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Intro to Data Visualization with R & ggplot2
 
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The R programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity is due, in part, to R’s rich and powerful data visualization capabilities. While tools like Excel, Power BI, and Tableau are often the go-to solutions for data visualizations, none of these tools can compete with R in terms of the sheer breadth of, and control over, crafted data visualizations. As an example, R’s ggplot2 package provides the R programmer with dozens of print-quality visualizations – where any visualization can be heavily customized with a minimal amount of code. In this webinar Dave Langer will provide an introduction to data visualization with the ggplot2 package. The focus of the webinar will be using ggplot2 to analyze your data visually with a specific focus on discovering the underlying signals/patterns of your business. Attendees will learn how to: • Craft ggplot visualizations, including customization of rendered output. • Choose optimal visualizations for the type of data and the nature of the analysis at hand. • Leverage ggplot2’s powerful segmentation capabilities to achieve “visual drill-in of data”. • Export ggplot2 visualizations from RStudio for use in documents and presentations. Repository: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Data%20Visualization%20with%20R%20and%20ggplot2 -- Learn more about Data Science Dojo here: https://hubs.ly/H0hz6V50 Watch the latest video tutorials here: https://hubs.ly/H0hz6W80 See what our past attendees are saying here: https://hubs.ly/H0hz5ZJ0 -- Like Us: https://www.facebook.com/datasciencedojo/ Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data-science-dojo Also find us on: Instagram: https://www.instagram.com/data_science_dojo/ Vimeo: https://vimeo.com/datasciencedojo #rtutorial #datavisualization
Views: 112962 Data Science Dojo
Introduction to Data Science with R - Data Analysis Part 1
 
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Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 1020179 David Langer
ggplot2 Tutorial | ggplot2 In R Tutorial | Data Visualization In R | R Training | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This "ggplot2 Tutorial" by Edureka is a comprehensive session on the ggplot2 in R. This tutorial will not only get you started with the ggplot2 package, but also make you an expert in visualizing data with the help of this package. This tutorial will comprise of these topics: 1) Base R Graphics 2) Grammar of Graphics 3) GGPLOT2 package Check out our R Playlist: https://goo.gl/huUh7Y Subscribe to our channel to get video updates. Hit the subscribe button above. #R #Rtutorial #Ronlinetraining #ggplot2 #ggplotinr How it Works? 1. This is a 5 Week Instructor led Online Course, 30 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course edureka's Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc. During our Data Analytics with R Certification training, our instructors will help you: 1. Understand concepts around Business Intelligence and Business Analytics 2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others 3. Apply various supervised machine learning techniques 4. Perform Analysis of Variance (ANOVA) 5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc 6. Use various packages in R to create fancy plots 7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights - - - - - - - - - - - - - - - - - - - Who should go for this course? This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics. - - - - - - - - - - - - - - - - Why learn Data Analytics with R? The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists. Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 40511 edureka!
R Tutorial: Data Visualization in R
 
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Introduction video to Data Visualization in R course by Ron Pearson. Learn more about the course here: https://www.datacamp.com/courses/data-visualization-in-r R supports four different graphics systems: base graphics, grid graphics, lattice graphics, and ggplot2. Base graphics is the default graphics system in R, the easiest of the four systems to learn to use, and provides a wide variety of useful tools, especially for exploratory graphics where we wish to learn what is in an unfamiliar dataset. Take Ron's course here: https://www.datacamp.com/courses/data-visualization-in-r
Views: 7733 DataCamp
Data Visualization In R | Data Science Tutorial | Simplilearn
 
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This video will teach you how to visualise your data using. R has several systems for making graphs, but ggplot2 is one of the most elegant and most versatile. It implements the grammar of graphics, a coherent system for describing and building graphs. With this, you can do more faster by learning one system and applying it in many places. Data Science Certification Training - R Programming: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Data-Visualization-_WyUme_H2ZQ&utm_medium=SC&utm_source=youtube What are the course objectives? This course will enable you to: 1. Gain a foundational understanding of business analytics 2. Install R, R-studio, and workspace setup. You will also learn about the various R packages 3. Master the R programming and understand how various statements are executed in R 4. Gain an in-depth understanding of data structure used in R and learn to import/export data in R 5. Define, understand and use the various apply functions and DPLYP functions 6. Understand and use the various graphics in R for data visualization 7. Gain a basic understanding of the various statistical concepts 8. Understand and use hypothesis testing method to drive business decisions 9. Understand and use linear, non-linear regression models, and classification techniques for data analysis 10. Learn and use the various association rules and Apriori algorithm 11. Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: IT professionals looking for a career switch into data science and analytics Software developers looking for a career switch into data science and analytics Professionals working in data and business analytics Graduates looking to build a career in analytics and data science Anyone with a genuine interest in the data science field Experienced professionals who would like to harness data science in their fields Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 3803 Simplilearn
Data Manipulation and Visualization with R | Examples using dplyr & ggplot2 Packages
 
