. Are there any distributions that are similar in shape to each other?
Use geom_point() to create a scatterplot that plots weekday versus season. Why is this plot not useful?
Create a geom_count() plot (an alternative to a mosaic plot) using the same variables you considered in question 5:
```
ggplot(data = rail_trail) +
  geom_count(mapping = aes(x = season, y = weekday))
```
Which circle in the plot takes up the most area? Explain the meaning of the different size circles in the plot and what information it contains that is missing in the previous scatter plot.
Run ?geom_bar in the Console window and read the documentation for geom_bar(), and then look at the entry for it on the ggplot2 cheatsheet Use geom_bar() to reproduce the following bar chart:

After reproducing the plot, explain what the height of each bar means.
Starting from the code snippet you deduced in question 7, create two more bar charts:
- Create a bar chart by supplying the input position = "dodge" to geom_bar()
- Create a bar chart by supplying the input position = "fill" to geom_bar().
After creating the visualizations, describe the feature that position controls.
Create a bar chart that maps its aesthetic aes() to precip > 0. Interpret what this bar chart means.
Create a scatter plot of volume versus hightemp using geom_point(). Describe any trends that you see.
Take the code snippet you wrote for question 10 and map the weekday variable to color. Then create a second plot where, instead of mapping weekday to color, you facet over weekday using either facet_wrap() or facet_grid(). Discuss the advantages and disadvantages to faceting instead of mapping to the color aesthetic. How might the balance change if you had a larger dataset?
Take the code snippet that you wrote down in question 11 that faceted over weekday and create a model for each facet panel using geom_smooth(). Discuss the trends in the number of rail trail users that geom_smooth() picks up.
Copy the code snippet you deduced in question 12 and use the input se = FALSE for geom_smooth(). What does the se input option for geom_smooth() control?

How to submit

When you are ready to submit, be sure to save, commit, and push your final result so that everything is synchronized to GitHub. To lock in your submission time, knit your R Markdown document to PDF, download the file from RStudio Server, and upload it to the R Markdown mini-assignment posting on Blackboard.

You are to also open a Pull Request on GitHub so that comments can be directly left on your R Markdown source files. After uploading to Blackboard, navigate to your copy of the GitHub repository you used for this assignment. You should see your repository, along with the updated files that you synchronized to GitHub. Do the following:

Click the Pull Requests tab near the top of the page.
Click the green button that says “New pull request”.
Click the dropdown menu button labeled “base:”, and select the option grading.
Confirm that the dropdown menu button labled “compare:” is set to master.
Click the green button that says “Create pull request”.
Give the pull request the following title: Submission: Homework 1, FirstName LastName, replacing FirstName and LastName with your actual first and last name.
In the messagebox, write My submission is ready for grading @jkglasbrenner.
Click “Create pull request” to lock in your submission.

Cheatsheets

You are encouraged to review and keep the following cheatsheets handy while working on this assignment:

DataCamp exercises (Week 5)

2018-10-01T23:59:00-04:00

Instructions

On Datacamp, watch the tutorial videos and complete the interactive coding challenges from the following lessons, which will let you practice and supplement this week’s content.

Important!

Your progress through these lessons is being tracked and completing them counts towards the Module exercises category of your grade.

From Data Visualization with ggplot2 (Part 1)

Aesthetics
Geometries

Week 6

2018-10-01T08:00:00-04:00

Data transformation

Homework assignment

Homework 1 (continued)

Module exercises

DataCamp exercises (Week 6)

Readings

Week 6 readings

Videos

Starter files for lecture demos

Below is the download link that you should click to get a copy of the starter files you’ll need to have when following along with the lecture demos.

dplyr demonstration starter files

Data transformation - 01 - What is data wrangling?

Data transformation - 02 - The presidential dataset

Data transformation - 03 - The select function

Data transformation - 04 - The pipe operator

Data transformation - 05 - The arrange function

Data transformation - 06 - The slice function

Data transformation - 07 - Boolean logic

Data transformation - 08 - The filter function

Slides

Week 5

2018-09-30T23:59:00-04:00

R for Data Science

From chapter 3: sections 3.6 through 3.10

DataCamp exercises (Week 4)

2018-09-25T23:59:00-04:00

Instructions

On Datacamp, watch the tutorial videos and complete the interactive coding challenges from the following lessons, which will let you practice and supplement this week’s content.

