Each year since 2005, the US Census Bureau surveys about 3.5 million households with The American Community Survey (ACS). Data collected from the ACS have been crucial in government and policy decisions, helping to determine the allocation of more than $400 billion in federal and state funds each year. For example, funds for the Adult Education and Family Literacy Act are distributed to states taking into consideration data from the ACS on number of adults 16 and over without a high school diploma. This act is the primary source of federal funding for adults with low basic skills seeking further education or English language services, and Department of Education uses ACS data to ensure the efficient distribute funds.
The ACS received a surge of media attention in Spring 2012 when the House of Representatives voted to eliminate the survey. Daniel Webster, a first-term Republican congressman from Florida, sponsored the legislation citing the following reasons:
In this assignment you will analyze data from the ACS.
The data can be found in the openintro package.
In your console, run the following to install this package:
install.packages("openintro")Then load the package with
library(openintro)and load the data with
data(acs12)Take a peek at the codebook with
?acs12or at https://www.rdocumentation.org/packages/openintro/versions/1.7.1/topics/acs12.
Go to the #assignment-links channel on Slack and click on the link for hw-01, and accept the assignment. This will automatically put you in the teams you created previously. You can confirm this by looking at the name of your repo (it will have your team name on it).
Then, each team member can follow the usual steps to clone the repo and get started with the analysis.
As a team, browse the data and select three variables that you think are interesting and that you think may have an interesting relationship.
For each of the variables you selected answer the following question: What are some of the properties of these variables that will be relevant (useful? problematic?) for data visualization? You are welcomed to add visualizations to your answer, but you don’t have to.
Select and design two different plots that visualize the relationship between these three variables. Each plot should have different “purpose” that guides your choices and justifies the differences between the plots.
What two plot types have you selected? What are some of the properties of these plots, and how do those properties match with your variables of interest?
Come up with target audiences for your plots. What task(s) should readers of each plot be able to undertake? Why are these tasks important for these variables?
What design choices did you make to try to help users accomplish the intended tasks?
What type of audience did you intend the plots for? What types of skills does this audience bring to the understanding of your plots? What elements of your plots might be difficult for the audience to understand, and how do you justify the choices you have made about those elements?
Use the #r-help or #github-help channels on Slack to ask questions, as well as your team channel. If your question is about an error you’re getting, make sure to provide the error as well as the code necessary to reproduce the error.
You are also welcomed and encouraged to attend office hours and ask questions there.
Remember that while you’re welcomed to broadly discuss the homework with other teams, you cannot share code or any other content across teams.
You’re working in the same repo as your teammates now, so merge conflics will happen, issues will arise, and that’s fine! Commit and push often, and ask questions when stuck.
Review the grading guidelines below and ask questions if any of the expectations are unclear.
Make sure each team member is contributing, both in terms of quality and quantity of contribution (we will be reviewing commits from different team members).
Set aside time to work together and apart (physically).
When you’re done, review the .md document on GitHub to make sure you’re happy with the final state of your work. Then go get some rest!
| Total | 100 pts |
|---|---|
| Part 1: Data properties | 15 pts |
| Part 2: Plots | 15 pts |
| Part 3: Plot properties | 15 pts |
| Part 4: Tasks | 5 pts |
| Part 5: Design context | 5 pts |
| Part 6: User skills | 5 pts |
| Code quality | 15 pts |
| Commit frquency and informative commit messages | 5 pts |
| Informatively named code chunks | 5 pts |
| Collaboration & contribution | 10 pts |
| Document organization (team name, code chunk names, overall organization, etc.) | 5 pt |