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Data Manipulation and Visualization with R R File link: https://goo.gl/nRmkwr Data file: https://goo.gl/UMYMZR For fiftystater and colorplaner, run following lines: devtools::install_github("wmurphyrd/fiftystater") devtools::install_github("wmurphyrd/colorplaner") R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 8355 Bharatendra Rai
R programming for beginners – statistic with R (t-test and linear regression) and dplyr and ggplot
 
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R programming for beginners - This video is an introduction to R programming. I have another channel dedicated to R teaching: https://www.youtube.com/c/rprogramming101 In this video I provide a tutorial on some statistical analysis (specifically using the t-test and linear regression). I also demonstrate how to use dplyr and ggplot to do data manipulation and data visualisation. Its R programming for beginners really and is filled with graphics, quantitative analysis and some explanations as to how statistics work. If you’re a statistician, into data science or perhaps someone learning bio-stats and thinking about learning to use R for quantitative analysis, then you’ll find this video useful. Importantly, R is free. If you learn R programming you’ll have it for life. This video was sponsored by the University of Edinburgh. Find out more about their programmes at http://edin.ac/2pTfis2 This channel focusses on global health and public health - so please consider subscribing if you’re someone wanting to make the world a better place – I’d love to you join this community. I have videos on epidemiology, study design, ethics and many more.
Data visualization using R studio
 
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In this video, you will learn how to construct line graph for time series data and adjust the attributes of the graphs and for interacting with the graph that will enable you to produce quality publication level graphical display. R is a free software and you can download it from the link given below https://www.r-project.org/ Downlaod link for R studio https://www.rstudio.com/products/rstudio/download/ Visit blog for more details: https://agroninfotech.blogspot.com/2018/06/visualizing-graphs-using-r.html Video contains: 1:00 Labeling the axis and adding a title 1:14 Changing the plot type and plotting symbol 1:34 PCH argument 1:58 Overlaying lines and line types 2:40 Line pattern 3:33 Using different colors for points and lines 4:00 Changing the format of the text Get connnected with us on ___________________________________________ G+: https://plus.google.com/u/1/101269397606526645442 Facebook page: https://www.facebook.com/AgronInfoTech/?ref=bookmarks Twitter: https://twitter.com/AgronInfoTech Linked In: https://www.linkedin.com/in/agron-info-tech-7429a6156/ Instagram: https://www.instagram.com/agroninfo/ ____________________________________________ If you have any question please comment below. Thanks for watching this video.
Views: 1083 AGRON Info-Tech
Intro to Data Analysis / Visualization with Python, Matplotlib and Pandas | Matplotlib Tutorial
 
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Python data analysis / data science tutorial. Let’s go! For more videos like this, I’d recommend my course here: https://www.csdojo.io/moredata Sample data and sample code: https://www.csdojo.io/data My explanation about Jupyter Notebook and Anaconda: https://bit.ly/2JAtjF8 Also, keep in touch on Twitter: https://twitter.com/ykdojo And Facebook: https://www.facebook.com/entercsdojo Outline - check the comment section for a clickable version: 0:37: Why data visualization? 1:05: Why Python? 1:39: Why Matplotlib? 2:23: Installing Jupyter through Anaconda 3:20: Launching Jupyter 3:41: DEMO begins: create a folder and download data 4:27: Create a new Jupyter Notebook file 5:09: Importing libraries 6:04: Simple examples of how to use Matplotlib / Pyplot 7:21: Plotting multiple lines 8:46: Importing data from a CSV file 10:46: Plotting data you’ve imported 13:19: Using a third argument in the plot() function 13:42: A real analysis with a real data set - loading data 14:49: Isolating the data for the U.S. and China 16:29: Plotting US and China’s population growth 18:22: Comparing relative growths instead of the absolute amount 21:21: About how to get more videos like this - it’s at https://www.csdojo.io/moredata
Views: 292718 CS Dojo
Data Visualization Using R
 