Important!

Your progress through these lessons is being tracked and completing them counts towards the Module exercises category of your grade.

From Data Visualization with ggplot2 (Part 1)

Introduction

R Markdown mini-assignment

2018-09-24T23:59:00-04:00

Due: September 24, 2018 @ 11:59pm

Instructions

First-time RStudio Server setup

Important! Follow all the directions in the repository linked below before starting work on this mini-assignment.

RStudio Server Configuration repository

Obtain the GitHub repository for the activity, which contains a starter file named rmarkdown.Rmd. Use this file to re-create the layout of rmarkdown-mini-assignment-template.html using Markdown. You will knit your file to the HTML format. When you’re done, save your file, commit, push (upload) it to GitHub, and follow the How to submit section below to setup a Pull Request, which will be used for feedback.

How to submit

When you are ready to submit, be sure to save, commit, and push your final result so that everything is synchronized to GitHub. To lock in your submission time, knit your R Markdown document to HTML, download the file from RStudio Server, and upload it to the R Markdown mini-assignment posting on Blackboard.

Click the Pull Requests tab near the top of the page.
Click the green button that says “New pull request”.
Click the dropdown menu button labeled “base:”, and select the option grading.
Confirm that the dropdown menu button labled “compare:” is set to master.
Click the green button that says “Create pull request”.
Give the pull request the following title: Submission: R Markdown mini-assignment, FirstName LastName, replacing FirstName and LastName with your actual first and last name.
In the messagebox, write My submission is ready for grading @jkglasbrenner.
Click “Create pull request” to lock in your submission.

Cheatsheets

You are encouraged to review and keep the following cheatsheets handy while working on this assignment:

Visualization mini-assignment

2018-09-24T23:59:00-04:00

Due: September 24, 2018 @ 11:59pm

Instructions

First-time RStudio Server setup

Important! Follow all the directions in the repository linked below before starting work on this mini-assignment.

RStudio Server Configuration repository

Obtain the GitHub repository you will use to complete the mini-assignment, which contains a starter file named visualization_mini-assignment.Rmd. The demos and exercises you will be completing are written inside visualization_mini-assignment.Rmd, complete them within the indicated spaces. You should knit your file to the HTML format to check how it looks. When you’re done, save your file, commit, push (upload) it to GitHub, and follow the How to submit section below to setup a Pull Request, which will be used for feedback.

How to submit

When you are ready to submit, be sure to save, commit, and push your final result so that everything is synchronized to GitHub. To lock in your submission time, knit your R Markdown document to HTML, download the file from RStudio Server, and upload it to the R Markdown mini-assignment posting on Blackboard.

Click the Pull Requests tab near the top of the page.
Click the green button that says “New pull request”.
Click the dropdown menu button labeled “base:”, and select the option grading.
Confirm that the dropdown menu button labled “compare:” is set to master.
Click the green button that says “Create pull request”.
Give the pull request the following title: Submission: Visualization mini-assignment, FirstName LastName, replacing FirstName and LastName with your actual first and last name.
In the messagebox, write My submission is ready for grading @jkglasbrenner.
Click “Create pull request” to lock in your submission.

Cheatsheets

You are encouraged to review and keep the following cheatsheets handy while working on this assignment:

Week 5

2018-09-24T08:00:00-04:00

Data visualization

Homework assignment

Homework 1

Module exercises

DataCamp exercises (Week 5)

Readings

Week 5 readings

Videos

Data visualization - 03 - Describing visualizations

Data visualization - 04 - Data visualization with ggplot2

Slides

Week 4

2018-09-23T23:59:00-04:00

R for Data Science

From chapter 3: sections 3.1 through 3.5

Introduction to computational and data sciences supplemental book

All of chapter 3 (short)

DataCamp exercises (Week 3)

2018-09-18T23:59:00-04:00

Instructions

You should have received an invitation in your Mason email address to register and join DataCamp. If you have not done so already, complete the registration so that you can join the Introduction to Computational and Data Sciences class there. You are to watch the tutorial videos and complete the interactive coding challenges from the following lessons, which will let you practice and supplement this week’s content.

