Chart and graphs in R programming language
Download as pptx, pdf0 likes2,160 views
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.
1 of 33
Downloaded 16 times
Ad
Recommended
Introduction to R and R Studio
Introduction to R and R StudioRupak Roy The document provides an overview of R, a programming language and platform for statistical computing and graphics, noting its open-source nature and extensive package availability. It distinguishes between R and R Studio, the integrated development environment for R, emphasizing that R can function independently while R Studio cannot. Additionally, it touches upon R's capacity for handling various data types and its integration with big data technologies like Hadoop, along with installation instructions for both R and R Studio.
Cloud security Presentation
Cloud security PresentationAjay p The document discusses cloud computing security. It begins with an introduction to cloud computing that defines it and outlines its characteristics, service models, and deployment models. It then discusses common security concerns and attacks in cloud computing like DDoS attacks, side channel attacks, and attacks on management consoles. It provides best practices for different security domains like architecture, governance, compliance, and data security. It also discusses current industry initiatives in cloud security.
Introduction of Cloud computing
Introduction of Cloud computingRkrishna Mishra Cloud computing involves delivering computing services over the Internet. Instead of running programs locally, users access software and storage that resides on remote servers in the "cloud." The concept originated in the 1950s but Amazon launched the first major public cloud in 2006. Cloud computing has three main components - clients that access the cloud, distributed servers that host applications and data, and data centers that house these servers. There are different types of clients, deployment models for clouds, service models, and cloud computing enables scalability, reliability, and efficiency for applications accessed over the Internet like email, social media, and search engines.
Cloud computing simple ppt
Cloud computing simple pptAgarwaljay The document provides an introduction to cloud computing, defining key concepts such as cloud, cloud computing, deployment models, and service models. It explains that cloud computing allows users to access applications and store data over the internet rather than locally on a device. The main deployment models are public, private, community, and hybrid clouds, while the main service models are Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). IaaS provides fundamental computing resources, PaaS provides development platforms, and SaaS provides software applications to users. The document discusses advantages such as lower costs and universal access, and disadvantages including internet dependence and potential security issues.
Cloud computing ppt
Cloud computing pptJagriti Rai This document provides an overview of cloud computing. It defines cloud computing as manipulating, configuring, and accessing applications online through virtualization of network resources that are managed and maintained remotely. The key components of cloud infrastructure are servers, storage, networking hardware, management software, deployment platforms, and hypervisors that allow sharing of physical resources. There are various cloud deployment models including public, private, hybrid, and community clouds. In addition, the document outlines several cloud service models such as IaaS, PaaS, SaaS, and IDaaS. Technologies that enable cloud computing are also discussed, including virtualization, service-oriented architecture, grid computing, and utility computing.
Cloud Computing Security
Cloud Computing SecurityNinh Nguyen This document discusses the history and definitions of cloud computing. It begins with various definitions of cloud computing from Wikipedia between 2007-2009 which evolved to emphasize dynamically scalable virtual resources provided over the internet. It then covers common characteristics of cloud computing like multi-tenancy, location independence, pay-per-use pricing and rapid scalability. The rest of the document details cloud computing models including public, private and hybrid clouds. It also outlines the different architectural layers of cloud computing from Software as a Service to Infrastructure as a Service. The document concludes with a discussion of security issues in cloud computing and a case study of security features in Amazon Web Services.
R programming slides
R programming slidesPankaj Saini The document provides an overview of R programming, emphasizing its functionality as a statistical processing environment and a tool for data analysis. It covers R Studio's interface components, basic arithmetic operations, and the creation and manipulation of vectors and data frames. Additionally, it discusses data visualization techniques, statistical testing, and model prediction with an emphasis on the importance of splitting datasets for improving predictive accuracy.
Method overloading
Method overloadingLovely Professional University The document discusses method overloading and overriding in Java. It defines method overloading as having multiple methods with the same name but different parameters, while overriding involves subclasses providing specific implementations of methods in the parent class. It provides examples of overloading methods by changing parameters and data types, and explains why overriding is not possible by only changing the return type due to ambiguity. The use of the super keyword to refer to parent class members is also explained.
Data frame operations
Data frame operations19MSS011dhanyatha DataFrames in R are used to store tabular data with rows and columns. DataFrames can contain different data types in each column. They are created using the data.frame() function by passing vectors as arguments. Operations that can be performed on DataFrames include creating, accessing and selecting subsets of rows and columns, editing data, and adding or deleting rows and columns. The structure of a DataFrame can be examined using the str() function.
Python exception handling
Python exception handlingMohammed Sikander Exception handling in Python allows programmers to handle errors and exceptions that occur during runtime. The try/except block handles exceptions, with code in the try block executing normally and code in the except block executing if an exception occurs. Finally blocks ensure code is always executed after a try/except block. Programmers can define custom exceptions and raise exceptions using the raise statement.
Strings in python
Strings in pythonPrabhakaran V M The document discusses strings in Python. It describes that strings are immutable sequences of characters that can contain letters, numbers and special characters. It covers built-in string functions like len(), max(), min() for getting the length, maximum and minimum character. It also discusses string slicing, concatenation, formatting, comparison and various string methods for operations like conversion, formatting, searching and stripping whitespace.
Data mining: Classification and prediction
Data mining: Classification and predictionDataminingTools Inc This document discusses various machine learning techniques for classification and prediction. It covers decision tree induction, tree pruning, Bayesian classification, Bayesian belief networks, backpropagation, association rule mining, and ensemble methods like bagging and boosting. Classification involves predicting categorical labels while prediction predicts continuous values. Key steps for preparing data include cleaning, transformation, and comparing different methods based on accuracy, speed, robustness, scalability, and interpretability.