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How to create histograms, density plots, boxplots, box and whisker plots, scatterplots, scatterplots matrices, fancy scatterplot matrices, conditional scatterplots, dotplots, qqplots, and access plot components.
Views: 39457 How To R
15 Useful R packages for Data Visualization
 
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This video will give you a brief overview of 15 useful interdisciplinary R visualization packages. I have listed below all the links and reference projects sites reviewed in the video. To find out more about the package, type help(package = "packagename") in your R console once you've installed it. My list of R packages for data viz : ggplot2 - CRAN http://ggplot2.org/ patchwork - GitHub https://github.com/thomasp85/patchwork ggiraph - CRAN http://davidgohel.github.io/ggiraph/ dygraphs - CRAN https://rstudio.github.io/dygraphs/index.html googleVis - CRAN https://github.com/mages/googleVis metricsgraphics - GitHub http://hrbrmstr.github.io/metricsgraphics/ taucharts - GitHub https://github.com/hrbrmstr/taucharts RColorBrewer - CRAN http://cran.r-project.org/web/packages/RColorBrewer/index.html Shiny - CRAN http://shiny.rstudio.com/ flexdashboard - CRAN https://rmarkdown.rstudio.com/flexdashboard/ rcdimple - GitHub https://github.com/timelyportfolio/rcdimple plotly - CRAN https://plot.ly/r/ Highcharter - CRAN http://jkunst.com/highcharter/ echarts4r - GitHub http://echarts4r.john-coene.com/index.html geofacet - CRAN https://github.com/hafen/geofacet If I’ve missed any of your favorite R data viz packages in this short list, let me know! comment below.
R Tutorial #2: Economic and Financial Data Visualization in R/RStudio
 
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Video content: ○ Discussion of packages used: 0:50 ○ Pulling in the data we'll use: 2:30 ○ Line Chart: 5:00 ○ Histogram: 16:35 ○ Scatter: 25:00 ○ Scatter with interaction: 31:30 ○ Scatter with interaction and animation: 38:06 ○ Chloropleth (map/geographic): 40:30 ○ Basic OHLC: 49:45 ○ OHLC with volume and RSI: 53:55 ○ Recap: 1:08:13 You can find the code I wrote here: https://github.com/joelalcedo/codewithjoel_r/tree/visualization ...but I recommend programming along with the video! ↓↓↓ Resources ↓↓↓ My first video on data wrangling: https://www.youtube.com/watch?v=S0n3o0HwNPU Install RStudio here: https://www.rstudio.com/products/rstudio/download/ Install R here: https://cran.r-project.org/ Learn more about tidyverse here: https://www.tidyverse.org/ Learn about the wbstats package here: https://cran.r-project.org/web/packages/wbstats/README.html Learn about quantmod here: https://www.quantmod.com/ Learn about plotly here: https://plot.ly/ Learn about highcharter here: http://jkunst.com/highcharter/ Check out these viridis color scales! https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html Don't forget to like 👍, comment 💬 and subscribe 🔔 if you learned something new!
Views: 1244 Joel Alcedo
Getting Started with Spatial Data Analysis in R
 
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Spatial and spatial-temporal data have become pervasive nowadays. We are constantly generating spatial data from route planners, sensors, mobile devices, and computers in different fields like Transportation, Agriculture, Social Media. These data need to be analyzed to generate hidden insights that can improve business processes, help fight crime in cities, and much more. Simply creating static maps from these data is not enough. In this webinar we shall look at techniques of importing and exporting spatial data into R; understanding the foundation classes for spatial data; manipulation of spatial data; and techniques for spatial visualization. This webinar is meant to give you introductory knowledge of spatial data analysis in R needed to understand more complex spatial data modeling techniques. In this webinar, we will cover the following topics: -Why use R for spatial analysis -Packages for spatial data analysis -Types of spatial data -Classes and methods in R for spatial data analysis -Importing and exporting spatial data -Visualizing spatial data in R
Views: 49659 Domino Data Lab
Data Visualization using ggplot2: Part 1
 
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Data Visualization in R using ggplot2
Views: 34 The Rise Academy
ggplot, Visualization in R: from basics to advanced plots
 
