Analysis 00 Directions

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Distribution

A template for this analysis can be found on RStudio Cloud.

• All required packages are already installed.
• Data is provided in the data/ directory.
• The file analysis-00.Rmd should be the only file that you edit. You must create the file analysis-00.html before submission. No additional files should be added or deleted. Do not change the names of any files. You should only modify chunks that contain a TODO with a task. Do not add any additional chunks. Do not modify the text, except in the discussion section.

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Tasks

For this analysis, do the following:

• Modify the header so that “Your Name” is your name, and netid@illinois.edu is your @illinois email address.
  • Please use only lower-case letters for your NetID.
• Write code to perform the requested tasks in any chunk that has a TODO statement in a comment. Delete the comment after completing the task.
  • You can search the document for TODO to check if there is anything left to do!
  • One of these chunks is called numeric-results. You will create a table of the numeric results. The output of this chunk will be considered in grading.
  • Another chunk is called graphical-results. You will create a some graphics that summarize the results. The output of this chunk will be considered in grading.
• Write a paragraph in the discussion section.
  • This paragraph should summarize the “why” of the analysis. It should place the results in context. See the template document for additional hints on how to do this.
• “Fix” the code in the chunk named this-is-bad-code.
  • This chunk doesn’t have anything to do with the analysis. This is simply practice cleaning up code. It will help you to understand how we will grade for style in the future.

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Submission

You will work on your analysis in RStudio Cloud, render your R Markdown document to .html, export the entire RStudio project, then submit to Compass2g.

• If you chose to not use RStudio Cloud, you do so at your own risk. The course staff will not be able to assist as easily. It is certainly possible to replicate the .zip file needed for submission, but following these steps will guarantee it.

R Markdown Rendering

Before downloading your analysis from RStudio Cloud, knit your document one final time.

RStudio Cloud Export

Export your analysis as a .zip file. (See the gif below.) Name the file analysis-00-netID.zip where netID is your NetID.

• In the bottom right pane, select “Files.”
• Click the box to the left of “Cloud.”
• Click “More.” (The gear icon.)
• Click “Export.”
• Type analysis-00-netID where netID is your NetID.
• Click “Download.”
• Follow the prompt and save your file locally.

Compass2g

Submit analysis-00-netID.zip (exported from RStudio Cloud) to the correct assignment on Compass2g. You are granted an unlimited number of submissions. Only your final submission will be graded.

• If you submit a .zip file generated by RStudio Cloud, it is extremely unlikely that you will encounter any issues. Creating the .zip yourself will likely cause issues. If you are using OSX and Safari, you might need to change your Safari preferences to stop Safari from automatically unzipping the file creating by RStudio Cloud
  • https://discussions.apple.com/thread/1483114

Late Submission

All analyses are due at 11:59 PM on the assigned due date. (See above.)

• Late analyses will be accepted up to 48 hours after the initial deadline.
  • Up to 24 hours late, the assignment will incur a 10 percent reduction.
  • Up to 48 hours late, the assignment will incur a 30 percent reduction.
  • No exceptions! Start early and make sure your environment is working correctly and you are able to produce a working document.

If you submit multiple attempts, the final attempt will be graded. If your first submission is on time, but your final submission is late, you will incur the late submission penalty.

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R Style

In addition to correctness, your code will be graded based on its style. We don’t expect you to have a mature coding style, so we have a list of rules which must be followed.

• Most infix operators (==, +, -, <-, etc.) should always be surrounded by spaces.
  • Notable exceptions: :, ::, $, [, [[, ], ]]
  • ^: Use x ^ 2 instead of x^2.
• All commas must be followed by a space.
• Use a consistent assignment operator. Either <- or =, not both.
  • If you choose to use the <- operator, you will need to replace the = operator in the given code.
• Do not use T or F.
• Do not use absolute paths.
• Do not use semicolons, ;.
• Do not use theattach() function.
• Do not use periods, ., or capital letters in variable and function names.
• Opening (left) curly braces should not be on their own line.
• Variable and function names (that are user created) should only contain lowercase letters, numbers, and underscores.
• A space should precede a parenthesis, except in a function call.
  • Good: for (i in 1:10)
  • Bad: for(i in 1:10)
  • Good: mean(x)
  • Bad: mean (x)
• No more than one newline (blank line) in a row in an R Markdown document.
• No more than one newline (blank line) in a row in an R chunk.
• A newline before and after each chunk in an R Markdown document.
• Except for the first, argument names should be written in function calls. (Exception for the predict() function.)
  • This will be encouraged, but not enforced, as often it is better to make a judgement call than to be dogmatic.

Much of this is derived from the tidyverse style guide. If you follow the tidyverse guide, be aware of our use of ^ and =.

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Grading

The analysis is worth a total of 10 points. Grading will be done in a 0 - 1 - 2 style in five categories:

• Numeric Results
  • Is the table summarizing the numeric results correct? (The results of the numeric-results chunk will be checked.)
• Graphical Results
  • Are the graphics summarizing the results correct? (The results of the graphical-results chunk will be checked.)
• Discussion
  • Has a correct and meaningful discussion been written? To be correct, you simply need to make no false statements. To be meaningful, it must consider context.
• Rendering
  • Does the submitted .Rmd file knit without error?
  • If during grading a human must modify your file in order to knit, you will lose these points.
• Code Style
  • Have you followed the suggested R coding style?
  • This will be checked automatically. There will be a small (undisclosed) margin of error for each analysis. That is, you will be allowed a small number of style mistakes.

The points will be awarded as follows:

• 0: No or poor effort.
• 1: Incorrect, but showing some understanding, with minor flaws.
• 2: An “A+” effort. Either 100% correct, or in the case of discussion, a well thought-out answer.

Note: This analysis is the “Intro Data Analysis” listed in the syllabus. This assignment will buffer Data Analysis I as noted in the syllabus. (Data Analysis I might end up being called Analysis II. Essentially, this analysis will buffer the first non-practice analysis.)

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