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In this assignment, you’ll practice collaborating around a code project with GitHub. You could consider our collective work as building out a book of examples on how to use TidyVerse functions.

You have two tasks:

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 completed both parts of the assignment, please submit your GitHub handle name in the submission link provided in the week 1 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 submission no later than end of day on Sunday, November 24th.

• GitHub repository: https://github.com/acatlin/FALL2019TIDYVERSE
• FiveThirtyEight.com datasets: https://data.fivethirtyeight.com/
• Kaggle datasets: https://www.kaggle.com/datasets

Author: Mario Pena

Part 1: Created a .Rmd document to demonstrate how to use some of the TidyVerse functions such as select(), mutate(), count() and ggplot(). Rpubs: http://rpubs.com/marioipena/553779

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• You can find this here:

Author: Stephen Haslett

GitHub Repository: https://github.com/stephen-haslett/FALL2019TIDYVERSE

Data Source: https://www.kaggle.com/residentmario/ramen-ratings/data

RPubs Location: http://rpubs.com/stephenhaslett/554575

Task 1: SHaslett-607-Tidyverse-assignment.Rmd. For task 1, I imported the ramen-ratings dataset from Kaggle (https://www.kaggle.com/residentmario/ramen-ratings/data) into R using the "readr" package. I then demonstrated the dplyr package's "slice", "filter", and "select" functions.

Author: Jai Jeffryes

A document term matrix (DTM) is a data structure that can serve as the input to machine learning models. Tidyverse tools can create this structure.

cf. candidate_email_dtm.Rmd

Author: Donny Lofland

FiveThirtyEight has aggregated polling data from a number of sources tracking Trump's approval rating since January 2017. In this programming vignette, we will use TidyVerse packages to load, manipulate, summarize and plot Trump's approval rating over time.

cf. trump_approval_ratings.Rmd

TidyVerse Assignment:

Part 1: Editor: Euclid Zhang Date Completed: 11/11/2019 Action: Create a demonstration of how to use the functions in the purrr package. Rpubs: http://rpubs.com/ezaccountz/TidyVerse_Assignment_EZ

Part 2: Creator: Lin Li Source: https://github.com/lincarrieli/FALL2019TIDYVERSE Updater: Euclid Zhang Date Completed: 11/17/2019 Action: Update with a few examples from TidyVerse package.

Author:Steven Ellingson

Part 1: Use forcats to easily change the way the levels of your factors are displayed in tables and plots.

cf. forcats vignette.Rmd

Part 2: Added some forcats and ggplot2 functions to C Rosemund's soccer data.

cf. steven_ellingson_tidyverse_part_2.Rmd

Author:Scott Reed

Part 1: Use readr, stringr, purr, and dplyr to analyze fightsongs and see if spelling is more common with authors with longer names.

cf. fightsongs_tidyvrese.rmd

Part 2: switch to readr then use stringr, lubridate to fix a problem with the data, and then graph using ggplot.

cf. NFL Runningback's BMI_sr.rmd

Author: Bryan Persaud

I am using the Avengers dataset from FiveThirtyEight and using the dplyr package to demonstrate the filter and full_join function. Avengers dataset: https://github.com/fivethirtyeight/data/tree/master/avengers

Extend an Existing Example

I extended Stephen Haslett's code.

Author: Zach Alexander

• Part 1: I will be making a TidyVerse vignette for the tidyr package, using the NFL elo ratings dataset from FiveThirtyEight.

  • You can find this here: http://rpubs.com/zachalexander/tidyverse

• Part 2: I extended Euclid Zhang's example, adding to his great plots by creating a ggplot2 plot of my own, as well as utilizing some tidyr and dplyr functions to manipulate the weather data he provided.

  • You can find this here: http://rpubs.com/zachalexander/tidyext

Author: Amber Ferger

• Part 1: I decided to use the dataset that corresponds to the What Do Men Think It Means To Be A Man? article on FiveThirtyEight.com. The article can be found here: https://fivethirtyeight.com/features/what-do-men-think-it-means-to-be-a-man/. This project focuses on the tibble, dplyr, and ggplot 2 packages. I focus on aggregating the data and displaying differences in responses between 2 demographic features. The final markdown file can be found here: http://rpubs.com/amberferger/DATA607_Tidyverse

• Part 2: I extended the Fight Songs vignette by Scott Reed to include additional aggregations.

Author: Forhad Akbar Part 01:

• GitHub handle: https://github.com/forhadakbar
• Data Source: https://www.kaggle.com/ronitf/heart-disease-uci/version/1
• slice() to filter out everything except a specified subset of the data.
• mutate() to add new variables that are functions of existing variables.
• summarise() to condense multiple values to a single value.
• group_by() Groups data by specified columns

Part 02: Tidyverse Assignment Part 02 Extended from SHaslett-607-Tidyverse-assignment.Rmd

• Added group_by()
• Added ggplot()

Author: Shovan Biswas Part 01:

The dplyr package makes these steps fast and easy:

• Provides appropriate functions manipulate data.
• Uses efficient backends.

Part 01:

• GitHub handle: https://github.com/ShovanBiswas

• Data Source: https://www.kaggle.com/ronitf/heart-disease-uci

• arrange() to order tbl rows by an expression involving its variables.

• group_by() Groups data by specified columns

• select() to Choose variables from a tbl. select() keeps only the variables you mention.

• summarise() to condense multiple values to a single value.

Part 02: Tidyverse Assignment Part 02 extended from 'purrr - Euclid Zhang.RMD'

• Added select(), as intermediate step.
• Added group_by()
• Added top_n()
• Added ggplot()

Author: Samuel Kigamba Data selected: https://projects.fivethirtyeight.com/polls-page/president_primary_polls.csv Github repository: https://github.com/igukusamuel/FALL2019TIDYVERSE/edit/master/README.md

Section 1 Load DAta rename() slice() groupby() tally() glimpse()

Section 2 Extended: https://github.com/forhadakbar/FALL2019TIDYVERSE/blob/master/DATA607_TidyVerse_ForhadAkbar_01.Rmd rename() function

Author: Fan Xu

• Part 1: I will be making a TidyVerse vignette for some handly functions in the tidyverse package, using the London bike sharing dataset from https://www.kaggle.com/datasets;.

  • You can find this here: [http://rpubs.com/oggyluky11/555248]

• Part 2: I extended C. Rosemond's example, utilizing some tidyr and dplyr functions to manipulate the Soccer Power Index data he provided and creating a ggplot.

  • You can find this here: [http://rpubs.com/oggyluky11/555371]

Author: Mengqin Cai Part1: Use readr(),map(), gather(),filter（）,mutate（）and summarise() to import the data and compare students math performance base on their parents education. ggplot2 to visualize the data.

Part2: Extend Amber Ferger's example, use arrange(), and ggplot2 to show the filtered dataset.

Rpubs: http://rpubs.com/DaisyCai/data607_Tidyverse Github:https://github.com/DaisyCai2019/Homework/blob/master/data607_tidyverse.Rmd https://github.com/DaisyCai2019/Homework/blob/master/data607_tidyverse.md