Important!

Your progress through these lessons is being tracked and completing them counts towards the Module exercises category of your grade.

From Introduction to R

Intro to basics
Vectors

From Reporting with R Markdown

Authoring R Markdown reports
Embedding Code

From Working with the RStudio IDE Part 1

Orientation

From Working with the RStudio IDE Part 2

Version Control

Week 4

2018-09-17T08:00:00-04:00

Data visualization

Mini-assignments

First-time RStudio Server setup

Important! Follow all the directions in the repository linked below before starting work on the two mini-assignments.

RStudio Server Configuration repository

Module exercises

DataCamp exercises (Week 4)

Readings

Week 4 readings

Videos

Data visualization - 01 - Data visualization as communication

Data visualization - 02 - Examining numerical data

Slides

Week 3

2018-09-16T23:59:00-04:00

R Markdown: The Definitive Guide

From chapter 2: Ch. 2 introduction, section 2.2, section 2.3, section 2.5, section 2.6

Week 3

2018-09-10T08:00:00-04:00

Reproducible toolbox

Module exercises

DataCamp exercises (Week 3)

Videos

01 - Reproducible toolbox - Reproducibility in science

02 - Reproducible toolbox - Reproducible research toolkit

Slides

Week 2

2018-09-09T23:59:00-04:00

R for Data Science

All of chapter 26 (short)
All of chapter 27

Introduction to computational and data sciences supplemental book

From chapter 2: sections 2.1, 2.2, and 2.3

Can Twitter predict election results?

2018-09-03T00:00:00-04:00

Due: September 3, 2018 2018 @ 12:00am

Appearances of candidates’ names on Twitter can help predict election results

The promise of using data science to predict election results has been received a lot of attention over the last decade. A lot of attention has focused on using polls to create models and predict results, such as what Nate Silver does over at fivethirtyeight.com. Besides polls, other kinds of data have been focused on for making predictions about elections. Consider the following 2013 study by a group of researchers at Indiana University. They wanted to see if the appearances of candidates’ names on Twitter can help predict election results.

The Washington Post picked this up in an editorial, written by one of the study’s authors:

New research in computer science, sociology and political science shows that data extracted from social media platforms yield accurate measurements of public opinion. It turns out that what people say on Twitter or Facebook is a very good indicator of how they will vote.

How good? […] co-authors Joseph DiGrazia, Karissa McKelvey, Johan Bollen and I show that Twitter discussions are an unusually good pre- dictor of U.S. House elections. Using a massive archive of billions of randomly sampled tweets stored at Indiana University, we extracted 542,969 tweets that mention a Democratic or Republican candidate for Congress in 2010. For each congressional district, we computed the percentage of tweets that mentioned these candidates. We found a strong correlation between a candidate’s tweet share and the final two-party vote share, especially when we account for a district’s economic, racial and gender profile. In the 2010 data, our Twitter data predicted the winner in 404 out of 406 competitive races.

Data science

This is a true data science research project, in that:

The data being analyzed was scraped from the Internet, not collected from a controlled, randomized trial. Typical statistical assumptions about random sampling, etc. do not apply!
The research question is addressed by combining domain knowledge (i.e. knowledge of how Congressional races work) with a data source (Twitter) that has no obvious relevance.
A large amount of data (500 million tweets!) was collected. [Note: only 500,000 tweets were analyzed.] In this case, the data was big enough that the Center for Complex Networks and Systems Research had to get involved!
The project was undertaken by a team of researchers from different fields (i.e. sociology, computing) working in different departments, and bringing different skills to the table.

Read the article like a scientist

Spend a few minutes reading the Rojas editorial and skimming the actual paper. Be sure to consider Figure 1 and Table 1 carefully, and address the questions below.

Write your responses in word processing software and export your submission to the PDF format before uploading it to the Blackboard submission link. Your responses should be written using full sentences and proper spelling and grammar.

Write a sentence summarizing the findings of the paper.
Think critically about the data visualization shown in Figure 1 and write what you observe about it. What is the figure’s purpose? What does it convey? If you were to create it, what would you do differently?
Now discuss the differences between the Bivariate model and the Full Model. Which one do you think does a better job of predicting the outcome of an election? Which one do you think best addresses the influence of tweets on an election?
Why do you suppose that the coefficient of RepublicanTweetShare is so much larger in the Bivariate model? How does this reflect on the influence of tweets in an election?
Do you think the study holds water? Why or why not? What are the shortcomings of this study?

