Data Visualization with R.ggplot2 and its extensions examples.

Jan 19, 2017Download as DOCX, PDF1 like585 views

ggplot2 and its extensions examples. lattice-latticeExtra-gridextra packages ref:http://r-statistics.co

prepared by VOLKAN OBAN
DATA VİSUALIZATION with R
ggplot2 and Its Extension Examples:

Data Visualization with R.ggplot2 and its extensions examples.

theme(panel.background = element_rect(fill = 'paleturquoise'), panel.grid.major = e
lement_line(colour = "purple2", size=3), panel.grid.minor = element_line(colour = "
red4", size=1))

corr <- round(cor(mtcars), 2)
df <- reshape2::melt(corr)
gg <- ggplot(df, aes(x=Var1, y=Var2, fill=value, label=value)) + geom_tile() + theme_bw()
+ geom_text(aes(label=value, size=value), color="white") + labs(title="mtcars - Correlation
plot") + theme(text=element_text(size=20), legend.position="none")
library(RColorBrewer)
p2 <- gg + scale_fill_distiller(palette="Reds")
p3 <- gg + scale_fill_gradient2()
gridExtra::grid.arrange(gg, p2, p3, ncol=3)

Code:
library(semPlot)
library(lavaan)
library(clusterGeneration) #this is to generate a positive definite covariance matrix
#simulate some data
set.seed(1222)
sig<-genPositiveDefMat("onion",dim=5,eta=4)$Sigma #the covariance matrix
mus<-c(10,5,120,35,6) #the vector of the means
data<-as.data.frame(mvrnorm(100,mu=mus,Sigma=sig)) #the dataset
names(data)<-c("CO2","Temp","Nitro","Biom","Rich") #giving it some names
#building an SEM with a latent variable
m<-'Abiot =~ CO2 + Temp + Nitro
Biom ~ Abiot
Rich ~ Abiot + Biom'
m.fit<-sem(m,data)
#the plot
#basic version, the what arguments specify what should be plotted, here we choose to look at the standardized
path coefficients
semPaths(m.fit,what="std",layout="circle")

Reference: http://r-statistics.co/
http://r-statistics.co/ggplot2-cheatsheet.html#Annotation
http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf

ggplot2 Examples. References: ggplot2 Elegant Graphics for Data Analysis; Wickham, Hadley http://www.stat.wisc.edu/~larget/stat302/chap2.pdf https://cran.r-project.org/web/packages/ggthemes/vignettes/ggthemes.html https://www3.nd.edu/~steve/computing_with_data/11_geom_examples/ggplot_examples.html http://seananderson.ca/ggplot2-FISH554/ http://ggobi.github.io/ggally/docs.html#columns_and_mapping

imager package in R and examples..Dr. Volkan OBAN

Advanced Data Visualization Examples with R-Part IIDr. Volkan OBAN

This document provides several examples of advanced data visualization techniques using R. It includes examples of 3D surface plots, contour plots, scatter plots and network graphs using various R packages like plot3D, scatterplot3D, ggplot2, qgraph and ggtree. Functions used include surf3D, contour3D, arrows3D, persp3D, image3D, scatter3D, qgraph, geom_point, geom_violin and ggtree. The examples demonstrate different visualization approaches for multivariate, spatial and network data.

ggtimeseries-->ggplot2 extensions Dr. Volkan OBAN

Basic Calculus in R. Dr. Volkan OBAN

Advanced Data Visualization in R- Somes Examples.Dr. Volkan OBAN

Rexsuns

R is a programming language and software environment for statistical analysis and graphics. It allows users to analyze data, create visualizations, and perform statistical tests. Common R commands include functions to get and set the working directory, list objects in the workspace, remove objects, view and set options, save and load the command history, and save and load the entire workspace. R supports various data structures like vectors, arrays, matrices, data frames, and lists to store and manipulate different types of data. Data can be input into R from files, databases, and Excel spreadsheets. Graphs and visualizations created in R can be exported to file formats like PNG, JPEG, PDF and others.

Some R Examples[R table and Graphics] -Advanced Data Visualization in R (Some...Dr. Volkan OBAN

Some R Examples[R table and Graphics] Advanced Data Visualization in R (Some Examples) References: http://zevross.com/blog/2014/08/04/beautiful-plotting-in-r-a-ggplot2-cheatsheet-3/ http://www.cookbook-r.com/ http://moderndata.plot.ly/trisurf-plots-in-r-using-plotly/ I hope that it would ne useful for UseRs. Umarım; R programı ile ilgilenen herkes için yararlı olur. Volkan OBAN

Data visualization using the grammar of graphicsRupak Roy

ggplot2 extensions-ggtree.Dr. Volkan OBAN

CLIM Undergraduate Workshop: (Attachment) Performing Extreme Value Analysis (...The Statistical and Applied Mathematical Sciences Institute

This document describes performing extreme value analysis on daily precipitation data from Fort Collins, Colorado from 1900 to 1999 using R. It first reads in and plots the data, summarizing seasonal variations. It then performs two extreme value analysis approaches: the block maxima approach, which fits a generalized extreme value distribution to summer maximum daily precipitation values within blocks; and the peak over threshold approach, which fits a generalized Pareto distribution to values exceeding a threshold. It estimates return levels such as the 100-year event and calculates confidence intervals.

CLUSTERGRAMDr. Volkan OBAN

Surface3d in R and rgl package.Dr. Volkan OBAN

Mosaic plot in R.Dr. Volkan OBAN

R meets HadoopHidekazu Tanaka

- The document describes a MapReduce workflow for analyzing airline flight data from multiple text files. - The map function parses the raw data by date, carrier, origin, destination, and converts time fields to datetime objects. - The reduce function aggregates the data by origin and destination airports to calculate inbound, outbound, and total flights. - The results are written to a new folder and then read back into R for further analysis and ranking of airports by flight volumes.

CLIM Undergraduate Workshop: Tutorial on R Software - Huang Huang, Oct 23, 2017The Statistical and Applied Mathematical Sciences Institute

This document provides an R tutorial for an undergraduate climate workshop. It introduces key concepts in R including data types, arrays, matrices, data frames, packages, and basic plotting. It demonstrates how to perform calculations, subset data, install and load packages, create different plot types like histograms and maps, and use functions like quantile and quilt.plot. Exercises include drawing a histogram of ozone values and calculating quantiles.

CUDA First Programs: Computer Architecture CSE448 : UAA Alaska : NotesSubhajit Sahu

The document provides examples of simple CUDA programs for adding vectors and 2D arrays using kernel functions. It begins with a "Hello World" CUDA program and explains how to compile and run it. It then shows a CUDA program that adds two numbers in a kernel function using thread indexing. Next, it presents a CUDA program for adding two vectors with one thread per element. Finally, it demonstrates how to map a 2D array to linear memory and write a kernel to add 2D arrays using block indexing.

Seminar psu 20.10.2013Vyacheslav Arbuzov

This document provides an overview and introduction to using the statistical programming language R. It begins with basic commands for performing calculations and creating vectors, matrices, and data frames. It then covers importing and exporting data, basic graphs and statistical distributions, correlations, linear and nonlinear regression, advanced graphics, and accessing financial data packages. The document concludes with proposing practical tasks for workshop participants to work with financial data in R.

peRm R group. Review of packages for r for market data downloading and analysisVyacheslav Arbuzov

This document summarizes R packages for downloading market data. It discusses packages such as quantmod, tseries, rdatamarket, and rBloomberg that can be used to access stock, economic, and financial time series data from various sources including Yahoo Finance, Google Finance, FRED, DataMarket, and Bloomberg. It provides examples of functions to download and visualize different types of market data using these packages.

Seminar PSU 10.10.2014 mmeVyacheslav Arbuzov

This document provides an overview of using R for financial modeling. It covers basic R commands for calculations, vectors, matrices, lists, data frames, and importing/exporting data. Graphical functions like plots, bar plots, pie charts, and boxplots are demonstrated. Advanced topics discussed include distributions, parameter estimation, correlations, linear and nonlinear regression, technical analysis packages, and practical exercises involving financial data analysis and modeling.

Seminar PSU 09.04.2013 - 10.04.2013 MiFIT, Arbuzov VyacheslavVyacheslav Arbuzov

Using R in financial modeling provides an introduction to using R for financial applications. It discusses importing stock price data from various sources and visualizing it using basic graphs and technical indicators. It also covers topics like calculating returns, estimating distributions of returns, correlations, volatility modeling, and value at risk calculations. The document provides examples of commands and functions in R to perform these financial analytics tasks on sample stock price data.

A Shiny Example-- RDr. Volkan OBAN

Script jantung copyNurwahidah Abidin

This document contains R code for analyzing survival data. It loads survival data from a file, fits Kaplan-Meier and Cox proportional hazards models, and generates Kaplan-Meier curves and log-rank test results. Functions are defined to plot single or multiple stratified Kaplan-Meier curves using ggplot. The curves and log-rank test are generated by fitting survival models to treatment groups in the loaded data and summarizing the results.

Perm winter school 2014.01.31Vyacheslav Arbuzov

This document provides an introduction to financial modeling in R. It begins with basic R commands for calculations, vectors, matrices, and data frames. It then covers importing and exporting data, basic graphs, distributions, correlations, and linear regression. More advanced topics include non-linear regression, graphics packages, downloading stock data, and estimating volatility and value at risk. Practical exercises are provided to work with financial data, estimate distributions, correlations, and models.

Python grassMargherita Di Leo

The document describes a Python module called r.ipso that is used in GRASS GIS to generate ipsographic and ipsometric curves from raster elevation data. The module imports GRASS and NumPy libraries, reads elevation and cell count statistics from a raster, calculates normalized elevation and area values, and uses these to plot the curves and output quantile information. The module demonstrates calling GRASS functionality from Python scripts.