3. R- list and data frame
3. R- list and data framekrishna singh The document provides a detailed explanation of R programming concepts, focusing on lists and data frames. It covers how to create, access, and manipulate lists, including naming elements and merging lists, as well as defining data frames with examples for creating and summarizing employee data. Additionally, it discusses vector and data frame operations, such as combining and manipulating columns and rows.
Decision trees in Machine Learning
Decision trees in Machine Learning Mohammad Junaid Khan This document provides an overview of decision trees and the CART (Classification and Regression Trees) algorithm, explaining their components such as root nodes, nodes, and leaves, as well as impurity measures like Gini index and entropy. It discusses the advantages and disadvantages of decision trees, including their interpretability and the risk of overfitting, while also highlighting their applications in various fields like business management and healthcare. Additionally, it includes references for further reading and resources for coding with decision trees.
Exploratory data analysis with Python
Exploratory data analysis with PythonDavis David The document discusses Exploratory Data Analysis (EDA), emphasizing its significance in identifying important variables, testing hypotheses, and ensuring data quality. It covers data types, distinguishing between structured and unstructured data, and categorizes various types of data such as categorical and numerical. Additionally, it lists Python packages for creating different types of graphs to visualize data effectively.
R Programming: Introduction To R Packages
R Programming: Introduction To R PackagesRsquared Academy The document provides an introduction to R packages, detailing their importance and how to manage them within R. It covers functions for installing, loading, updating, and removing packages from various sources, including CRAN, GitHub, R-Forge, and Bioconductor. Additionally, it emphasizes the significance of contributing new packages to the R community and outlines resources for further learning.
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.
Python programming : Files
Python programming : FilesEmertxe Information Technologies Pvt Ltd The document discusses files in Python. It defines a file as an object that stores data, information, settings or commands used with a computer program. There are two main types of files - text files which store data as strings, and binary files which store data as bytes. The document outlines how to open, read, write, append, close and manipulate files in Python using functions like open(), read(), write(), close() etc. It also discusses pickling and unpickling objects to binary files for serialization. Finally, it covers working with directories and running other programs from Python.
3.5 model based clustering
3.5 model based clusteringKrish_ver2 The document discusses various model-based clustering techniques for handling high-dimensional data, including expectation-maximization, conceptual clustering using COBWEB, self-organizing maps, subspace clustering with CLIQUE and PROCLUS, and frequent pattern-based clustering. It provides details on the methodology and assumptions of each technique.
Integrity Constraints
Integrity Constraintsmadhav bansal Integrity constraints are rules that help maintain data quality and consistency in a database. The main types of integrity constraints are:
1. Domain constraints specify valid values and data types for attributes to restrict what data can be entered.
2. Entity constraints require that each row have a unique identifier and prevent null values in primary keys.
3. Referential integrity constraints maintain relationships between tables by preventing actions that would invalidate links between foreign and primary keys.
4. Cascade rules extend referential integrity by automatically propagating updates or deletes from a primary table to its related tables.
Union in C programming
Union in C programmingKamal Acharya Unions allow a variable to hold objects of different types in the same memory location. All members of a union share the same memory location, which is the size of the largest member. This means unions save memory by storing all members in one block, but the programmer must ensure the correct member is being accessed based on the data currently stored. The example program defines a union called Student containing different data types, reads values into members, and displays the members to demonstrate unions share the same memory location.
K mean-clustering algorithm
K mean-clustering algorithmparry prabhu K-means clustering is a method for partitioning a dataset into k distinct groups based on attributes, seeking to minimize the sum of squared distances between data points and their respective cluster centroids. The process involves initializing centroids, assigning data points to clusters based on proximity, updating centroids, and iterating until no changes occur. While efficient and widely used in various fields, k-means has limitations, including sensitivity to initial conditions and the requirement of specifying the number of clusters in advance.
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.
Relational algebra in dbms
Relational algebra in dbmsVignesh Saravanan The document provides an introduction to relational algebra, detailing its procedural query language framework and fundamental operations such as select, project, union, set difference, cartesian product, and rename. It explains unary and binary operations with examples for each operation, highlighting the need for type compatibility between relations for operations like union and intersection. Additionally, it briefly covers SQL built-in functions related to numeric and string manipulation.
Attribute oriented analysis
Attribute oriented analysisHirra Sultan The document discusses the challenges of data mining analysis on large databases and introduces attribute-oriented analysis as a method for analyzing data based on selected attributes. It covers concepts such as attribute generalization, attribute relevance analysis, and class comparison methods to better understand and visualize data distinctions. Additionally, it highlights the importance of descriptive statistics like mean, median, mode, variance, and standard deviation in analyzing data distributions.
Data preprocessing in Machine learning
Data preprocessing in Machine learning pyingkodi maran Data preprocessing is the process of preparing raw data for analysis by cleaning it, transforming it, and reducing it. The key steps in data preprocessing include data cleaning to handle missing values, outliers, and noise; data transformation techniques like normalization, discretization, and feature extraction; and data reduction methods like dimensionality reduction and sampling. Preprocessing ensures the data is consistent, accurate and suitable for building machine learning models.
Unit I Role of Mathematical Model in BI and BI Cycle.pdf
Unit I Role of Mathematical Model in BI and BI Cycle.pdfShivarkarSandip 1. Mathematical models play an important role in business intelligence systems by providing decision makers with information and knowledge extracted from data through the application of models and algorithms.
2. Mathematical models in BI can take different forms but generally include variables, parameters, and metrics to evaluate alternative options through what-if analysis.
3. The BI cycle involves four phases - analysis, insight, decision, and evaluation - to transform data into information, knowledge, decisions, and actions.