Credits

This module exercise is a derivative of Can Twitter predict election results? by Mine Çetinkaya-Rundel used under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Credit for the dataset example belongs to Ben Baumer. Adapted by James Glasbrenner and licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Week 1

2018-09-03T00:00:00-04:00

R for Data Science

All of chapter 1

Introductory Statistics with Randomization and Simulation

All of Chapter 1, except skip sections 1.3.5, all of 1.4, all of 1.5, skip 1.6.8.

Fall 2018 Schedule

2018-08-27T09:00:00-04:00

Week	Dates	Topic	Assignments
Week 1	Aug-27 – Aug-31	Course orientation	Week 1 readings Week 1 module exercise
Week 2	Sep-03 – Sep-07 Labor Day	What are the computational and data sciences?	Week 2 readings
Week 3	Sep-10 – Sep-14	Course toolbox	Week 3 readings Week 3 module exercises
Week 4	Sep-17 – Sep-21	Data visualization	Week 4 readings Week 4 module exercises R Markdown mini-assignment Visualization mini-assignment
Week 5	Sep-24 – Sep-28	Data visualization	Week 5 readings Week 5 module exercises Homework 1
Week 6	Oct-01 – Oct-05	Data transformation	Week 6 readings Week 6 module exercises Homework 1 (continued)
Week 7	Oct-08 – Oct-12 Columbus Day	Midterm Exam week	Midterm Exam Homework 1 (Group)
Week 8	Oct-15 – Oct-19	Data transformation	Week 8 readings (Due: Oct-21) Week 8 module exercises (Due: Oct-22) Homework 2 (Due: Oct-26)
Week 9	Oct-22 – Oct-26	Data reshaping (Tidy data)	Week 9 readings (Due: Oct-28) Week 9 module exercises (Due: Oct-29) Homework 2 (continued) (Due: Oct-26)
Week 10	Oct-29 – Nov-02	Catch-up week	Week 10 module exercises (Due: Nov-05)
Week 11	Nov-05 – Nov-09	Data distributions	Week 11 readings (Due: Nov-11) Homework 2 (Group) (Due: Nov-09)
Week 12	Nov-12 – Nov-16	Inference and simulation	Week 12 readings (Due: Nov-18) Homework 3 (Due: Nov-27)
Week 13	Nov-19 – Nov-23 Thanksgiving Day	Inference and simulation	Week 13 readings (Due: Nov-25) Homework 3 (continued) (Due: Nov-30)
Week 14	Nov-26 – Nov-30	Inference and simulation	Week 14 readings (Due: Dec-02) Homework 3 (continued) (Due: Nov-30)
Week 15	Dec-03 – Dec-07	Final project	Final project question list (Due: Dec-07)
Week 16	Dec-10 – Dec-14	Final project	Final project report (Due: Dec-14)

CDS 101 - DL1 Syllabus

2018-08-27T08:00:00-04:00

Description

During this course, students will develop basic skills for obtaining, cleaning, transforming, and visualizing real-world datasets using the R programming language and the RStudio integrated development environment. Statistical methods for analyzing, interpreting, and predicting dataset trends are then introduced and approached from a computational point of view using randomization and simulation. Additional topics may be covered, such as an introduction to advanced or special topics like cross-validation. Throughout the course emphasis is placed on documenting one’s scientific work using RStudio in conjunction with Github to fulfill the principles of reproducible research. Connections are highlighted between statistical inference and the scientific method and how this is related to both the scientific method’s power and its limitations. These tools will also be used to critically examine statistical claims reported in mass media, demonstrating how scientific literacy and a basic knowledge in statistics are indispensable tools to making sense of our modern world.

Classroom: Online
Meeting times: Asynchronous
University holidays: Labor Day, September 3, Columbus Day, October 8, Thanksgiving Recess, November 21 – 25
Credit hours: 3.0 credit hours
Prerequisites: None, but a background in algebra is assumed.
Mason Core: Natural science (+lab when taken with CDS 102)

Instructor

Dr. James K. Glasbrenner

Office: 253 Research Hall
Phone: (703) 993-4512
Email: jglasbr2@gmu.edu
Office hours: Virtual office hours held on Blackboard Collaborate Ultra every Wednesday between 4:00pm – 5:00pm. Individual video conferencing sessions or on-campus meetings must be made by appointment.