Plot3D Package and Example in R.-Data visualizat,onDr. Volkan OBAN

Experement no 6Smita Batti

This experiment finds the minimum cost spanning tree of an undirected graph using Kruskal's algorithm. It takes the number of vertices and cost matrix as input, initializes parent and minimum cost variables, then iterates through the edges and adds the minimum cost edge between two unconnected vertices to the spanning tree until all vertices are connected. It outputs the edges of the minimum spanning tree and their total cost.

The simplest existential graph systemArmahedi Mahzar

This document summarizes the Peirce Existential Graph system and the Simple Existential Graph system. It defines the axioms and rules of inference for each system. It then proves several theorems about the rules of inference for the Peirce system, including theorems showing rules P3, P4, and P5 are valid. It introduces some lemmas about insertion, deletion, and inversion and uses these to prove rules P1 and P2 are also valid.

Data Visualization With RRsquared Academy

Data Visualization in RSophia Banton

Make beautiful plots and graphs using the open source R programming language. How we represent our data is often as important as the quality of the data itself. In this course, you will learn how to make functional and elegant plots using the R language. R is a free/open source programming language that has become very popular in academia and among data scientists across all disciplines. In this course, you will learn how to quickly make bar plots scatter plots line plots pie charts and more... You will also learn how to show trends over time and how to plot correlations and geographical data in this course. This course is intended for students, professional, entrepeneurs and everyone in between. Happy Plotting!

More Related Content

What's hot (20)

Data visualization using the grammar of graphicsRupak Roy

ggplot2 extensions-ggtree.Dr. Volkan OBAN

CLIM Undergraduate Workshop: (Attachment) Performing Extreme Value Analysis (...The Statistical and Applied Mathematical Sciences Institute

CLUSTERGRAMDr. Volkan OBAN

Surface3d in R and rgl package.Dr. Volkan OBAN

Mosaic plot in R.Dr. Volkan OBAN

R meets HadoopHidekazu Tanaka

CLIM Undergraduate Workshop: Tutorial on R Software - Huang Huang, Oct 23, 2017The Statistical and Applied Mathematical Sciences Institute

CUDA First Programs: Computer Architecture CSE448 : UAA Alaska : NotesSubhajit Sahu

Seminar psu 20.10.2013Vyacheslav Arbuzov

peRm R group. Review of packages for r for market data downloading and analysisVyacheslav Arbuzov

Seminar PSU 10.10.2014 mmeVyacheslav Arbuzov

Seminar PSU 09.04.2013 - 10.04.2013 MiFIT, Arbuzov VyacheslavVyacheslav Arbuzov

A Shiny Example-- RDr. Volkan OBAN

Script jantung copyNurwahidah Abidin

Perm winter school 2014.01.31Vyacheslav Arbuzov

Python grassMargherita Di Leo

Plot3D Package and Example in R.-Data visualizat,onDr. Volkan OBAN

Experement no 6Smita Batti

The simplest existential graph systemArmahedi Mahzar

Data visualization using the grammar of graphicsRupak Roy

ggplot2 extensions-ggtree.Dr. Volkan OBAN

CLIM Undergraduate Workshop: (Attachment) Performing Extreme Value Analysis (...The Statistical and Applied Mathematical Sciences Institute

CLUSTERGRAMDr. Volkan OBAN

Surface3d in R and rgl package.Dr. Volkan OBAN

Mosaic plot in R.Dr. Volkan OBAN

R meets HadoopHidekazu Tanaka

CLIM Undergraduate Workshop: Tutorial on R Software - Huang Huang, Oct 23, 2017The Statistical and Applied Mathematical Sciences Institute

CUDA First Programs: Computer Architecture CSE448 : UAA Alaska : NotesSubhajit Sahu

Seminar psu 20.10.2013Vyacheslav Arbuzov

peRm R group. Review of packages for r for market data downloading and analysisVyacheslav Arbuzov

Seminar PSU 10.10.2014 mmeVyacheslav Arbuzov

Seminar PSU 09.04.2013 - 10.04.2013 MiFIT, Arbuzov VyacheslavVyacheslav Arbuzov

A Shiny Example-- RDr. Volkan OBAN

Script jantung copyNurwahidah Abidin

Perm winter school 2014.01.31Vyacheslav Arbuzov

Python grassMargherita Di Leo

Plot3D Package and Example in R.-Data visualizat,onDr. Volkan OBAN

Experement no 6Smita Batti

The simplest existential graph systemArmahedi Mahzar

Viewers also liked (18)

Data Visualization With RRsquared Academy

Data Visualization in RSophia Banton

Mini Innovation Lab: Community Foundations and Shared DataBeth Kanter

The document summarizes a workshop on using human-centered design and data to drive innovation at community foundations. It discusses how community foundations can effectively communicate the value of shared data and become recognized knowledge connectors in their communities. The workshop utilized human-centered design techniques like engaging stakeholders, iterating solutions, and collaboration. It provided examples from the Sacramento Regional Community Foundation and Community Foundation for Greater New Haven on their use of shared data and challenges faced. The goal was for participants to leave with new ideas on improving communications and knowledge sharing through shared data.

