5 R Tutorial Data Visualization
3 likes2,256 views
The document discusses various data visualization techniques in R including bar plots, pie charts, histograms, kernel density plots, line charts, box plots, heat maps, and word clouds. It provides code examples for creating basic and customized visualizations of different types using the R programming language and exporting the visualizations to file formats like PDF, PNG, and JPEG.
1 of 33
Downloaded 83 times
![Data visualization
Heat Map
data <- read.csv("HEATMAP.csv",header = TRUE)
#convert Data frame into matrix
data <- data.matrix(data[,-1])
heatmap(data,Rowv=NA, Colv=NA,
col = heat.colors(256), scale="column")
http://shakthydoss.com 20](https://image.slidesharecdn.com/5-r-tutorial-data-visualization-151030161703-lva1-app6892/85/5-R-Tutorial-Data-Visualization-20-320.jpg)
Ad
Recommended
1 R Tutorial Introduction
1 R Tutorial IntroductionSakthi Dasans R is a programming language developed as an alternative for S at AT&T Bell Laboratories. It excels at statistical computation and graphic visualization. R is free, open source, and available across platforms. It has over 3,000 packages on CRAN that extend its functionality. R has a steep learning curve and working with large datasets is limited by RAM size. Major companies use R in business.
Data visualization using R
Data visualization using RUmmiya Mohammedi This document discusses visualizing data in R using various packages and techniques. It introduces ggplot2, a popular package for data visualization that implements Wilkinson's Grammar of Graphics. Ggplot2 can serve as a replacement for base graphics in R and contains defaults for displaying common scales online and in print. The document then covers basic visualizations like histograms, bar charts, box plots, and scatter plots that can be created in R, as well as more advanced visualizations. It also provides examples of code for creating simple time series charts, bar charts, and histograms in R.
Introduction to Rstudio
Introduction to RstudioOlga Scrivner This document provides an introduction to using R Studio for statistical analysis. It discusses how to install both R and R Studio on Windows and Mac systems. It then covers creating scripts and files in R Studio, basic R syntax including assigning values to variables, vectors, and strings. The document also demonstrates how to install and load packages to access additional functions, and how to access built-in datasets to practice working with data in R.
Data visualization with R
Data visualization with RBiswajeet Dasmajumdar 1) The document discusses data visualization using R and provides an introduction to key concepts. It explains why data visualization is important for understanding large and complex data.
2) Basic concepts for effective visual analytics are covered, including understanding the data, determining what to visualize, knowing the audience, and using simple visuals.
3) Different types of plots in R are described like histograms, bar plots, scatter plots, box plots, and plots for descriptive statistics. Steps to install R and RStudio are also provided.
Introduction to R Graphics with ggplot2
Introduction to R Graphics with ggplot2izahn This document outlines an introduction to R graphics using ggplot2 presented by the Harvard MIT Data Center. The presentation introduces key concepts in ggplot2 including geometric objects, aesthetic mappings, statistical transformations, scales, faceting, and themes. It uses examples from the built-in mtcars dataset to demonstrate how to create common plot types like scatter plots, box plots, and regression lines. The goal is for students to be able to recreate a sample graphic by the end of the workshop.
Data Visualization With R
Data Visualization With RRsquared Academy This document outlines a course on data visualization using R, detailing objectives such as understanding data visualization concepts, building plots, and modifying visual elements. It introduces the main graphics systems in R—graphics, ggplot2, and lattice—along with practical use of the plot() function for different types of data visualization scenarios. Additionally, it covers enhancing plots by adding titles, labels, and modifying colors and ranges.
Introduction to R Programming
Introduction to R Programmingizahn The document is a workshop guide for an intermediate/advanced R programming course, focusing on data types, function application, object manipulation, and control flow. It includes practical exercises using the built-in iris dataset to demonstrate statistical functions and data extraction methods. Key topics covered include vector and data.frame manipulation, S3 object systems, and debugging functions.
Unit 1 - R Programming (Part 2).pptx
Unit 1 - R Programming (Part 2).pptxMalla Reddy University This document provides an introduction and overview of using R for data visualization and analysis. It discusses installing both R and RStudio, basics of R programming including data types, vectors, matrices, data frames and control structures. Descriptive statistical analysis functions are also introduced. The document is intended to teach the fundamentals of the R programming language with a focus on data visualization and analysis.
2 R Tutorial Programming
2 R Tutorial ProgrammingSakthi Dasans This document discusses various programming concepts in R including operators, control flow statements, and functions. It covers arithmetic, relational, and logical operators. It also covers if/else statements, for loops, while loops, and the repeat loop. Commonly used functions like summary(), str(), and length() are also mentioned. The document is intended to teach basic concepts in R programming.
PL/SQL Code for Sample Projects
PL/SQL Code for Sample Projectsjwjablonski This document discusses sample PL/SQL code projects including:
1) Using multiple cursors and parameters to create reports from database tables on regions, countries, and languages.
2) Handling user defined exceptions when querying and deleting from a database table based on condition checks.
3) Creating functions.
4) Creating packages with overloaded procedures.
5) Creating triggers to maintain data integrity where check constraints would be ineffective.
Introduction to R programming
Introduction to R programmingVictor Ordu This document outlines an introductory course on R programming for a web monitoring group, highlighting prerequisites such as basic computer skills and a willingness to learn. It discusses the advantages of R, including its cost-effectiveness, ease of installation, and the ability to produce high-quality graphics, while also mentioning challenges like its steep learning curve. The course aims to equip participants with practical skills through exercises in the R console, ultimately welcoming them to the R programming environment.
R Programming: Importing Data In R
R Programming: Importing Data In RRsquared Academy The document provides a comprehensive guide to learning R programming for data analysis. It covers course modules, data handling concepts, including importing data from various file types, and functions for reading data from console and files. Specific functions are detailed with their syntax and usage examples for practical understanding.
Using Python Libraries.pdf
Using Python Libraries.pdfSoumyadityaDey Python uses modules, packages, and libraries to organize code. A module is a .py file containing functions, classes, and variables. Related modules are grouped into packages, which are directories containing an __init__.py file. Libraries are collections of packages that provide specific functionality. The Python standard library includes common modules like math and random. Modules can be imported and used to reuse code in other files or packages.
