| Field | Excellent (3) | Competent (2) | Needs Work (1) |
|---|---|---|---|
| Reproducible | All graphs, code, and answers are created from text files. Answers are never hardcoded but instead are inserted using inline R code. An automatically generated references section with properly formatted citations when appropriate and sessionInfo() are provided at the end of the document. |
All graphs, code, and answers are created from text files. Answers are hardcoded. No sessionInfo() is provided at the end of the document. References are present but not cited properly or not automatically generated. |
Document uses copy and paste with graphs or code. Answers are hardcoded; and references, when appropriate are hardcoded. |
| Statistical Understanding | Answers to questions demonstrate clear statistical understanding by comparing theoretical answers to simulated answers. When hypotheses are tested, classical methods are compared and contrasted to randomization methods. When confidence intervals are constructed, classical approaches are compared and contrasted with bootstrap procedures. The scope of inferential conclusions made is appropriate for the sampling method. | Theoretical and simulated answers are computed but no discussion is present comparing and contrasting the results. When hypotheses are tested, results for classical and randomization methods are presented but are not compared and contrasted. When confidence intervals are constructed, classical and bootstrap approaches are computed but the results are not compared and contrasted. The scope of inferential conclusions made is appropriate for the sampling method. | Theoretical and simulated answers are not computed correctly. No comparison between classical and randomization approaches is present when testing hypotheses. When confidence intervals are constructed, there is no comparison between classical and bootstrap confidence intervals . |
| Graphics | Graphs for categorical data (barplot, mosaic plot, etc.) have appropriately labeled axes and titles. Graphs for quantitative data (histograms, density plots, violin plots, etc.) have appropriately labeled axes and titles. Multivariate graphs use appropriate legends and labels. Computer variable names are replaced with descriptive variable names. | Appropriate graphs for the type of data are used. Not all axes have appropriate labels or computer variable names are used in the graphs. | Inappropriate graphs are used for the type of data. Axes are not labeled and computer variable names appear in the graphs. |
| Coding | Code (primarily R) produces correct answers. Non-standard or complex functions are commented. Code is formatted using a consistent standard. | Code produces correct answers. Commenting is not used with non-standard and complex functions. No consistent code formatting is used. | Code does not produce correct answers. Code has no comments and is not formatted. |
| Clarity | Few errors of grammar and usage; any minor errors do not interfere with meaning. Language style and word choice are highly effective and enhance meaning. Style and word choice are appropriate for the assignment. | Some errors of grammar and usage; errors do not interfere with meaning. Language style and word choice are, for the most part, effective and appropriate for the assignment. | Major errors of grammar and usage make meaning unclear. Language style and word choice are ineffective and/or inappropriate. |
Sign-up for a free account on GitHub.
When you register for a free individual GitHub account, request a student discount to obtain a few private repositories as well as unlimited public repositories. Please use something similar to FirstNameLastName as your username when you register with GitHub. For example, my username on GitHub is alanarnholt. If you have a popular name such as John Smith, you may need to provide some other distinguishing characteristic in your username.
Complete Introduction to R (DataCamp)
Introduction to R slides
You may want to install Git, R, RStudio, zotero, and optionally \(LaTeX\) on your personal computer. If you do, you will want to follow Jenny Bryan’s excellent advice for installing R and RStudio and installing Git. Jenny’s advice is also in chapters 6 and 7 of Happy Git and GitHub for the useR. Note: Git, R, RStudio, and \(LaTeX\) are installed on the Appstate RStudio server.
Watch the following videos as appropriate:
Become familiar with the Appstate RStudio server. You will use your Appstate user name and password to log in to the server. You must be registered in the class to access the server.
Complete A Hands-on Introduction to Statistics with R (DataCamp)
Complete Intro to Statistics with R: Student’s T-test (DataCamp)
Complete Foundations of Probability in R (DataCamp)
Start Reporting with R Markdown (DataCamp)
Test yourself:
Complete Reporting with R Markdown (DataCamp)
Complete Introduction to Data (DataCamp)
Test drive RStudio by following the directions from Jenny Bryan’s STAT 545 course. Additional material can be found in the detailed Bookdown document Happy Git and GitHub for the useR. Chapters 8, 10-13 of Happy Git and GitHub for the useR will be helpful if you need more directions for test driving RStudio. Note: Git, R, and the RStudio IDE have already been installed for you on the RStudio server.
Read the Git and GitHub chapter from Hadley Wickham’s book R Packages
Brian Caffo’s take on R IDEs
dplyr (DataCamp)Complete Data Visualization with ggplot2 (Part 1) (DataCamp)
Work on Class Assignment
Some ideas for how to answer the Class Assignment
Complete Foundations of Inference (DataCamp)
Write a summary (less than two pages) using the bookdown format html_document2 of the key ideas in Foundations of Inference. Your summary should include a discussion/definition of the following: hypotheses (null and alternative), null distribution, test statistic, p-value, types of errors in hypothesis testing, confidence intervals, and bootstrapping. Properly labeled Figures, Tables, and Equations will be extra credit. Print out and turn in a hard copy of your html document with your source code stapled to the back of the html no later than 5pm October 26, 2017.
Chapter 3 of MSWRAR
Complete the problems in the R Markdown file and publish your solution to RPubs no later than 5pm November 2, 2017. Make sure to write down on the sign-up sheet (available in class) the url where your work is published.
Chapter 4 of MSWRAR
Suggested Problems: 2, 5, 12–16
Complete the problems in the R Markdown file and publish your solution to RPubs no later than 5pm November 9, 2017. Make sure to write down on the sign-up sheet (available in class) the url where your work is published.
Chapter 5 of MSWRAR
Bootstrap Example
Quiz on Tuesday (\(\text{E}(X)\) and \(\text{Var}(X)\))
Quiz on Thursday (Bootstrap SE and Bias)
Complete the problems in the R Markdown file and publish your solution to RPubs no later than 5pm November 21, 2017. Make sure to write down on the sign-up sheet (available in class) the url where your work is published.
Chapter 5 of MSWRAR
Complete the problems in the R Markdown file and publish your solution to RPubs no later than 5pm November 21, 2017. Make sure to write down on the sign-up sheet (available in class) the url where your work is published.
Chapter 7 of MSWRAR
Complete the problems in the R Markdown file and publish your solution to RPubs no later than 5pm November 30, 2017. Make sure to write down on the sign-up sheet (available in class) the url where your work is published. Note: Problems 1-5 Due Thursday
Note: Problems 6-11 Due Wednesday NLT 5 pm
Follow the MWF meeting pattern on the University Exam Schedule to determine your final exam time and date.