Module 4: Data visualization (8 hrs)
Introduction, Types of data visualization, Data for visualization: Data types, Data encodings, Retinal variables, Mapping variables to encodings, Visual encodings, Data Visualization in Python-Superset or in Microsoft Power BI
A walk through the maze of understanding Data Visualization using several tools such as Python, R, Knime and Google Data Studio.
This workshop is hands-on and this set of presentations is designed to be an agenda to the workshop
A Production Quality Sketching Library for the Analysis of Big DataDatabricks
In the analysis of big data there are often problem queries that don’t scale because they require huge compute resources to generate exact results, or don’t parallelize well.
Data visualization in data science: exploratory EDA, explanatory. Anscobe's quartet, design principles, visual encoding, design engineering and journalism, choosing the right graph, narrative structures, technology and tools.
Business Intelligence Barista: What DataViz Tool to Use, and When?Jen Stirrup
Choosing a data visualization tool is like being a barista serving coffee: everyone wants their data, their way, personalized, fast, and perfect. Many organizations have a cottage industry of data visualization tools, and it's difficult to know what tool to use, and when. Different tools exist in different departments, and if it doesn't meet the user requirements, the default position is to go back to Excel and move the data around there.
This session will examine data visualization tools such as SSRS Excel, Tableau, QlikView, Datazen, Kibana and PowerBI, in order to craft and blend your data visualization tools to serve your data customers better.
Business Intelligence Barista: What DataViz Tool to Use, and When?Jen Stirrup
Choosing a data visualization tool is like being a barista serving coffee: everyone wants their data, their way, personalized, fast, and perfect. Many organizations have a cottage industry of data visualization tools, and it's difficult to know what tool to use, and when. Different tools exist in different departments, and if it doesn't meet the user requirements, the default position is to go back to Excel and move the data around there.
This session will examine data visualization tools such as SSRS Excel, Tableau, QlikView, Datazen, Kibana and PowerBI, in order to craft and blend your data visualization tools to serve your data customers better.
A walk through the maze of understanding Data Visualization using several tools such as Python, R, Knime and Google Data Studio.
This workshop is hands-on and this set of presentations is designed to be an agenda to the workshop
A Production Quality Sketching Library for the Analysis of Big DataDatabricks
In the analysis of big data there are often problem queries that don’t scale because they require huge compute resources to generate exact results, or don’t parallelize well.
Data visualization in data science: exploratory EDA, explanatory. Anscobe's quartet, design principles, visual encoding, design engineering and journalism, choosing the right graph, narrative structures, technology and tools.
Business Intelligence Barista: What DataViz Tool to Use, and When?Jen Stirrup
Choosing a data visualization tool is like being a barista serving coffee: everyone wants their data, their way, personalized, fast, and perfect. Many organizations have a cottage industry of data visualization tools, and it's difficult to know what tool to use, and when. Different tools exist in different departments, and if it doesn't meet the user requirements, the default position is to go back to Excel and move the data around there.
This session will examine data visualization tools such as SSRS Excel, Tableau, QlikView, Datazen, Kibana and PowerBI, in order to craft and blend your data visualization tools to serve your data customers better.
Business Intelligence Barista: What DataViz Tool to Use, and When?Jen Stirrup
Choosing a data visualization tool is like being a barista serving coffee: everyone wants their data, their way, personalized, fast, and perfect. Many organizations have a cottage industry of data visualization tools, and it's difficult to know what tool to use, and when. Different tools exist in different departments, and if it doesn't meet the user requirements, the default position is to go back to Excel and move the data around there.
This session will examine data visualization tools such as SSRS Excel, Tableau, QlikView, Datazen, Kibana and PowerBI, in order to craft and blend your data visualization tools to serve your data customers better.
Machine Learning Foundations for Professional ManagersAlbert Y. C. Chen
20180804@Taiwan AI Academy, Hsinchu
6 hour lecture for those new to machine learning, to grasps the concepts, advantages and limitations of various classical machine learning methods. More importantly, to learn the skills to break down large complicated AI projects into manageable pieces, where features and functionalities could be added incrementally and annotated data accumulated. Take home message: machine learning is always a delicate balance between model complexity M and number of data N so that the trained classifier generalizes well and does not overfit.