Css color and background properties
Css color and background propertiesJesus Obenita Jr. <body style="background: green; color: white;">
<style>
body {
background: url("image_7.png") fixed repeat-y top right;
}
</style>
Introduction to HTML5 and CSS3 (revised)
Introduction to HTML5 and CSS3 (revised)Joseph Lewis Joseph R. Lewis of Sandia National Laboratories gave a presentation on HTML5 and CSS3 at Oak Ridge National Laboratory. The presentation provided an overview of the history that led to the development of HTML5, described new HTML5 semantic elements and attributes, and covered features of HTML5 like Canvas, SVG, and MathML. It also covered new CSS3 properties and exercises for attendees to experiment with the new technologies.
A c program of Phonebook application
A c program of Phonebook applicationsvrohith 9 The document describes a mini project for a phonebook application developed in C, which aims to teach basic concepts of functions, file handling, and data structure. It outlines the application's functionalities including adding, listing, modifying, searching, and deleting contact information, along with its advantages and limitations. Additionally, it includes software and hardware details, design modules, and the structure of the code used to implement the application.
CSS
CSSseedinteractive CSS (Cascading Style Sheets) is a style sheet language used to describe the presentation of structured documents written in HTML. CSS controls the layout of multiple documents from a single style sheet and allows for more precise control over layouts and different styles for different media like screens and print. CSS syntax uses selectors to apply styles denoted by properties and values to HTML elements. Styles can be applied inline, internally in the <style> tag, or externally in a separate .css file linked via the <link> tag.
State of the Word 2016
State of the Word 2016photomatt The document provides an overview of WordCamps and meetups organized by WordPress in 2016, including statistics on the number of events, attendees, speakers, and sponsors. There were a total of 41 WordCamps held in 89 countries, with 36,569 tickets sold and 1,036 speakers. Additionally, there were 58 meetups across various countries, attracting 62,566 attendees.
Data Structures Practical File
Data Structures Practical File Harjinder Singh The document describes programs to implement various operations on singly linked lists including insertion, deletion, counting nodes, creating a list, traversing a list, and copying a list. It provides functions for insertion at the beginning, end, and before/after a given node. Deletion functions remove from the beginning, end, or by item value. Counting returns the total nodes or occurrences of a value. Traversal and copying print or duplicate the list.
Data Analysis and Visualization using Python
Data Analysis and Visualization using PythonChariza Pladin The document is a presentation about data analysis and visualization using Python libraries. It discusses how data is everywhere and growing exponentially, and introduces a 5-step process for data analysis and decision making. It emphasizes the importance of visualizing data to analyze patterns, discover insights, support stories, and teach others. The presentation then introduces Jupyter Notebook and highlights several Python libraries for data visualization, including matplotlib, seaborn, ggplot, Bokeh, pygal, plotly, and geoplotlib.
Artificial intelligence ||practical file||art integrated work activity||delh...
Artificial intelligence ||practical file||art integrated work activity||delh...unknown brain The document outlines a practical file for Artificial Intelligence, including programming exercises and tasks for a class 10 student. It presents examples of string concatenation and a program to print a multiplication table using a for loop. The document appears to include instructional content focused on basic programming concepts.
cpp input & output system basics
cpp input & output system basicsgourav kottawar This document discusses console I/O operations in C++. It covers C++ stream classes that implement input and output operations, unformatted I/O using operators and functions, and formatted I/O using stream manipulators and member functions. The key stream classes are istream for input and ostream for output. Unformatted I/O uses operators like >> and << while formatted I/O uses width(), precision(), and fill() functions as well as custom manipulators.
HTML, CSS and Java Scripts Basics
HTML, CSS and Java Scripts BasicsSun Technlogies The document provides an introduction to HTML basics including HTML document structure, common tags, and formatting. It discusses the <!DOCTYPE> declaration, <head> and <body> sections, common text formatting tags, headings, paragraphs, comments, and includes code examples.
Looking at how Scratch and Python compare
Looking at how Scratch and Python compareEmily de la Pena The document compares Scratch with Python, highlighting that while Scratch is a beginner-friendly tool for animations, Python is a more powerful programming language used for various applications like data analytics and game development. Key differences include Scratch's block-based coding and sprite interactions versus Python's line-by-line execution and greater versatility with data structures and libraries. The author emphasizes mastering Python for more complex projects and its extensibility and widespread use in the programming community.
C Programming Project
C Programming ProjectVijayananda Mohire This document appears to be a student project submission for a post graduate diploma in information technology. It contains two questions and answers involving C programming. Question 1 involves accepting integer values from the user, sorting an array in ascending order, calculating the sum of odd values and number of even values. Question 2 involves reading from a file passed as a command line argument, displaying each word on a new line, counting vowels, occurrences of "the", and copying the file contents to a new file. The code provided implements the specified tasks for each question.
Programming in C Presentation upto FILE
Programming in C Presentation upto FILEDipta Saha The document provides an overview of the C programming language, detailing its history, basic syntax, variable types, operators, decision-making statements, loops, functions, arrays, pointers, and file handling. It includes example codes for various concepts and syntax for declarations and operations. This serves as a foundational guide for understanding C programming.
Cheat Sheet for Machine Learning in Python: Scikit-learn
Cheat Sheet for Machine Learning in Python: Scikit-learnKarlijn Willems The document is a cheat sheet for using the scikit-learn library in Python, detailing various machine learning algorithms, preprocessing steps, and model evaluation techniques. It includes examples of loading data, creating and training models, and assessing model performance using metrics like accuracy score and confusion matrix. Additionally, it covers parameter optimization methods such as grid search and randomized search.
Let us C (by yashvant Kanetkar) chapter 3 Solution
Let us C (by yashvant Kanetkar) chapter 3 SolutionHazrat Bilal The document contains solutions to questions on loops in C programming. It includes:
- Sample code to demonstrate while, for, and do-while loops with explanations of output
- Solutions to problems involving calculating overtime pay, finding factorials, exponents, ASCII values, Armstrong numbers, and more using various loop structures
- Explanations of parts of for and do-while loops, and statements like break and continue
- Additional problems solved using nested loops to produce patterns, tables, and series calculations
Emotion detection from text using data mining and text mining
Emotion detection from text using data mining and text miningSakthi Dasans The document outlines the development of an emotion detection system named 'Emotelligence', which uses data mining and text mining to identify human emotions from textual content. It focuses on eight basic emotions—joy, trust, fear, surprise, sadness, disgust, anger, and anticipation—and describes a three-step approach for emotion detection: identifying emotions, collecting data, and processing it through classifiers. The tests conducted on a dataset of 1800 training examples and 200 testing examples resulted in an overall accuracy of 71%, with varied accuracy across different emotions.
