| Field | Excellent (3) | Competent (2) | Needs Work (1) |
|---|---|---|---|
| Reproducible | All graphs, code, and answers are created from text files. Answers are never hard-coded 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 hard coded. 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 hard coded; and references, when appropriate are hard coded. |
| 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. |
Before the first class meeting, read chapter 1 (Getting Started with Data in R) of MD—pgs 1-20
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.
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.
Work through chapter 1 (Git and GitHub) of DSWR. Make sure RStudio is set up to communicate with Git by following the directions in HappyGitWithR for introducing yourself to Git.
Work through chapter 2 (Introduction to R) of DSWR
Complete PS-01 by 5:00 pm Aug 21—(assignment link in asulearn)
Me create and insert video here for how to accept assignments.
Introduction to R slides
Watch Paul the Octopus clip (61 seconds).
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:
Before class read chapter 3 (Data Wrangling) of MD — pgs 65-96
Before class read chapter 4 (Data Importing and “Tidy” Data) of MD — pgs 99-117
Work through chapter 3 (Starting with Data) of DSWR
Work through chapter 4 (Data Manipulation) of DSWR
Complete the Data Wrangling chapter of Introduction to the Tidyverse— DataCamp — Due NLT 5:00 pm Aug 28
Complete the Data Visualization chapter of Introduction to the Tidyverse— DataCamp — Due NLT 5:00 pm Aug 28
Complete PS-03 by 5:00 pm Aug 28—(assignment link in asulearn)
Before class read chapter 2 (Data Visualization) of MD — pgs 21-62
Work through chapter 5 (Using ggplot2) of DSWR
Complete the Grouping and Summarizing chapter of Introduction to the Tidyverse— DataCamp — Due NLT 5:00 pm Sep 4
Complete the Types of Visualizations chapter of Introduction to the Tidyverse— DataCamp — Due NLT 5:00 pm Sep 4
Complete PS-02 by 5:00 pm Sep 4—(assignment link in asulearn)
Test yourself:
Complete Data Visualization with ggplot2 (Part 1) (DataCamp)
Before class read chapter 5 (Basic Regression) of MD — pgs 119-160
In class go over this document
Complete the Introduction to Modeling chapter of Modeling with Data in the Tidyverse— DataCamp — Due NLT 5:00 pm Sep 11
Complete the Modeling with Basic Regression chapter of Modeling with Data in the Tidyverse— DataCamp — Due NLT 5:00 pm Sep 11
Complete PS-04 by 5:00 pm Sep 11—(assignment link in asulearn)
Read the Git and GitHub chapter from Hadley Wickham’s book R Packages
Brian Caffo’s take on R IDEs
Before class read chapter 6 (Multiple Regression) of MD — pgs 161-191
Complete the Modeling with Multiple Regression chapter of Modeling with Data in the Tidyverse— DataCamp — Due NLT 5:00 pm Sep 18
Complete the Model Assessment and Selection chapter of Modeling with Data in the TIdyverse— DataCamp — Due NLT 5:00 pm Sep 18
Complete PS-05 by 5:00 pm Sep 18—(assignment link in asulearn)
dplyr (DataCamp)
Before class read chapter 6 (Multiple Regression) of MD — pgs 161-191
Complete the Getting Started with R Markdown chapter of Reporting with R Markdown— DataCamp — Due NLT 5:00 pm Sep 25
Complete the Adding Analyses and Visualizations chapter of Reporting with R Markdown— DataCamp — Due NLT 5:00 pm Sep 25
Complete PS-06 by 5:00 pm Sep 25—(assignment link in asulearn)
Read through Misc Regression
Answer the questions at the end of Misc Regression for extra credit (Turn in before Oct 2)
Work on Is this Discrimination?
Some ideas for how to answer the Is this Discrimination?
Complete the Improving the Report chapter of Reporting with R Markdown— DataCamp — Due NLT 5:00 pm Oct 2
Complete the Customizing the Report chapter of Reporting with R Markdown— DataCamp — Due NLT 5:00 pm Oct 2
Complete all chapters (1–4) of Foundations of Probability in R— DataCamp — Due NLT 5:00 pm Oct 9
Before class read chapter 7 (Sampling) of MD — pgs 195-232
Complete the Completing a randomization test: gender discrimination chapter of Foundations of Inference— DataCamp — Due NLT 5:00 pm Oct 9
Complete the Introduction to ideas of inference chapter of Foundations of Inference— DataCamp — Due NLT 5:00 pm Oct 9
Complete PS-07 by 5:00 pm Oct 9—(assignment link in asulearn)
Read Chapter 4 of MSWR — Sampling Distributions; Problems 2, 5, 12-16
Read Chapter 5 of MSWR
Read Chapter 8 (Bootstrapping and Confidence Intervals) of MD — pgs 233-305
Complete the Confidence intervals chapter of Foundations of Inference— DataCamp — Due NLT 5:00 pm Oct 16
Complete the Hypothesis testing errors: opportunity cost chapter of Foundations of Inference— DataCamp — Due NLT 5:00 pm Oct 16
Compete Sampling Distributions Lab by 5:00 pm Oct 16—(assignment link in asulearn)
Read Chapter 8 (Bootstrapping and Confidence Intervals) of MD — pgs 233-305
Complete the Bootstrapping for Estimating a Parameter chapter in Inference for Numerical Data in R— DataCamp — Due NLT 5:00 pm Oct 23
Complete the Introducing the t-distribution chapter in Inference for Numerical Data in R— DataCamp — Due NLT 5:00 pm Oct 23
Bootstrap Example
Read Chapter 9 (Hypothesis Testing) of MD — pgs 307-360
Complete the Inference for Difference in Two Parameters chapter in Inference for Numerical Data in R— DataCamp — Due NLT 5:00 pm Oct 30
Complete the Comparing Many Means chapter in Inference for Numerical Data in R— DataCamp — Due NLT 5:00 pm Oct 30
Complete PS-08 by 5:00 pm Oct 30—(assignment link in asulearn)
Read Chapter 9 (Hypothesis Testing) of MD — pgs 307-360
Complete the Inference for a Single Parameter chapter in Inference for Categorical Data in R— DataCamp — Due NLT 5:00 pm Nov 6
Complete the Proportions Testing and Power chapter in Inference for Categorical Data in R— DataCamp — Due NLT 5:00 pm Nov 6
Complete the Comparing Many Parameters: Independence chapter in Inference for Categorical Data in R— DataCamp — Due NLT 5:00 pm Nov 13
Complete the Comparing Many Parameters: Goodness of Fit chapter in Inference for Categorical Data in R— DataCamp — Due NLT 5:00 pm Nov 13
Complete PS-09 by 5:00 pm Nov 13—(assignment link in asulearn)
Start Reproduction of Inference for Numerical Data in R — assignment will be in GitHub Classroom - Due NLT 5:00 PM Dec 4, 2020
Read Chapter 7 of MSWR
Work on Reproduction of Inference for Numerical Data in R — assignment will be in GitHub Classroom - Due NLT 5:00 PM Dec 4, 2020
Last Day! :)
Work on Reproduction of Inference for Numerical Data in R — assignment will be in GitHub Classroom - Due NLT 5:00 PM Dec 4, 2020