R Programming

Final project - create your own R Package Issaiah Jennings  In this blog post, I’ll walk you through how I created an R package, combinePredictR, to analyze the 2019 NFL Combine data. This package includes functions for cleaning, visualizing, and summarizing key player statistics, such as the 40-yard dash time. Along the way, I’ll explain the steps I took, the challenges I encountered, and how I addressed them.  Step 1: Setting Up the Package I started by setting up my R package using usethis and devtools, which are great tools for package development in R. Here's a quick rundown of what I did: I used the usethis::create_package() function to initialize the basic structure of the package. I created a new R script for each function: clean_combine_data.R: For data cleaning. plot_40yd_by_position.R: To plot the 40-yard dash times by player position. summarize_40yd_by_position.R: To create a summary table showing average, minimum, and maximum times by position. Step 2: Cleani...

--- title: "Markdown Assignment for LIS4370" author: "Issaiah Jennings" date: "2025-04-13" output: html_document --- ## Introduction In this project, I worked on functions that help analyze student grade data. I used R to build functions that calculate averages, give letter grades, and filter students who are doing well. This file explains what each function does, how it works, and includes example code. --- ## Function 1: `average_grade()` **What it does:**   Takes a list of grades and gives back the average. **Inputs:**   - A vector of numbers (grades) **Outputs:**   - One number (the average) **Code:** ```{r} average_grade <- function(grades) {   return(mean(grades, na.rm = TRUE)) } # Example average_grade(c(90, 85, 88))      # 87.67 average_grade(c(100, 95, NA, 85)) # 93.33