FALL2024TIDYVERSE

Sample

Initial Description and Link:

• Demonstrates how to flip ggplot axes
• ggplot_forcats.qmd

Revision/Addition Description and Link:

• Modified ggplot/forcats example to demonstrate sorting, other category
• ggplot_forcats.qmd

Amish R.

Initial Description and Link: group_by and summarize tidyverse functions amish_tidyverse_vignette.rmd

Revision/Addition Description and Link:

• Modified amish_tidyverse_vignette.rmd to include an example of the ungroup() command
• Extension begins on line 61 ======= Revision/Addition Description and Link: additional examples demonstrating data transformation and visualization through the use of mutate() and other dyplr functions.

dplyr+ggplot2-extended.Rmd

Anthony R.

Initial Description and Link:

• Using pivot_longer() to reshape data for visualization
• Utilizing multiple methods to visualize data
• roman_tidyverse.qmd
• Here is a link to my github source

Revision/Addition Description and Link:

• Added advanced text analysis techniques using Tidyverse for Alexander Wrubel
• Extension is here

Benjamin W.

Initial Description and Link:

• Demonstrates how to use the gather function to collpse multiple columns of a datadrame into key-value pairs.
• BW_Gather.RMD

Revision/Addition Description and Link:

Crystal Q.

Initial Description and Link:

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Daniel B.

Initial Description and Link: This dataset explores maternal health risks for pregnant women based on factors such as age and blood pressure. It focuses on the RiskLevel column and uses ggplot2 to better explore how age and blood pressure relate to the risk level.
Tidyverse create Daniel Brusche.Rmd Revision/Addition Description and Link:

John F.

Initial Description and Link:

• Basic Usage of the group_by() function for aggregate metrics.
• groupby_vingette.Rmd

Revision/Addition Description and Link:

• Extended the initial file unnesting.Rmd from tillmawitz
• Around line 114 is where my extension begins.
• Extended with group_split() from kevinhav

Julian A-N.

Initial Description and Link:

Revision/Addition Description and Link:

Keith R.

Initial Description and Link:

• Using dplyr's count() function for calculating subtotals of observations in a group.
• count()_vignette.Rmd

Revision/Addition Description and Link:

• Extended groupby_vingette.rmd to include an example of how to use the count() function (starting at line 77).

Kevin H.

Initial Description and Link:

• Using across() with mutate() for column-wise transformations
• across_vignette.Rmd

Revision/Addition Description and Link:

Kevin K.

Initial Description and Link:

• Combining tidytext for text mining with gganimate for animated visuals to create dynamic visualizations
• kkirby_tidytext.Rmd

Revision/Addition Description and Link:

Kimberly K.

Initial Description and Link:

• Using 'dense_rank()' with 'group_by()' and 'desc()' to perform equivalent to rank() over (partition by order by) in SQL
• Using case_when() to perform equivalent to case when in SQL
• DATA_607_Koon_Tidyverse_Create.Rmd

Revision/Addition Description and Link:

• Extended Zach R's ggplot vignette, Data607TidyVerse.Rmd
• Found the dataset on fivethirtyeight and edited the dataset loading to point to a publicly available path
• Demonstrated the use of scale_color_manual() to map colors to the data in ggplot

Luis M.

Initial Description and Link: For the CREATE assignment, I provided an example of the use of some functions from the forcats package:

• fct_reorder(): Reordering a factor by another variable.
• fct_relevel(): Changing the order of a factor by hand. Link

Revision/Addition Description and Link: For the EXTEND assignment, I chose gather() by Ben W to include an explanation of separate () and mutate() Link

Matthew T.

Exploring how to work with nested data using unnest()

Initial Description and Link:
Nested data occurs when a cell of a dataframe contains a list or similar object. Properly unnesting can range from very simple to incredibly complicated, and we will explore a few examples and explain how to handle them using the tidyr package.

Click here to see the example!

Revision/Addition Description and Link:
Extending the work done by Kevin H. on the across() function we explore the similar if_any() and if_all() functions which can be used with the filter() function as well as mutate(). The if_any() and if_all() functions follow the same pattern as across() and can be used when conditional operations are desired.

Check out the extension here!

Tenzin D.

My dataset is World happiness report. Initial Description and Link: Purrr is a popular R Programming package that provides a consistent and powerful set of tools for working with functions and vectors. (Tidyverse.Rmd) -Data filter and maping -Calculating the Average -Purrr map function

Revision/Addition Description and Link:(TidyVerseExtend.Rmd) -GGPLOT -PLOTLY This visualization gave me many insights regarding the correlation between a country’s GDP per capita and their happiness.

Nwe M.

Initial Description and Link:

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Robert G.

Initial Description and Link:

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Tiffany H.

Initial Description and Link:This vignette explores analyzing election deniers' stances by state, using mutate() and case_when() from dplyr for efficient conditional transformations and recoding. It also demonstrates group_by(), summarize(), and arrange() for data aggregation. ggplot2 is used for visualization.

file name: dplyr+ggplot2.Rmd

Revision/Addition Description and Link:

I expanded Kimberly K.'s vignette by incorporating visualizations using the ggplot2 package. These visualizations include a detailed breakdown of salary proportions by subdiscipline and an exploration of the relationship between salary and education level. The extension begins on line 10.

file name: DATA_607_Koon_Tidyverse_Extension.rmd

Zachary R.

Initial Description and Link:A vignette demonstrating the use of TidyVerse packages (dplyr, ggplot2, and kable) to analyze polling trends using FiveThirtyEight data on Donald Trump's polling percentages over time. Data607TidyVerse.Rmd

Revision/Addition Description and Link: Created new dataset featuring total and average consumption rates by year to the file 'BW_Gather.RMD'. Created additional visualizations using ggplot and kable for cleaner visibility.

Alinzon Simon

Initial Description and Link: purrr, is part of the Tidyverse and is designed for working with functional programming concepts in R; especially for lists, vectors, and data frames. https://purrr.tidyverse.org/

purrr_tidyverse.Rmd

Revision/Addition Description and Link:

Alexander Wrubel

Initial Description and Link:

• using stringr package to clean and prep data
• Data607_tidyverseAW.Rmd

Revision/Addition Description and Link: