R Data Visualization: Learn To Add Text Annotations To Plots
1 like873 views
The document provides guidance on using the text() and mtext() functions in R for adding text annotations to plots. It covers key arguments for both functions, including positioning, color, font styles, and alignment options. Additionally, it includes examples to demonstrate the practical application of these functions.
1 of 24
Ad
Recommended
Data Visualization With R: Learn To Modify Font Of Graphical Parameters
Data Visualization With R: Learn To Modify Font Of Graphical ParametersRsquared Academy The document provides instructions on modifying font and font size in R graphical parameters using the plot() function. It explains how to customize the fonts for titles, subtitles, axes, and labels, as well as the corresponding arguments (font.main, font.sub, font.axis, font.lab, cex.main, cex.sub, cex.axis, cex.lab) and their value ranges. The document also includes example code for each modification along with visual representations.
Data Visualization With R: Learn To Modify Color Of Plots
Data Visualization With R: Learn To Modify Color Of PlotsRsquared Academy The document is a course overview on data visualization using R, focusing on how to add and modify colors in plots. It outlines various color customization options for plot symbols, titles, axes, labels, and foregrounds using specific arguments in the plot() function. Resources related to the course materials are available on the DataCrunch website, including slides, scripts, and tutorial videos.
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.
Data Visualization With R: Learn To Combine Multiple Graphs
Data Visualization With R: Learn To Combine Multiple GraphsRsquared Academy The document provides a comprehensive guide on how to combine multiple graphs in R using the par() and layout() functions. It explains the functionalities of these functions, including customizing graphical parameters and the use of different matrix structures to arrange plots. Several code examples illustrate specific combinations of plots in various configurations.
Data Visualization With R: Introduction
Data Visualization With R: IntroductionRsquared Academy The document serves as an introduction to data visualization using R, specifically focusing on the 'plot()' function and its applications with the mtcars dataset. It covers six different cases demonstrating how to plot one or two continuous and categorical variables, emphasizing the interpretation of various plot types created. Additional resources for learning about R programming and data visualization are available on the datacrunch website.
Data Visualization With R: Learn To Modify Title, Axis Labels & Range
Data Visualization With R: Learn To Modify Title, Axis Labels & RangeRsquared Academy This document contains slides from a data visualization course in R. It discusses how to modify the title, axis labels, and range of plots created in R. Specifically, it shows how to add these elements either by including arguments in the plot() function or by using the title() function. The title(), xlab, ylab, xlim, and ylim arguments can be used in plot() to customize the title, axis labels, and ranges. Alternatively, the title() function can be used after plotting but may overwrite default axis labels, so the ann argument should be set to FALSE in plot().
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.
NCCU: Statistics in the Criminal Justice System, R basics and Simulation - Pr...
NCCU: Statistics in the Criminal Justice System, R basics and Simulation - Pr...The Statistical and Applied Mathematical Sciences Institute R is a free software environment for statistical computing and graphics that provides a wide variety of statistical techniques and graphical methods. It includes base functions and packages, and is used through interfaces like RStudio. R represents data using objects like vectors, matrices, and data frames. Common operations include calculations, generating random variables, and visualizing data. R can be used to analyze a glass fragment dataset to visualize compositions and potentially classify an unknown fragment.
Access intermediate 2010 final project new
Access intermediate 2010 final project newclscott1 The document provides instructions for a final Access Intermediate project to modify an existing Vacation database for Griffin and Emma MacElroy's travel agency, GEM Ultimate Vacations. Students are asked to download the database, save it under a new name, and perform tasks like modifying data, creating queries to find specific property records and allow country parameter selection, designing a form to display guest and reservation records, creating a report to group properties by country and count reservations, and generating mailing labels for guests. The modified Access database file is to be submitted for grading.
Chapter13 two-dimensional-array
Chapter13 two-dimensional-arrayDeepak Singh The document discusses two-dimensional arrays in C++. It explains that a two-dimensional array is an array of arrays, with elements arranged in rows and columns. It provides examples of declaring, initializing, accessing, inputting, and passing two-dimensional arrays as parameters to functions. It also gives examples of functions that perform operations on two-dimensional arrays, such as addition, multiplication, finding row/column sums, diagonal sums, and transposing the array.
An introduction to matlab
An introduction to matlabDr. Andrew Wallace PhD This document provides an introduction to MATLAB by covering key topics like the command window, inputs and outputs, M files, and basic programming structures in MATLAB such as conditionals and loops. Some key points covered include how to enter and manipulate matrices in the command window, perform basic math operations, load and save data, plot graphs, write M files to organize commands and create functions, and use conditional statements and loops in M files.
R Programming: Numeric Functions In R
R Programming: Numeric Functions In RRsquared Academy The document outlines a comprehensive R programming course focused on data analysis, covering fundamental concepts like data types, programming elements, and various numeric functions including signif(), abs(), round(), ceiling(), floor(), trunc(), and jitter(). It provides syntax, descriptions, and examples for each function, facilitating understanding through practical illustrations. Additionally, it hints at future modules on string manipulation and invites learners to connect with R-squared for further resources.
Extreme querying with_analytics
Extreme querying with_analyticsGary Myers The document provides insights into various analytical functions and aggregate operations, detailing how to implement them using SQL. It covers concepts such as min/max with added 'keep', ranking functions like dense_rank and row_number, and advanced aggregation methods like rollup and cube. Additionally, it emphasizes the importance of understanding context when applying these functions to prevent confusion.
Lecture 1 mte 407
Lecture 1 mte 407rumanatasnim415 The document discusses arrays in C programming. It defines arrays as groups of same data types that can store integer, float, character, or other data. Arrays allow storing multiple values in a single variable and accessing elements using indexes. The document provides examples of one-dimensional and two-dimensional arrays, and explains how to initialize, declare, and access array elements. It also discusses using for loops and nested loops to iterate through arrays.
Lecture 1 mte 407
Lecture 1 mte 407rumanatasnim415 The document discusses arrays in C programming. It defines arrays as groups of same data types that can store integer, float, character, or other data. Arrays allow storing multiple values in a single variable and accessing elements using indexes. The document provides examples of one-dimensional and two-dimensional arrays, and using for loops to initialize, input, and output array elements. Nested for loops are described for traversing two-dimensional or multi-dimensional arrays like matrices.
R studio
R studio Kinza Irshad This document provides an overview and introduction to using the statistical software R. It outlines R's interface, workspace, help system, packages, input/output functions, and how to reuse results. It also discusses downloading and installing R, basic functions and syntax, data manipulation techniques like sorting and merging, creating graphs, and performing statistical analyses such as t-tests, regression, ANOVA, and multiple comparisons. The document recommends several tutorials that provide more in-depth information on using R for statistical modeling, data analysis, and graphics.
Excel/R
Excel/RAndrija Djurovic This document provides an example of running an R script from Excel to create plots. It describes setting up an Excel file with buttons to run an R script and open the resulting PDF. The R script generates random data, plots it, and saves the plots to a PDF. Clicking the first button runs the R script, passing cell values as arguments. Clicking the second button opens the PDF if it was created.
Introduction to matlab
Introduction to matlabSourabh Bhattacharya This document provides an introduction and overview of the MATLAB programming environment and its core functionality. It describes how to perform basic operations and calculations, write scripts and functions, work with vectors and matrices, and use common plotting and programming commands. Key features covered include MATLAB's command-line interface, help system, variables, operators, functions, file types (m-files and function files), flow control, and short summaries of many common commands.
R-Excel Integration
R-Excel IntegrationAndrija Djurovic This document provides an example of integrating R and Excel using the XLConnect package. It summarizes data in R, generates a graph, and writes the results to an Excel template, applying formatting. Key steps include: loading an Excel template, writing an R data frame and summary results to sheets, adding a pre-generated graph image, and saving the updated workbook.
11)recap forms and queries
11)recap forms and queriespanks172 The document outlines steps to create an employee database with Emp and Dept tables, including setting primary keys, modifying fields, entering sample records, designing queries to display specific fields, and creating tabular and auto forms from the tables and a query. The database is to be used to store and retrieve employee and department information.
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.
Assigment 3
Assigment 3sukumaraser The document provides 6 programming assignments involving functions, recursion, pointers, structures, and unions in C language. Assignment 1 asks to write a program to display a word descending in length using a function. Assignment 2 asks to solve an equation using recursion. Assignment 3 asks to solve an equation using pointers. Assignment 4 asks to get and display system details using a structure. Assignment 5 asks to call and display output from a function without arguments. Assignment 6 asks to get and display book details using a union containing structures.
Doc 20180130-wa0004
Doc 20180130-wa0004HarithaRanasinghe This document contains the questions from an examination on algorithms and data structures. Question 1 asks about asymptotic analysis and simplifying Big-O expressions. Question 2 involves analyzing and implementing an insertion sort algorithm. Question 3 covers queue implementation using a circular array. Question 4 is about linear search. Question 5 deals with binary search trees, including drawing a tree from insertions, traversing methods, defining a node structure, and finding the minimum value.
Easy Blogging With Emacs -- Cheatsheet
Easy Blogging With Emacs -- CheatsheetDashamir Hoxha This document provides a cheatsheet for using Emacs for blogging, including general Emacs commands like saving files and navigating text, Org mode commands for formatting text like expanding headings, and Org mode examples for formatting paragraphs, headings, lists, inline markup, hyperlinks, and preformatted text.
Matlab Visualizing Data
Matlab Visualizing DataDataminingTools Inc The document discusses visualizing data in Matlab. It describes how the plot function can be used to create graphs with different parameters. It also explains how to create animations by saving multiple frames from figures using getframe and playing them back with movie. An example is provided to generate an animation by plotting the FFT of an identity matrix over increasing sizes and saving each frame.
QMC: Undergraduate Workshop, Tutorial on 'R' Software - Yawen Guan, Feb 26, 2...
QMC: Undergraduate Workshop, Tutorial on 'R' Software - Yawen Guan, Feb 26, 2...The Statistical and Applied Mathematical Sciences Institute R is a free software environment for statistical computing and graphics that provides a wide variety of statistical techniques and graphical methods. It includes base functions as well as packages for additional techniques. RStudio is a popular integrated development environment for R that includes a console, editor, and tools.
Common R objects include vectors to store multiple values, matrices to store values in rows and columns, and data frames to store different types of data. Functions like cbind(), rbind(), and matrix() can be used to create matrices from vectors. Random variables can be simulated using functions like runif(), rnorm(), and sample(). User-defined functions allow custom calculations to be performed on data in R.
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.
Tools for research plotting
Tools for research plottingNimrita Koul This document provides information on tools for research plotting in Python and R. It discusses matplotlib and R for creating plots in Python and R respectively. It provides examples of different plot types that can be created such as line plots, bar plots, scatter plots, and histograms. It also discusses installing and working with matplotlib and R Studio, and provides code examples to generate various plots from data.
Tools for research plotting
Tools for research plottingNimrita Koul This document provides information on tools for research plotting in Python and R. It discusses matplotlib and R for creating plots in Python and R respectively. It provides examples of different plot types that can be created such as line plots, scatter plots, bar plots, and includes code samples. It also discusses installing and working with matplotlib and R Studio, and reading in data from files to create plots.
Presentation: Plotting Systems in R
Presentation: Plotting Systems in RIlya Zhbannikov The document provides an overview of plotting systems in R, including base, lattice, and ggplot2, explaining how to create and save plots using various functions. It details the various graphic devices, plotting functions, and layered approaches in ggplot2 for visualizing data, with examples using the 'mtcars' dataset. Additionally, it includes resources for further learning about R graphics.
More Related Content
What's hot (18)
Access intermediate 2010 final project new
Access intermediate 2010 final project newclscott1 The document provides instructions for a final Access Intermediate project to modify an existing Vacation database for Griffin and Emma MacElroy's travel agency, GEM Ultimate Vacations. Students are asked to download the database, save it under a new name, and perform tasks like modifying data, creating queries to find specific property records and allow country parameter selection, designing a form to display guest and reservation records, creating a report to group properties by country and count reservations, and generating mailing labels for guests. The modified Access database file is to be submitted for grading.
Chapter13 two-dimensional-array
Chapter13 two-dimensional-arrayDeepak Singh The document discusses two-dimensional arrays in C++. It explains that a two-dimensional array is an array of arrays, with elements arranged in rows and columns. It provides examples of declaring, initializing, accessing, inputting, and passing two-dimensional arrays as parameters to functions. It also gives examples of functions that perform operations on two-dimensional arrays, such as addition, multiplication, finding row/column sums, diagonal sums, and transposing the array.
An introduction to matlab
An introduction to matlabDr. Andrew Wallace PhD This document provides an introduction to MATLAB by covering key topics like the command window, inputs and outputs, M files, and basic programming structures in MATLAB such as conditionals and loops. Some key points covered include how to enter and manipulate matrices in the command window, perform basic math operations, load and save data, plot graphs, write M files to organize commands and create functions, and use conditional statements and loops in M files.
R Programming: Numeric Functions In R
R Programming: Numeric Functions In RRsquared Academy The document outlines a comprehensive R programming course focused on data analysis, covering fundamental concepts like data types, programming elements, and various numeric functions including signif(), abs(), round(), ceiling(), floor(), trunc(), and jitter(). It provides syntax, descriptions, and examples for each function, facilitating understanding through practical illustrations. Additionally, it hints at future modules on string manipulation and invites learners to connect with R-squared for further resources.
Extreme querying with_analytics
Extreme querying with_analyticsGary Myers The document provides insights into various analytical functions and aggregate operations, detailing how to implement them using SQL. It covers concepts such as min/max with added 'keep', ranking functions like dense_rank and row_number, and advanced aggregation methods like rollup and cube. Additionally, it emphasizes the importance of understanding context when applying these functions to prevent confusion.
Lecture 1 mte 407
Lecture 1 mte 407rumanatasnim415 The document discusses arrays in C programming. It defines arrays as groups of same data types that can store integer, float, character, or other data. Arrays allow storing multiple values in a single variable and accessing elements using indexes. The document provides examples of one-dimensional and two-dimensional arrays, and explains how to initialize, declare, and access array elements. It also discusses using for loops and nested loops to iterate through arrays.
Lecture 1 mte 407
Lecture 1 mte 407rumanatasnim415 The document discusses arrays in C programming. It defines arrays as groups of same data types that can store integer, float, character, or other data. Arrays allow storing multiple values in a single variable and accessing elements using indexes. The document provides examples of one-dimensional and two-dimensional arrays, and using for loops to initialize, input, and output array elements. Nested for loops are described for traversing two-dimensional or multi-dimensional arrays like matrices.
R studio
R studio Kinza Irshad This document provides an overview and introduction to using the statistical software R. It outlines R's interface, workspace, help system, packages, input/output functions, and how to reuse results. It also discusses downloading and installing R, basic functions and syntax, data manipulation techniques like sorting and merging, creating graphs, and performing statistical analyses such as t-tests, regression, ANOVA, and multiple comparisons. The document recommends several tutorials that provide more in-depth information on using R for statistical modeling, data analysis, and graphics.
Excel/R
Excel/RAndrija Djurovic This document provides an example of running an R script from Excel to create plots. It describes setting up an Excel file with buttons to run an R script and open the resulting PDF. The R script generates random data, plots it, and saves the plots to a PDF. Clicking the first button runs the R script, passing cell values as arguments. Clicking the second button opens the PDF if it was created.
Introduction to matlab
Introduction to matlabSourabh Bhattacharya This document provides an introduction and overview of the MATLAB programming environment and its core functionality. It describes how to perform basic operations and calculations, write scripts and functions, work with vectors and matrices, and use common plotting and programming commands. Key features covered include MATLAB's command-line interface, help system, variables, operators, functions, file types (m-files and function files), flow control, and short summaries of many common commands.
R-Excel Integration
R-Excel IntegrationAndrija Djurovic This document provides an example of integrating R and Excel using the XLConnect package. It summarizes data in R, generates a graph, and writes the results to an Excel template, applying formatting. Key steps include: loading an Excel template, writing an R data frame and summary results to sheets, adding a pre-generated graph image, and saving the updated workbook.
11)recap forms and queries
11)recap forms and queriespanks172 The document outlines steps to create an employee database with Emp and Dept tables, including setting primary keys, modifying fields, entering sample records, designing queries to display specific fields, and creating tabular and auto forms from the tables and a query. The database is to be used to store and retrieve employee and department information.
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.
Assigment 3
Assigment 3sukumaraser The document provides 6 programming assignments involving functions, recursion, pointers, structures, and unions in C language. Assignment 1 asks to write a program to display a word descending in length using a function. Assignment 2 asks to solve an equation using recursion. Assignment 3 asks to solve an equation using pointers. Assignment 4 asks to get and display system details using a structure. Assignment 5 asks to call and display output from a function without arguments. Assignment 6 asks to get and display book details using a union containing structures.
Doc 20180130-wa0004
Doc 20180130-wa0004HarithaRanasinghe This document contains the questions from an examination on algorithms and data structures. Question 1 asks about asymptotic analysis and simplifying Big-O expressions. Question 2 involves analyzing and implementing an insertion sort algorithm. Question 3 covers queue implementation using a circular array. Question 4 is about linear search. Question 5 deals with binary search trees, including drawing a tree from insertions, traversing methods, defining a node structure, and finding the minimum value.
Easy Blogging With Emacs -- Cheatsheet
Easy Blogging With Emacs -- CheatsheetDashamir Hoxha This document provides a cheatsheet for using Emacs for blogging, including general Emacs commands like saving files and navigating text, Org mode commands for formatting text like expanding headings, and Org mode examples for formatting paragraphs, headings, lists, inline markup, hyperlinks, and preformatted text.
Matlab Visualizing Data
Matlab Visualizing DataDataminingTools Inc The document discusses visualizing data in Matlab. It describes how the plot function can be used to create graphs with different parameters. It also explains how to create animations by saving multiple frames from figures using getframe and playing them back with movie. An example is provided to generate an animation by plotting the FFT of an identity matrix over increasing sizes and saving each frame.
QMC: Undergraduate Workshop, Tutorial on 'R' Software - Yawen Guan, Feb 26, 2...
QMC: Undergraduate Workshop, Tutorial on 'R' Software - Yawen Guan, Feb 26, 2...The Statistical and Applied Mathematical Sciences Institute R is a free software environment for statistical computing and graphics that provides a wide variety of statistical techniques and graphical methods. It includes base functions as well as packages for additional techniques. RStudio is a popular integrated development environment for R that includes a console, editor, and tools.
Common R objects include vectors to store multiple values, matrices to store values in rows and columns, and data frames to store different types of data. Functions like cbind(), rbind(), and matrix() can be used to create matrices from vectors. Random variables can be simulated using functions like runif(), rnorm(), and sample(). User-defined functions allow custom calculations to be performed on data in R.
QMC: Undergraduate Workshop, Tutorial on 'R' Software - Yawen Guan, Feb 26, 2...
QMC: Undergraduate Workshop, Tutorial on 'R' Software - Yawen Guan, Feb 26, 2...The Statistical and Applied Mathematical Sciences Institute
Similar to R Data Visualization: Learn To Add Text Annotations To Plots (20)
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.
Tools for research plotting
Tools for research plottingNimrita Koul This document provides information on tools for research plotting in Python and R. It discusses matplotlib and R for creating plots in Python and R respectively. It provides examples of different plot types that can be created such as line plots, bar plots, scatter plots, and histograms. It also discusses installing and working with matplotlib and R Studio, and provides code examples to generate various plots from data.
Tools for research plotting
Tools for research plottingNimrita Koul This document provides information on tools for research plotting in Python and R. It discusses matplotlib and R for creating plots in Python and R respectively. It provides examples of different plot types that can be created such as line plots, scatter plots, bar plots, and includes code samples. It also discusses installing and working with matplotlib and R Studio, and reading in data from files to create plots.
Presentation: Plotting Systems in R
Presentation: Plotting Systems in RIlya Zhbannikov The document provides an overview of plotting systems in R, including base, lattice, and ggplot2, explaining how to create and save plots using various functions. It details the various graphic devices, plotting functions, and layered approaches in ggplot2 for visualizing data, with examples using the 'mtcars' dataset. Additionally, it includes resources for further learning about R graphics.
Introduction to R Short course Fall 2016
Introduction to R Short course Fall 2016Spencer Fox The document provides instructions for an introductory R session, including downloading materials from a GitHub repository and opening an R project file. It outlines logging in, downloading an R project folder containing intro materials, and opening the project file in RStudio.
UNIT_4_data visualization.pptx
UNIT_4_data visualization.pptxBhagyasriPatel2 The document discusses various data visualization techniques using Matplotlib in Python. It covers creating basic line plots and scatter plots, customizing plots by adding labels, legends, colors and styles. It also discusses different chart types like pie charts, bar charts, histograms and boxplots. Advanced techniques like showing correlations and time series analysis are also covered. The document provides code examples for each visualization technique.
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.
Matlab ch1 (6)
Matlab ch1 (6)mohsinggg This chapter covers two-dimensional (2D) plots in MATLAB. It discusses various plot commands like plot, fplot, and errorbar that can be used to create basic and specialized 2D plots. It also describes how to format plots by adding labels, titles, legends, text annotations and grids. Plots with logarithmic and polar axes as well as histograms are demonstrated. The chapter shows how to create multiple plots on the same figure using subplots or in separate figure windows.
UNit-III. part 2.pdf
UNit-III. part 2.pdfMastiCreation This document discusses how to create line charts, bar charts, pie charts, histograms, and scatter plots using Matplotlib in Python. It covers how to import Matplotlib, customize line styles, colors, markers, legends, titles and labels. It provides code examples for plotting single and multiple lines, formatting plots, saving figures, and using different chart types like pie charts, bar charts and histograms.
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.
MatplotLib.pptx
MatplotLib.pptxParas Intotech The document provides a comprehensive guide on using the Matplotlib library for data visualization in Python, including instructions for installation and basic plotting functions. It details various plotting techniques such as line plots, scatter plots, bar graphs, histograms, and pie charts, along with customization options for markers, lines, and labels. Additionally, it covers advanced features like subplots, grid lines, and legends to enhance visual representation of data.
a9bf73_Introduction to Matplotlib01.pptx
a9bf73_Introduction to Matplotlib01.pptxRahidkhan10 The document provides a comprehensive introduction to matplotlib, a Python library used for creating various types of plots including line graphs, bar plots, scatter plots, and histograms. It covers basics like importing pyplot, customizing plots, adding multiple lines, creating subplots, and exploring advanced techniques for better data visualization. Additionally, it includes mini-projects to apply the concepts learned, such as visualizing sales data and temperature variations.
UNIT-5-II IT-DATA VISUALIZATION TECHNIQUES
UNIT-5-II IT-DATA VISUALIZATION TECHNIQUEShemalathab24 The document provides a comprehensive overview of data visualization techniques using the matplotlib library in Python, including creating various types of plots such as line plots, scatter plots, and histograms. It explains the importance of visualizing data for better understanding and insights, while also detailing customization options like labels, annotations, and error bars. Additionally, the document covers advanced plotting techniques, including density and contour plots for representing multidimensional data.
ggplot2: An Extensible Platform for Publication-quality Graphics
ggplot2: An Extensible Platform for Publication-quality GraphicsClaus Wilke The document discusses ggplot2, an R package that enables the creation of publication-quality graphics through a grammar of graphics, highlighting its powerful styling options, wide variety of plot types, and sophisticated text annotations. It includes examples of different plot types such as ridgeline plots and simple features with geographic data, as well as mentions of additional packages like ggrepel and patchwork for enhanced annotations and plot composition. Acknowledgments are given to the contributors and supporters of the ggplot2 project and its related extensions.
Rtips123
Rtips123 Mahendra Babu Rtips is being revived after being difficult to update previously. The latest versions will be available online as HTML and PDF formats. The document provides tips and examples for importing and exporting data, working with data frames, matrices and vectors, applying functions, and creating graphs in R. It includes over 100 sections covering topics such as merging data, selecting observations, generating random numbers, plotting regression lines, and formatting axes.
Introduction to GGVIS Visualization
Introduction to GGVIS VisualizationHemantSingh311 This document provides an introduction to ggvis visualization in R. It discusses how ggvis allows creating interactive graphics that can be played with locally in RStudio or in a browser. ggvis graphics combine the modeling capabilities of R with the interactivity of web graphics. The document covers key topics such as:
- Creating basic ggvis plots using layers like layer_points() and adding interactivity.
- Adding variables to control visual properties like color, size and shape.
- Using different layer types including histograms, boxplots, and smoothing.
- Combining multiple layers to build richer graphics.
- Controlling plot elements interactively using sliders.
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.
Visualization and Matplotlib using Python.pptx
Visualization and Matplotlib using Python.pptxSharmilaMore5 This document provides an overview of Matplotlib, a Python data visualization library. It discusses Matplotlib's pyplot and OO APIs, how to install Matplotlib, create basic plots using functions like plot(), and customize plots using markers and line styles. It also covers displaying plots, the Matplotlib user interface, Matplotlib's relationships with NumPy and Pandas, and examples of different types of graphs and charts like line plots that can be created with Matplotlib.
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.
Ad
More from Rsquared Academy (20)
Handling Date & Time in R
Handling Date & Time in RRsquared Academy The document outlines a comprehensive curriculum for learning date and time manipulation using R programming, including practical exercises related to invoicing, date extraction, and time zones. It covers subjects such as leap years, parsing dates, and interval calculations, providing examples and tasks for the reader to complete. Additionally, it emphasizes data sanitization and the use of various R functions to handle date-related operations effectively.
Market Basket Analysis in R
Market Basket Analysis in RRsquared Academy This document provides information about association rule mining on market basket analysis data. It discusses connecting with the company on various platforms, accessing resources like slides and code, and the key concepts of association rule mining including what it is, why it is used, how it works, and example use cases. It then demonstrates the process of generating, inspecting, and filtering rules from transaction data to understand common purchases and influence of products. Top rules are examined by support, confidence, and lift. Association rule mining can uncover frequently bought item sets and has applications in retail and other industries.
Practical Introduction to Web scraping using R
Practical Introduction to Web scraping using RRsquared Academy This document summarizes information about the governors of the Reserve Bank of India (RBI) by extracting a table from the Wikipedia page on the topic. It lists the 15 governors of the RBI in order from longest to shortest terms in office. It also analyzes the backgrounds of the governors, finding that most were economists (7) or bureaucrats from the IAS or ICS (7), with some also having a banking background (2) or being a career RBI officer (1).
Joining Data with dplyr
Joining Data with dplyrRsquared Academy The document discusses various types of SQL joins (inner, left, right, semi, anti, and full) applied to customer and order data from an online source. It includes a case study that demonstrates these joins using R libraries dplyr and readr with example customer and order data. The results showcase how different joins affect the output of combined datasets.
Explore Data using dplyr
Explore Data using dplyrRsquared Academy The document outlines data manipulation techniques using R's dplyr and readr libraries, demonstrating methods to extract, rename, and sample data from a tibble containing website visitor metrics. It includes information about distinct traffic sources, device types, and offers examples of tabulating and counting observations related to referrers and purchases. Additionally, it provides instructions on data extraction, slicing, and conditional mutation for analysis.
Data Wrangling with dplyr
Data Wrangling with dplyrRsquared Academy The document discusses data manipulation techniques using the dplyr library, emphasizing the importance of efficient data cleaning for data scientists. It provides various examples for filtering, selecting, and summarizing data, including case studies analyzing average order values and user behavior by device type. The document concludes with practical applications of grouping and arranging data to derive insights from an example dataset.
Writing Readable Code with Pipes
Writing Readable Code with PipesRsquared Academy The document provides an overview of using pipes in R programming, particularly with the magrittr package, to improve code readability and maintainability by avoiding nested function calls. It details various types of pipes (%>% , %<>% , %$%) and includes practical examples of data manipulation, arithmetic operations, and visualizations. The document explains how to implement these concepts using R libraries and contains sample data for hands-on application.
Introduction to tibbles
Introduction to tibblesRsquared Academy The document provides an overview of tibbles, a modern version of data frames in R that offer advantages such as not altering variable names or types and enhanced printing for complex data sets. It details how to create and manipulate tibbles, highlighting their unique features like handling of row names, and provides examples of operations like adding and removing rows and columns. Tibbles encourage cleaner and more expressive code by forcing users to directly address data issues.
Read data from Excel spreadsheets into R
Read data from Excel spreadsheets into RRsquared Academy The document provides a comprehensive guide on how to read data from various file formats such as Excel, SAS, SPSS, and Stata using R programming. It includes specific commands and examples for reading sheets, cells, columns, and rows from Excel files and integrating data from other statistical software formats. Key functionalities and illustrative data outputs are showcased for clarity.
Read/Import data from flat/delimited files into R
Read/Import data from flat/delimited files into RRsquared Academy This document provides examples of using the readr package in R to read data from CSV files. It demonstrates how to handle column names, skip text lines, specify column types as numeric, integer or factor, and read specific columns. Functions used include read_csv(), read_delim(), spec_csv(), and arguments like col_names, skip, col_types, and cols_only to control reading of columns.
Variables & Data Types in R
Variables & Data Types in RRsquared Academy The document provides an introduction to variables and data types in the R programming language. It covers the definition and creation of variables, naming conventions, and different data types such as numeric, integer, character, and logical. Additionally, it explains how to assign values to variables and check their data types using various functions.
How to install & update R packages?
How to install & update R packages?Rsquared Academy This document provides guidance on R packages, highlighting that there are around 12,000 available on CRAN and offering best practices for using and sharing them. It covers how to install packages from various sources like CRAN, GitHub, and Bioconductor, as well as how to manage installed packages and their versions. Additionally, it explains library paths and the necessity of loading packages into the R session for functionality.
How to get help in R?
How to get help in R?Rsquared Academy This document provides an overview of methods for seeking help in R, emphasizing both online resources like Stack Overflow and R Bloggers, and the built-in help functions available in R. Key functions discussed include help(), help.search(), demo(), example(), and various ways to access package documentation and vignettes. The content is aimed at helping new R users navigate support effectively.
Introduction to R
Introduction to RRsquared Academy A brief introduction to the R ecosystem for absolute beginners. You will learn about the history and capabilities of R as a modern language for data science.
RMySQL Tutorial For Beginners
RMySQL Tutorial For BeginnersRsquared Academy The document is a tutorial for accessing MySQL databases using the R programming language with the rmysql package. It covers installation of the package, connecting to a database, querying, importing/exporting data, and managing database tables. Various functions and their usage for interacting with MySQL from R are explained in detail, providing clear examples and commands.
R Markdown Tutorial For Beginners
R Markdown Tutorial For BeginnersRsquared Academy This document provides an introduction to R Markdown. It explains that R Markdown combines Markdown syntax and R code chunks to create dynamic reports and documents. The document outlines the key topics that will be covered, including what Markdown and R Markdown are, Markdown syntax like headers, emphasis, lists, links and images, R code chunks and options, and RStudio settings. Resources for learning more about Markdown, R Markdown, and related tools are provided.
R Programming: Introduction to Matrices
R Programming: Introduction to MatricesRsquared Academy The document is a course material for an R programming class focusing on matrices, including creation, operations, and manipulation methods. Key topics covered include matrix creation syntax, operations such as addition and multiplication, and methods to index and subset matrices. It also introduces concepts like lists and data coercion, providing a comprehensive guide for handling matrices in R.
R Programming: Introduction to Vectors
R Programming: Introduction to VectorsRsquared Academy El curso de R Academy se centra en la comprensión y creación de vectores, un tipo de estructura de datos fundamental en R. Se abordan temas como la coerción de tipos de datos, operaciones básicas en vectores, manejo de datos faltantes y técnicas de indexación. Además, se proporcionan recursos adicionales en línea para apoyar el aprendizaje y la práctica.
R Programming: Variables & Data Types
R Programming: Variables & Data TypesRsquared Academy The document provides an introduction to variables and data types in R programming, explaining their fundamental components and creation methods. It covers various data types including numeric, integer, character, logical, and date/time, along with naming conventions and testing functions. Additional resources and future topics such as vectors and data manipulation are also mentioned.
R Programming: Mathematical Functions In R
R Programming: Mathematical Functions In RRsquared Academy The document outlines a course on R programming, covering fundamental data analysis skills including programming basics, working with data, and various mathematical functions. It includes specific examples of arithmetic, cumulative, and set operations, along with detailed explanations of commands and functions used within R. The course also offers insights into the next topics, such as selection statements and connecting with additional resources for further learning.
Ad
Recently uploaded (20)
Measurecamp Copenhagen - Consent Context
Measurecamp Copenhagen - Consent ContextHuman37 Measurecamp Copenhagen 2025 presentation by Glenn Vanderlinden, Human37
最新版美国佐治亚大学毕业证（UGA毕业证书）原版定制
最新版美国佐治亚大学毕业证（UGA毕业证书）原版定制Taqyea 2025原版佐治亚大学毕业证书pdf电子版【q薇1954292140】美国毕业证办理UGA佐治亚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。
【复刻佐治亚大学成绩单信封,Buy The University of Georgia 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。
佐治亚大学成绩单能够体现您的的学习能力，包括佐治亚大学课程成绩、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】佐治亚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，办理美国成绩单佐治亚大学毕业证【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
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
最新版美国威斯康星大学河城分校毕业证（UWRF毕业证书）原版定制
最新版美国威斯康星大学河城分校毕业证（UWRF毕业证书）原版定制taqyea 2025原版威斯康星大学河城分校毕业证书pdf电子版【q薇1954292140】美国毕业证办理UWRF威斯康星大学河城分校毕业证书多少钱？【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。
【复刻威斯康星大学河城分校成绩单信封,Buy University of Wisconsin-River Falls 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。
威斯康星大学河城分校成绩单能够体现您的的学习能力，包括威斯康星大学河城分校课程成绩、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】威斯康星大学河城分校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，办理美国成绩单威斯康星大学河城分校毕业证【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
Residential Zone 4 for industrial village
Residential Zone 4 for industrial villageMdYasinArafat13 based on assumption that failure of such a weld is by shear on the
effective area whether the shear transfer is parallel to or
perpendicular to the axis of the line of fillet weld. In fact, the
strength is greater for shear transfer perpendicular to the weld axis;
however, for simplicity the situations are treated the same.
定制OCAD学生卡加拿大安大略艺术与设计大学成绩单范本,OCAD成绩单复刻
定制OCAD学生卡加拿大安大略艺术与设计大学成绩单范本,OCAD成绩单复刻taqyed 2025年极速办安大略艺术与设计大学毕业证【q薇1954292140】学历认证流程安大略艺术与设计大学毕业证加拿大本科成绩单制作【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。
【复刻安大略艺术与设计大学成绩单信封,Buy OCAD 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。
安大略艺术与设计大学成绩单能够体现您的的学习能力，包括安大略艺术与设计大学课程成绩、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.安大略艺术与设计大学毕业证【q微1954292140】安大略艺术与设计大学成绩单、留信认证、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.办理国外各大学文凭(一对一专业服务,可全程监控跟踪进度)
Boost Business Efficiency with Professional Data Entry Services
Boost Business Efficiency with Professional Data Entry Serviceseloiacs eloiacs Boost Business Efficiency with Professional Data Entry Services
In today’s digital-first world, businesses generate and handle massive amounts of data every day — customer records, sales data, inventory logs, survey results, and much more. But raw data has no value unless it is well-organized, accurate, and easily accessible. That’s where professional data entry services come in.
By outsourcing data entry tasks to experts, businesses can streamline operations, reduce manual errors, and improve overall efficiency — while focusing their internal resources on core activities like growth and customer engagement.
What Are Data Entry Services?
Data entry services refer to the process of converting information from various formats (handwritten, scanned, PDF, image, or audio) into structured, digital formats such as Excel sheets, CRM databases, or cloud storage systems. This work may be done online or offline, manually or using automation tools, depending on the client’s requirements.
Popular Data Entry Services Include:
Manual data entry from paper documents or scanned files
Online data entry directly into websites, forms, or portals
Offline data entry in formats like MS Word, Excel, or custom software
Product data entry for eCommerce platforms like Shopify, Amazon, and Flipkart
Document and image conversion into editable formats
Data cleansing and validation to remove duplicates and fix errors
Remote data entry support for real-time operations
CRM and ERP data management
These services are essential for organizing business data and making it usable for analysis, reporting, and decision-making.
Who Can Benefit from Data Entry Services?
Outsourcing data entry is not limited to any one industry — it's a universal need for businesses of all types and sizes. Here are some examples:
eCommerce Businesses – For managing product catalogs, inventory updates, pricing, and customer orders.
Healthcare Providers – For digitizing patient records, prescriptions, and insurance documents.
Education Institutes – To maintain student records, exam results, and staff data.
Financial Services – For processing invoices, bank statements, transaction records.
Real Estate Companies – To handle property listings, legal paperwork, client records.
Research & Marketing Firms – To compile survey data, leads, and analytics reports.
Even startups and freelancers often require virtual data entry services to stay organized and competitive.
Top Benefits of Outsourcing Data Entry Services
Outsourcing data entry work to a professional company or virtual assistant offers multiple benefits — whether you're running a small business or managing a large enterprise.
1. Reduced Costs
Maintaining an in-house data entry team means salaries, hardware, training, and software expenses. Outsourcing eliminates these costs and provides flexible, pay-as-you-go solutions.
