Introduction to pell

The pell R package contains one dataset that provides data about pell award distribution by the universities/colleges across the United States since 1999 to 2017. This introductory vignette provides some overall statistics and visualization about the data to inspire potential use of this data.

Installation

You can install the released version of pell from CRAN with:

install.packages("pell")

Or install the development version from GitHub with:

install.packages("devtools")
devtools::install_github("Curious-Joe/pell")

The pell package

This package contains one dataset called - pell. Take a glimpse at the data:

dplyr::glimpse(pell)
#> Rows: 100,474
#> Columns: 6
#> $ STATE     <fct> AK, AK, AK, AK, AK, AK, AL, AL, AL, AL, AL, AL, AL, AL, AL, …
#> $ AWARD     <dbl> 197232.9, 133148.0, 107287.0, 3425148.8, 2441864.0, 353170.0…
#> $ RECIPIENT <dbl> 109, 69, 72, 1920, 1256, 221, 2369, 837, 3236, 854, 2842, 16…
#> $ NAME      <fct> "Alaska Pacific University", "Alaska Vocational Technical Ce…
#> $ SESSION   <fct> 1999-00, 1999-00, 1999-00, 1999-00, 1999-00, 1999-00, 1999-0…
#> $ YEAR      <int> 1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999, …

The pell::pell data contains 100470 complete cases, with 4 missing values.

visdat::vis_dat(pell)

Highlights

Without going much into the details, here are few code snippet to get you started with the pell dataset. You can check out more in vignette("examples").

Exploring factors

The pell data has three factor variables:

pell %>%
  dplyr::select(where(is.factor)) %>% 
  dplyr::glimpse()
#> Rows: 100,474
#> Columns: 3
#> $ STATE   <fct> AK, AK, AK, AK, AK, AK, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL…
#> $ NAME    <fct> "Alaska Pacific University", "Alaska Vocational Technical Cent…
#> $ SESSION <fct> 1999-00, 1999-00, 1999-00, 1999-00, 1999-00, 1999-00, 1999-00,…

Get the top 10 states with the highest median Pell grant record:

# Top 10 institutions with the highest pell grant disbursements
pell %>%
  dplyr::group_by(STATE) %>%
  dplyr::summarise(
    Median = median(.data$AWARD, na.rm = TRUE)
  ) %>%
  dplyr::arrange(desc(Median)) %>%
  head(10) %>%
  knitr::kable(caption = "Top 10 States with the Highest Median Grant Distribution")

Top 10 States with the Highest Median Grant Distribution
STATE	Median
FM	8787878
AS	4181457
AL	3067752
MS	2989801
MH	2826818
GU	2706895
VI	2388619
PW	2206829
NC	2113618
MP	1920043

Get a treemap of all the states based on their total paid out grant dollars:

treemap::treemap(pell,
            index=c("STATE"),
            vSize="AWARD",
            type="index",
            
            )

If you are a Python user, you may find interest in checking a dash app that I created earlier using the same data. Check out the app repository here.

I will try to put some more R examples in vignette("examples") but currently it’s not populated.

So keep an eye on that or do you own analysis and contribute your own!

Package citation

Please cite the pell R package using:

citation("pell")
#> 
#> To cite package 'pell' in publications use:
#> 
#>   Hossain A (????). _pell: Data About Historic Pell Grant Distribution
#>   in the US_. https://github.com/Curious-Joe/pell,
#>   https://curious-joe.github.io/pell/.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {pell: Data About Historic Pell Grant Distribution in the US},
#>     author = {Arafath Hossain},
#>     note = {https://github.com/Curious-Joe/pell,
#> https://curious-joe.github.io/pell/},
#>   }

Have fun with the pell grant data!