In Edgar-Zamora/bbccIR: Instutional Research package

knitr::opts_chunk$set(
  collapse = TRUE,
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Welcome to {bbccIR}!!! Getting started with the {bbccIR} package only requires that you download the package from GitHub and that you are connected to Big Bend databases. This article will help walk you from downloading the package to connecting to a database and finally using a function.

The original intention of this package was to help Institutional Research and Planning team at Big Bend Community College when it cames to querying, wranging, and reporting on instiutional data. Many of the function contained within {bbccIR} have the ability to be applied to other community colleges throughout the State of Washington. Currently the package is developed using Legacy connections and will require revisting when Big Bend transitions to PeopleSoft.

Downloading & Library {bbccIR}

To install and libary {bbccIR}, all it take is running the following lines of code:

devtools::install_github("Edgar-Zamora/bbccIR")
library(bbccIR)

Violà!! You have installed and libraried {bbccIR}. It work noting that you will only need to install {bbccIR} once per machine. Next time you start a new session of RStudio you will just have to library(bbccIR) and not install it.

Connecting to Big Bend Databases

In order to get the most out of this package, it is beneficial that you are connected to the Big Bends databases through the RStudio "Connections" tab. In order to connect to a database you will first need to create a odbc connection using the ODBC Data Source Adminstor in Windows. The steps should be similar on Mac. After creating a connection to your database, you will need to install and library the {DBI} and {odbc} package. From their, you should see the connection you just created in the "Connections" tab in RStudio.

The step above is probably where you will encounter the most challenging but only has to be once per database. If you decided to forgo connecting to the databases you can still use the funtions contained in this package but will need to save the necessary tables.

Delving into more detail about connecting and querying databases would outside the scope of this package. The documention for {dbplyr} is robust and provides a great intro into the area. For now I will provide snippet of what your code may look like:

con <- DBI::dbConnect(odbc::odbc(), "connection_name")

database_tbl <-tbl(con, "database_tbl") %>%
    dplyr::collect() %>%
    janitor::clean_names() %>%
    filter(year == 'B90')

Using {bbccIR} functions

After you library(bbccIR) and connect to a Big Bend database, you can start to use the functions contained within this package. The rest of this section will outline some examples of which can bee seen when using {bbccIR}. For a detailed walkthrough, you can visit the article about Big Bend retention.

Edgar-Zamora/bbccIR documentation built on Feb. 3, 2022, 11:06 a.m.