DATA VISUALIZATION WITH R PACKAGES
DATA VISUALIZATION WITH R PACKAGESFatma ÇINAR The document outlines a data mining workshop held in Muğla, Turkey, focusing on data visualization using R programming, specifically employing packages like Lattice and ggplot2. It discusses the complexities of big data and presents case studies for visualizing multidimensional datasets, including various graphs such as xyplots, histograms, and dotplots. Furthermore, it covers the Grammar of Graphics concept, allowing participants to create visually appealing and informative data representations.
More Related Content
What's hot (20)
2 R Tutorial Programming
2 R Tutorial ProgrammingSakthi Dasans This document discusses various programming concepts in R including operators, control flow statements, and functions. It covers arithmetic, relational, and logical operators. It also covers if/else statements, for loops, while loops, and the repeat loop. Commonly used functions like summary(), str(), and length() are also mentioned. The document is intended to teach basic concepts in R programming.
PL/SQL Code for Sample Projects
PL/SQL Code for Sample Projectsjwjablonski This document discusses sample PL/SQL code projects including:
1) Using multiple cursors and parameters to create reports from database tables on regions, countries, and languages.
2) Handling user defined exceptions when querying and deleting from a database table based on condition checks.
3) Creating functions.
4) Creating packages with overloaded procedures.
5) Creating triggers to maintain data integrity where check constraints would be ineffective.
Introduction to R programming
Introduction to R programmingVictor Ordu This document outlines an introductory course on R programming for a web monitoring group, highlighting prerequisites such as basic computer skills and a willingness to learn. It discusses the advantages of R, including its cost-effectiveness, ease of installation, and the ability to produce high-quality graphics, while also mentioning challenges like its steep learning curve. The course aims to equip participants with practical skills through exercises in the R console, ultimately welcoming them to the R programming environment.
R Programming: Importing Data In R
R Programming: Importing Data In RRsquared Academy The document provides a comprehensive guide to learning R programming for data analysis. It covers course modules, data handling concepts, including importing data from various file types, and functions for reading data from console and files. Specific functions are detailed with their syntax and usage examples for practical understanding.
Using Python Libraries.pdf
Using Python Libraries.pdfSoumyadityaDey Python uses modules, packages, and libraries to organize code. A module is a .py file containing functions, classes, and variables. Related modules are grouped into packages, which are directories containing an __init__.py file. Libraries are collections of packages that provide specific functionality. The Python standard library includes common modules like math and random. Modules can be imported and used to reuse code in other files or packages.
Css color and background properties
Css color and background propertiesJesus Obenita Jr. <body style="background: green; color: white;">
<style>
body {
background: url("image_7.png") fixed repeat-y top right;
}
</style>
Introduction to HTML5 and CSS3 (revised)
Introduction to HTML5 and CSS3 (revised)Joseph Lewis Joseph R. Lewis of Sandia National Laboratories gave a presentation on HTML5 and CSS3 at Oak Ridge National Laboratory. The presentation provided an overview of the history that led to the development of HTML5, described new HTML5 semantic elements and attributes, and covered features of HTML5 like Canvas, SVG, and MathML. It also covered new CSS3 properties and exercises for attendees to experiment with the new technologies.
A c program of Phonebook application
A c program of Phonebook applicationsvrohith 9 The document describes a mini project for a phonebook application developed in C, which aims to teach basic concepts of functions, file handling, and data structure. It outlines the application's functionalities including adding, listing, modifying, searching, and deleting contact information, along with its advantages and limitations. Additionally, it includes software and hardware details, design modules, and the structure of the code used to implement the application.
CSS
CSSseedinteractive CSS (Cascading Style Sheets) is a style sheet language used to describe the presentation of structured documents written in HTML. CSS controls the layout of multiple documents from a single style sheet and allows for more precise control over layouts and different styles for different media like screens and print. CSS syntax uses selectors to apply styles denoted by properties and values to HTML elements. Styles can be applied inline, internally in the <style> tag, or externally in a separate .css file linked via the <link> tag.
State of the Word 2016
State of the Word 2016photomatt The document provides an overview of WordCamps and meetups organized by WordPress in 2016, including statistics on the number of events, attendees, speakers, and sponsors. There were a total of 41 WordCamps held in 89 countries, with 36,569 tickets sold and 1,036 speakers. Additionally, there were 58 meetups across various countries, attracting 62,566 attendees.
Data Structures Practical File
Data Structures Practical File Harjinder Singh The document describes programs to implement various operations on singly linked lists including insertion, deletion, counting nodes, creating a list, traversing a list, and copying a list. It provides functions for insertion at the beginning, end, and before/after a given node. Deletion functions remove from the beginning, end, or by item value. Counting returns the total nodes or occurrences of a value. Traversal and copying print or duplicate the list.
Data Analysis and Visualization using Python
Data Analysis and Visualization using PythonChariza Pladin The document is a presentation about data analysis and visualization using Python libraries. It discusses how data is everywhere and growing exponentially, and introduces a 5-step process for data analysis and decision making. It emphasizes the importance of visualizing data to analyze patterns, discover insights, support stories, and teach others. The presentation then introduces Jupyter Notebook and highlights several Python libraries for data visualization, including matplotlib, seaborn, ggplot, Bokeh, pygal, plotly, and geoplotlib.
Artificial intelligence ||practical file||art integrated work activity||delh...