Buscadores especializados para docentes ad sjuliol 2013TICS & Partners

Este documento ofrece instrucciones sobre cómo buscar información en Internet de manera efectiva. Explica que menos del 40% de la población mundial tiene acceso a Internet y que la mayor parte de la información en línea es generada por usuarios comunes. Luego, detalla los pasos para realizar una búsqueda estratégica, incluyendo definir el tema y palabras clave, seleccionar un motor de búsqueda apropiado, determinar el formato deseado y validar las fuentes. También explica el uso de operadores lógicos para optimizar los

Marketing analyticsData Science Thailand

Microsoft R Server for Data ScienceaData Science Thailand

R and Visualization: A match made in HeavenEdureka!

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.

Analytical method developmentSagar K Savale

The drug or drug combination may not be official in any pharmacopoeias. A proper analytical procedure for the drug may not be available in the literature due to patent regulations. Analytical methods may not be available for the drug in the form of a formulation due to the interference caused by the formulation excipients. Analytical methods for the quantitation of the drug in biological fluids may not be available. Analytical methods for a drug in combination with other drugs may not be available. The existing analytical procedures may require expensive reagents and solvents. It may also involve cumbersome extraction and separation procedures and these may not be reliable.

Method Development and Method Validation for the estimation of Valganciclovir...google

Microsoft R ServerEduardo Castro

Analytical method validation by manoj ingale(best ppts)Indus Biotech Pvt.Ltd.

analytical method validation and validation of hplcvenkatesh thota

The document summarizes a seminar on analytical method validation and validation of HPLC. It discusses parameters for method validation according to USP, BP, and ICH guidelines such as accuracy, precision, linearity, range, specificity, detection limit, and quantitation limit. It also covers validation of typical HPLC systems through qualification, design, installation, operational, and performance qualification. Key parameters evaluated during HPLC method validation are discussed, including system suitability tests involving retention factor, relative retention, theoretical plates, resolution, and tailing factor.

analytical method validationDr. Raja Abhilash

The document discusses analytical method validation, including defining method validation as ensuring an analytical method provides acceptable data for its intended use. It outlines the common steps in method development and validation and the validation parameters that should be assessed, including accuracy, precision, specificity, linearity, range, and robustness. The document provides details on how each of these parameters should be evaluated during the validation process.

My Life #OutsideWork (Intuit Employees)Intuit Inc.

Why Are You Attracted to That Person? SlideShop.com

This document summarizes several factors that can lead to interpersonal attraction according to social psychology research: physical appearance/beauty, personality, proximity, and similarity. Studies discussed found that people tend to see attractive, beautiful people as more desirable and competent. Additionally, having a warm, kind, or exciting personality is attractive to others. Proximity, or spending time near others, increases comfort levels and likelihood of attraction. Similarity in characteristics and interests between people also reduces conflicts. Reciprocated liking and gaining the approval of someone who was initially unimpressed can be especially rewarding.

Free Download Powerpoint SlidesGeorge

Poor Self-esteem: Just Beat It!SlideShop.com

Data Visualization With RRsquared Academy

Data Visualization in RSophia Banton

Mini Innovation Lab: Community Foundations and Shared DataBeth Kanter

Buscadores especializados para docentes ad sjuliol 2013TICS & Partners

Marketing analyticsData Science Thailand

Microsoft R Server for Data ScienceaData Science Thailand

R and Visualization: A match made in HeavenEdureka!

Introduction to R Graphics with ggplot2izahn

Analytical method developmentSagar K Savale

Method Development and Method Validation for the estimation of Valganciclovir...google

Microsoft R ServerEduardo Castro

Analytical method validation by manoj ingale(best ppts)Indus Biotech Pvt.Ltd.

analytical method validation and validation of hplcvenkatesh thota

analytical method validationDr. Raja Abhilash

My Life #OutsideWork (Intuit Employees)Intuit Inc.

Why Are You Attracted to That Person? SlideShop.com

Free Download Powerpoint SlidesGeorge

Poor Self-esteem: Just Beat It!SlideShop.com

Similar to Data Visualization with R.ggplot2 and its extensions examples. (20)

Survey DemoAnthony Castellani

This document demonstrates analyzing responses to Likert-scale survey questions using R. It generates dummy survey data with 1000 responses across 5 questions. It then uses dplyr and ggplot2 to: 1) Summarize responses to a single question as a percentage bar graph. 2) Plot responses to two related questions side-by-side using gridExtra. 3) Compare responses to one question between two demographic groups in another side-by-side graph. The goal is to showcase analyzing survey data with R packages like dplyr and ggplot2.