AMIA 2015 Visual Analytics in Healthcare Tutorial Part 1David Gotz
A concise introduction to the topic of visualization. Designed for beginners with no prior experience with visualization. These slides were the first part of a half-day tutorial on Visual Analytics held in conjunction with the 2015 AMIA Annual Symposium. It was sponsored by the AMIA Visual Analytics Working Group. For more information, please see www.visualanalyticshealthcare.org or contact the author of the slides: David Gotz @ http://gotz.web.unc.edu
Graph Analytics (or network analytics), is an area of analysis with numerous applications that increasingly draws more and more attention. From fraud detection and money laundering, illegal transactions and other forms of financial crime, to identify key influencers in social networks, communities of frequently interacting individuals and route optimisation or even bioinformatics; graph analytics offer a vast variety of solutions that keep on evolving on a daily basis; allowing for experts in various fields to tackle every day challenges, extract insights and drive decision making.
Mainly, Graph Analytics is divided into 4 categories:
* Path Analytics;
* Connectivity Analytics;
* Centrality Analytics, and;
* Community Detection analytics;
each of which relies on different algorithms and address different problems.
So, how does one apply these techniques effectively in order to drive hypothesis testing and, eventually, the extraction of actionable insights?
What are the steps that should be followed?
What is the impact of visualisation tools in this process?
How should we sample from graphs?
What tools should one use or be familiar with?
Furthermore, how can scalable and high-quality production-ready solutions be implemented that apply Graph Analytics, giving direct access to visualisations and on-demand analytics’ dashboards, which can serve as an intuitive and amenable means of information interpretation?
In this talk, we present and discuss the different categories of graph analytics and their areas of application. In addition, to address the above questions, we define a methodology for the application of these analytics technologies through example use-cases, studying the steps that need to be followed before assumptions can be confirmed and insights can be extracted.
Finally, we will discuss how distributed programming models, such as Spargel, have been developed to allow for the adoption of graph analytics algorithms by frameworks like Apache Spark and Apache Flink; the challenges and limitations that come with their adoption by these frameworks, and how one can build scalable distributed graph analytics solutions using them.
Big Data, a space adventure - Mario Cartia - Codemotion Rome 2015Codemotion
Codemotion Rome 2015 - I Big Data sono indubbiamente tra i temi più "caldi" del panorama tecnologico attuale. Ad oggi nel mondo sono stati prodotti circa 5 Exabytes di dati che costituiscono una potenziale fonte di "intelligenza" che è possibile sfruttare, grazie alle tecnologie più recenti, in svariati ambiti che spaziano dalla medicina alla sociologia passando per il marketing. Il talk si propone, tramite una gita virtuale nello spazio, di introdurre i concetti, le tecniche e gli strumenti che consentono di iniziare a sfruttare il potenziale dei Big Data nel lavoro quotidiano.
Data visualization is the representation of data through use of common graphi...samarpeetnandanwar21
Data and information visualization (data viz/vis or info viz/vis)[2] is the practice of designing and creating easy-to-communicate and easy-to-understand graphic or visual representations of a large amount[3] of complex quantitative and qualitative data and information with the help of static, dynamic or interactive visual items. Typically based on data and information collected from a certain domain of expertise, these visualizations are intended for a broader audience to help them visually explore and discover, quickly understand, interpret and gain important insights into otherwise difficult-to-identify structures, relationships, correlations, local and global patterns, trends, variations, constancy, clusters, outliers and unusual groupings within data (exploratory visualization).[4][5][6] When intended for the general public (mass communication) to convey a concise version of known, specific information in a clear and engaging manner (presentational or explanatory visualization),[4] it is typically called information graphics.
Data visualization is concerned with visually presenting sets of primarily quantitative raw data in a schematic form. The visual formats used in data visualization include tables, charts and graphs (e.g. pie charts, bar charts, line charts, area charts, cone charts, pyramid charts, donut charts, histograms, spectrograms, cohort charts, waterfall charts, funnel charts, bullet graphs, etc.), diagrams, plots (e.g. scatter plots, distribution plots, box-and-whisker plots), geospatial maps (such as proportional symbol maps, choropleth maps, isopleth maps and heat maps), figures, correlation matrices, percentage gauges, etc., which sometimes can be combined in a dashboard.
Machine Learning Foundations for Professional ManagersAlbert Y. C. Chen
20180804@Taiwan AI Academy, Hsinchu
6 hour lecture for those new to machine learning, to grasps the concepts, advantages and limitations of various classical machine learning methods. More importantly, to learn the skills to break down large complicated AI projects into manageable pieces, where features and functionalities could be added incrementally and annotated data accumulated. Take home message: machine learning is always a delicate balance between model complexity M and number of data N so that the trained classifier generalizes well and does not overfit.
AMIA 2015 Visual Analytics in Healthcare Tutorial Part 1David Gotz
A concise introduction to the topic of visualization. Designed for beginners with no prior experience with visualization. These slides were the first part of a half-day tutorial on Visual Analytics held in conjunction with the 2015 AMIA Annual Symposium. It was sponsored by the AMIA Visual Analytics Working Group. For more information, please see www.visualanalyticshealthcare.org or contact the author of the slides: David Gotz @ http://gotz.web.unc.edu
Graph Analytics (or network analytics), is an area of analysis with numerous applications that increasingly draws more and more attention. From fraud detection and money laundering, illegal transactions and other forms of financial crime, to identify key influencers in social networks, communities of frequently interacting individuals and route optimisation or even bioinformatics; graph analytics offer a vast variety of solutions that keep on evolving on a daily basis; allowing for experts in various fields to tackle every day challenges, extract insights and drive decision making.
Mainly, Graph Analytics is divided into 4 categories:
* Path Analytics;
* Connectivity Analytics;
* Centrality Analytics, and;
* Community Detection analytics;
each of which relies on different algorithms and address different problems.
So, how does one apply these techniques effectively in order to drive hypothesis testing and, eventually, the extraction of actionable insights?
What are the steps that should be followed?
What is the impact of visualisation tools in this process?
How should we sample from graphs?
What tools should one use or be familiar with?
Furthermore, how can scalable and high-quality production-ready solutions be implemented that apply Graph Analytics, giving direct access to visualisations and on-demand analytics’ dashboards, which can serve as an intuitive and amenable means of information interpretation?
In this talk, we present and discuss the different categories of graph analytics and their areas of application. In addition, to address the above questions, we define a methodology for the application of these analytics technologies through example use-cases, studying the steps that need to be followed before assumptions can be confirmed and insights can be extracted.
Finally, we will discuss how distributed programming models, such as Spargel, have been developed to allow for the adoption of graph analytics algorithms by frameworks like Apache Spark and Apache Flink; the challenges and limitations that come with their adoption by these frameworks, and how one can build scalable distributed graph analytics solutions using them.
Big Data, a space adventure - Mario Cartia - Codemotion Rome 2015Codemotion
Codemotion Rome 2015 - I Big Data sono indubbiamente tra i temi più "caldi" del panorama tecnologico attuale. Ad oggi nel mondo sono stati prodotti circa 5 Exabytes di dati che costituiscono una potenziale fonte di "intelligenza" che è possibile sfruttare, grazie alle tecnologie più recenti, in svariati ambiti che spaziano dalla medicina alla sociologia passando per il marketing. Il talk si propone, tramite una gita virtuale nello spazio, di introdurre i concetti, le tecniche e gli strumenti che consentono di iniziare a sfruttare il potenziale dei Big Data nel lavoro quotidiano.
Data visualization is the representation of data through use of common graphi...samarpeetnandanwar21
Data and information visualization (data viz/vis or info viz/vis)[2] is the practice of designing and creating easy-to-communicate and easy-to-understand graphic or visual representations of a large amount[3] of complex quantitative and qualitative data and information with the help of static, dynamic or interactive visual items. Typically based on data and information collected from a certain domain of expertise, these visualizations are intended for a broader audience to help them visually explore and discover, quickly understand, interpret and gain important insights into otherwise difficult-to-identify structures, relationships, correlations, local and global patterns, trends, variations, constancy, clusters, outliers and unusual groupings within data (exploratory visualization).[4][5][6] When intended for the general public (mass communication) to convey a concise version of known, specific information in a clear and engaging manner (presentational or explanatory visualization),[4] it is typically called information graphics.