Artificial intelligence ||practical file||art integrated work activity||delh...unknown brain The document outlines a practical file for Artificial Intelligence, including programming exercises and tasks for a class 10 student. It presents examples of string concatenation and a program to print a multiplication table using a for loop. The document appears to include instructional content focused on basic programming concepts.
cpp input & output system basics
cpp input & output system basicsgourav kottawar This document discusses console I/O operations in C++. It covers C++ stream classes that implement input and output operations, unformatted I/O using operators and functions, and formatted I/O using stream manipulators and member functions. The key stream classes are istream for input and ostream for output. Unformatted I/O uses operators like >> and << while formatted I/O uses width(), precision(), and fill() functions as well as custom manipulators.
HTML, CSS and Java Scripts Basics
HTML, CSS and Java Scripts BasicsSun Technlogies The document provides an introduction to HTML basics including HTML document structure, common tags, and formatting. It discusses the <!DOCTYPE> declaration, <head> and <body> sections, common text formatting tags, headings, paragraphs, comments, and includes code examples.
Looking at how Scratch and Python compare
Looking at how Scratch and Python compareEmily de la Pena The document compares Scratch with Python, highlighting that while Scratch is a beginner-friendly tool for animations, Python is a more powerful programming language used for various applications like data analytics and game development. Key differences include Scratch's block-based coding and sprite interactions versus Python's line-by-line execution and greater versatility with data structures and libraries. The author emphasizes mastering Python for more complex projects and its extensibility and widespread use in the programming community.
C Programming Project
C Programming ProjectVijayananda Mohire This document appears to be a student project submission for a post graduate diploma in information technology. It contains two questions and answers involving C programming. Question 1 involves accepting integer values from the user, sorting an array in ascending order, calculating the sum of odd values and number of even values. Question 2 involves reading from a file passed as a command line argument, displaying each word on a new line, counting vowels, occurrences of "the", and copying the file contents to a new file. The code provided implements the specified tasks for each question.
Programming in C Presentation upto FILE
Programming in C Presentation upto FILEDipta Saha The document provides an overview of the C programming language, detailing its history, basic syntax, variable types, operators, decision-making statements, loops, functions, arrays, pointers, and file handling. It includes example codes for various concepts and syntax for declarations and operations. This serves as a foundational guide for understanding C programming.
Cheat Sheet for Machine Learning in Python: Scikit-learn
Cheat Sheet for Machine Learning in Python: Scikit-learnKarlijn Willems The document is a cheat sheet for using the scikit-learn library in Python, detailing various machine learning algorithms, preprocessing steps, and model evaluation techniques. It includes examples of loading data, creating and training models, and assessing model performance using metrics like accuracy score and confusion matrix. Additionally, it covers parameter optimization methods such as grid search and randomized search.
Let us C (by yashvant Kanetkar) chapter 3 Solution
Let us C (by yashvant Kanetkar) chapter 3 SolutionHazrat Bilal The document contains solutions to questions on loops in C programming. It includes:
- Sample code to demonstrate while, for, and do-while loops with explanations of output
- Solutions to problems involving calculating overtime pay, finding factorials, exponents, ASCII values, Armstrong numbers, and more using various loop structures
- Explanations of parts of for and do-while loops, and statements like break and continue
- Additional problems solved using nested loops to produce patterns, tables, and series calculations
Viewers also liked (6)
Emotion detection from text using data mining and text mining
Emotion detection from text using data mining and text miningSakthi Dasans The document outlines the development of an emotion detection system named 'Emotelligence', which uses data mining and text mining to identify human emotions from textual content. It focuses on eight basic emotions—joy, trust, fear, surprise, sadness, disgust, anger, and anticipation—and describes a three-step approach for emotion detection: identifying emotions, collecting data, and processing it through classifiers. The tests conducted on a dataset of 1800 training examples and 200 testing examples resulted in an overall accuracy of 71%, with varied accuracy across different emotions.
DATA VISUALIZATION WITH R PACKAGES
DATA VISUALIZATION WITH R PACKAGESFatma ÇINAR The document outlines a data mining workshop held in Muğla, Turkey, focusing on data visualization using R programming, specifically employing packages like Lattice and ggplot2. It discusses the complexities of big data and presents case studies for visualizing multidimensional datasets, including various graphs such as xyplots, histograms, and dotplots. Furthermore, it covers the Grammar of Graphics concept, allowing participants to create visually appealing and informative data representations.
Emotion Detection from Text
Emotion Detection from TextIJERD Editor This document discusses emotion detection from text. It presents an emotion detection model that extracts emotion from text at the sentence level without relying on existing affect lexicons. The model detects emotion by searching for direct emotional keywords and emotion-affect words/phrases. Experiments show the method achieves over 77% accuracy in detecting Ekman's six basic emotions from text. The document also reviews related work on emotion detection approaches, including keyword-based, rule-based, and machine learning methods. It discusses challenges like the lack of large annotated training data and limitations of dictionary-based approaches.
4 R Tutorial DPLYR Apply Function
4 R Tutorial DPLYR Apply FunctionSakthi Dasans - Apply functions in R are used to apply a specified function to each column or row of R objects. Common apply functions include apply(), lapply(), sapply(), tapply(), vapply(), and mapply().
- The dplyr package is a powerful R package for data manipulation. It provides verbs like select(), filter(), arrange(), mutate(), and summarize() to work with tabular data.
- Functions like apply(), lapply(), sapply() apply a function over lists or matrices. Arrange() reorders data, mutate() adds new variables, and summarize() collapses multiple values into single values.
Microsoft NERD Talk - R and Tableau - 2-4-2013
Microsoft NERD Talk - R and Tableau - 2-4-2013Tanya Cashorali The document compares and discusses the integration of R, an open-source statistical programming environment, and Tableau, a data visualization tool, showcasing their capabilities in analyzing NFL play-by-play data. It includes examples of data processing in R, insights derived from the data regarding Baltimore Ravens' offensive predictability, and the creation of visualizations in Tableau. It concludes with references to resources for further exploration and an appendix outlining features of different Tableau editions.
Performance data visualization with r and tableau
Performance data visualization with r and tableauEnkitec This document discusses using R and Tableau for performance data visualization. It provides an agenda that covers why data visualization is useful, an overview of the tools R and Tableau, how to transform raw data into visualizations, and use cases. R is an open source statistical computing language with thousands of packages for tasks like bioinformatics, spatial statistics, and financial analysis. Tableau is a fast data visualization tool that allows users to interact with and analyze data through drag and drop functionality.
