The Tutorial Book project is designed to have students develop tutorials to teach new R users about a new topic. The goal is to have students conduct research, learn about the topic, learn the code, and create a tutorial to teach the topic. For each tutorial, assume the new R user has taken STAT 100A (or STAT 010) and has the first 2 weeks of R knowledge from this course.
These are the tutorials for your book:
Tutorial must be written with an RMD File.
Tutorials must provide resources.
Tutorials must be knitted to an html file.
Meet the requirements listed for each topic.
Provide code for each tutorial.
The Tutorial Book will be scored out of 75 points. Two tutorials will be graded based on the rubric. The last tutorial will be graded based on completion (15 points; must meet minimum requirements listed). Students have the opportunity to tell the instructor which tutorials they want graded by the rubric. If a student does not notify the instructor which tutorials to grade, two tutorials will be randomly selected.
The Tutorial Book is Due Wed (7/21/21) @ 11:59 PM.
Here are the guidelines how each tutorial will be graded.
How well does the tutorial communicate the topic? Is it written in a way that is concise and clear? Does the tutorial use images or videos to further explain the topic?
Does the tutorial provide a reasonable explanation about the topic? Does the tutorial provide code on how to conduct the topic? Does the tutorial explain relevant arguments for each function? Is the code well commented? Does the tutorial go beyond the minimal requirements?
What resources does the tutorial provide? Does the resource provide links or videos suitable for multiple audiences?
Multiple Audiences:
Resources are good for different learning styles.
Resources may be in different languages (provide 2-3 sentence description of resource in English in tutorial).
Resources attempt to support individuals with disabilities (Larger Font, videos, night mode, …).
Any resources suitable for an audience not listed here.
| Topic | 1-2 | 3-5 | 6-8 | 9-10 |
|---|---|---|---|---|
| Communication/Organization | The tutorial is challenging to understand. | The tutorial is understandable, but lacks organization. | The tutorial is concise and easy to understand with only minor errors. | The tutorial organized well. Suitable for multiple audiences. Goes beyond expectations. |
| Knowledge/Code | No understanding of the topic. No code present. | Understands how to program the topic, but does not understand the topic completely. Code is present, but does not provide proper comments. | Clear understanding the topic and programming. Code is readable and well commented. | Clear understanding of the topic. Goes beyond the topic and incorporates related topics. Code is present, well commented, and arguments are clearly explained. |
| Resources/Citations | Resources are not provided. | Resources are listed but not specified. | Resources are listed and well documented. | Resources are well researched and designed to meet diverse learning needs. |
R has the capability to compute different probabilities from different distributions. Below is a list of select distributions R is capable to compute:
| Normal | Binomial | Poisson | F | Student’s t |
| Beta | Gamma | \(\chi²\) | Exponential | Tukey |
| Cauchy | Negative Binomial | Hypergeometric | Multinomial | Uniform |
Each distribution has functions for your use: dXXXX,
pXXXX, qXXXX, rXXXX.
Create tutorial on the following distributions:
Normal
Binomial
Each tutorial must provide a brief description of the distribution, why would you use the distribution, explanation of each functions and their arguments, and examples how to use these distributions of each function.
R has the capabilities to analyze categorical data, particularly proportion tests. Explore how you can analyze different proportions in R.
One-Sample Proportion Test
Two-Sample Proportion Test
Write a brief description the statistical tests, why would you use them, explanation of the R functions you can use for proportion tests, and an example.
R has functionality to handle data of dates and times. This can be used to analyze data with time-series models. Explore the data types and how they are handled in R.
Dates
Times
Write a brief description of how R handles both dates and times. Provide at least one example working with dates and times in R. For example, converting times for different time zones or finding the day of the week.