There are quite a few reasons for this. When you add R to your solution, a vast opportunity of analytics opens up like statistics, predictive data modelling, forecasting, machine learning, visualization and much more.
R is developed by statisticians, scientists or professional analysts using the script but the reports and the results generated by them on the desktop can be easily emailed or presented in the form of presentation, but that is limiting the business use and other potential uses.
1. Deploy R open
Through Deploy R opens you can easily embed results of various R functions like- data and charts into any application. This specific structure is an open source server-based system planned especially for R, which makes it simple to call the R code at a real time.
As I have already mentioned that R is an open source analytical software, it can create high dimensional data visualizations. Ggplot2 is a standout among the most downloaded bundle that has helped R to accomplish best quality level as a data visualization tool.
• Information is perused into R
• Data is handled (and conceivably controlled) by R
• Information is mapped to plotting highlights and rendered
Now let us discuss some of the data visualization packages:
• r d3 package
It is really very easy to create plots in R, but you may ask me whether it is same for creating custom plots, the answer is “yes”, and that is the primary motivation behind why ggplot came into existence. With ggplot, you can make complex multi-layered designs effectively.
Here you can start plotting with axes then add points and lines. But the only drawback that it has it is relatively slower than base R, and new developers might find it difficult to learn.
The leaflet has found its profound use in GIS (mapping), this is an open source library. The R packages that backings this is composed and kept up by RStudio and ports. Using this developer can create pop up text, custom zoom levels, tiles, polygon, planning and many more.
Lattice helps in plotting visualized multivariate data. Here you can have tilled plots that help in comparing values or subgroups of a given variable. Here you will discover numerous lattice highlights has been acquired as utilizes grid package for its usage. The underlying logic used by lattice is very much similar to base R.
For the graphical representation of nodes and edges, the visual network is referred. Vis.js is a standout amongst the most famous library among numerous that can do this sort of plotting. visNetwork is the related with R package for this.
Network plots ought to be finished remembering nodes and edges. For visNetwork, these two should be separated into two different data frames one for the nodes and the other
This is another visualization tool which is very similar to D3. You can use this tool for a variety of plots like line, spline, arealinerange, column range, polar chart and many more. For the commercial use of Highcarter, you need to get a license while for the non-commercial you don’t need one.
Highcarter library can be accessed very easily using various chart () functions. Using this function, you can create a plot in a single task. This function is very much similar to qplot() of ggplot2 of D3. chart () can produce different types of scenarios depending on the data inputs and specifications.
• RColor Brewer
With this package, you can use color for your plots, graphs, and maps. This package works nicely with schemes.
It is a well distinguish podium for data visualization that works inordinately with R and Python notebook. It has similarity with the high career as both are known for interactive plotting. But here you get some extra as it offers something that most of the package don’t like contour plots, candlestick chart, and 3d charts.
It is the way for representing data visualization as it nicely describes the sequence of events. The diagram that it produces speaks about itself. You don’t need an explanation for the chart as it is self-explanatory.
For creating three-dimensional plots in R you should check out RGL. It has comparability with lattice, and on the off chance that you are an accomplished R developer you will think that its simple.
Some of the visualization function three are as follows:
• Graphjs: this is used for implementing 3D interactive data visualization. This function accepts igraph as the first argument. This manages definition for nodes and edges.
• Scatterplot3js: this function is used for creating three dimensional scatter plot.
Shiny is created and maintained by R Studio. It is a software application development instrument, to a great extent employed for making wise interfaces with R. R shiny tutorial will take in more about shiny.
Shiny is a podium for facilitating R web development.