R Data Visualization: Learn To Add Text Annotations To Plots
- 1. www.r-squared.in/git-hub dataCrunch Data Visualization With R Learn To Add Text Annotations To Plots
- 3. dataCrunchText Annotations: Objectives Slide 3 In this section, we will learn to: Add text annotations to the plots using ● text() function ● mtext() function
- 4. dataCrunchText Annotations: Introduction Slide 4 The text() and the mtext() functions allow the user to add text annotations to the plots. While the text() function places the text inside the plot, the mtext() function places the text on the margins of the plot. Below is the syntax for both the functions: # the text function text(x, y = NULL, labels = seq_along(x), adj = NULL, pos = NULL, offset = 0.5, vfont = NULL, cex = 1, col = NULL, font = NULL, ...) # the mtext function mtext(text, side = 3, line = 0, outer = FALSE, at = NA, adj = NA, padj = NA, cex = NA, col = NA, font = NA, ...) Let us explore each function and its arguments one by one:
- 5. dataCrunchText Annotations: text() Slide 5 To add text annotations using the text() function, the following 3 arguments must be supplied: ● x: x axis coordinate ● y: y axis coordinate ● text: the text to be added to the plot Below is a simple example: # create a basic plot plot(mtcars$disp, mtcars$mpg) # add text text(340, 30, "Sample Text") The text appears at the coordinates (340, 30) on the plot. Ensure that the text is enclosed in double quotes and the coordinates provided are within the range of the X and Y axis variable.
- 6. dataCrunchtext(): Color Slide 6 # create a basic plot plot(mtcars$disp, mtcars$mpg) # modify the color of the text text(340, 30, "Sample Text", col = "red") Description The color of the text can be modified using the col argument in the text() function. Code
- 7. dataCrunchtext(): Color Slide 7 The below plot depicts the appearance of the text when different options for col are applied:
- 8. dataCrunchtext(): Font Slide 8 # create a basic plot plot(mtcars$disp, mtcars$mpg) # modify the font of the text text(340, 30, "Sample Text", font = 2) Description The font of the text can be modified using the font argument in the text() function. Code
- 9. dataCrunchtext(): Font Slide 9 The below plot depicts the appearance of the text when different options for font are applied:
- 10. dataCrunchtext(): Font Family Slide 10 # create a basic plot plot(mtcars$disp, mtcars$mpg) # modify the font family of the text text(340, 30, "Sample Text", family = mono) Description The font family of the text can be modified using the family argument in the text() function. Code
- 11. dataCrunchtext(): Font Family Slide 11 The below plot depicts the appearance of the text when different options for font family are applied:
- 12. dataCrunchtext(): Font Size Slide 12 # create a basic plot plot(mtcars$disp, mtcars$mpg) # modify the size of the text text(340, 30, "Sample Text", cex = 1.5) Description The size of the text can be modified using the cex argument in the text() function. Code
- 13. dataCrunchtext(): Font Size Slide 13 The below plot depicts the appearance of the text when different options for font size are applied:
- 14. dataCrunchmtext(): Introduction Slide 14 The mtext() function places text annotations on the margins of the plot instead of placing them inside the plot. It allows the user to modify the location of the text in multiple ways and we will explore them one by one. Below is a simple example: # create a basic plot plot(mtcars$disp, mtcars$mpg) # add text mtext("Sample Text") As you can see, the text is placed on the margin of the plot and not inside the plot. Next, we will learn to specify the margin where the text should be placed.
- 15. dataCrunchmtext(): Margin Slide 15 # create a basic plot plot(mtcars$disp, mtcars$mpg) # specify the margin on which the text should appear mtext("Sample Text", side = 1) Description The margin on which we want to place the text can be specified using the side argument. It takes 4 values from 1-4 each representing one side of the plot. Code
- 16. dataCrunchmtext(): Margin Options Slide 16 The side argument can be used to specify the margin on which the text should be placed. side Margin 1 Bottom 2 Left 3 Top 4 Right
- 17. dataCrunchmtext(): Margin Slide 17 The below plot depicts the appearance of the text when different options for side are applied:
- 18. dataCrunchmtext(): Line Slide 18 # create a basic plot plot(mtcars$disp, mtcars$mpg) # place the text away from the margin mtext("Sample Text", line = 1) Description The line argument places the text at the specified distance from the margin. The default value is 0 and as the value increases the text is placed farther from the margin and outside the plot, and as the value decreases the text is placed inside the plot and farther from the margin. Code
- 19. dataCrunchmtext(): Line Slide 19 # create a basic plot plot(mtcars$disp, mtcars$mpg) # place the text away from the plot mtext("Sample Text", line = -1) Description The line argument places the text inside the plot when the values is less than zero. Code
- 20. dataCrunchmtext(): Line Slide 20 The below plot depicts the appearance of the text when different options for line are applied:
- 21. dataCrunchmtext(): adj Slide 21 # create a basic plot plot(mtcars$disp, mtcars$mpg) # align the text to the left mtext("Sample Text", adj= 0) Description The adj argument is used for horizontal alignment of the text. If set to 0, the text will be left aligned and at 1, it will be right aligned. Code
- 22. dataCrunchmtext(): adj Slide 22 # create a basic plot plot(mtcars$disp, mtcars$mpg) # align the text to the right mtext("Sample Text", adj= 1) Description When the value is set to 1, the text will be right aligned. Code
- 23. dataCrunchmtext(): adj Slide 23 The below plot depicts the appearance of the text when different options for text() are applied:
- 24. dataCrunch Slide 24 Visit dataCrunch for tutorials on: → R Programming → Business Analytics → Data Visualization → Web Applications → Package Development → Git & GitHub