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Stanford SCI01: Introduction to Data Science You can access the slides from the following link: https://drive.google.com/file/d/1biwZi04XFmuV1jgvQAUtcXbSQLJO6O8D/view?usp=sharing
Social Network Analysis with R | Examples
 
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Social network analysis with several simple examples in R. R file: https://goo.gl/CKUuNt Data file: https://goo.gl/Ygt1rg Includes, - Social network examples - Network measures - Read data file - Create network - Histogram of node degree - Network diagram - Highlighting degrees & different layouts - Hub and authorities - Community detection R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 23475 Bharatendra Rai
Introduction to Data Science with R - Data Analysis Part 2
 
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Part 2 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 148100 David Langer
Text Analysis and Data Visualization Using R  - 3/22/2018
 
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Ball State’s Digital Scholarship Lab hosted a workshop on doing text analysis (and other forms of data visualization) with R. R is a powerful statistical software that can be employed to analyze textual data, which can include novels, newspaper articles, interview transcripts, and collections of tweets. R enables researchers to detect patterns of language and meaning in large and complex sets of texts. The workshop introduced participants to methods for analyzing and creating plots from textual data (e.g. most frequent words) using the R statistical programming language.
Introduction to Data Visualization with ggplot2
 
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The R programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity is due, in part, to R’s rich and powerful data visualization capabilities. While tools like Excel, Power BI, and Tableau are often the go-to solutions for data visualizations, none of these tools can compete with R in terms of the sheer breadth of, and control over, crafted data visualizations. As an example, R’s ggplot2 package provides the R programmer with dozens of print-quality visualizations – where any visualization can be heavily customized with a minimal amount of code. In this webinar Arham Akheel will provide an introduction to data visualization with the ggplot2 package. The focus of the webinar will be using ggplot2 to analyze your data visually with a specific focus on discovering the underlying signals/patterns of your business. Attendees will learn how to: • Craft ggplot visualizations, including customization of rendered output. • Choose optimal visualizations for the type of data and the nature of the analysis at hand. • Leverage ggplot2’s powerful segmentation capabilities to achieve “visual drill-in of data”. • Export ggplot2 visualizations from RStudio for use in documents and presentations. Repository: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Data%20Visualization%20with%20R%20and%20ggplot2 Software: https://www.rstudio.com/products/rstudio/download2/ Dataset: https://www.kaggle.com/c/titanic/data About the Presenter: Arham holds a Masters degree in Technology Management from Texas A&M University and has a background of managing information systems. -- Learn more about Data Science Dojo here: https://hubs.ly/H0hC0n50 Watch the latest video tutorials here: https://hubs.ly/H0hB_Y_0 See what our past attendees are saying here: https://hubs.ly/H0hB_Zh0 -- Like Us: https://www.facebook.com/datasciencedojo/ Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data-science-dojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo/ Vimeo: https://vimeo.com/datasciencedojo
Views: 3157 Data Science Dojo
Introduction to Data Science with R - Data Analysis Part 3
 
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Part 3 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 66616 David Langer
Jeffrey Heer - Interactive Data Analysis: Visualization and Beyond
 
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Data analysis is a complex process with frequent shifts among data formats, tools and models, as well as between symbolic and visual thinking. How might the design of improved tools accelerate people's exploration and understanding of data? Covering both interactive demos and principles from academic research, my talk will examine how to craft a careful balance of interactive and automated methods, combining concepts from data visualization, machine learning, and computer systems to design novel interactive analysis tools. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 8079 PyData
Data Visualization with R by Matthew Renze
 
