1. Create an Example Using one or more TidyVerse packages, and any dataset from fivethirtyeight.com or Kaggle, create a programming sample “vignette” that demonstrates how to use one or more of the capabilities of the selected TidyVerse package with your selected dataset. (25 points)

2. Extend an Existing Example Using one of your classmate’s examples (as created above), extend his or her example with additional annotated code. (15 points)

You should fork the provided repository and then clone it locally if you wish. Once you have code to submit, you should make a pull request on the shared repository. Minimally, you should submit .Rmd files. Ideally, you should also submit an .md file and update the README.md file with your example.

If you are going to use RStudio as your version control software, make sure to add *.Rproj and .gitignore to your .gitignore file before you make any commits. Otherwise you run the risk of trying to push that to the master repository in a pull request.

After you’ve read each part of the assignment, please submit your GitHub handle name in the submission link provided in the Major Assignments folder! This will let your instructor know that your work is ready to be graded.

You should complete both parts of the assignment and make your submissions on the schedule specified in our ourse syllabus

• GitHub repository: https://github.com/acatlin/SPRING2020TIDYVERSE
• FiveThirtyEight.com datasets: https://data.fivethirtyeight.com/
• Kaggle datasets: https://www.kaggle.com/datasets

• https://acatlin.github.io/SPRING2020TIDYVERSE/forcats_makes_plots_better.html - how to use capabilities of forcats package to improve your plots! Andy Catlin and Ait Elmouden Abdellah

dbplyr is great and simple to use backend solution for working with data that is stored on a SQL server. dbplyr follows the same grammar and formatting as dplyr, but translates R code into a SQL query to directly access the SQL database.

From the forcats package, this function allows you to quickly group levels of a categorical variable if they are above/below a certain count or proportion.

This vignette loads the Lubridate package and performs a few date transformations. Ken Popkin

This file should help you understand how to export a table from a website, gather the data into a tall format, and plot the variables of interest into several plots for easy comparison

A vignette describing the prepration of an online table for ggplot and facet_wrap Author: Thomas Hill

A vignette describing the progressive use of as_tibble(), dplyr functions select, arrange, filter and a gpplot to visualizae data. Author: Neil Shah

Please follow along with the steps I took to prepare date for ggplot exploration. I based this off of a previous project and added some annotations about the functions I called.

Using titanic dataset from Kaggle, clean and manipulate data using various functions of tidyvesrse package. This vignette was extended to show how to deal with NA values and pipe to statistically compare columns. (Subhalaxmi Rout & Neil Shah)