Ad
Similar to 5 R Tutorial Data Visualization (20)
Data visualization
Data visualizationBaijayanti Chakraborty Baijayanti Chakraborty, a postgraduate student in business analytics at the Great Lakes Institute of Management, discusses data visualization and its importance in transforming data into understandable insights. The document covers basic and advanced visualization techniques using tools like ggplot2 and Plotly, detailing various chart types such as histograms, box plots, and scatter plots. Additionally, it provides resources and references for further learning about data visualization in R.
Data Visualization in R (Graph, Trend, etc)
Data Visualization in R (Graph, Trend, etc)Rudyansyah - The document outlines a short course on data visualization in R, covering essential topics like standard graphics, grid & lattice graphics, and the ggplot2 package. Key components include understanding graphic roles in data analysis and learning to create various types of plots using R's capabilities. Resources and recommended packages for effective data visualization and project organization in R are also provided.
17TH October 2023 DOE SRM University.pptx
17TH October 2023 DOE SRM University.pptxVikash431966 The document discusses the use of R for graphical data analysis and visualization, highlighting its various functions and commands for creating different types of graphs and charts. It introduces key concepts in data visualization, such as selecting the right graph type and utilizing the ggplot2 package for designing effective visuals. Examples of chart types are provided, including bar charts, pie charts, histograms, and scatter plots, to illustrate how they represent data distributions and relationships.
R and Visualization: A match made in Heaven
R and Visualization: A match made in HeavenEdureka! This document discusses using R for data visualization and analytics. It covers creating basic and advanced graphs in R, customizing graphical parameters, and learning the basics of grammar of graphics. The document outlines topics that will be covered, including creating pie charts, scatter plots, line graphs, bar graphs, histograms, density plots, box plots, and more advanced graphs. It also discusses customizing graphs by changing colors, titles, axes, and backgrounds. Color palettes and the RColorBrewer package are presented as ways to customize colors in graphs.
Data Visualization by David Kretch
Data Visualization by David KretchSummit Consulting, LLC The document discusses the importance of data visualization, advocating for the use of graphs over tables to better understand data through visual means like color, size, and position. It outlines different types of graphs suitable for various data types, such as density plots for continuous variables and bar charts for categorical ones, and emphasizes the need for clear visual representation to highlight important trends and relationships. Additionally, it provides a checklist for effective graphing practices in data analysis.
M4_DAR_part1. module part 4 analystics with r
M4_DAR_part1. module part 4 analystics with rLalithauLali This document outlines the fundamentals of exploratory data analysis (EDA) using R, emphasizing the use of various graphical methods and packages such as base graphics, grid graphics, lattice, and ggplot2 for data visualization. It provides insights into techniques for summarizing data, detecting outliers, and constructing parsimonious models, along with specific functions for creating different types of plots, including pie charts and scatter plots. Key concepts include maximizing data insight through statistics and visualizations, uncovering patterns, and optimizing factor settings.
Science Online 2013: Data Visualization Using R
Science Online 2013: Data Visualization Using RWilliam Gunn This document discusses data visualization using R. It begins with a brief history of data visualization and introduces R as an open-source tool for data analysis and visualization that allows working with larger and more diverse datasets than Excel. It then covers important R concepts like data structures, loading and selecting data, and the ggplot2 package for creating graphs and visualizations by layering different geoms like points, lines, bars and boxplots.
Exploratory data analysis using r
Exploratory data analysis using rTahera Shaikh The document provides an overview of exploratory data analysis in R, focusing on the distinction between categorical and continuous variables, and how to handle both types within R using factors and numeric data. It explains different visualization techniques such as pie charts, bar charts, boxplots, histograms, line graphs, and scatterplots, detailing their syntax and parameters for effective data representation. Additionally, it addresses handling missing values in datasets, showcasing methods to test for, recode, and exclude missing data.
Data visualization
Data visualizationVivian S. Zhang The document outlines a data visualization class focusing on the application of drawing functions in R, emphasizing data exploration through visualization techniques. It covers basic principles, mapping elements of graphs, and various R packages like ggplot2 and lattice for creating visualizations. Additionally, it includes exercises and examples illustrating statistical functions, graphing, and customizing plots.
Exploratory Data Analysis
Exploratory Data AnalysisUmair Shafique The document discusses exploratory data analysis (EDA) techniques in R. It explains that EDA involves analyzing data using visual methods to discover patterns. Common EDA techniques in R include descriptive statistics, histograms, bar plots, scatter plots, and line graphs. Tools like R and Python are useful for EDA due to their data visualization capabilities. The document also provides code examples for creating various graphs in R.
Overview data analyis and visualisation tools 2020
Overview data analyis and visualisation tools 2020Marié Roux This document provides an overview of various tools for data analysis and visualization. It discusses tools for data cleaning like Microsoft Excel, DataWrangler, and OpenRefine. For statistical analysis, it outlines R, RStudio, and Notepad++. Visualization applications mentioned include Tableau Public, Microsoft Power BI, and Google Data Studio. Qualitative data analysis software like Atlas.ti and Dedoose are also highlighted. Code libraries like D3.js are presented as options for helping with coding.
R and Visualization: A match made in Heaven
R and Visualization: A match made in HeavenEdureka! This document outlines an R training course on data visualization and spatial analysis. The course covers basic and advanced graphing techniques in R, including customizing graphs, color palettes, hexbin plots, tabplots, and mosaics. It also demonstrates spatial analysis examples using shapefiles and raster data to visualize and analyze geographic data in R.
Data visualization with R and ggplot2.docx
Data visualization with R and ggplot2.docxkassaye4 The document explains data visualization in R using the ggplot2 package, which is based on the grammar of graphics and provides multiple layering components for building visualizations. It covers key concepts such as data layers, aesthetics, geometrics, facets, statistics, coordinates, and themes using the mtcars dataset. Additionally, it demonstrates creating different plot types, saving plots, and using various packages to enhance ggplot2 visualizations.
Introduction to Data Visualization, Importance and types
Introduction to Data Visualization, Importance and typesgrsssyw24 Data visualization is the process of converting data into visual formats like charts and graphs to enhance understanding and communication. It is essential for identifying patterns, making informed decisions, and facilitating effective communication across various fields. Popular tools and libraries for data visualization include Tableau, Power BI, and programming libraries like Matplotlib and ggplot2.
