Building My First R Package: hamad

I recently created my first R package called hamad, and it's been a solid learning experience. The package focuses on streamlining exploratory data analysis with practical utility functions.

What the Package Does

The hamad package includes three core functions designed to speed up initial data assessment:

summary_stats() - Automatically identifies and summarizes all numeric variables in a dataset. This eliminates the need to manually select columns every time you want a quick overview.

missing_report() - Generates a comprehensive report showing which variables contain missing values, along with counts and percentages. This helps identify data quality issues upfront.

quick_hist() - Creates histogram visualizations for any variable, making it easy to quickly examine distributions during exploratory analysis.

These functions address common tasks that come up repeatedly in data analysis workflows.

Package Design Decisions

The DESCRIPTION file required several key decisions:

Dependencies: I included ggplot2, dplyr, and tidyr in the Imports field. These are widely-used packages in the R ecosystem that complement data analysis workflows. While not all are actively used in the current version, they provide a foundation for future functionality.

License: I selected the MIT License for its simplicity and permissiveness. It allows users to freely use, modify, and distribute the package, which aligns well with open-source collaboration.

Versioning: Following R conventions, I started with version 0.0.0.9000. The .9000 suffix indicates this is a development version, setting appropriate expectations for users.

Installation and Access

You can install the development version from GitHub:

devtools::install_github("ohamad03/hamad")

Future Development

I plan to expand the package with additional visualization and data cleaning functions based on common analysis needs. Contributions and feedback are welcome as the package evolves. The code's all on GitHub: https://github.com/ohamad03/hamad


Location: 5QPCXF2W+26

Comments

Post a Comment

Popular posts from this blog

R Programming Journal - Omar Hamad

Image
  RStudio Installation Experience Yesterday, I switched from Windows to Mac, which was new for me. Installing R and RStudio on macOS  It was simple, but I had to change some security settings in my Mac to allow the software to run.  I downloaded the correct files from the official sites, installed them, and opened RStudio successfully.  What is an R Vector? An R vector is a basic data structure that stores a sequence of elements all of the same type, such as  numbers, words, or logical values. It’s a core part of R because it allows you to organize and work with  data efficiently. Vectors are the foundation for many operations in R, making data manipulation and  analysis faster and easier. Vectors are essential in R because they enable you to perform operations on entire collections of data at  once, rather than handling each element individually. This makes your code more concise and efficient.  Understanding vectors is crucial because most ...
Read more

Assignment 6

Code: # Task 1: Matrix Addition & Subtraction A = matrix(c(2, 0, 1, 3), ncol = 2) B = matrix(c(5, 2, 4, -1), ncol = 2) cat("Matrix A:\n") print(A) cat("\nMatrix B:\n") print(B) A_plus_B = A + B cat("\nA + B:\n") print(A_plus_B) A_minus_B = A - B cat("\nA - B:\n") print(A_minus_B) # Task 2: Create a Diagonal Matrix D = diag(c(4, 1, 2, 3)) cat("\nDiagonal Matrix D:\n") print(D) # Task 3: Construct a Custom 5x5 Matrix first_col = c(3, 2, 2, 2, 2) remaining_block = rbind(   c(1, 1, 1, 1),   diag(3, 4) ) custom_matrix = cbind(first_col, remaining_block) cat("\nCustom 5x5 Matrix:\n") print(custom_matrix) Output: > # Task 1: Matrix Addition & Subtraction > A = matrix(c(2, 0, 1, 3), ncol = 2) > B = matrix(c(5, 2, 4, -1), ncol = 2) > cat("Matrix A:\n") Matrix A: > print(A)      [,1] [,2] [1,]    2    1 [2,]    0    3 > cat("\nMatrix B:\n") Matrix B: > print(B)      [,1] [,2] [1,]   ...
Read more

Generic Functions in R

What I Did   This assignment helped me learn how R's generic functions work and the difference between S3 and S4 objects. I used the mtcars dataset to see how functions change behavior based on what kind of object they're working with.   Loading the Dataset   First, I loaded mtcars data(" mtcars ") head( mtcars ) str( mtcars )   This dataset has 11 columns with things like miles per gallon, horsepower, Etc . How Generic Functions Work   I tested some basic R functions to see how they adapt to different objects:   summary( mtcars ) plot( mtcars )   lm_model = lm (mpg ~ wt + hp, data = mtcars ) summary( lm_model )   What I noticed: when you run summary( ) on a data frame, you get min/max/mean values. But when you run it on a linear model, you get totally different outputs, like coefficients and p-values. Same function name, different behavior!   Creating S3 Objects   S3 is the simpler object system. Here's how I made one:   s3...
Read more