Data visualization with R and ggplot2.docxkassaye4

Geo Spatial Plot using R Rupak Roy

This document provides an example of creating geospatial plots in R using ggmap() and ggplot2. It includes 3 steps: 1) Get the map using get_map(), 2) Plot the map using ggmap(), and 3) Plot the dataset on the map using ggplot2 objects like geom_point(). The example loads crime and neighborhood datasets, filters the data, gets a map of Seattle, and plots crime incidents and dangerous neighborhoods on the map. It demonstrates various geospatial plotting techniques like adjusting point transparency, adding density estimates, labeling points, and faceting by crime type.

Genomic GraphicsDr. Volkan OBAN

This document demonstrates how to create genomic graphics and plots using the ggbio and GenomicFeatures R packages. It shows examples of: 1) Creating tracks plots to visualize genomic data over time using qplot and tracks functions. 2) Plotting genomic ranges data from a GRanges object using autoplot with options to facet by strands or calculate coverage. 3) Creating bar plots of coverage data from a GRanges object grouped by chromosome and strand. 4) Drawing circular genome plots from GRanges data using layout_circle with options to add multiple track types like rectangles, bars, points and links between ranges.

Optimization and Mathematical Programming in R and ROI - R Optimization Infra...Dr. Volkan OBAN

Company_X_Data_Analyst_ChallengeMark Yashar

The document describes a GPS data analysis challenge to combine two streams of timestamped GPS data collected from devices at different heights into a single stream. The goals are to create a single stream from the two inputs and indicate how each point was computed. Code snippets are provided that read the raw GPS data, perform processing to average and calculate values from the two streams, and write the results to a new consolidated file along with plotting scripts to visualize the data.

Compact and safely: static DSL on KotlinDmitry Pranchuk

ggplot2: An Extensible Platform for Publication-quality GraphicsClaus Wilke

Idea for ineractive programming languageLincoln Hannah

Notebooks such as Jupyter give programming languages a level of interactivity approaching that of spreadsheets. I present here an idea for a programming language specifically designed for an interactive environment similar to a notebook. It aims to combining the power of a programming language with the usability of a spreadsheet. Instead of free-form code, the user creates fields / columns, but these can be combined into tables and object classes. By decoratively cycling through field elements, loops and other programming constructs can be created. I give examples from classical computer science, machine learning and mathematical finance, specifically: Nth Prime Number, 8 Queens, Poker Hand, Travelling Salesman, Linear Regression, VaR Attribution

Big datacourseMassimiliano Ruocco

The document outlines an introduction to analyzing and visualizing geo-data in R. It discusses exploring the structure of spatially distributed point data through point process statistics like the Complete Spatial Randomness test and Ripley's K-function. It also covers visualizing maps and point patterns with packages like maps, ggmap, rworldmap, and ggplot2. The document provides examples of mapping different regions, geocoding location data, and plotting point patterns on maps in R.

Grokking Monads in ScalaTim Dalton

This document summarizes a presentation about monads in Scala. It discusses how monads allow structuring computations and combining them. Some key monads described include Option for handling failures, State for managing state, and Identity. For comprehensions in Scala emulate do notation in Haskell. Monads are demonstrated through an evaluator for arithmetic expressions that uses different monadic types like Identity, Option and State.

Tech talk ggplot2jalle6

Exploratory Analysis Part1 Coursera DataScience SpecialisationWesley Goi

The document discusses exploratory data analysis techniques in R, including various plotting systems and graph types. It provides code examples for creating boxplots, histograms, bar plots, and scatter plots in Base, Lattice, and ggplot2. It also covers downloading data, transforming data, adding scales and themes, and creating faceted plots. The final challenge involves creating a boxplot with rectangles to represent regions and jittered points to show trends over years.

r for data science 2. grammar of graphics (ggplot2) clean -refMin-hyung Kim

REFERENCES #1. RStudio Official Documentations (Help & Cheat Sheet) Free Webpage) https://www.rstudio.com/resources/cheatsheets/ #2. Wickham, H. and Grolemund, G., 2016.R for data science: import, tidy, transform, visualize, and model data. O'Reilly. Free Webpage) https://r4ds.had.co.nz/ Cf) Tidyverse syntax (www.tidyverse.org), rather than R Base syntax Cf) Hadley Wickham: Chief Scientist at RStudio. Adjunct Professor of Statistics at the University of Auckland, Stanford University, and Rice University

第１３回数学カフェ「素数！！」二次会 LT資料「乱数！！」Ken'ichi Matsui

- The document discusses random number generation and probability distributions. It presents methods for generating random numbers from Bernoulli, binomial, beta, and multinomial distributions using random bits generated from linear congruential generators. - Graphical examples are shown comparing histograms of generated random samples to theoretical probability density functions. Code examples in R demonstrate how to simulate random number generation from various discrete distributions. - The goal is to introduce different methods for random number generation from basic discrete distributions that are important for modeling random phenomena and Monte Carlo simulations.

decision tree regressionAkhilesh Joshi

This document discusses decision tree regression for predicting salary based on position level. It shows how to import data, build a decision tree regression model using scikit-learn in Python and rpart in R, make predictions, and plot the results. It notes that decision trees are discrete models, so the plots need to treat the x-axis as discrete rather than continuous to properly visualize the model's piecewise constant predictions.