R programming
R programmingShantanu Patil R is a programming language developed for statistical computing and graphics, first designed in 1993 and stable since October 2014. It features extensive statistical modeling capabilities, is open-source, and supports cross-platform usage, but has demerits like slower performance compared to compiled languages and a steeper learning curve. R is widely utilized by statisticians and data miners, with a rich repository of packages for various analyses.
R programming.pptx r language easy concept
R programming.pptx r language easy conceptMuhammadjunaidgulMuh1 R programming.pptx r language easy concept
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.
More Related Content
What's hot (20)
Data frame operations
Data frame operations19MSS011dhanyatha DataFrames in R are used to store tabular data with rows and columns. DataFrames can contain different data types in each column. They are created using the data.frame() function by passing vectors as arguments. Operations that can be performed on DataFrames include creating, accessing and selecting subsets of rows and columns, editing data, and adding or deleting rows and columns. The structure of a DataFrame can be examined using the str() function.
Python exception handling
Python exception handlingMohammed Sikander Exception handling in Python allows programmers to handle errors and exceptions that occur during runtime. The try/except block handles exceptions, with code in the try block executing normally and code in the except block executing if an exception occurs. Finally blocks ensure code is always executed after a try/except block. Programmers can define custom exceptions and raise exceptions using the raise statement.
Strings in python
Strings in pythonPrabhakaran V M The document discusses strings in Python. It describes that strings are immutable sequences of characters that can contain letters, numbers and special characters. It covers built-in string functions like len(), max(), min() for getting the length, maximum and minimum character. It also discusses string slicing, concatenation, formatting, comparison and various string methods for operations like conversion, formatting, searching and stripping whitespace.
Data mining: Classification and prediction
Data mining: Classification and predictionDataminingTools Inc This document discusses various machine learning techniques for classification and prediction. It covers decision tree induction, tree pruning, Bayesian classification, Bayesian belief networks, backpropagation, association rule mining, and ensemble methods like bagging and boosting. Classification involves predicting categorical labels while prediction predicts continuous values. Key steps for preparing data include cleaning, transformation, and comparing different methods based on accuracy, speed, robustness, scalability, and interpretability.
3. R- list and data frame
3. R- list and data framekrishna singh The document provides a detailed explanation of R programming concepts, focusing on lists and data frames. It covers how to create, access, and manipulate lists, including naming elements and merging lists, as well as defining data frames with examples for creating and summarizing employee data. Additionally, it discusses vector and data frame operations, such as combining and manipulating columns and rows.
Decision trees in Machine Learning
Decision trees in Machine Learning Mohammad Junaid Khan This document provides an overview of decision trees and the CART (Classification and Regression Trees) algorithm, explaining their components such as root nodes, nodes, and leaves, as well as impurity measures like Gini index and entropy. It discusses the advantages and disadvantages of decision trees, including their interpretability and the risk of overfitting, while also highlighting their applications in various fields like business management and healthcare. Additionally, it includes references for further reading and resources for coding with decision trees.
Exploratory data analysis with Python
Exploratory data analysis with PythonDavis David The document discusses Exploratory Data Analysis (EDA), emphasizing its significance in identifying important variables, testing hypotheses, and ensuring data quality. It covers data types, distinguishing between structured and unstructured data, and categorizes various types of data such as categorical and numerical. Additionally, it lists Python packages for creating different types of graphs to visualize data effectively.
R Programming: Introduction To R Packages
R Programming: Introduction To R PackagesRsquared Academy The document provides an introduction to R packages, detailing their importance and how to manage them within R. It covers functions for installing, loading, updating, and removing packages from various sources, including CRAN, GitHub, R-Forge, and Bioconductor. Additionally, it emphasizes the significance of contributing new packages to the R community and outlines resources for further learning.
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.
Python programming : Files
Python programming : FilesEmertxe Information Technologies Pvt Ltd The document discusses files in Python. It defines a file as an object that stores data, information, settings or commands used with a computer program. There are two main types of files - text files which store data as strings, and binary files which store data as bytes. The document outlines how to open, read, write, append, close and manipulate files in Python using functions like open(), read(), write(), close() etc. It also discusses pickling and unpickling objects to binary files for serialization. Finally, it covers working with directories and running other programs from Python.
3.5 model based clustering
3.5 model based clusteringKrish_ver2 The document discusses various model-based clustering techniques for handling high-dimensional data, including expectation-maximization, conceptual clustering using COBWEB, self-organizing maps, subspace clustering with CLIQUE and PROCLUS, and frequent pattern-based clustering. It provides details on the methodology and assumptions of each technique.
Integrity Constraints
Integrity Constraintsmadhav bansal Integrity constraints are rules that help maintain data quality and consistency in a database. The main types of integrity constraints are:
1. Domain constraints specify valid values and data types for attributes to restrict what data can be entered.
2. Entity constraints require that each row have a unique identifier and prevent null values in primary keys.
3. Referential integrity constraints maintain relationships between tables by preventing actions that would invalidate links between foreign and primary keys.
4. Cascade rules extend referential integrity by automatically propagating updates or deletes from a primary table to its related tables.
Union in C programming
Union in C programmingKamal Acharya Unions allow a variable to hold objects of different types in the same memory location. All members of a union share the same memory location, which is the size of the largest member. This means unions save memory by storing all members in one block, but the programmer must ensure the correct member is being accessed based on the data currently stored. The example program defines a union called Student containing different data types, reads values into members, and displays the members to demonstrate unions share the same memory location.
K mean-clustering algorithm
K mean-clustering algorithmparry prabhu K-means clustering is a method for partitioning a dataset into k distinct groups based on attributes, seeking to minimize the sum of squared distances between data points and their respective cluster centroids. The process involves initializing centroids, assigning data points to clusters based on proximity, updating centroids, and iterating until no changes occur. While efficient and widely used in various fields, k-means has limitations, including sensitivity to initial conditions and the requirement of specifying the number of clusters in advance.