Data visualization is concerned with visually presenting sets of primarily quantitative raw data in a schematic form. The visual formats used in data visualization include tables, charts and graphs (e.g. pie charts, bar charts, line charts, area charts, cone charts, pyramid charts, donut charts, histograms, spectrograms, cohort charts, waterfall charts, funnel charts, bullet graphs, etc.), diagrams, plots (e.g. scatter plots, distribution plots, box-and-whisker plots), geospatial maps (such as proportional symbol maps, choropleth maps, isopleth maps and heat maps), figures, correlation matrices, percentage gauges, etc., which sometimes can be combined in a dashboard.
Introduction, Terminology and concepts, Introduction to statistics, Central tendencies and distributions, Variance, Distribution properties and arithmetic, Samples/CLT, Basic machine learning algorithms, Linear regression, SVM, Naive Bayes
TIME STUDY: Time Study, Definition, time study equipment, selection of job, steps in time study. Breaking jobs into elements, recording information. Rating & standard Rating, standard performance, scale of rating, factors of affecting rate of working, allowances and standard time determination. Predetermined motion time study – Method time measurement (MTM)
MICRO AND MEMO MOTION STUDY: Charts to record moment at work place – principles of motion economy, classification of moments two handed process chart, SIMO chart, and micro motion study. Development, definition and installation of the improved method, brief concept about synthetic motion studies. Management
Unit 2 :
Work Study: Definition, objective and scope of work study. Human factors in work study. Work study and management.
Method Study: Definition, objective and scope of method study, activity recording and exam aids. Charts to record moments in shop operation – process charts, flow process charts, travel charts and multiple activity charts.
Work Study: Definition, objective and scope of work study. Human factors in work study. Work study and management.
Method Study: Definition, objective and scope of method study, activity recording index aids. Charts to record moments in shop operation – process charts, flow process charts, travel charts and multiple activity charts.
Industrial Management: Meaning, Definition, Objective, Need, Scope, Evolution and developments.
Productivity: Definition of productivity, Measurement of productivity, factors affecting the productivity, productivity improvement programs.
Industrial Management: Meaning, Definition, Objective, Need, Scope, Evolution and developments.
Productivity: Definition of productivity, Measurement of productivity, factors affecting the productivity, productivity improvement programs.
Industrial Management: Meaning, Definition, Objective, Need, Scope, Evolution and developments.
Productivity: Definition of productivity, Measurement of productivity, factors affecting the productivity, productivity improvement programs.
Industrial Management: Meaning, Definition, Objective, Need, Scope, Evolution and developments.
Productivity: Definition of productivity, Measurement of productivity, factors affecting the productivity, productivity improvement programs.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
4. • we focus on visualization=information visualization
• Data→Visualization→Analysis
• Related concepts
• Computer graphics: Data is not necessary present, analysis is not normally assumed
• Example: Computer games
• Scientific visualization: similar to information visualization, often engineering data,
statistical/machine learning analysis is normally not assumed
• Example: Industrial robots
27. • Which graphs can be used for analysis of my data?
• How to create these graphs?
• How should these graphs be analysed?
• How to make these graphs looking good enough for publication or presentation?
28. • Human sight = primary resource for information understanding
• Visualization is often the quickest way for data understanding
• The way of data visualization may affect decision making dramatically
29. Decision here: population does not
increase so much, no intervention
needed
Decision here: population
increases quickly, intervention is
required
Visual perception problem
30. Source: Elting Linda S, Martin Charles
G, Cantor Scott B, Rubenstein Edward
B. Influence of data display formats on
physician investigators' decisions to
stop clinical trials: prospective trial with
repeated measures BMJ 1999; 318
:1527
31. • Visualization for exploration
• Clusters
• Trends
• Anomalies
• …
• Confirmatory visualization
• Example 1: Perform linear regression, analyse residuals→ was linear regression reasonable
• Example 2: Discover clusters by K-means, visualize clusters→ are they clusters actually?
