Module 10.

 GitHub link: https://github.com/christyj777/tidycleanr/tree/main

Scope and Purpose: 

The goal of tidycleanr is to make everyday data cleaning faster and more consistent for analysts and students who work with messy CSVs and survey data. Instead of repeating the same wrangling steps by hand, users can call short helper functions to standardize column names, guess variable types, and handle missing values. The package focuses on simple utilities that make it easy to prepare data for visualization or modeling. 

Key Functions:

clean_names() – wraps janitor to quickly convert messy column names into snake_case and fix duplicates.

guess_types() – automatically converts character columns to numeric, date, or factor types where appropriate.

impute_fast() – fill missing values using median or mode for numeric and categorical variables.

drop_dupes() – remove duplicate rows across one or more key columns using dplyr.


Description: 


Some of the fields above such as (dependencies and license) was provided, so I used those to maintain integrity. The author has my personal details. The description and title were based on the package itself and its capabilities. Imports include janitor, dplyr, tidyr, and stringr, since these packages provide the backbone of data manipulation and have used them many times. LazyData is set to true to make it easy to include small example datasets in the future. 








Comments

Post a Comment

Popular posts from this blog

R Programming Journal – Christine Jacob

Image
 Studio Console screenshot.  Installation Report: I began by downloading the latest version of R according to my operating system. After the R installation completed, I proceeded to download RStudio Desktop. Both installers were straightforward, making sure to select the correct version for my system. Once installed, I opened RStudio to confirm that it recognized R properly. The console displayed the version information successfully, and I captured a screenshot of the console window as required, as seen above. During the installation, I did not encounter any major issues. The only minor challenge was ensuring that my operating system’s security settings allowed the installer to run, on macOS. Once I approved it, the installation proceeded without further problems. My system is running macOS Ventura 13.5, with R version 4.4.2 installed. I am using RStudio version 2024.12.0+467, which provides the interface for working with R. An R vector is a one-dimensional structure that...
Read more

Module 2. Assignment: Function Debugging and Evaluation in R

Image
 Assignment Instructions: The initial test results can be found below and included the following error message: "Error in myMean(assignment2) : object 'assignment' not found".  The reason why this function failed is because assignment and someData are undefined variable names. R cannot identify their function and they are not existing objects in the environment, and as a result produces an error message.  Now, a corrected version of myMean that correctly returns the mean of assignment2 will be computed.  The mean is  19.07692. In this iteration, we changed the parameters to include 'data' and both sum(data) and length(data) use the same parameter. Consistent variable names are important in R programming to ensure data integrity and cohesion! 
Read more

Module 8. Assignment CSV files

Image
  (please click images for higher clarity) The images above showcase file‑write operations succeeded and the R code.  The first step imports the data without commas, from the dataset.txt file. It then creates a summarized table that shows the average grade for males and females. The resulting data frame, gender_mean, is then written to a tab-delimited text file named gender_mean.txt for reference or sharing. The second step filters the data to find all students whose names contain the letter “i” or “I,” using the grepl() function. A smaller data frame, i_students, is created from these matching rows and saved to a csv file. The final step produces a new csv file, created including all original columns, for just the names including the letter "I". This is the final document for this project, creating a new data set containing just those names. 
Read more