Module 10: Building Your Own R Package

The Curtis R package is designed to help students, researchers, and data analysts explore and visualize vehicle efficiency and emissions data from the U.S. Environmental Protection Agency’s SmartWay program. Its purpose is to simplify the process of cleaning, analyzing, and comparing fuel efficiency trends across different vehicle classes and model years.

The target audience includes learners in data science and environmental analytics courses, as well as professionals interested in sustainability reporting and transportation efficiency research. By combining robust data-handling functions with clear visualization tools, Curtis allows users to generate high-quality plots and insights without needing extensive coding experience.

Key Functions

The creation of "Curtis" package will include several core functions designed for accessibility and analytical depth:

  • read_smartway(path) – Imports and cleans EPA SmartWay CSV files, ensuring consistent column names and data types.

  • plot_mpg_emissions(data) – Creates static ggplot2 scatterplots of MPG versus CO₂ emissions, colored by vehicle class.

  • rank_best_in_class(data, metric = "mpg") – Ranks and returns the most fuel-efficient vehicles in each class or model year.

  • trim_compare(data) – Uses Plotly to generate interactive charts comparing trims or models within a class.

  • year_over_year(data) – Visualizes changes in MPG and emissions over time using trend lines or facet grids.

Choices in the DESCRIPTION File

The fields in the DESCRIPTION file were carefully selected to make the package reproducible, lightweight, and easy to maintain:

  • Title and Description clearly summarize the purpose within 80-character line limits so the file remains machine-readable and compliant with CRAN standards.

  • Version (0.0.0.9000) follows the development convention for early-stage packages.

  • Authors@R lists myself, Austin Curtis, as both author and maintainer for transparent responsibility and contact.

  • Depends: R (>= 3.1.2) ensures compatibility with most R installations while maintaining backward flexibility.

  • Imports include ggplot2, dplyr, tidyr, readr, and plotly the essential libraries for data wrangling, visualization, and interactivity.

  • License: CC0 was chosen to promote open academic sharing, allowing free reuse in coursework and collaborative research.

  • LazyData: true enables efficient inclusion of prebuilt datasets for faster demonstrations.

  • URL and BugReports connect the package directly to its GitHub repository for open development and issue tracking.

GitHub Repository

The "Curtis" package source, documentation, and example scripts are hosted on GitHub: AustinTCurtis/AustinCurtis: For LIS4370

Comments

Post a Comment

Popular posts from this blog

Module #4 Visualizing and Interpreting Hospital Patient Data

Image
Github Repository:   r-programming-assignments/Assignment_04.R at main · AustinTCurtis/r-programming-assignments CODE FROM R Studio: > # Define numeric vectors > Frequency <- c(0.6, 0.3, 0.4, 0.4, 0.2, 0.6, 0.3, 0.4, 0.9, 0.2) > BloodPressure <- c(103, 87, 32, 42, 59, 109, 78, 205, 135, 176) > > # Convert categorical strings to numeric codes > # First assessment: bad=1, good=0 > FirstAssess <- c(1, 1, 1, 1, 0, 0, 0, 0, NA, 1) > > # Second assessment: low=0, high=1 > SecondAssess <- c(0, 0, 1, 1, 0, 0, 1, 1, 1, 1) > > # Final decision: low=0, high=1 > FinalDecision <- c(0, 1, 0, 1, 0, 1, 0, 1, 1, 1) > > # Create data frame > df_hosp <- data.frame( + Frequency, BloodPressure, FirstAssess, + SecondAssess, FinalDecision, + stringsAsFactors = FALSE + ) > > # Inspect data > summary(df_hosp) Frequency BloodPressure FirstAssess SecondAssess FinalDecision Min. :0.20 Min. : 32.0...
Read more

Module # 2 Assignment Importing Data and Function Evaluation in R

Image
  Evaluate  myMean  Function Use the vector: assignment2 <- c(16, 18, 14, 22, 27, 17, 19, 17, 17, 22, 20, 22) Consider this function: myMean <- function(assignment2) { return(sum(assignment) / length(someData)) } In RStudio, run  myMean(assignment2)  and record the output or error. After running the code in R Studio this is the error that occurred: > assignment2 <- c(16, 18, 14, 22, 27, 17, 19, 17, 17, 22, 20, 22) > myMean <- function(assignment2) { + return(sum(assignment) / length(someData)) + } > assignment2 <- c(16, 18, 14, 22, 27, 17, 19, 17, 17, 22, 20, 22) > myMean <- function(assignment2) { + return(sum(assignment) / length(someData)) + } > myMean(assignment2) Show Traceback Rerun with Debug Error in myMean(assignment2) : object 'assignment' not found The function fails because of inconsistent variable names : You pass in "assignment2", but inside the function you try to use "assignment" and "someData...
Read more

Module 6: Matrix Operations and Construction

GitHub Repository:  r-programming-assignments/Assignment_06.R at main · AustinTCurtis/r-programming-assignments Part I — Matrix Addition & Subtraction In this section, I’m creating two matrices A and Busing the matrix() function. Both matrices have 2 columns. After defining them, I perform matrix addition (A+B)  and matrix subtraction (A-B)  These operations are performed elementwise, meaning R adds or subtracts each corresponding element from the same position in both matrices. R Code: # Define two 2x2 matrices A and B A <- matrix(c(2, 0, 1, 3), ncol = 2) B <- matrix(c(5, 2, 4, -1), ncol = 2) # Display A and B A B # Matrix addition A_plus_B <- A + B A_plus_B # Matrix subtraction A_minus_B <- A - B A_minus_B Part II — Creating a Diagonal Matrix Here, I use the diag() function to create a 4×4 diagonal matrix called D. A diagonal matrix has non-zero values only along its main diagonal (from top left to bottom right), with zeros everywhere else. I specify th...
Read more