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.
Relational algebra in dbms
Relational algebra in dbmsVignesh Saravanan The document provides an introduction to relational algebra, detailing its procedural query language framework and fundamental operations such as select, project, union, set difference, cartesian product, and rename. It explains unary and binary operations with examples for each operation, highlighting the need for type compatibility between relations for operations like union and intersection. Additionally, it briefly covers SQL built-in functions related to numeric and string manipulation.
Attribute oriented analysis
Attribute oriented analysisHirra Sultan The document discusses the challenges of data mining analysis on large databases and introduces attribute-oriented analysis as a method for analyzing data based on selected attributes. It covers concepts such as attribute generalization, attribute relevance analysis, and class comparison methods to better understand and visualize data distinctions. Additionally, it highlights the importance of descriptive statistics like mean, median, mode, variance, and standard deviation in analyzing data distributions.
Data preprocessing in Machine learning
Data preprocessing in Machine learning pyingkodi maran Data preprocessing is the process of preparing raw data for analysis by cleaning it, transforming it, and reducing it. The key steps in data preprocessing include data cleaning to handle missing values, outliers, and noise; data transformation techniques like normalization, discretization, and feature extraction; and data reduction methods like dimensionality reduction and sampling. Preprocessing ensures the data is consistent, accurate and suitable for building machine learning models.
Unit I Role of Mathematical Model in BI and BI Cycle.pdf
Unit I Role of Mathematical Model in BI and BI Cycle.pdfShivarkarSandip 1. Mathematical models play an important role in business intelligence systems by providing decision makers with information and knowledge extracted from data through the application of models and algorithms.
2. Mathematical models in BI can take different forms but generally include variables, parameters, and metrics to evaluate alternative options through what-if analysis.
3. The BI cycle involves four phases - analysis, insight, decision, and evaluation - to transform data into information, knowledge, decisions, and actions.
R programming
R programmingShantanu Patil R is a programming language developed for statistical computing and graphics, first designed in 1993 and stable since October 2014. It features extensive statistical modeling capabilities, is open-source, and supports cross-platform usage, but has demerits like slower performance compared to compiled languages and a steeper learning curve. R is widely utilized by statisticians and data miners, with a rich repository of packages for various analyses.
Similar to Chart and graphs in R programming language (20)
R programming.pptx r language easy concept
R programming.pptx r language easy conceptMuhammadjunaidgulMuh1 R programming.pptx r language easy concept
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.
R graphics
R graphicsDHIVYADEVAKI This document provides an overview of various plotting functions in R for visualizing data including quantile-quantile plots, barplots, boxplots, interaction plots, density plots, 3D plots, and adjusting graphical parameters such as colors, sizes, fonts and saving plots. Key functions discussed are qqnorm, qqline, barplot, boxplot, interaction.plot, density, persp, contour, par, and devices like pdf, jpeg for saving plots.
Lectures r-graphics
Lectures r-graphicsetyca This document provides an overview of graphics functions in R for creating plots and customizing plot elements. It discusses how to start a new plot, set axis scales and limits, draw axes, add titles and labels, and draw different types of graphical elements like points, lines, and shapes. Functions covered include plot.new(), plot.window(), axis(), title(), points(), lines(), abline(), segments(), and box(). The document uses examples to demonstrate how to construct basic and customized plots programmatically in R.
statistical computation using R- an intro..
statistical computation using R- an intro..Kamarudheen KV The document provides an overview of R as a statistical software and programming environment, detailing its features, functionalities, and data structures. It covers various aspects such as preliminary commands, functions, graphics, and probability distributions, emphasizing R's flexibility and utility in statistical computing. Conclusions highlight R's importance in data analysis, statistics, and its integration with commercial packages.
pie chart ppt.pdf ppt on pie chart. Veri formal
pie chart ppt.pdf ppt on pie chart. Veri formalrjyotisingh123 Pie charts represent proportional data by slicing a circle into sections. This document discusses pie charts in R, including how to create them from single numeric vectors or data frames, customize aspects like labels, colors, and direction, and provides examples. Pie charts in R are created using the pie() function, which takes a positive numeric vector as input and can be customized through additional parameters to control visual properties and labels. Examples demonstrate basic pie charts on sample data and how to add customization parameters.
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.
CIV1900 Matlab - Plotting & Coursework
CIV1900 Matlab - Plotting & CourseworkTUOS-Sam This document provides an overview of plotting and image processing capabilities in Matlab. It discusses how to generate basic scatter plots and customize axis properties. It also explains how digital images are constructed as arrays and can be displayed, rotated, and converted to grayscale using commands like plot, surf, image, and imagesc. The document demonstrates plotting multiple lines and images on the same figure. It describes how image processing techniques like Sobel filtering can be used to detect edges in an image.
R training5
R training5Hellen Gakuruh This document provides an introduction and overview of graphics and plotting in R. It discusses high level and low level plotting functions, interacting with graphics, and modifying plots. It also covers plotting different variable types including dichotomous, categorical, ordinal, and continuous variables. Examples are provided for various plot types including histograms, bar plots, dot plots, boxplots, and more.
Introduction to r
Introduction to rGolden Julie Jesus This document provides an introduction to using R and RStudio. It discusses installing R and RStudio, the four windows in RStudio (source editor, console, environment/history, and plots/files), and basic commands and functions for running code, saving scripts, clearing the screen, commenting lines, and getting help. It also covers creating and manipulating variables and vectors, importing and exporting data, generating basic plots like bar plots, pie charts and histograms, and importing/exporting data.