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R is a popular open-source programming language for data analysis. Its interactive programming environment and data visualization capabilities make R an ideal tool for creating professional data visualizations. This session will provide an introduction to the R programming language using RStudio. In addition, we will demonstrate how we can use R to create data visualizations to transform our data into actionable insight. Matthew is a data science consultant with over 17 years of professional experience. He is an international public speaker, an author for Pluralsight, a Microsoft MVP, an ASPInsider, and an open-source software contributor. His interests include data analytics, data visualization, and machine learning.
Views: 765 Devoxx
Data visualization in R | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) The nuances in data can be both analyzed and visualized using R language. R helps in creating data visualizations that provide insights about the data metrics. Following are the contents that have been covered in this video: 1.Data Visualization 2.Library (Corrgram) 3.Graphs in R 4.Spatial Analysis 5.Spatial Analysis: Example 6.Spatial Analysis using Deducer GUI 7.Data Visualization: Facets 8.Interactive Graphs in R (Demo) 9.Tableau Software – Public Related Posts: http://www.edureka.co/blog/core-data-scientist-skills/ http://www.edureka.co/blog/r-for-marketing-professionals/ Edureka is a New Age e-learning platform that provides Instructor-Led Live Online classes for learners who would prefer a hassle free and self paced learning environment, accessible from any part of the world. The topics, related to Data Visualization, have been widely covered in our course ‘Business Analytics with R’. For more information, please write back to us at [email protected] Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004
Views: 15548 edureka!
Data Visualization in R Part-1
 
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https://sites.google.com/site/raibharatendra/home/data-visualization Shows how to make graphs and charts in R. Examples include histogram, pie chart, bar chart, scatter plot, box plot, etc. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 8859 Bharatendra Rai
Dashboards with R | Interactive Data Visualization including  Plots, Data Tables and Pivot Charts
 
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Interactive Dashboards with multiple pages in R. Data File: https://goo.gl/7PLsYx R Markdown File: https://goo.gl/a1bHYK Includes, - Interactive plots - Interactive data tables - Interactive pivot tables - Interactive geographic plots R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 14568 Bharatendra Rai
Data Visualization with R: global climate change
 
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leonawicz.github.io github.com/leonawicz twitter.com/leonawicz linkedin.com/in/leonawicz blog.snap.uaf.edu An example animation of modeled historical and projected global temperature change. The data analysis, processing and generation of still frames for the video is done using R. Typically an ensemble of models would be used but this video demonstrates a basic animation using one climate model, both with a monthly time series and a monthly moving average time series. If wondering about the y-axis range, the animation shows anomalies, or delta change, from the climate model's historical baseline monthly average temperatures using a given climatology window. In a later video I will use annual and seasonal averages, which will display a smoother signal than monthly series.
Views: 6887 Matthew Leonawicz
Data Visualization in R  Part-2
 
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Visualization using R includes examples of, - Multiple plots - Color coded scatter plots - Motion charts (googleVis package) - Geographic chart - World (googleVis package) - Geographic chart - States (googleVis package) - Network diagram - Interactive pivot chart Visualizing data is an important tool related to analyzing big data or data science. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 5489 Bharatendra Rai
Vinayak Hegde - Data analysis and Visualization using R
 
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Vinayak gives a brief talk about what he is going to cover at this workshop for fifthelephant.in. The Fifth Elephant is a conference on Big Data organized by HasGeek. http://hasgeek.com/
Views: 99 HasGeek TV
Data Visualization and R, part 4, lattice and the small multiple
 
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Ryan Womack, Data Librarian Rutgers University http://ryanwomack.com twitter: @ryandata https://github.com/ryandata/DataViz Screencast version of a workshop at Rutgers University. This segment reviews the use of the lattice package in R for data analysis, including the display of "small multiples" of data in Trellis panels, as well as the historical role of William Cleveland.
Views: 2156 librarianwomack
Ashton Drew  | Interactive Data Visualization Tools in R
 
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PyData Carolinas 2016 An introduction to the R packages that generate interactive table and graphics. The focus will be on R Shiny, but I will also summarize value of other options, such as leaflet, plotly, ggvis, and rCharts. With interactive and reactive data visualizations, your audience directly engages with your data for stronger communication and better understanding. The Shiny apps can easily be launched directly to the web via shinyapps.io or Shiny Server to be run by anyone (they don't need to download your data or have R). This course assumes participants can already perform data analysis and visualization in R, but want to expand their skills with R Shiny. Therefore, the class exercises focus on transcribing code from a static to an interactive presentation of data products and information. Students must bring their own laptop with a current version of R and R Studio. Outline: - Overview of available interactive tools in R - Exercise to build a simple Shiny app - Shiny setup and orientation - Introduction to Shiny code structure - Overview of basic widgets - Example of conditional reactivity
Views: 4066 PyData
Linear Regression Algorithm | Linear Regression in R | Data Science Training | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Edureka Linear Regression tutorial will help you understand all the basics of linear regression machine learning algorithm along with examples. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial: 1) Introduction to Machine Learning 2) What is Regression? 3) Types of Regression 4) Linear Regression Examples 5) Linear Regression Use Cases 6) Demo in R: Real Estate Use Case Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LinearRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 71411 edureka!
Data Visualization and R, part 1, Outline
 