Objectives

By the end of the course, students will be able to:

Use Github for collaborating on a reproducible research document
Obtain, clean, transform, and visualize a dataset using the R programming language
Interpret, and predict dataset trends using statistical inference and models
Critically examine and interpret statistical claims reported in mass media

Materials

Textbooks

This course utilizes two free textbooks that are made available under Creative Commons licenses:

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
- Hadley Wickham
- Garrett Grolemund

Click here to read the textbook for free online.

This is the free, open-source data science textbook that we will use throughout the course. This textbook is available under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 United States License.

If you would like a physical copy of the textbook, you can order it from Amazon.

Introductory Statistics with Randomization and Simulation
- David M Diez
- Christopher D Barr
- Mine Çetinkaya-Rundel

Click here to download a free PDF copy of the textbook.

This is the free, open-source statistics textbook that we will use to supplement the R for Data Science textbook during the course. This textbook is available under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license (CC BY-NC-SA).

If you would like a physical copy of the textbook, you can order it from Amazon.

Software

During the course we will use RStudio Server available at https://rstudio.cos.gmu.edu, which provides a complete computing environment that is accessible using any computer with a modern web browser (Firefox and Chrome). Students are welcome to install RStudio on their own computers and will need to install the following applications in order to match what is available on RStudio Server:

Programming language: R (https://www.r-project.org)
- Windows: https://cran.cnr.berkeley.edu/bin/windows/base/
- Mac: https://cran.cnr.berkeley.edu/bin/macosx/
Version control: Git (https://git-scm.com)
- Windows: https://git-scm.com/download/win
- Mac: https://git-scm.com/download/mac
PDF export: LaTeX (https://www.latex-project.org)
- Windows: https://mirrors.rit.edu/CTAN/systems/win32/miktex/setup/windows-x64/basic-miktex-2.9.6753-x64.exe
- Mac: http://tug.org/cgi-bin/mactex-download/MacTeX.pkg
Programming software: RStudio (https://www.rstudio.com)
- Windows: https://s3.amazonaws.com/rstudio-ide-build/desktop/windows/RStudio-1.2.830.exe
- Mac: https://s3.amazonaws.com/rstudio-ide-build/desktop/macos/RStudio-1.2.830.dmg

Technical support will only be provided for RStudio Server.

Platforms

The course will be administered through the following online platforms:

Course website: http://fall18.cds101.com
Github: https://github.com
Slack: https://masoncds101.slack.com
Blackboard: https://mymasonportal.gmu.edu

The course website operates as the central repository for course materials, including an up-to-date schedule, copies of the lecture slides and handouts, homework instructions, and links to the lecture videos. Slack is the primary communication medium, replacing email (see the Contact policy below) and serving as a discussion board. Github is used for connecting your class files to RStudio Server, tracking changes, distributing starter files for homework assignments and certain module exercises, handing out example code, and for project collaborations. Blackboard will also contain the lecture videos, video quizzes, and module exercises, which will also be cross-linked on the course website, and be used for submitting PDF versions of your assignments and returning grades.

Policies

Contact policy

All correspondence is to be done using the private, invite-only Slack workspace for the course. Direct messages on Slack are to be used for contacting me instead of emails. Your Slack username must be registered and associated with your Mason @gmu.edu email address at all times.

My ground rules for direct messages are as follows:

I check and respond to messages during normal university hours
Allow up to approximately 24 hours for a response during normal hours
Questions about homework problems or troubleshooting a technical issue should be sent to the relevant Slack channels.
Just because I view a message does not mean I will respond right away
I generally don’t respond to messages over weekends and school holidays
If your questions are involved enough, I will ask you to talk to me in person
On email: Emails sent during the first week of classes will be responded to, but I will respond to you using Slack. Emails sent to me after the first week will be ignored.¹

Tech support: R, RStudio, Github, and your computer

Post all technical issues or error messages for R, RStudio, Github, and your computer in the designated Slack channel #r-rstudio-github-help. This is so that other students can either help out or see how to resolve what is likely a common problem. If it becomes clear that the error or issue is highly specific, then discussion can be moved to Direct Message or handled via a remote desktop sharing session.