Line graph bar graph
Line graph bar graphAyesha Arshad This document provides information about different types of graphs, including line graphs and bar graphs. It defines a line graph as a diagram that connects points on an x-y plane to show the relationship between two variables. Bar graphs use vertical or horizontal bars to show comparisons between categories. The document explains the key parts of line graphs and bar graphs, such as the title, labels, scales, points and bars. It also provides steps to construct a line graph using sample data on daily earnings. For bar graphs, it outlines how to create bars of uniform width and height according to a chosen scale. An example bar graph shows the number of children in different activities.
Lecture_3.pptx
Lecture_3.pptxSungaleliYuen The document discusses various types of plots that can be created in MATLAB, including:
1. Standard two-dimensional plots created using the plot command, which connects data points with lines. Additional lines and graphs can be added to the same plot using hold on/off or the line command.
2. Plots with logarithmic axes created using semilogy, semilogx, and loglog for situations where data spans a wide range of values.
3. Formatted plots where elements like titles, labels, legends, grids can be added using various commands.
4. Specialized plots like bar plots, stem plots, and pie charts for different data visualization needs.
5. The ability to place
Line & Bar Graphs
Line & Bar Graphsherbison This document discusses different types of graphs, including line graphs, bar graphs, and pie charts. It describes when each graph type is most appropriate based on the data being visualized. Line graphs are best for comparing values over time, while bar graphs compare values across categories. The document also provides examples of correctly formatted line plots and instructs students to complete worksheets to practice creating different graph types.
R Data Visualization Tutorial: Bar Plots
R Data Visualization Tutorial: Bar PlotsRsquared Academy The document provides a comprehensive tutorial on creating univariate bar plots using the R programming language, specifically using the graphics package's barplot function. It covers various aspects such as modifying bar width, space, colors, labels, and adding legends. Additionally, it includes information on adjusting axis properties and introduces future topics like bivariate and stacked bar plots.
1. Introduction.pptx
1. Introduction.pptxSungaleliYuen This document provides an overview of mathematical functions and plotting in MATLAB. It discusses:
- The many predefined mathematical functions available in MATLAB, such as sin, cos, and exp.
- How to generate vectors and matrices for input.
- How to perform basic plotting of data and functions using commands like plot and linspace.
- How to customize plots by adding titles, labels, and changing colors.
- Matrix operations in MATLAB like transposes, inverses, concatenation and arithmetic operations.
visual representation with BOX PLOT,BAR PLOTS
visual representation with BOX PLOT,BAR PLOTSanjanasharma77573 The document provides an overview of box plots, describing their purpose as a graphical method to visualize data distributions through quartiles, including details about elements such as median, quartiles, and outliers. It also explains how to create box plots, interpret their skewness, and compare different plots based on median, dispersion, and outliers. Additionally, the document touches on bar charts, their types, properties, and emphasizes the use of R and ggplot2 for data visualization.
Domain and Range of a Function- Day3.pptx
Domain and Range of a Function- Day3.pptxlorryswift847 The document explains the concepts of domain and range in mathematics, defining the domain as the set of permissible x-values and the range as the set of permissible y-values. It provides examples of different representations of functions, including ordered pairs, mapping diagrams, tables of values, and graphs. Additionally, it outlines how to determine the domain and range from a mapping diagram and a table of values.
Graphing Data
Graphing Datashas595 This document discusses different types of graphs used to represent data: pie charts, bar graphs, line graphs, and pictographs. It provides details on the key elements of graphs, including titles, axes, legends, labels, and how to properly plot and organize data. Graphs are useful tools to help understand amounts and how things change over time by visually depicting relationships between variables.
Lesson 3
Lesson 3Vinnu Vinay This document introduces new commands in Matlab lesson 3 for creating plots, including plot(x,y) to create Cartesian plots, semilogx(x,y) to plot log(x) vs y, and bar(x) to create bar graphs. It also discusses using titles, labels, text and grids with plots, and describes polar, multiple, and fancy plots using different line styles and point markers. The document concludes with instructions for printing and saving graphic plots.
Ad
More from CHANDAN KUMAR (13)
Raid technology
Raid technologyCHANDAN KUMAR RAID (Redundant Array of Independent Disks) technology was invented in 1987 to improve data storage performance and reliability. It combines multiple disk drive components into one or more logical units. There are different RAID levels that determine how disk arrays are used, with RAID 0 through RAID 6 being the standard levels. RAID levels use techniques like striping, mirroring, and parity to provide features like fault tolerance, high throughput, and data redundancy. Each level has advantages and disadvantages for performance, reliability, and capacity.
Pointers in c
Pointers in cCHANDAN KUMAR The document provides an extensive overview of pointers in C programming, explaining their definitions, functions, and memory representation. It covers pointer declaration, assignment, arithmetic operations, and comparisons, alongside practical coding examples. It highlights the application of pointers in dynamic memory allocation and their importance in improving code efficiency and performance.
Sorting algorithms
Sorting algorithmsCHANDAN KUMAR This document provides an overview of sorting algorithms. It defines sorting as arranging data in a particular order like ascending or descending. Common sorting algorithms discussed include bubble sort, selection sort, insertion sort, merge sort, and quick sort. For each algorithm, the working method, implementation in C, time and space complexity is explained. The document also covers sorting terminology like stable vs unstable sorting and adaptive vs non-adaptive algorithms. Overall, the document serves as a comprehensive introduction to sorting and different sorting techniques.
Searching in c language
Searching in c languageCHANDAN KUMAR The document discusses searching techniques in computer science, focusing on linear and binary search methods. Linear search works by sequentially checking each element in a list, while binary search efficiently finds an element in a sorted array using a divide-and-conquer approach. The document outlines the steps and algorithms for each search method, including examples of implementation in C programming.