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Ryan Womack, Data Librarian Rutgers University http://ryanwomack.com twitter: @ryandata https://github.com/ryandata/DataViz Part 1 provides a brief outline of what is and is not covered by this series.
Views: 3836 librarianwomack
Day 2: Data exploration and visualization
 
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The video elaborates on Data Exploration and Visualization in R. The main package used is lattice and with some summary functions like tapply, table etc. The R Script and dataset used for the tutorial is provided in below link https://drive.google.com/open?id=0B37uVuAnn1eUbWFmNWxTMTF4LXM The session is an initiative by Shashi online classes. Shashi Kumar, Arun Sharma and Ankit Shaw are the core mentors. This session is conducted by Ankit. For more information, you can reach out to them on their on below link Ankit Shaw - https://www.linkedin.com/in/ankit-shaw-2b098681/ Shashi Kumar - https://www.linkedin.com/in/shashi-kumar-078877a7/ Arun Sharma - https://www.linkedin.com/in/arun-sharma-786a7378/
Views: 860 Shashi
Introduction to Exploring Social Network Structure with Visualization in R
 
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This video is meant as an introductory excursion into using the research software R to explore social network structure through visualization. Examples of visualization by layout, by node arrangement, and by changing visual node characteristics are provided. Recorded for the University of Maine at Augusta.
Views: 9450 James Cook
PLOTCON 2016: Irene Ros, Text is data! Analysis and visualization methods
 
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Text is one of the most interesting and varied data sources on the web and beyond, but it is one of the most difficult to deal with because it is fundamentally a messy, fragmented, and unnormalized format. If you have ever wanted to analyze and visualize text, but don’t know where to get started, this talk is for you. Irene will go through examples of text visualization approaches and the analysis methods required to create them. Irene is an information visualization researcher and developer creating engaging, informative and interactive data-driven interfaces and visualizations. Irene's career in data and its visual forms started at The Visual Communication Lab @ IBM Research and now continues at Bocoup where she is the Director of the Data Visualization team. Irene is also the organizer and program co-chair of Bocoup's OpenVis Conf, a two day conference about the practice of data visualization on the open web.
Views: 3707 Plotly
Basic Data Visualisation Techniques
 
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Learn basic data visualization techniques in this tutorials. For Training & Study packs on Analytics/Data Science/Big Data, Contact us at [email protected] Find all free videos & study packs available with us here: http://analyticsuniversityblog.blogspot.in/ SUBSCRIBE TO THIS CHANNEL for free tutorials on Analytics/Data Science/Big Data/SAS/R/Hadoop
Views: 14031 Analytics University
Data Mining with R & RStudio - KMeans Clustering and Visualization
 
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Simple overview of data mining with R and RStudio.
Views: 3332 Gaurav Jetley
R Tutorial: Data Visualization in R (part 4)
 
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Part 4 of our Data Visualization in R course by Ron Pearson. Learn more about the course here: https://www.datacamp.com/courses/data-visualization-in-r As we have seen, base R graphics provides tremendous flexibility in creating plots with multiple lines, points of different shapes and sizes, and added text, along with arrays of multiple plots. If we attempt to add too many details to a plot or too many plots to an array, however, the result can become too complicated to be useful. This chapter focuses on how to manage this visual complexity so the results remain useful to ourselves and to others.
Views: 706 DataCamp
Careers in Data Analytics and Visualization
 
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In this webinar, Andy Catlin, Director of the Katz School's Data Analytics and Visualization program discusses the many career opportunities available in the growing field of data analytics. The M.S. in Data Analytics and Visualization is a 30-credit Master's degree offered in-person or fully online. Visit yu.edu/katz, email [email protected], or call 833-241-4723 for more information.
Skills Needed For Data Scientist and Data Analyst
 
