R Graphics Tutorial Series
 06/01/2017
 659
 0 Like
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During one of the recent R workshops I conducted in my organization, one of my colleague asked for the reference material for "Graphics using R" and I thought of putting together this series of blogs which would help readers delve into the details of 'R Graphics' starting from the basics of Graphics.
This tutorial series comprises of 6 Blogs on 'R Graphics' inspired from an excellent book R in Action by Rob Kabacoff (This book not only comprises of some excellent material on R basics but also on understanding the nittygritty of Graphics using R.). While there are lot of excellent R packages for creating some amazing graphs, the focus of these blogs has been on R Base Graphics and 'ggplot2' package.
Here is a summary of the 6 Blogs for your perusal 
 R : Graphics Tutorial Series ( Part 1 ) : This blog focuses on the various graphical features available in "R Base Graphics" like basic plots, text, colors, Axes, Legends, annotations etc. using Base Graphics (Remember : R comes with builtin functionality for charts and graphs, typically referred to as base graphics. Then there are R packages that extend functionality.).
 R : Graphics Tutorial Series ( Part 2 ) : Here you would learn plotting different types of graphs/plots in R using BaseGraphics, specifically graphical methods for displaying relationships between two variables (bivariate relationships). For example, you will read about BaseGraphics plots like BarPlots, Density Plots, Box Plots, Dot Plots etc.
 R : Graphics Tutorial Series ( Part 3 ) : This is an extension of the Part 2 which begins with scatter plots and scatter plot matrices. Thereafter, it talks about line charts of various types. These approaches are well known and widely used in research. It also covers basics of 3D graphs using 3D scatterplots.
 R : Graphics Tutorial Series ( Part 4 ) : In this blog, you’ll learn the use of correlograms for visualizing correlations and mosaic plots for visualizing multivariate relationships among categorical variables.

R : Graphics Tutorial Series ( Part 5 ) : This blog introduces 'ggplot2' package beginning with the "Grammar of Graphics" using ggplot2 thereby getting into the details of creating various types of plots using ggplot2.
 R : Graphics Tutorial Series ( Part 6 ) : This blog summarizes previous 5 blogs through an interesting exercise towards reproducing a graph that appeared inEconomist sometime back. This blog has been inspired from the Harvard Labs class of Introduction to R Graphics.
I wanted to write another one for sharing my thoughts on the "Interactive Graphics" using R as Interactive visualization allows deeper exploration of data than static plots. With the help of Javascript libraries such as d3 you may explore data in numerous interesting ways. With the power of R , you may easily create interactive visualizations without knowing any javascript with minimal lines of R code using some cool packagesrCharts, plotly and googleVis . Not sure when I would write a detailed blog on "Interactive Graphics using R" (given my lassitude) , however, here is a summary of some of the interesting R packages used for Interactive Graphics 
 rCharts : This package is used to create, customize and publish interactive JavaScript visualizations from R.
 plotly – This converts ggplot2 figures to interactive plots easily. It is an interactive, browserbased charting library built on the open source JavaScript graphing library, plotly.js. It works entirely locally, through the HTML widgets framework
 htmlwidgets  It is one of the latest interactive data visualization technologies used to produce D3 graphics or Leaflet maps with few lines of code.
 googleVis  The googleVis package provides an interface between R and the Google Charts API. Google Charts offer interactive charts which can be embedded into web pages. The best known of these charts is probably the Motion Chart, popularised by Hans Rosling in his TED talks.
 iPlots  This is the R package which provides high interaction statistical graphics, written in Java. It offers a wide variety of plots, including histograms, barcharts, scatterplots, boxplots, fluctuation diagrams, parallel coordinates plots and spineplots. All plots support interactive features, such as querying, linked highlighting, color brushing, and interactive changing of parameters.
Also other libraries for creating interactive visualizations from R do exist, such as clickme,RIGHT, ggobi, iplots, gg2v, rVega, cranvas and r2d3. Some of these are not under active development anymore.The d3Network package is also worth checking if you need cool interactive network visualizations.
 06/01/2017
 659
 0 Like
R Graphics Tutorial Series
 06/01/2017
 659
 0 Like
Ankit Agarwal
Analytics Manager  Deloitte Advisory at Deloitte
Opinions expressed by Gladwin Analytics members are their own.