R Language IntroductionKhaled Al-Shamaa

The document outlines various statistical and data analysis techniques that can be performed in R including importing data, data visualization, correlation and regression, and provides code examples for functions to conduct t-tests, ANOVA, PCA, clustering, time series analysis, and producing publication-quality output. It also reviews basic R syntax and functions for computing summary statistics, transforming data, and performing vector and matrix operations.

CDMA simulation code for wireless Network.docxMaryamAziz47

Scaling up data science applicationsSalesforce Engineering

R (Shiny Package) - Server Side Code for Decision Support SystemMaithreya Chakravarthula

This document loads various libraries and reads in multiple csv files containing transportation data. It then performs some data cleaning and preprocessing steps. Various outputs are defined to render tables and plots of subsets of the data. Plots are created to visualize relationships between weighted time, cost, and safety metrics. Interactive elements are added to output text describing user input from the plots. Maps and motion charts are also defined as outputs to visualize additional data aspects.

Survey DemoAnthony Castellani

Data visualization with R and ggplot2.docxkassaye4

Geo Spatial Plot using R Rupak Roy

Genomic GraphicsDr. Volkan OBAN

Optimization and Mathematical Programming in R and ROI - R Optimization Infra...Dr. Volkan OBAN

Company_X_Data_Analyst_ChallengeMark Yashar

Compact and safely: static DSL on KotlinDmitry Pranchuk

ggplot2: An Extensible Platform for Publication-quality GraphicsClaus Wilke

Idea for ineractive programming languageLincoln Hannah

Big datacourseMassimiliano Ruocco

Grokking Monads in ScalaTim Dalton

Tech talk ggplot2jalle6

Exploratory Analysis Part1 Coursera DataScience SpecialisationWesley Goi

r for data science 2. grammar of graphics (ggplot2) clean -refMin-hyung Kim

第１３回数学カフェ「素数！！」二次会 LT資料「乱数！！」Ken'ichi Matsui

decision tree regressionAkhilesh Joshi

R Language IntroductionKhaled Al-Shamaa

CDMA simulation code for wireless Network.docxMaryamAziz47

Scaling up data science applicationsSalesforce Engineering

R (Shiny Package) - Server Side Code for Decision Support SystemMaithreya Chakravarthula

More from Dr. Volkan OBAN (20)

Conference Paper:IMAGE PROCESSING AND OBJECT DETECTION APPLICATION: INSURANCE...Dr. Volkan OBAN

1) The document discusses using image processing and object detection techniques for insurance claims processing and underwriting. It aims to allow insurers to realistically assess images of damaged objects and claims. 2) Artificial intelligence, including computer vision, has been widely adopted in the insurance industry to analyze data like images, extract relevant information, detect fraud, and predict costs. Computer vision can recognize objects in images and help route insurance inquiries. 3) The document examines several computer vision applications for insurance - image similarity, facial recognition, object detection, and damage detection from images. It asserts that computer vision can expedite claims processing and improve key performance metrics for insurers.

Covid19py Python Package - ExampleDr. Volkan OBAN

Object detection with Python Dr. Volkan OBAN

Python - Rastgele Orman(Random Forest) ParametreleriDr. Volkan OBAN

Linear Programming wi̇th R - ExamplesDr. Volkan OBAN

The document discusses using the lpSolveAPI package in R to solve linear programming problems. It provides three examples: 1) A farmer's profit maximization problem is modeled and solved using functions from lpSolveAPI like make.lp(), add.constraint(), and solve(). 2) A simple minimization problem is created and solved to illustrate setting up the objective function and constraints. 3) A more complex problem is modeled to demonstrate setting sparse matrices, integer/binary variables, and customizing variable and constraint names.

"optrees" package in R and examples.(optrees:finds optimal trees in weighted ...Dr. Volkan OBAN

k-means Clustering in PythonDr. Volkan OBAN

k-means Clustering in Python scikit-learn--Machine Learning in Python from sklearn.cluster import KMeans k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. The problem is computationally difficult (NP-hard); however, there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both algorithms. Additionally, they both use cluster centers to model the data; however, k-means clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes.[wikipedia] ref: http://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_iris.html

Naive Bayes Example using R Dr. Volkan OBAN

R forecasting ExampleDr. Volkan OBAN

k-means Clustering and Custergram with RDr. Volkan OBAN

k-means Clustering and Custergram with R. K Means Clustering is an unsupervised learning algorithm that tries to cluster data based on their similarity. Unsupervised learning means that there is no outcome to be predicted, and the algorithm just tries to find patterns in the data. In k means clustering, we have the specify the number of clusters we want the data to be grouped into. The algorithm randomly assigns each observation to a cluster, and finds the centroid of each cluster. ref:https://www.r-bloggers.com/k-means-clustering-in-r/ ref:https://rpubs.com/FelipeRego/K-Means-Clustering ref:https://www.r-bloggers.com/clustergram-visualization-and-diagnostics-for-cluster-analysis-r-code/