When posting about an issue, here are some basic questions to answer that will help with troubleshooting:

What did you expect to happen when you ran your code?
What is actually happening when you run your code?
1. If there’s an error message, tell us what it is. A screenshot works, provided you a) don’t crop the image as that can remove useful information by accident, and b) take a real screenshot, not a photograph of your screen using your phone.
Is there any other context we should know? For example, if a file won’t load, did you check that you are in the correct project or that the file actually exists? Did your issue appear only after you worked on a different assignment? Did you recently install a package not used in class?

Illness and emergencies

It is a student’s responsibility to inform me about illnesses or personal/family emergencies that will interfere with submitting work on time. This must be done as soon as possible. In case of illness, you may be asked to provide a doctor’s note before being granted an assignment extension or exemption.

I understand that certain emotional or physical situations can impact a student’s willingness and ability to communicate what is going on and that it can take a few days to inform me about a personal emergency or severe illness. At the same time, all students are expected to exercise personal responsibility. It is not acceptable to wait to tell me about the impacts of a personal illness or emergency until you’re about to fail the course due to missing multiple submission deadlines.

Late work policy

Unless otherwise noted, assignments are to be submitted by 11:59pm on the due date. The following penalties apply for most assignments (please note that weekends count as days):

First day late, by 11:59pm: -15%
Second day late, by 11:59pm: -30%
Third day late or later: no credit

The above does not pertain to the module exercises, which must be completed within a week of posting to receive credit. The midterm exam will be available for a limited amount of time and must be completed within time window, otherwise you will receive a zero. Reports and related materials for the final project are also exceptions and must be submitted by the due date. Late submissions for the final project will not be accepted.

Students are responsible for informing me about any religious holidays, scheduled varsity sports trips, or other school-sponsored activities that will interfere with submitting an assignment on time. Extensions are to be completed within the time-frame I set forth. Exemptions may be granted at my discretion.

Regrading appeals policy

Regrade appeals need to be written and formatted as a formal letter and submitted to me as a PDF file within 48 hours of receiving back an assignment (not including weekends). Appeals sent in plain text via Slack or email will not be accepted, no exceptions. Appeals are only to be used for correct answers being marked as incorrect, misapplication of the grading rubric, or incorrectly tallied points. Submissions need to clearly state what you want regraded and justify the request by citing evidence.² The number of points a question, exercise, or rubric category is worth or that were deducted for an incorrect answer or mistake cannot be appealed and are not up for debate or negotiation.

Extra credit and grading curves policy

Individual requests for extra credit or a grading curve will not be granted, no exceptions. Any opportunities to earn extra points will be offered to the entire class. Grading curves are handled on a per-assignment basis and are applied to all students equally.

Accommodations policy

Students with disabilities who need academic accommodations, please see me and contact the Office of Disability Services (ODS) at (703) 993-2474. All academic accommodations must be arranged through the ODS: http://ods.gmu.edu/.

Grading

Breakdown

Category	Weight
Participation	10%
Module exercises	15%
Homework	25%
Midterm exam	25%
Final project	25%

Schema

Based on the final total score, your final grade will be determined as follows:

Score Range	Final Grade
97–100	A+
93–96	A
90–92	A-
87–89	B+
83–86	B
80–82	B-
77–79	C+
73–76	C
70–72	C-
65–69	D
<65	F

Expectations

Module exercises

Video quiz questions for checking your understanding and end-of-unit exercises to reenforce what you’ve learned will be a regular part of the course and collectively factor into the Module exercises grade category. Your grade in this category will factor in both completion, meaning how many questions did you attempt to answer, and correctness.

Readings

Reading assignments will be regularly scheduled during the semester and students are expected to complete all readings on their own. These readings cover the same general topics as the lecture slides, but often contain different examples and ways of explaining a method or concept. The readings can contain material that is not part of the lecture slides. While there is not a grade category for the course readings, students are responsible for understanding the content and being able to apply the discussed methods in the end-of-unit exercises and homeworks.