Top Authors
Ad: 50000 Data Science Jobs Globally  Over 10000 Hours of Free Data Science Video Tutorials: Register Now
During one of the recent R workshops I conducted in my organization, one of my colleague asked for the reference material for "Graphics using R" and I thought of putting together this series of blogs which would help readers delve into the details of 'R Graphics' starting from the basics of Graphics.
This tutorial series comprises of 6 Blogs on 'R Graphics' inspired from an excellent book R in Action by Rob Kabacoff (This book not only comprises of some excellent material on R basics but also on understanding the nittygritty of Graphics using R.). While there are lot of excellent R packages for creating some amazing graphs, the focus of these blogs has been on R Base Graphics and 'ggplot2' package.
Here is a summary of the 6 Blogs for your perusal 
 R : Graphics Tutorial Series ( Part 1 ) : This blog focuses on the various graphical features available in "R Base Graphics" like basic plots, text, colors, Axes, Legends, annotations etc. using Base Graphics (Remember : R comes with builtin functionality for charts and graphs, typically referred to as base graphics. Then there are R packages that extend functionality.).
 R : Graphics Tutorial Series ( Part 2 ) : Here you would learn plotting different types of graphs/plots in R using BaseGraphics, specifically graphical methods for displaying relationships between two variables (bivariate relationships). For example, you will read about BaseGraphics plots like BarPlots, Density Plots, Box Plots, Dot Plots etc.
 R : Graphics Tutorial Series ( Part 3 ) : This is an extension of the Part 2 which begins with scatter plots and scatter plot matrices. Thereafter, it talks about line charts of various types. These approaches are well known and widely used in research. It also covers basics of 3D graphs using 3D scatterplots.
 R : Graphics Tutorial Series ( Part 4 ) : In this blog, you’ll learn the use of correlograms for visualizing correlations and mosaic plots for visualizing multivariate relationships among categorical variables.

R : Graphics Tutorial Series ( Part 5 ) : This blog introduces 'ggplot2' package beginning with the "Grammar of Graphics" using ggplot2 thereby getting into the details of creating various types of plots using ggplot2.
 R : Graphics Tutorial Series ( Part 6 ) : This blog summarizes previous 5 blogs through an interesting exercise towards reproducing a graph that appeared inEconomist sometime back. This blog has been inspired from the Harvard Labs class of Introduction to R Graphics.
I wanted to write another one for sharing my thoughts on the "Interactive Graphics" using R as Interactive visualization allows deeper exploration of data than static plots. With the help of Javascript libraries such as d3 you may explore data in numerous interesting ways. With the power of R , you may easily create interactive visualizations without knowing any javascript with minimal lines of R code using some cool packagesrCharts, plotly and googleVis . Not sure when I would write a detailed blog on "Interactive Graphics using R" (given my lassitude) , however, here is a summary of some of the interesting R packages used for Interactive Graphics 
 rCharts : This package is used to create, customize and publish interactive JavaScript visualizations from R.
 plotly – This converts ggplot2 figures to interactive plots easily. It is an interactive, browserbased charting library built on the open source JavaScript graphing library, plotly.js. It works entirely locally, through the HTML widgets framework
 htmlwidgets  It is one of the latest interactive data visualization technologies used to produce D3 graphics or Leaflet maps with few lines of code.
 googleVis  The googleVis package provides an interface between R and the Google Charts API. Google Charts offer interactive charts which can be embedded into web pages. The best known of these charts is probably the Motion Chart, popularised by Hans Rosling in his TED talks.
 iPlots  This is the R package which provides high interaction statistical graphics, written in Java. It offers a wide variety of plots, including histograms, barcharts, scatterplots, boxplots, fluctuation diagrams, parallel coordinates plots and spineplots. All plots support interactive features, such as querying, linked highlighting, color brushing, and interactive changing of parameters.
Also other libraries for creating interactive visualizations from R do exist, such as clickme,RIGHT, ggobi, iplots, gg2v, rVega, cranvas and r2d3. Some of these are not under active development anymore.The d3Network package is also worth checking if you need cool interactive network visualizations.
 06/01/2017
 659
 0 Like
Ankit Agarwal
Analytics Manager  Deloitte Advisory at Deloitte
Opinions expressed by Gladwin Analytics members are their own.