stringr

🔍 An RStudio addin slash regex utility belt

Shiny Application to test regular expressions in R

Collocates retriever and Collocation association measure

The Programm tries to determine the cosine similarity scores for a set of words in question. Cosine similarity scores indicate the contextual similarity between words.

PU de stringr

An introduction to the tidyverse. Includes packages such as dplyr, stringr, ggplot2, etc.

This open-source, R-based web application allows the conversion of video captions (subtitles) from the Web Video Text Tracks (WebVTT) Format into plain texts. For this purpose, users upload a WebVTT file with the extension of 'vtt' or 'txt'. Automatically, metadata such as timestamps are removed, and the text is formatted.

Repo for lab assign. and the final capstone project for CS 631 Data Visualization. Implemented data visualization principles - fonts, text, color theory, gestalt principles using RStudio and Markdown on different datasets

Kentucky, like many states, maintains a historical marker database(KHMD). Scraping the database yielded 2226 total markers which were plotted to the center of the county located.

Series of codes to extract some information from books and recorded earthquakes written in R.

Implementation of k-means task by DBSCAN with focus on noise reduction

A R-shiny application to detect personally identifiable information in a given text corpus using regex and "stringr"

Google Analytics Capstone Project - Bellabeat Smart Watch Analysis.

Analyze your Twitter archive and explore some aspects of your tweeting behavior. When do you tweet, how much do retweet people, do you use hashtags and much more

R functions to deal with character vectors

A broker wants to use linear regression to find out which factors have a large influence on the price of a property.

My role in this group project was to perform regression analysis on quarterly financial data to predict a company's market capitalization. I used R to develop ordinary least squares (OLS), stepwise, ridge, lasso, relaxed lasso, and elastic net regression models. I first used stepwise and OLS regression to develop a model and examine its residual plots. The plot displaying the residuals against the predicted values indicated multiplicative errors. I, therefore, took the natural log transformation of the dependent variable. The resulting model's R2 was significantly, negatively impacted. After examining scatter plots between the log transformation of market capitalization and the independent variables, I discovered the independent variables also had to be transformed to produce a linear relationship. Using the log transformation of both the dependent and independent variables, I developed models using all the regression techniques mentioned to strike a balance between R2 and producing a parsimonious model. All the models produced similar results, with an R2 of around .80. Since OLS is easiest to explain, had similar residual plots, and the highest R2 of all the models, it was the best model developed.

Data Wrangling in R, Lesson 4 - Working with String Variables

This project was created to showcase the skills learnt in the DA-5020 Collecting Storing and Retrieving Data Course in the Spring 2017 Semester.

Notes on string manipulation using R

This application aims to help you recode your variables after introducing the data as an RDS saved dataframe and given the variable to recode it will automatically generate fields where variable levels are adjustable and you perform your modification before downloading your data via the button