Data Science and its Relationship to Big Data and Data-Driven Decision MakingDr. Volkan OBAN

Data Science and its Relationship to Big Data and Data-Driven Decision Making To cite this article: Foster Provost and Tom Fawcett. Big Data. February 2013, 1(1): 51-59. doi:10.1089/big.2013.1508. Foster Provost and Tom Fawcett Published in Volume: 1 Issue 1: February 13, 2013 ref:http://online.liebertpub.com/doi/full/10.1089/big.2013.1508 https://www.researchgate.net/publication/256439081_Data_Science_and_Its_Relationship_to_Big_Data_and_Data-Driven_Decision_Making

Scikit-learn Cheatsheet-PythonDr. Volkan OBAN

Python Pandas for Data Science cheatsheet Dr. Volkan OBAN

The Pandas library provides easy-to-use data structures and analysis tools for Python. It uses NumPy and allows import of data into Series (one-dimensional arrays) and DataFrames (two-dimensional labeled data structures). Data can be accessed, filtered, and manipulated using indexing, booleans, and arithmetic operations. Pandas supports reading and writing data to common formats like CSV, Excel, SQL, and can help with data cleaning, manipulation, and analysis tasks.

Pandas,scipy,numpy cheatsheetDr. Volkan OBAN

ReporteRs package in R. forming powerpoint documents-an exampleDr. Volkan OBAN

R Machine Learning packages( generally used)Dr. Volkan OBAN

treemap package in R and examples.Dr. Volkan OBAN

R-Data table Cheat SheetDr. Volkan OBAN

This document provides an overview of using data.tables in R. It discusses how to create and subset data.tables, manipulate columns by reference, perform grouped operations, and use keys and indexes. Some key points include: - Data.tables allow fast subsetting, updating, and grouping of large data sets using keys and indexes. - Columns can be manipulated by reference using := to efficiently add, update, or remove columns. - Grouped operations like summing are performed efficiently using by to split the data.table into groups. - Keys set on one or more columns allow fast row selection similar to SQL queries on indexed columns.

Rcommands-for those who interested in R.Dr. Volkan OBAN

Conference Paper:IMAGE PROCESSING AND OBJECT DETECTION APPLICATION: INSURANCE...Dr. Volkan OBAN

Covid19py Python Package - ExampleDr. Volkan OBAN

Object detection with Python Dr. Volkan OBAN

Python - Rastgele Orman(Random Forest) ParametreleriDr. Volkan OBAN

Linear Programming wi̇th R - ExamplesDr. Volkan OBAN

"optrees" package in R and examples.(optrees:finds optimal trees in weighted ...Dr. Volkan OBAN

k-means Clustering in PythonDr. Volkan OBAN

Naive Bayes Example using R Dr. Volkan OBAN

R forecasting ExampleDr. Volkan OBAN

k-means Clustering and Custergram with RDr. Volkan OBAN

Data Science and its Relationship to Big Data and Data-Driven Decision MakingDr. Volkan OBAN

Scikit-learn Cheatsheet-PythonDr. Volkan OBAN

Python Pandas for Data Science cheatsheet Dr. Volkan OBAN

Pandas,scipy,numpy cheatsheetDr. Volkan OBAN

ReporteRs package in R. forming powerpoint documents-an exampleDr. Volkan OBAN

R Machine Learning packages( generally used)Dr. Volkan OBAN

treemap package in R and examples.Dr. Volkan OBAN

R-Data table Cheat SheetDr. Volkan OBAN

Rcommands-for those who interested in R.Dr. Volkan OBAN

Recently uploaded (20)

Cleaned_Lecture 6666666_Simulation_I.pdfalcinialbob1234

Data Analytics Overview and its applicationsJanmejayaMishra7

Calories_Prediction_using_Linear_Regression.pptxTijiLMAHESHWARI

Developing Security Orchestration, Automation, and Response ApplicationsVICTOR MAESTRE RAMIREZ

presentation of first program exist.pptxMajidAzeemChohan

EDU533 DEMO.pptxccccvbnjjkoo jhgggggbbbbJessaMaeEvangelista2

Andhra Pradesh Micro Irrigation Project”vzmcareers

AI Competitor Analysis: How to Monitor and Outperform Your CompetitorsContify

DPR_Expert_Recruitment_notice_Revised.pdfinmishra17121973

Adobe Analytics NOAM Central User Group April 2025 Agent AI: Uncovering the S...gmuir1066

How iCode cybertech Helped Me Recover My Lost Fundsireneschmid345

I was devastated when I realized that I had fallen victim to an online fraud, losing a significant amount of money in the process. After countless hours of searching for a solution, I came across iCode cybertech. From the moment I reached out to their team, I felt a sense of hope that I can recommend iCode Cybertech enough for anyone who has faced similar challenges. Their commitment to helping clients and their exceptional service truly set them apart. Thank you, iCode cybertech, for turning my situation around! [email protected]

Day 1 - Lab 1 Reconnaissance Scanning with NMAP, Vulnerability Assessment wit...Abodahab