Greedy algorithm
Greedy algorithmCHANDAN KUMAR The document discusses greedy algorithms, which are used to solve optimization problems that seek the best solution under constraints. It explains the components and strategies of greedy methods, emphasizing their straightforward and efficient nature. Additionally, it highlights various applications of greedy algorithms, such as finding the shortest path and solving problems like the traveling salesman and job scheduling.
Divide and conquer algorithm
Divide and conquer algorithmCHANDAN KUMAR The document provides an overview of the divide-and-conquer algorithm design paradigm, which breaks problems into sub-problems that are solved recursively before combining their solutions. It includes a C program example for computing powers and lists various algorithms like binary search and merge sort that utilize this approach. Additionally, it discusses the complexity analysis using the master theorem and highlights advantages such as efficiency in matrix multiplication and suitability for multiprocessing systems.
Arrays in c
Arrays in cCHANDAN KUMAR The document explains arrays as a collection of fixed-size sequential elements of the same data type, detailing their characteristics, types (one-dimensional, two-dimensional, multidimensional), and initialization methods. It provides examples of array declarations, memory layouts, and simple C programs demonstrating how to read and print numbers using arrays. Additionally, it touches on more complex usages of arrays, including pointers, structures, and dynamic data structures.
Loops in c programming
Loops in c programmingCHANDAN KUMAR The document provides an overview of looping in programming using C, detailing the use of while, do-while, and for loops to execute statements repeatedly. It explains the differences between pretest and posttest loops, along with syntax and examples for each loop type. The conclusion emphasizes the importance of loops for code efficiency and readability.
Linked List
Linked ListCHANDAN KUMAR The document provides an overview of linked lists, a linear data structure used to store collections of elements where each element is represented by a node containing data and a pointer to the next node. It compares linked lists with arrays, highlighting advantages like dynamic sizing and efficient insertion/deletion, and outlines different types of linked lists such as singly linked lists, doubly linked lists, and circular linked lists. Additionally, it details key operations on linked lists including creation, insertion, deletion, traversing, searching, concatenation, and displaying elements.
Stack and queue
Stack and queueCHANDAN KUMAR This document discusses different types of data structures, including linear and non-linear structures. It focuses on linear structures like arrays, stacks, and queues. Stacks follow LIFO principles with push and pop operations. Queues follow FIFO principles with enqueue and dequeue operations. Real-world examples and algorithms for common stack and queue operations are provided.
Technical questions for interview c programming
Technical questions for interview c programmingCHANDAN KUMAR The document presents a series of technical interview questions designed by Dr. Chandan Kumar, focusing on C programming concepts such as syntax errors, data types, and program outputs. Each question includes a specific code snippet and multiple-choice answers, with a correct answer provided at the end of each question. The questions cover various topics, including variable manipulation, function declarations, and struct definitions.
Decision making using if statement
Decision making using if statementCHANDAN KUMAR The document provides an overview of decision-making using 'if' statements in programming, detailing four variants: if, if-else, nested if, and else-if statements. Each variant is explained with syntax, flow charts, and examples in C programming to illustrate their use for making decisions based on test expressions. Example programs are included to demonstrate how these statements are executed based on user input.
"A short and knowledgeable concept about Algorithm "
"A short and knowledgeable concept about Algorithm "CHANDAN KUMAR This document discusses algorithms and their properties. It defines an algorithm as a finite set of well-defined instructions to solve a problem. There are five criteria for writing algorithms: they must have inputs and outputs, be definite, finite, and effective. Algorithms use notation like step numbers, comments, and termination statements. Common algorithm types are dynamic programming, greedy, brute force, and divide and conquer. An example algorithm calculates the average of four numbers by reading inputs, computing the sum, calculating the average, and writing the output. Key patterns in algorithms are sequences, decisions, and repetitions.
Ad
Recently uploaded (20)
WIRELESS COMMUNICATION SECURITY AND IT’S PROTECTION METHODS
WIRELESS COMMUNICATION SECURITY AND IT’S PROTECTION METHODSsamueljackson3773 In this paper, the author discusses the concerns of using various wireless communications and how to use
them safely. The author also discusses the future of the wireless industry, wireless communication
security, protection methods, and techniques that could help organizations establish a secure wireless
connection with their employees. The author also discusses other essential factors to learn and note when
manufacturing, selling, or using wireless networks and wireless communication systems.
社内勉強会資料_Chain of Thought .
社内勉強会資料_Chain of Thought .NABLAS株式会社 本資料「To CoT or not to CoT?」では、大規模言語モデルにおけるChain of Thought（CoT）プロンプトの効果について詳しく解説しています。
CoTはあらゆるタスクに効く万能な手法ではなく、特に数学的・論理的・アルゴリズム的な推論を伴う課題で高い効果を発揮することが実験から示されています。
一方で、常識や一般知識を問う問題に対しては効果が限定的であることも明らかになりました。
複雑な問題を段階的に分解・実行する「計画と実行」のプロセスにおいて、CoTの強みが活かされる点も注目ポイントです。
This presentation explores when Chain of Thought (CoT) prompting is truly effective in large language models.
The findings show that CoT significantly improves performance on tasks involving mathematical or logical reasoning, while its impact is limited on general knowledge or commonsense tasks.
Week 6- PC HARDWARE AND MAINTENANCE-THEORY.pptx
Week 6- PC HARDWARE AND MAINTENANCE-THEORY.pptxdayananda54 Basics of computer hardware for beginners
SEW make Brake BE05 – BE30 Brake – Repair Kit
SEW make Brake BE05 – BE30 Brake – Repair Kitprojectultramechanix A SEW-EURODRIVE brake repair kit is needed for maintenance and repair of specific SEW-EURODRIVE brake models, like the BE series. It includes all necessary parts for preventative maintenance and repairs. This ensures proper brake functionality and extends the lifespan of the brake system
David Boutry - Mentors Junior Developers
David Boutry - Mentors Junior DevelopersDavid Boutry David Boutry is a Senior Software Engineer in New York with expertise in high-performance data processing and cloud technologies like AWS and Kubernetes. With over eight years in the field, he has led projects that improved system scalability and reduced processing times by 40%. He actively mentors aspiring developers and holds certifications in AWS, Scrum, and Azure.