• Visualization for presentation
33. • Human visual system has limitations
• These limitations may lead to wrong/incomplete analysis of graphs
• Understanding how we see→ better displays
• Misleading graphics needs to be avoided
34. • Color= hue + saturation + value (lightness)
• 8% of males are color deficient→ what are good colors?
https://henrydangprg.com/2016/06/26/color-detection-in-python-with-opencv/
38. • Viewing raw data is often prefered
• Sometimes some preprocessing is needed
• Missing values and Data cleaning
• Discard the bad record →may remove almost all data
• Assign sentinel value
• Column mean imputation
• Nearest neighbor imputation
• Other imputations
39. • Normalization
• Converting column to range [0,1]. Useful in for ex. color mapping
• Centering and scaling 0/1
• Nonlinear transformations: log, sqrt
• Segmentation
• Split data according to some column
40. • Sampling, subsetting and expanding
• Random sampling reduces size of data and facilitates overplotting (for ex. scatterplots)
• Interpolation: linear (one dimension), bilinear (two dimensions), nonlinear. Select necessary
amount of intrepolation points.
• Dimension reduction
• PCA
• MDS
• Other techniques (ex. ICA, Autoencoders), welcome to Machine Learning...
41. • Mapping nominal dimensions to numbers
• Random mapping should never be done unless intrinsic ordering is present
• Use other numeric variables to measure in the data to measure “closeness” of values in the
nominal variable
• Correspondence analysis
• Aggregation and summarization
1. Grouping observations
2. Computing summary statistics per group
42. • Smoothing and filtering
• Replace original values with a smoothed versions
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61. Commercial:
• SAS and SAS JMP – environment. Special visual tools are available (JMP), require separate license. Well documented.
Good even for large sets. SAS Enterprise Guide has many visual static tools
• Spotfire – Many static and interactive visualization tools
• Tableau– Many static and interactive visualization tools
• InfoScope – visualizing maps, interactive visualizations
• Infographics-
Free:
• R – programming language. Set of packages is constantly updated. A lot of statistical tools (even the newest methods)
Badly documented
• Plotly – a tool for interactive and dynamic graphics, R interface available
• Shiny - – a tool for R-based web applications using graphics
• GraphViz – visualization of graph data, coding needed
• Jigsaw – Text analysis
62. Tools for the web (used by web designers):
• ActionScript
• JavaScript
• Prefuse
• VTK
… much more references given in the course book
65. • R base: basic plotting, not
publication quality Grob package: low-level plotting
tools, new types of plots
Zhou, Lutong, and W. John Braun. "Fun with the r grid package." Journal of Statistics Education
18.3 (2010).
66. Ggplot2 package: based on
grammar of graphics, close to
publication quality
Plotly package: Ggplot2 + interactivity
67. Base R graphical procedures:
• plot(x,..) plots time series
• plot(x,y) scatter plot
• plot(x,y) followed by points(x,y) plots several
scatterplots in one coordinate system
• hist(x,..) plots a hitogram
• persp(x,y,z,…) creates surface plots
• cloud(formula,data..) creates 3D scatter plot
68. • Visualization for exploration
• Default settings
• Visualization for presentation for publication
• Higher quality graphics is required
• Improve the graph quality in the software (often requires quite a bit of
programming)
• Use postprocessing tools, such as Inkscape or Adobe Illustrator
70. • Install Inkscape
• http://inkscape.org/
• Inkscape is an open source, SVG-based
vector drawing program
• file format that Inkscape uses is
compact and quickly transmittable over
the Internet.
• Vector graphics: image is defined in
terms of lines, not pixels
• Benefit: can be enlarged without loss
of picture quality
1. Save your R plot as PDF and import it to
Inkscape
2. Make changes and export your plot as a
PNG-file or save it as PDF.
Bitmap image and vector
image (enlarged)
71. • Menu bar (Important: File, Object, Path, Text, View→Grid)
• Command bar (Zoom to fit page, Edit object’s colors)
• Tool controls
• Tool box (Select and transform, zoom, Text, Eraser, Fill)
• Color palette
• Status bar
72. • Course book, chapters 1.1, 1.3-1.8 and 2
• Manual to InkScape: http://tavmjong.free.fr/INKSCAPE/MANUAL/html/index.php
