---
title: "Download pell data from US Department of Education"
vignette: >
  %\VignetteIndexEntry{download}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

## Download data from a US Department of Education website

In case you are curious, you can also directly get the data from the website of US Department of Education. The website provides all the data in separate files one for each year. The reporting format is not consistent across the files. So, if you want to derive the dataset by yourself, you will need to do some data cleaning. Which has already been done in the `pell` package.

You can find the data in this link: [Pell grant data](https://www2.ed.gov/finaid/prof/resources/data/pell-institution.html).

You can use the following code snippet to download all the data without needing to manually click and save the excel files: 

```{r eval = FALSE}
# 0.1 Loading Libraries ----
library(readxl)
library(rvest)
library(tidyverse)
library(httr)
library(janitor)
library(patchwork)
library(glue)

# 0.0 Variables ----
raw_data_loc = "data-raw/yearly-data/"
url <- "https://www2.ed.gov/finaid/prof/resources/data/pell-institution.html"
downloadString <- "https://www2.ed.gov/finaid/prof/resources/data/"

# 1.0 DATA SOURCING ----

# 1.1 Web Scraping ----
htmlOutput <- read_html(url)

# collect years
years <- htmlOutput %>%
  html_nodes(".smallindent") %>%
  html_text() %>%
  substr(1, 7)

# collects download link
reportLinks <- htmlOutput %>%
  html_nodes(".smallindent > a") %>%
  html_attr('href') %>%
  unique()

# year and report match
reportSources <- tibble(
  years = years,
  report_link = paste0(downloadString, reportLinks)
)


# 1.2 Collecting Data ----
downloadData <- function(reportSourcesDF, storageLocation){

  df <- tibble()

  for(i in seq(1, nrow(reportSourcesDF), 1)){
    reportYear <- reportSourcesDF[i, 1]$years
    report <- reportSourcesDF[i,2]
    fileExt <- tools::file_ext(report)
    outputLocation <- glue::glue("{storageLocation}/{reportYear}.csv")

    # downloads and save file temporarily
    GET(report$report_link, write_disk(
      yearlyReport <- tempfile(fileext = fileExt))
    )
    # removes introductory paragraphs from the excel files
    yearlyReport = read_excel(yearlyReport, col_names = F)
    yearlyReport <- yearlyReport[!is.na(yearlyReport[,1]), ]
    yearlyReport <- yearlyReport[!is.na(yearlyReport[,2]), ]
    yearlyReport <- yearlyReport[!is.na(yearlyReport[,3]), ]

    # promotes first full row as column names
    yearlyReport <- yearlyReport %>%
      janitor::row_to_names(row_number = 1)

    # writes back to the desired location
    yearlyReport %>%
      write_excel_csv(outputLocation)

    # populates a summary report with col names
    cols <- as_tibble(names(yearlyReport))
    cols$year <- reportYear
    df <- rbind(df, cols)
  }

  return(df)
}

# downloads all the files and saving them in the location provided in downloadData() as storageLocation argument.
reportSourcesSummary <- downloadData(reportSources, raw_data_loc)

```