Decoding Kotlin - Your Guide to Solving the Mysterious in Kotlin - Devoxx PL ...
Decoding Kotlin - Your Guide to Solving the Mysterious in Kotlin - Devoxx PL ...João Esperancinha Kotlin can be very handy and easy to use. Kotlin offers the possibility to develop code that is easy to understand, safe, immutable, and thus predictable and follows standards that avoid side effects. I realized that very quickly after I started my Kotlin journey that already amounts to more than 5 years.
This is the third version of this presentation focused on more detail explaining inline, crossinline, tailrec and as a bonus a quick run through unnamed classes.
How Binning Affects LED Performance & Consistency.pdf
How Binning Affects LED Performance & Consistency.pdfMina Anis 🔍 What’s Inside:
📦 What Is LED Binning?
• The process of sorting LEDs by color temperature, brightness, voltage, and CRI
• Ensures visual and performance consistency across large installations
🎨 Why It Matters:
• Inconsistent binning leads to uneven color and brightness
• Impacts brand perception, customer satisfaction, and warranty claims
📊 Key Concepts Explained:
• SDCM (Standard Deviation of Color Matching)
• Recommended bin tolerances by application (e.g., 1–3 SDCM for retail/museums)
• How to read bin codes from LED datasheets
• The difference between ANSI/NEMA standards and proprietary bin maps
🧠 Advanced Practices:
• AI-assisted bin prediction
• Color blending and dynamic calibration
• Customized binning for high-end or global projects
22PCOAM16 _ML_Unit 3 Notes & Question bank
22PCOAM16 _ML_Unit 3 Notes & Question bankGuru Nanak Technical Institutions 22PCOAM16 _ML_Unit 3 Notes & Question bank
Blood bank management system project report.pdf
Blood bank management system project report.pdfKamal Acharya The main objective of the “Blood Bank management System” all the details in the Blood
Bank’sprocess. This project has some tasks to maintain the Blood Bank through computerization.
Using this blood bank system people can search blood group available which they are needed.
They check it on using our blood bank management website. If in case blood group is not available
in blood bank they can also contact numbers of the persons who has the same blood group he is
need. And he can request the person to done the blood for saving someone life.
The Project describes the smart Blood Bank management system. This report will help you
to know in deep the actual work that has been done as a team work. The main objective of this
application is to automate the complete operations of the blood bank. They need to maintain
hundreds of thousands of records. Also searching should be very faster, so they can find required
details instantly. Main objective is to create a system which helps them to complete their work
faster in simple way by using computer not the oldest way which is used paper. Also our project
contains updated information and many things else.
The project consists of a central repository containing various blood deposits available
along with associated details. These details include blood type, storage area and date of storage.
These details help in maintaining and monitoring the blood deposits. The project is an online
system that allows checking weather required blood deposits of a particular group are available in
the blood bank. Moreover the system also has added features such as patient name and contacts,
blood booking and even need for certain blood group is posted on the website to find available
donors for a blood emergency. This online system is developed on PHP platform and supported
by an MYSQL database to store blood and user specific details.
Engineering Mechanics Introduction and its Application
Engineering Mechanics Introduction and its ApplicationSakthivel M Engineering Mechanics Introduction
Structural Design for Residential-to-Restaurant Conversion
Structural Design for Residential-to-Restaurant ConversionDanielRoman285499 Overview of a 4-month project working on designing the structural components of a residential building to meet new code
Impurities of Water and their Significance.pptx
Impurities of Water and their Significance.pptxdhanashree78 Impart Taste, Odour, Colour, and Turbidity to water.
Presence of organic matter or industrial wastes or microorganisms (algae) imparts taste and odour to water.
Presence of suspended and colloidal matter imparts turbidity to water.
Chart and graphs in R programming language
- 1. Charts and Graphs- R Programming
- 2. Introduction Since we know that a huge amount of data is generated when it comes to interpreting any sector To acquire significant insights, it is usually preferable to depict data through charts and graphs rather than scanning large Excel sheets. The R programming language is mostly used to depict data graphically in software for statistics and data analytics.
- 3. The R programming language includes some simple and easy techniques for converting data into visually appealing features such as graphs and charts. In R, charts and graphs are used to graphically depict the data. R Programming language has numerous libraries to create charts and graphs. There are numerous types of charts and graphs are present in R language such as pie chart, scatter graph, bar plot, box plot, mosaic plot, dot chart, coplot, histogram, etc.
- 4. Pie Chart A pie chart is a visual depiction of values as colored slices of a circle. The slices are identified, and the graphic also shows the numbers that correlate to each slice. The pie chart is made in R using the pie() function, which requires a vector input of positive values. The extra options are used to customize labels, color, and title, among other things.
- 5. Syntax: pie(x, labels, radius, main, col, clockwise) vector Description radius of the circle Direction color palette Title
- 6. Here, x indicates a vector that contain numerical values. labels is used to describe the slices. radius indicates the radius of the circle of the pie chart. (value between −1 and +1). The chart's title is indicated by the main. The color palette is indicated by col. The logical value clockwise indicates whether the slices are drawn clockwise or anticlockwise.
- 7. Example-1 We can create a very simple pie-chart with the help of only two parameters such as the input vector and labels. # Firstly we Create data i.e. vector for the chart. x <- c(35, 60, 20, 40) # After that naming labels of slices labels <- c(“INDIA", "NEW YORK", “NEW DELHI", "MUMBAI") # Call pie() function to Plot the chart pie(x,labels)
- 8. After executing these commands R Console shows the given chart.