Students are encouraged to ask questions about and discuss the readings in the fa18-dl1-discussion Slack channel. To maintain a high quality discussion, here’s a list of things you should do before submitting your question in the Slack channel:

Do…
- use the search feature to check if your question has already been asked, and if so, contribute to that ongoing discussion thread instead of asking the question again
- ask questions that are on-topic and about the reading’s subject matter instead of asking about tangential commentary made by the reading’s author or extraneous details in an example
- explain, as part of your question, which part of a concept or method you do not understand and why, instead of just saying you don’t understand it
- look up words you do not understand and information you can quickly do a web search for to help you better phrase and contextualize your question

Homework

There will be 5 homework assignments to complete during the semester. The homeworks are cumulative and will require you to draw on and apply concepts and methods you learned about earlier in the course. The homework assignments will include instructions on how you are to submit your work, which will typically involve uploading a file to Blackboard and also opening a pull request on Github against the branch labeled grading.

Homework assignments will completed in two stages. The first is the “rough draft” stage that each student must complete on their own. Grades for these submissions will be primarily based on the correctness of your answers to each question. The second is the “group draft” stage, which will begin approximately 3 days after the rough drafts are due. You will be assigned into groups where you will discuss the questions and assemble a final draft together. You will not know these groups beforehand, and they will change with each assignment. Grades for the group-submitted final drafts will, in addition to correctness, be based on document formatting, visualization quality, writing quality, and code style.

Both the individual rough draft and group report homework submissions are to be completed using the R Markdown format and they must successfully knit to HTML and PDF in a clean RStudio environment. In the individual submissions, full sentences are required when the question is asking for a written response. In the group reports, full sentences with proper grammar and punctuation are to be used throughout the report. The group reports should explain what you are doing with each chunk of code and to interpret the meaning of what you calculate so that a person that is not familiar with the problem could follow your logic.

Group final drafts are to have a style and formatting that is clean, readable, and follows the conventions demonstrated in the lecture slides and readings. Use your common sense when getting ready to submit an assignment. For example, the knitted PDF for your submission should not contain figures with unreadable overlapping labels, code blocks that run off the edge of the page, or be 50 – 100 pages in length because you keep printing out long tables. Submissions must use the Markdown syntax correctly and not engage in unorthodox practices such as writing sentences in a section header or placing code outside of code blocks. Unless specifically requested in the instructions, screenshots should never be included as part of your submission.

Questions about the homework assignments during the rough draft stage should be posted in the fa18-dl1-homework Slack channel. Group discussion channels will be created for the group draft stage. Take care to ensure that your submissions are in your own words and code. See the academic integrity section for specifics.

Participation

Your participation grade will be determined by evaluating the quality of your contributions and activity history within the homework groups. Contributions that factor into the participation grade include: how often you participated in group discussions on Slack and GitHub, if your comments were understandable and substantive, if you committed content and edits to the draft in your group’s GitHub repository, and what the group’s CONTRIBUTIONS.md log says you contributed to the final submission.

Midterm exam

There will be a midterm exam around the halfway point of the course and will be available for you to start at any time during a specified time window. The midterm exam is cumulative and tests your knowledge of the material covered in the lecture videos, readings, and homeworks during the first several weeks of the course. The structure and content of this exam will be explained in more detail before it is assigned. The midterm exam must be completed alone and the Mason Honor Code, as always, is in effect. Evidence of collaboration on the midterm exam with another student will result in, at minimum, an automatic zero for the exam.

Final project

Students will complete a final project in assigned groups where you will perform an exploratory data analysis on a dataset. Groups will focus on asking and answering questions about their dataset and then submit a final collaborative report. More detailed information about the project is to come later in the semester.

Conduct

Academic integrity

Student members of the George Mason University community pledge not to cheat, plagiarize, steal, or lie in matters related to academic work.³

Students are permitted to ask questions about the homeworks on Slack and discuss assignments in private communications, however it is important to make sure that you write your assignments by yourself and in your own words, meaning that students are not permitted to collaborate on write-ups for individual assignments and projects. In the same vein, do not duplicate or paraphrase another person’s material or ideas and represent them as your own. “Individual assignment” is the default classification for all assignments, exams, and projects in the course; any exceptions to the rule will be noted in the instructions. Content that comes from a resource or another student should be properly cited.