R visualization: ggplot2, googlevis, plotly, igraph Overview
R visualization: ggplot2, googlevis, plotly, igraph OverviewOlga Scrivner The document provides an extensive overview of data visualization techniques in R, focusing on the use of several packages including ggplot2, googlevis, plotly, and igraph. It covers topics such as the grammar of graphics, plot creation, and interactive visualizations along with practical examples and resources for each method. Additionally, it addresses the capabilities of each package and their integration for creating effective visual representations of data.
R programming for data science
R programming for data scienceSovello Hildebrand The document is a comprehensive guide on R programming for data science, covering its history, installation instructions for Windows and Linux, data handling, and data visualization techniques. It emphasizes the functionality of R and its packages, particularly ggplot2 and the tidyverse, for effective data analysis and visualization. Additionally, it provides practical examples and commands for users to get started with R, including creating visualizations and utilizing different aesthetics.
Chart and graphs in R programming language
Chart and graphs in R programming language CHANDAN KUMAR The document provides an introduction to using R programming for visualizing data through various types of charts and graphs, including pie charts, histograms, bar charts, and line graphs. It explains the functions and syntax to create these visualizations, along with examples to illustrate the usage of parameters for customization. Additionally, it highlights the importance of graphical representation in data interpretation for better insights.
Overview of tools for data analysis and visualisation (2021)
Overview of tools for data analysis and visualisation (2021)Marié Roux The document provides an overview of various tools for data analysis and visualization, highlighting applications for data cleaning, statistical analysis, qualitative analysis, and visualization techniques. It discusses specific tools such as Microsoft Excel, R, Tableau, and GIS applications, emphasizing their functionalities and best use cases. The document serves as a resourceful guide for selecting appropriate tools without delving into in-depth training for each tool.
Introduction to Data Visualization for Agriculture and Allied Sciences using ...
Introduction to Data Visualization for Agriculture and Allied Sciences using ...Shubham Shah Title: Introduction to Data Visualization for Agriculture and Allied Sciences using R-Studio
Description:
This presentation introduces the fundamental concepts and practical applications of data visualization specifically tailored for agriculture and allied sciences. Designed for students, researchers, and professionals, it highlights the role of visual tools in making data-driven insights clearer, quicker, and more impactful.
Through this presentation, you will explore how data visualization enhances the understanding of complex datasets, reveals hidden trends, and supports effective decision-making in research, agronomy, environmental sciences, and allied fields.
The slides provide:
📊 Introduction to Data Visualization — Understand the importance of turning raw data into visual formats such as scatter plots, bar charts, histograms, density plots, and more, for easier interpretation and communication.
💻 Hands-on with R-Studio & ggplot2 — Learn to generate elegant and informative visualizations using R’s powerful ggplot2 package, including:
• Scatter plots
• Box plots
• Histograms
• Density plots
• Multiple plot arrangements with GGally
🔍 Exploring the Grammar of Graphics — Grasp key concepts like aesthetic mapping, geometric objects, statistical transformations, coordinate systems, and faceting for advanced visualization workflows.
💡 Use Case Example:
The presentation demonstrates real-world examples using the famous iris dataset, guiding learners through each step — from simple scatter plots to polished, customized graphs suitable for research publications.
⚖️ Pros & Cons of Data Visualization in R
The slides also discuss the practical advantages of using R for data visualization, such as enhanced clarity, efficiency, and spatial mapping, while also addressing limitations like potential costs and the risk of over-focusing on visual aesthetics over substance.
🎓 Author:
Shubham Shah — M.Sc. Forest Resource Management
For more research and learning resources:
Google Scholar: https://scholar.google.com/citations?user=tR4uhq8AAAAJ&hl=en
Academia.edu: https://skuast-k.academia.edu/ShubhamShah
Ad
Recently uploaded (20)
Microsoft Power BI - Advanced Certificate for Business Intelligence using Pow...
Microsoft Power BI - Advanced Certificate for Business Intelligence using Pow...Prasenjit Debnath Completed Power BI - Advanced Masterclass from Skill Nation
Prescriptive Process Monitoring Under Uncertainty and Resource Constraints: A...
Prescriptive Process Monitoring Under Uncertainty and Resource Constraints: A...Mahmoud Shoush We introduced Black-Box Prescriptive Process Monitoring (BB-PrPM) – a reinforcement learning approach that learns when, whether, and how to intervene in business processes to boost performance under real-world constraints.
This work is presented at the International Conference on Advanced Information Systems Engineering CAiSE Conference #CAiSE2025
NVIDIA Triton Inference Server, a game-changing platform for deploying AI mod...
NVIDIA Triton Inference Server, a game-changing platform for deploying AI mod...Tamanna36 NVIDIA Triton Inference Server! 🌟
Learn how Triton streamlines AI model deployment with dynamic batching, support for TensorFlow, PyTorch, ONNX, and more, plus GPU-optimized performance. From YOLO11 object detection to NVIDIA Dynamo’s future, it’s your guide to scalable AI inference.
