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Data-Science-with-R-programming

This R script provides a comprehensive overview of data visualization and data manipulation techniques using various libraries like DPLYR, TIDYR, & Data Visualization using GGPLOT, PLOTLY etc.

R Data Visualization and Manipulation Guide

Overview

This repository provides a comprehensive guide to data visualization and manipulation in R, utilizing powerful libraries such as dplyr, tidyr, ggplot2, and plotly. It covers essential techniques for handling and visualizing data efficiently.

Key Sections

Data Visualization Keywords:

  • ggplot2
  • plotly
  • plot(), hist(), boxplot()
  • geom_bar(), geom_point(), geom_boxplot()
  • aes(), facet_wrap(), coord_flip()
  • scatter plot, bar chart, histogram, box plot, pie chart, heatmap, word cloud

Data Manipulation (DPLYR) Keywords:

  • dplyr
  • filter(), select(), mutate(), arrange(), summarise()
  • group_by(), rename(), distinct(), count()
  • left_join(), right_join(), inner_join(), full_join()
  • %>% (Pipe Operator)

Data Reshaping (TIDYR) Keywords:

  • tidyr
  • gather(), spread()
  • separate(), unite()

General R Functions and Libraries:

  • install.packages(), library()
  • View(), head(), tail()
  • sample_n(), sample_frac()
  • dataset: flights, mtcars, airquality, mpg
  • Base R, grid graphics, MASS
  • data transformation, data visualization, tidy data, data wrangling

📊 Charts and Graphs

Demonstrates various visualization techniques, including:

  • Pie charts

  • Bar charts

  • Histograms

  • Box plots

  • Scatter plots

  • Heatmaps

  • Word clouds

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🛠 DPLYR for Data Manipulation

Learn how to efficiently manipulate datasets with functions like:

  • filter(), select(), mutate(), arrange(), summarise()

  • group_by(), rename(), distinct(), count()

  • Data joining with left_join(), right_join(), inner_join(), full_join()

🔄 TIDYR for Data Reshaping

Transform and tidy datasets using:

  • gather() to make wide data longer

  • spread() to make long data wider

  • separate() to split columns

  • unite() to combine columns

📈 Data Visualization Techniques

Base R Graphics

  • plot(), hist(), boxplot() for quick and easy visualizations.

ggplot2 for Advanced Visualizations

Create professional and customizable visualizations using the grammar of graphics.

plotly for Interactive Plots

Build interactive and dynamic web-based visualizations.

📂 Examples and Use Cases

Demonstrates practical applications using datasets such as:

  • flights (from nycflights13)

  • mtcars

  • airquality

  • mpg

🔧 Installation

Ensure you have R installed and then install the required libraries:

install.packages(c("dplyr", "tidyr", "ggplot2", "plotly", "nycflights13", "MASS", "grid", "plotrix"))