https://github.com/gracysapra/r-in-data-science

This repository contains essential guides for data analysis using R, covering topics like data preparation, data reshaping, and data visualization. Each file focuses on fundamental techniques to manipulate, clean, and visualize data effectively using R programming.
https://github.com/gracysapra/r-in-data-science

data-analysis data-preparation data-reshaping data-science data-visualization data-visualizations ggplot r r-for-data-science

Last synced: 19 days ago
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This repository contains essential guides for data analysis using R, covering topics like data preparation, data reshaping, and data visualization. Each file focuses on fundamental techniques to manipulate, clean, and visualize data effectively using R programming.

• Host: GitHub
• URL: https://github.com/gracysapra/r-in-data-science
• Owner: Gracysapra
• Created: 2024-09-09T04:32:25.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-09-18T05:02:20.000Z (4 months ago)
• Last Synced: 2024-12-15T11:08:23.732Z (19 days ago)
• Topics: data-analysis, data-preparation, data-reshaping, data-science, data-visualization, data-visualizations, ggplot, r, r-for-data-science
• Language: Jupyter Notebook
• Homepage:
• Size: 40 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# R-in-data-Science

This repository provides a comprehensive guide to working with data in R. It is divided into three key sections:

## Data Preparation Using R:

This section focuses on the essential steps to clean and preprocess data for analysis. It covers techniques like handling missing values, outlier detection, normalization, and data transformation using popular packages like dplyr. The guide is designed to help you ensure your data is ready for analysis by addressing common issues found in raw datasets.

## Data Reshaping Using R: 

In this section, we dive into the methods of reshaping data to fit the required format for analysis. Topics include transposing data, merging datasets, and using functions like cbind(), rbind(), and merge(). Advanced topics like melting and casting data frames are also covered to enable smooth transitions between wide and long data formats.

## Data Visualization Using R:

This section is dedicated to creating insightful and aesthetically pleasing visualizations using ggplot2. It guides users through creating various types of plots such as scatter plots, histograms, box plots, and bar charts. Additionally, it explains how to layer elements and customize themes to make your visualizations both informative and visually appealing.