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In this Video, We will be discussing about the skills needed for data analyst and data scientist roles. The reason for making one video to discuss both data analyst and data scientist roles is because there are a lot things in common between both these two role. Data Analyst does a lot of descriptive analytics. On the other hand, Data Scientist also does descriptive analytics. But also data scientists do something called predictive analytics. So let's try to understand what Descriptive and Predictive analytics mean. Descriptive Analytics is all about analyzing the historical data to answer this particular question which is "WHAT HAS HAPPENED TILL NOW??". Predictive Analytics also involves analysis of historical data but, predictive analytics is mainly all about answering the question which is.. "WHAT WILL HAPPEN IN THE FUTURE??" Let's understand this with a simple example. I have sales data of XYZ company in a table format. As part of descriptive analytics, we can simply create a scatter chart so that we can quickly understand how the company has been performing in terms of sales in the previous years. Now let's look at predictive analytics. So now that we know how the company has been performing in the previous years, can we predict what's gonna happen to the sales in the coming years?.. Will the sales increase, or decrease or does it remain the same??.. If we are able to answer these questions, then it is called as predictive analytics. So coming back to the comparison of Data Analyst and Data Scientist roles, Now that we have some idea about the differences between the two roles, lets now look at skills needed for each of these two roles. Data Analysts should be good with Math and Statistics. They should be good with handling the data. -- This includes knowledge of ETL (or Extract Transform and Load) operations on data and experience working with popular ETL tools such as Informatica – PowerCenter,IBM – Infosphere Information Server, alteryx, Microsoft – SQL Server Integrated Services (SSIS), Talend Open Studio, SAS – Data Integration Studio ,SAP – BusinessObjects Data Integrator, QlikView Expressor or any other popular ETL tool. -- They should be comfortable in handling data from different sources and in different formats such as text, csv, tsv, excel, json, rdbms and others popular formats. -- They should have excellent knowledge of SQL (or Structured Query Language). Its a Bonus to have -- The knowledge of Big data tools and technologies to handle large data sets. -- NoSQL databases such as HBase, Cassandra and MongoDB. They should be expert in Analysing and Visualizing the data. -- They Should have experience working with popular data analysis and visualization packages in python and R such as numpy, scipy, pandas, matplotlib, ggplot and others. -- Experience with popular data analysis and visualization BI tools such as Tableau, Microsoft Power BI, SAP BI, SAS BI, Oracle BI, QlikView or any other popular BI tool They should have good communication and storytelling skills. Lets now look at the skills needed for data scientist role. Data scientist also does descriptive analytics just like data analysts. Apart from that, they also do predictive analytics. So as part of Descriptive analytics: Data Scientists should be excellent with Math and Statistics. Data scientists should be good with handling data -- So yes, they should have experience working with popular ETL frameworks. -- They should have excellent knowledge of SQL. -- Many companies expect data scientists to have mandatory knowledge of big data tools and technologies to work with large datasets and also to work with structured, semi-structured and unstructured data. -- Its good to have the knowledge of NoSQL databases such as HBase, Cassandra and MongoDB. They should be expert in Analysing and Visualizing the data. -- Experience working with popular data analysis and visualization packages in python and R. -- Experience with popular data analysis and visualization BI tools such as Tableau, Microsoft Power BI, SAP BI, SAS BI, Oracle BI, QlikView or any other popular BI tool. They should also have excellent communication and storytelling skills. And as part of predictive analytics, They should be good in using the techniques in artificial intelligence, data mining, machine learning, and statistical modeling to make future predictions using the historical data. Exposure to popular predictive analytics tools such as SAP Predictive analytics, Minitab, SAS Predictive Analytics, Alteryx Analytics, IBM predictive analytics or any other popular predictive analytics tool. They should have very good exposure to popular machine learning and deep learning packages available for Python and R such as scikit learn, tensorflow, theano,rpart, caret, randomForest, nnet, and other popular libraries.
Views: 58587 Art of Engineer
R - Sentiment Analysis and Wordcloud with R from Twitter Data | Example using Apple Tweets
 
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Provides sentiment analysis and steps for making word clouds with r using tweets about apple obtained from Twitter. Link to R and csv files: https://goo.gl/B5g7G3 https://goo.gl/W9jKcc https://goo.gl/khBpF2 Topics include: - reading data obtained from Twitter in a csv format - cleaning tweets for further analysis - creating term document matrix - making wordcloud, lettercloud, and barplots - sentiment analysis of apple tweets before and after quarterly earnings report R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 20585 Bharatendra Rai
Twitter data analysis visualization using R language Hadoop projects
 
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Views: 45 Phdtopic com