Check out the slides and share your thoughts! 👇
#AI #NVIDIA #TritonInferenceServer #MachineLearning
最新版美国约翰霍普金斯大学毕业证（JHU毕业证书）原版定制
最新版美国约翰霍普金斯大学毕业证（JHU毕业证书）原版定制Taqyea 2025原版约翰霍普金斯大学毕业证书pdf电子版【q薇1954292140】美国毕业证办理JHU约翰霍普金斯大学毕业证书多少钱？【q薇1954292140】海外各大学Diploma版本，因为疫情学校推迟发放证书、证书原件丢失补办、没有正常毕业未能认证学历面临就业提供解决办法。当遭遇挂科、旷课导致无法修满学分，或者直接被学校退学，最后无法毕业拿不到毕业证。此时的你一定手足无措，因为留学一场，没有获得毕业证以及学历证明肯定是无法给自己和父母一个交代的。
【复刻约翰霍普金斯大学成绩单信封,Buy The Johns Hopkins University Transcripts】
购买日韩成绩单、英国大学成绩单、美国大学成绩单、澳洲大学成绩单、加拿大大学成绩单（q微1954292140）新加坡大学成绩单、新西兰大学成绩单、爱尔兰成绩单、西班牙成绩单、德国成绩单。成绩单的意义主要体现在证明学习能力、评估学术背景、展示综合素质、提高录取率，以及是作为留信认证申请材料的一部分。
约翰霍普金斯大学成绩单能够体现您的的学习能力，包括约翰霍普金斯大学课程成绩、专业能力、研究能力。（q微1954292140）具体来说，成绩报告单通常包含学生的学习技能与习惯、各科成绩以及老师评语等部分，因此，成绩单不仅是学生学术能力的证明，也是评估学生是否适合某个教育项目的重要依据！
我们承诺采用的是学校原版纸张（原版纸质、底色、纹路）我们工厂拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有成品以及工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！
【主营项目】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理毕业证|办理文凭: 买大学毕业证|买大学文凭【q薇1954292140】约翰霍普金斯大学学位证明书如何办理申请？
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理美国成绩单约翰霍普金斯大学毕业证【q薇1954292140】国外大学毕业证, 文凭办理, 国外文凭办理, 留信网认证
美国毕业证范本中华盛顿大学学位证书CWU学生卡购买
美国毕业证范本中华盛顿大学学位证书CWU学生卡购买Taqyea 1:1原版中华盛顿大学毕业证+CWU成绩单【Q微:1954 292 140】鉴于此，CWUdiploma中华盛顿大学挂科处理解决方案CWU毕业证成绩单专业服务学历认证【Q微:1954 292 140】办理教育部学历认证，留学回国证明，中华盛顿大学毕业证、中华盛顿大学成绩单、中华盛顿大学文凭（留信学历认证+永久存档查询）办理本科+硕士+博士毕业证成绩单学历认证，我们一直是留学生的首选，质量行业第一，诚信可靠。
【中华盛顿大学成绩单一站式办理专业技术完美呈现Central Washington University Transcripts】
购买日韩成绩单、英国大学成绩单、美国大学成绩单、澳洲大学成绩单、加拿大大学成绩单（q微1954292140）新加坡大学成绩单、新西兰大学成绩单、爱尔兰成绩单、西班牙成绩单、德国成绩单。成绩单的意义主要体现在证明学习能力、评估学术背景、展示综合素质、提高录取率，以及是作为留信认证申请材料的一部分。
中华盛顿大学成绩单能够体现您的的学习能力，包括中华盛顿大学课程成绩、专业能力、研究能力。（q微1954292140）具体来说，成绩报告单通常包含学生的学习技能与习惯、各科成绩以及老师评语等部分，因此，成绩单不仅是学生学术能力的证明，也是评估学生是否适合某个教育项目的重要依据！
【主营项目】
一.毕业证【q微1954292140】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
Flextronics Employee Safety Data-Project-2.pptx
Flextronics Employee Safety Data-Project-2.pptxkilarihemadri This PPT are using to show employee safety
Measurecamp Copenhagen - Consent Context
Measurecamp Copenhagen - Consent ContextHuman37 Measurecamp Copenhagen 2025 presentation by Glenn Vanderlinden, Human37
Shifting Focus on AI: How it Can Make a Positive Difference
Shifting Focus on AI: How it Can Make a Positive Difference1508 A/S This morgenbooster will share how to find the positive impact of AI and how to integrate it into your own digital process.
英国毕业证范本利物浦约翰摩尔斯大学成绩单底纹防伪LJMU学生证办理学历认证
英国毕业证范本利物浦约翰摩尔斯大学成绩单底纹防伪LJMU学生证办理学历认证 taqyed LJMU利物浦约翰摩尔斯大学毕业证书多少钱【q薇1954292140】1:1原版利物浦约翰摩尔斯大学毕业证+LJMU成绩单【q薇1954292140】完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。
【主营项目】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理毕业证|办理文凭: 买大学毕业证|买大学文凭【q薇1954292140】学位证明书如何办理申请？
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理利物浦约翰摩尔斯大学毕业证|LJMU成绩单【q薇1954292140】国外大学毕业证, 文凭办理, 国外文凭办理, 留信网认证
三.材料咨询办理、认证咨询办理请加学历顾问【微信:1954292140】毕业证购买指大学文凭购买，毕业证办理和文凭办理。学院文凭定制，学校原版文凭补办，扫描件文凭定做，100%文凭复刻。
5 R Tutorial Data Visualization
- 1. R Programming Sakthi Dasan Sekar http://shakthydoss.com 1
- 2. Data visualization R Graphics R has quite powerful packages for data visualization. R graphics can be viewed on screen and saved in various format like pdf, png, jpg, wmf,ps and etc. R packages provide full control to customize the graphic needs. http://shakthydoss.com 2
- 3. Data visualization Simple bar chart A bar graph are plotted either horizontal or vertical bars to show comparisons among categorical data. Bars represent lengths or frequency or proportion in the categorical data. barplot(x) http://shakthydoss.com 3
- 4. Data visualization Simple bar chart counts <- table(mtcars$gear) barplot(counts) #horizontal bar chart barplot(counts, horiz=TRUE) http://shakthydoss.com 4
- 5. Data visualization Simple bar chart Adding title, legend and color. counts <- table(mtcars$gear) barplot(counts, main="Simple Bar Plot", xlab="Improvement", ylab="Frequency", legend=rownames(counts), col=c("red", "yellow", "green") ) http://shakthydoss.com 5
- 6. Data visualization Stacked bar plot # Stacked Bar Plot with Colors and Legend counts <- table(mtcars$vs, mtcars$gear) barplot(counts, main="Car Distribution by Gears and VS", xlab="Number of Gears", col=c("grey","cornflowerblue"), legend = rownames(counts)) http://shakthydoss.com 6
- 7. Data visualization Grouped Bar Plot # Grouped Bar Plot counts <- table(mtcars$vs, mtcars$gear) barplot(counts, main="Car Distribution by Gears and VS", xlab="Number of Gears", col=c("grey","cornflowerblue"), legend = rownames(counts), beside=TRUE) http://shakthydoss.com 7
- 8. Data visualization Simple Pie Chart slices <- c(10, 12,4, 16, 8) lbls <- c("US", "UK", "Australia", "Germany", "France") pie( slices, labels = lbls, main="Simple Pie Chart") http://shakthydoss.com 8
- 9. Data visualization Simple Pie Chart slices <- c(10, 12,4, 16, 8) pct <- round(slices/sum(slices)*100) lbls <- paste(c("US", "UK", "Australia", "Germany", "France"), " ", pct, "%", sep="") pie(slices, labels=lbls2, col=rainbow(5),main="Pie Chart with Percentages") http://shakthydoss.com 9
- 10. Data visualization Simple pie chart – 3D library(plotrix) slices <- c(10, 12,4, 16, 8) lbls <- paste( c("US", "UK", "Australia", "Germany", "France"), " ", pct, "%", sep="") pie3D(slices, labels=lbls,explode=0.0, main="3D Pie Chart") http://shakthydoss.com 10
