R is an open source language for data analysis and graphics. It is platform-independent and allows users to load various packages as well as develop their own packages to interpret data better.This video is packed with practical recipes, designed to provide you with all the guidance needed to get to grips with data visualization with R.
We start - off with the basics of R plots and an introduction to heat maps and customizing them. After this, we gradually take you through creating interactive maps using the googleVis package. Finally, we generate choropleth maps and contouring maps, bubble plots, and pie charts.
About the Author
Atmajit Singh Gohil works as a senior consultant at a consultancy firm in New York City. After graduating, he worked in the financial industry as a Fixed Income Analyst.
He has a master's degree in financial economics from the State University of New York (SUNY), Buffalo. He also graduated with a Master of Arts degree in economics from the University of Pune, India. He loves to read blogs on data visualization and loves to go out on hikes in his free time.
R comes loaded with some basic packages, but the R community is rapidly growing and active R users are constantly developing new packages for R.
Everything in R is in the form of objects. Objects can be manipulated in R.
We will dive into R's capability with regard to matrices and edit (add, delete, or replace) elements of a matrix.
One of the useful and widely used functions in R is the data.frame() function.
Once we have processed our data, we need to save it to an external device or send it to our colleagues. It is possible to export data in R in many different formats.
Most of the tasks in R are performed using functions. A function in R has the same utility as functions in Arithmetic.
If we want to perform an action repeatedly in R, we can utilize the loop functionality.
R has some very handy functions, such as apply, sapply, tapply, and mapply, that can be used to reduce the task of writing complicated statements.
One quick and easy way to edit a plot is by generating the plot in R and then using Inkspace or any other software to edit it.
Scatter plots are used primarily to conduct a quick analysis of the relationships among different variables in our data.
We will display multivariate data on a scatter plot and also introduce interactive scatter plots.
The advantage of using the Google Chart API in R is the flexibility it provides in making interactive plots.
Line plots are simply lines connecting all the x and y dots. They are very easy to interpret and are widely used to display an upward or downward trend in data.
Gantt charts are used to track the progress of a project displayed against time.
Plot a histogram using the googleVis package and merge more than one histogram on the same page.
The advantage of the Google Chart API is the interactivity and the ease with which they can be attached to a web page.
The waterfall plots or staircase plots are observed mostly in financial reports.
This video helps you get introduced to the concept of dendrograms.
This video teaches you to create a plot which is easy to study and more informative.
Heat maps are a visual representation of data, wherein each value in a matrix is represented with a color. This video shows you how to create a heat map.
This video dives into plotting a heat map by customizing colors.
This video teaches you to integrate a dendrogram and heat map into a single plot.
R allows us to plot three-dimensional interactive heat maps using the heat map package.
Tree maps are basically rectangles placed adjacent to each other. The size of each rectangle is directly proportional to the data being used in the visualization.
We encounter maps on a daily basis, be it for directions or to infer information regarding the distribution of data. Maps have been widely used to plot various types of data in R.
Choropleth maps can be state level as well as county level. In this video, we will plot well-being data on a state level.
Contour maps are used to display data related to temperature or topographic information.
For each region, a bubble or a pie chart is used that represents percentage.
Overlaying maps with text is not a very prominent medium of displaying information.
The shapefile package in R can be used to read a shapefile, add the processed data to our shapefile, and then save it in the shapefile format.
The idea of a cartogram is to show the gravity of the issue or data being studied.
Pie charts are a great visualization technique to represent data and help viewers understand statistical data.
Labels are important as they give the information about the sections of the pie chart. We will include labels inside the pie chart in this video.
Donut charts have advantages over pie charts with respect to the area and efficiency in visualizing information.
Instead of using multiple pie charts for comparing data, we can use slope charts.
Fan plots are an alternative to pie charts and are useful in differential and comparative analysis.
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