Thingyan is now a global treasure! See how people around the world are search...Pixellion

Chromatography_Detailed_Information.docxNohaSalah45

Induction Program of MTAB online sessionLOHITH886892

CTS EXCEPTIONSPrediction of Aluminium wire rod physical properties through AI...ThanushsaranS

i_o updated.pptx 6=₹cnjxifj,lsbd ধ and vjcjcdbgjfu n smn u cut the lb, it ও o...ggg032019

Ppt. Nikhil.pptxnshwuudgcudisisshvehsjkspanchariyasahil

Data Science Courses in India iim skillsdharnathakur29

This comprehensive Data Science course is designed to equip learners with the essential skills and knowledge required to analyze, interpret, and visualize complex data. Covering both theoretical concepts and practical applications, the course introduces tools and techniques used in the data science field, such as Python programming, data wrangling, statistical analysis, machine learning, and data visualization.

Simple_AI_Explanation_English somplr.pptxssuser2aa19f

Cleaned_Lecture 6666666_Simulation_I.pdfalcinialbob1234

Data Analytics Overview and its applicationsJanmejayaMishra7

Calories_Prediction_using_Linear_Regression.pptxTijiLMAHESHWARI

Developing Security Orchestration, Automation, and Response ApplicationsVICTOR MAESTRE RAMIREZ

presentation of first program exist.pptxMajidAzeemChohan

EDU533 DEMO.pptxccccvbnjjkoo jhgggggbbbbJessaMaeEvangelista2

Andhra Pradesh Micro Irrigation Project”vzmcareers

AI Competitor Analysis: How to Monitor and Outperform Your CompetitorsContify

DPR_Expert_Recruitment_notice_Revised.pdfinmishra17121973

Adobe Analytics NOAM Central User Group April 2025 Agent AI: Uncovering the S...gmuir1066

How iCode cybertech Helped Me Recover My Lost Fundsireneschmid345

Day 1 - Lab 1 Reconnaissance Scanning with NMAP, Vulnerability Assessment wit...Abodahab

Thingyan is now a global treasure! See how people around the world are search...Pixellion

Chromatography_Detailed_Information.docxNohaSalah45

Induction Program of MTAB online sessionLOHITH886892

CTS EXCEPTIONSPrediction of Aluminium wire rod physical properties through AI...ThanushsaranS

i_o updated.pptx 6=₹cnjxifj,lsbd ধ and vjcjcdbgjfu n smn u cut the lb, it ও o...ggg032019

Ppt. Nikhil.pptxnshwuudgcudisisshvehsjkspanchariyasahil

Data Science Courses in India iim skillsdharnathakur29

Simple_AI_Explanation_English somplr.pptxssuser2aa19f

Data Visualization with R.ggplot2 and its extensions examples.

1. prepared by VOLKAN OBAN DATA VİSUALIZATION with R ggplot2 and Its Extension Examples:

8. theme(panel.background = element_rect(fill = 'paleturquoise'), panel.grid.major = e lement_line(colour = "purple2", size=3), panel.grid.minor = element_line(colour = " red4", size=1))

12. corr <- round(cor(mtcars), 2) df <- reshape2::melt(corr) gg <- ggplot(df, aes(x=Var1, y=Var2, fill=value, label=value)) + geom_tile() + theme_bw() + geom_text(aes(label=value, size=value), color="white") + labs(title="mtcars - Correlation plot") + theme(text=element_text(size=20), legend.position="none") library(RColorBrewer) p2 <- gg + scale_fill_distiller(palette="Reds") p3 <- gg + scale_fill_gradient2() gridExtra::grid.arrange(gg, p2, p3, ncol=3)

13. lattice example:

14. Code: library(semPlot) library(lavaan) library(clusterGeneration) #this is to generate a positive definite covariance matrix #simulate some data set.seed(1222) sig<-genPositiveDefMat("onion",dim=5,eta=4)$Sigma #the covariance matrix mus<-c(10,5,120,35,6) #the vector of the means data<-as.data.frame(mvrnorm(100,mu=mus,Sigma=sig)) #the dataset names(data)<-c("CO2","Temp","Nitro","Biom","Rich") #giving it some names #building an SEM with a latent variable m<-'Abiot =~ CO2 + Temp + Nitro Biom ~ Abiot Rich ~ Abiot + Biom' m.fit<-sem(m,data) #the plot #basic version, the what arguments specify what should be plotted, here we choose to look at the standardized path coefficients semPaths(m.fit,what="std",layout="circle")

15. Reference: http://r-statistics.co/ http://r-statistics.co/ggplot2-cheatsheet.html#Annotation http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf

Data Visualization with R.ggplot2 and its extensions examples.

Recommended

More Related Content

What's hot (20)

Viewers also liked (18)

Similar to Data Visualization with R.ggplot2 and its extensions examples. (20)

More from Dr. Volkan OBAN (20)

Recently uploaded (20)

Data Visualization with R.ggplot2 and its extensions examples.