A note on sharing or reusing code found on other Github repos or on websites like Wikipedia or Stack Overflow. I am aware that there are solution sets, sample snippets of code, etc. that can be of use while working on your assignments, projects and exercises during the course. It’s common knowledge that researchers in both industry and academia will use search engines while writing code. Being able to search for existing solutions so that you don’t “reinvent the wheel” is a useful skill. Therefore, unless I specify otherwise, you are permitted to use these resources as long as you provide a citation.

Exceptions to this rule are:

For individual assignments, you cannot reuse anything from another student’s work (past or present), including but not limited to RMarkdown documents, code, plain text explanations, etc.
For the group assignments, you cannot use any material from another group, such as code and plain text explanations
You are not permitted to consult or use solution sets for the two main textbooks or any of the assignments, activities, and projects for the course
You are not permitted to ask other students from this or previous semesters for copies of their submitted assignments or projects, even to use for reference.

Any material that is taken in whole or in part from another source and not properly cited will be treated as a violation of Mason’s Academic Honor Code.

Other violations of Mason’s Honor Code will be treated similarly. Suspected violations will be reported to the Office of Academic Integrity. Please see the Honor Code page for details.

Decorum/discourse

Students are expected to be civil in their conduct and respectful of their fellow classmates and the instructor for the duration of the course on all discussion platforms. Students are expected to follow proper grammar and punctuation in their posted messages and to refrain from using internet slang, abbreviations, and sarcasm.

I will address violations of classroom decorum on a case-by-case basis and reserve the right to enact grade-based penalties for disruptive or repeat violations. Penalties for decorum violations cannot be negotiated or appealed.

Mason diversity statement

George Mason University promotes a living and learning environment for outstanding growth and productivity among its students, faculty and staff. An emphasis upon diversity and inclusion throughout the campus community is essential to achieve these goals. Diversity is broadly defined to include such characteristics as, but not limited to, race, ethnicity, gender, religion, age, disability, and sexual orientation. Diversity also entails different viewpoints, philosophies, and perspectives. Attention to these aspects of diversity will help promote a culture of inclusion and belonging, and an environment where diverse opinions, backgrounds and practices have the opportunity to be voiced, heard and respected.

Support services

The Math Tutoring Center is in 344 Johnson Center; http://math.gmu.edu/tutor-center.php. The Math Department also maintains a list of persons that have identified themselves as math tutors: http://math.gmu.edu/tutor-list.php

Mason’s Writing Center is in A114 Robinson Hall; (703) 993-1200; http://writingcenter.gmu.edu/

George Mason provides Counseling and Psychological Services (CAPS) for students. Contact them at (703) 993-2380 or http://caps.gmu.edu/.

Disclaimer

The instructor reserves the right to modify this syllabus at any time during the course to improve the learning experience and classroom environment. The digital version of the syllabus on the course website will be updated to reflect the changes. The pacing of the course and the list of covered topics may also be altered in response to student progress.

The course objectives reflect what a student is expected to understand by the end of the course after putting in the necessary time and effort both inside and outside the classroom and completing all assignments. These outcomes are not a guarantee, and students will get more out of the course the more they put into it. Any acquired skills and knowledge can fade over time if not reviewed or practiced after the course concludes.

If there are special circumstances requiring that we communicate via email, it is your responsibility to inform me about it as soon as possible.↩
Acceptable evidence includes in-class notes (provide date of class), a reading passage (provide full citation), or another valid source (textbooks, official publications, etc).↩
Office for Academic Integrity. 2017-2018 Honor Code and Honor System. Web. 27 Aug. 2017.↩

Datasets

2018-08-27T08:00:00-04:00

Name	Formats	Description
rail_trail	rds	Dataset counting the number of people that pass through a sensor on a rail trail Dataset for Homework 1

Weeks 1 and 2

2018-08-27T08:00:00-04:00

What are the computational and data sciences?

Videos

01 - What are the computational and data sciences - Prologue

02 - What are the computational and data sciences - Science

03 - What are the computational and data sciences - Data Overview

04 - What are the computational and data sciences - Data Sampling

05 - What are the computational and data sciences - Computation

Slides