- 11. Data visualization Histograms Histograms display the distribution of a continuous variable. It by dividing up the range of scores into bins on the x-axis and displaying the frequency of scores in each bin on the y-axis. You can create histograms with the function hist(x) http://shakthydoss.com 11
- 12. Data visualization Histograms mtcars$mpg #miles per gallon data hist(mtcars$mpg) # Colored Histogram with Different Number of Bins hist(mtcars$mpg, breaks=8, col="lightgreen") http://shakthydoss.com 12
- 13. Data visualization Kernal density ploy Histograms may not be the efficient way to view distribution always. Kernal density plots are usually a much more effective way to view the distribution of a variable. plot(density(x)) http://shakthydoss.com 13
- 14. Data visualization Kernal density plot # kernel Density Plot density_data <- density(mtcars$mpg) plot(density_data) # Filling density Plot with colour density_data <- density(mtcars$mpg) plot(density_data, main="Kernel Density of Miles Per Gallon") polygon(density_data, col="skyblue", border="black") http://shakthydoss.com 14
- 15. Data visualization Line Chart The line chart is represented by a series of data points connected with a straight line. Line charts are most often used to visualize data that changes over time. lines(x, y,type=) http://shakthydoss.com 15
- 16. Data visualization Line Chart weight <- c(2.5, 2.8, 3.2, 4.8, 5.1, 5.9, 6.8, 7.1, 7.8,8.1) months <- c(0,1,2,3,4,5,6,7,8,9) plot(months, weight, type = "b", main="Baby weight chart") http://shakthydoss.com 16
- 17. Data visualization Box plot The box plot (a.k.a. whisker diagram) is another standardized way of displaying the distribution of data based on the five number summary: minimum, first quartile, median, third quartile, and maximum. http://shakthydoss.com 17
- 18. Data visualization Box Plot vec <- c(3, 2, 5, 6, 4, 8, 1, 2, 3, 2, 4) summary(vec) boxplot(vec, varwidth = TRUE) #varwidth=TRUE to make box plot proportionate to width http://shakthydoss.com 18
- 19. Data visualization Heat Map A heat map is a two-dimensional representation of data in which values are represented by colors. A simple heat map provides an immediate visual summary of information. More elaborate heat maps allow the viewer to understand complex data sets. http://shakthydoss.com 19
- 20. Data visualization Heat Map data <- read.csv("HEATMAP.csv",header = TRUE) #convert Data frame into matrix data <- data.matrix(data[,-1]) heatmap(data,Rowv=NA, Colv=NA, col = heat.colors(256), scale="column") http://shakthydoss.com 20
- 21. Data visualization Word cloud A word cloud (a.ka tag cloud) can be an handy tool when you need to highlight the most commonly cited words in a text using a quick visualization. R packages : wordcloud http://shakthydoss.com 21
- 22. Data visualization Word cloud install.packages("wordcloud") library("wordcloud") data <- read.csv("TEXT.csv",header = TRUE) head(data) wordcloud(words = data$word, freq = data$freq, min.freq = 2, max.words=100, random.order=FALSE) http://shakthydoss.com 22
- 23. Data visualization Graphic outputs can be redirected to files. pdf("filename.pdf") #PDF file win.metafile("filename.wmf") #Windows metafile png("filename.png") #PBG file jpeg("filename.jpg") #JPEG file bmp("filename.bmp") #BMP file postscript("filename.ps") #PostScript file http://shakthydoss.com 23
- 24. Data visualization Graphic outputs can be redirected to files. Example jpeg("myplot.jpg") counts <- table(mtcars$gear) barplot(counts) dev.off() http://shakthydoss.com 24
- 25. Data visualization Graphic outputs can be redirected to files. Function dev.off( ) should be used to return the control back to terminal. Another way saving graphics to file. dev.copy(jpeg, filename="myplot.jpg"); counts <- table(mtcars$gear) barplot(counts) dev.off() http://shakthydoss.com 25
- 26. Data visualization Export graphs in RStudio In Graphic panel of RStuido Step1 : Select Plots tab Click Explore menu and chose Save as Image. Step 2: Save image window will open. Step3 : Select image format and the directory to save the file. Step4 : Click save. http://shakthydoss.com 26
- 27. Data visualization Export graphs in RStudio To Export as pdf Step 1: Click Export Menu and click save as PDF. Step 2:Select the directory to save the file. Step3: Click Save. http://shakthydoss.com 27
- 29. Data visualization ____________ represent lengths or frequency or proportion in the categorical data. A. Line charts B. Bot plot C. Bar charts D. Kernal Density plot Answer C http://shakthydoss.com 29
- 30. Data visualization ___________ displays the distribution of data based on the five number summary: minimum, first quartile, median, third quartile, and maximum. A. Line charts B. Bot plot C. Bar charts D. Kernal Density plot Answer B http://shakthydoss.com 30
- 31. Data visualization Histograms display the distribution of a continuous variable. A. TRUE B. FALSE Answer A http://shakthydoss.com 31
- 32. Data visualization Graphic outputs can be redirected to file using _____________ function. A. save("filename.png") B. write.table("filename.png") C. write.file("filename.png") D. png("filename.png") Answer D http://shakthydoss.com 32
- 33. Data visualization ___________ visualization can be used highlight the most commonly cited words in a text. A. Word Stemmer B. Word cloud C. Histograms D. Line chats Answer B http://shakthydoss.com 33