- 9. Example- 2 #The below script will create a pie chart and save the pie chart in the current R working directory. x <- c(35, 60, 20, 40) labels <- c(“KANPUR", “BAREILLY", “NEW DELHI", "MUMBAI") # Call png() function to give the chart file name. png(file = "city.png") pie(x,labels) # To Save the file. dev.off()
- 10. Example- 3 #We can expand the features of the pie chart by adding more parameters to the function. The below script shows it. pie(x, labels, main="City Pie Chart")
- 11. Example- 4 pie(x,labels, radius= -1, main="City Pie Chart")
- 12. Example- 5 pie(x, labels, radius= 1, main="City Pie Chart", col="RED") Note: For rainbow color pallet, Use rainbow function.
- 13. 3D Pie Chart We can also draw pie chart with 3 dimensions i.e. 3D using additional packages. plotrix package has a function called pie3D() that is used for creating 3D pie chart.
- 14. Example- 6 # Install Package Install.packages(“plotrix”) # Get the library. library(plotrix) # Create data for the graph. x <- c(35, 60, 20, 40) labels <- c(“KANPUR", “BAREILLY", “NEW DELHI", "MUMBAI") pie3D(x,labels = lbl,explode = 0.1, main = "Pie Chart of Countries ")
- 15. Histograms A histogram represents the frequencies of values of a variable bucketed into ranges. Histogram is similar to bar chat but the difference is it groups the values into continuous ranges i.e. In a histogram, there are no gaps between the bars, unlike a bar graph. Each bar in histogram represents the height of the number of values present in that range.
- 16. R creates histogram using hist() function. This function takes a vector as an input and uses some more parameters to plot histograms. We Use histograms when we have continuous measurements and want to understand the distribution of values and look for outliers.
- 17. Syntax: hist(v,main,xlab,xlim,ylim,breaks,col,border) vector Description description of x-axis width of each bar range of values on the y-axis. range of values on the x-axis color palette set border color
- 18. Here v indicates vector that contain numeric values. Title of the chart is indicated by main col parameter is used to set color of the bars. To set border color of each bar, border parameter is used. xlab is used to give description of x-axis. xlim and ylim are used to specify the range of values on the x- axis and y-axis respectively. breaks is used to mention the width of each bar.
- 19. Example- 1 # Create histogram # Create data for the graph. v <- c(7,11,19,7,40,12,22,31,44,55,43) # Create the histogram. hist( v, xlab = "Weight", col = "yellow", border = "blue")
- 20. Example- 2 The xlim and ylim parameters can be used to set the range of values allowed in the X and Y axes, respectively. Breaks can be used to determine the width of each bar. v <- c(7,11,19,7,40,12,22,31,44,55,43) hist(v,xlab = "Weight",col = "green",border = "red",xlim = c(0,40), ylim = c(0,5), breaks = 5)
- 21. Bar Charts A bar chart depicts data as rectangular bars whose length is proportionate to the variable's value. The function barplot() in R is used to make bar charts. In a bar chart, R can create both vertical and horizontal bars. Each of the bars in a bar chart can be colored differently.
- 22. Syntax: barplot(H,xlab,ylab,main, names.arg,col) vector label for x axis width of each bar label for y axis Title names appearing under each bar colors to the bars
- 23. Here, H indicates vector or matrix that contain numeric values. xlab is the label for x axis. ylab is the label for y axis. main is the title of the bar chart. names.arg is a vector of names appearing under each bar. col is used to give colors to the bars in the graph.
- 24. Example- 1 Create a simple bar chart # Create the data for the bar chart H <- c(9,15,23,13,22) # Plot the bar chart barplot(H)
- 25. Example- 2 Create a simple bar chart using vector and names of each bar. # Create the data for the bar chart H <- c(9,15,23,13,22) names <- c("March","April","May","June","July") # Plot the bar chart barplot(H,names.arg = names)
- 26. Example- 3 Create a bar chart using other parameters. H <- c(9,15,23,13,22) names <- c("March","April","May","June","July") barplot(H,names.arg=names,xlab="Months",ylab=“ Expenditure",col="yellow", main="Expenditure chart",border="black") Or We also use parameters in this sequence. barplot(H,xlab="Months",ylab="Expenditure",main="Expenditur e chart", names.arg = names, col= "blue")
- 27. Line Graph A line chart is a graph that connects a set of points by connecting them with line segments. These points are sorted according to the value of one of their coordinates (typically the x-coordinate). Line charts are commonly used to identify data trends. The line graph was created using R's plot() function.
- 28. Syntax: plot(v, type, main, col, xlab, ylab) vector Title Draw points or lines label for x axis label for y axis colors to both the points and lines
- 29. Here The numeric values are stored in vector V Type takes value "p" is used to draw only points, "l" is used to draw just lines, and "o" is used to draw both points and lines. The x axis is labeled as xlab. The y axis label is ylab. The main is the chart's title. The col is used to color both the points and the lines.
- 30. Example- 1 We can create a simple line chart using two parameters the input vector and the type parameter as “o". v <- c(10,15,22,32) plot (v,type = “o”)
- 31. Example - 2 Create using other parameters v <- c(10,15,11,17,28) plot (v, main = “Line Graph”, xlab = “Months”, ylab = “Expenditure”, type = “o”, col = “RED”)
- 32. Example - 3 Using lines()function, More than one line can be drawn on the same chart v <- c(7,12,28,3,41) t <- c(14,7,6,19,3) plot(v,type = "o",col = “blue", xlab = "Months", ylab = “Expenditure", main = “Expenditure chart") lines(t, type = "o", col = “yellow")