Monday 6 July 2020
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How an IT Certification in R will Enhance your Knowledge About its Robust Libraries?

R is considered one of the most popular programming languages of the last decade which was primarily developed for statistical computing.

The best thing about R is that it provides multiple packages for several data sciences and machine learning tasks. As of now the number of available packages available in the CRAN repository of R packages is 12,550.

We all know how much we are dependent on Data. One can say:

“Data is no less than oxygen to survive.”

With growing data and its management, there is a need for all tech professionals and developers to enhance their skills by undertaking work specific IT Certifications which would be a big plus in your career field, thus improving your dexterity and performance. The certifications available, no doubt, stand you out but also gives a higher edge over the rests.

Top R Libraries and its Useful Packages

Here are the Top R libraries and packages that have set the standards for Data Visualization and Data Import Tasks for Web Development. With the help of these packages, you can perform any data visualization and data import task you can imagine, from analyzing security-breach to visualizing cancer genomes.

# ggplot2

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Creating standard points in R is very easy however it is challenging to make a custom plot. To solve this problem, ggplot2 was developed by skilled R developers and coders.

ggplot2 takes care of the tiny details which make plotting difficult. Producing complex multi-layered graphics is also more comfortable using ggplot2 as it provides a powerful model of graphics.

The library is based on the understanding that graphics are made of several layers and used together to create a plot.

In ggplot2, you need first to start creating your plot with the help of axes, and later you can add lines, points, and others details.

Many coders feel that ggplot2 is slower than base R and then the learning curve is a little bit steep. However, if you can master it, they would be beneficial for any data scientist working in R.

# Lattice

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Lattice is used to visualize multivariate data. It is a type of plotting which is influenced by the Trellis graphics.

With the help of Lattice, you can develop tiled panels of plots which look like a garden trellis and is used to compare different values of the same variable.

Lattice inherited many grid features as it was built using the grid package.

Many base R users feel familiar with the logic of Lattice as the grid has been folded into base R.

# highcharter

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Highcharter is an interactive visualization library with a robust API.

With the help of highcharter, dynamic charting has been made easy. It can draw any plots of different R object classes such as dendrogram, phylo and data frame.

The R developers and coders find it easy to access other well-known plot types like Highmaps which is used for schematic maps and Highstocks which is used for financial charting.

The themes available along with the library are straightforward to customize. It also has many built-in themes including “538”, “economist” and many more.

# Leaflet

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The leaflet is another charting library of R which is based on the previous JavaScript library of the same name.

With the help of Leaflet, you can build powerful yet lightweight interactive maps similar to the maps available on popular websites such as The Washington Post, The New York Times, CartoDB and many more.

The interface of the library is developed using the popular htmlwidgets framework which helps in integrating and controlling the maps efficiently in Shiny applications, RStudio, and R Markdown documents.

# RColorBrewer

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RColorBrewer is a type of library which helps in taking advantage of one of the best strength of R, i.e., manipulating colors in maps, plots, and graphs.

The library is inspired by the use of color in cartography in Cynthia Brewer’s work. It helps you to create sequential and qualitative color palettes.

# Plotly

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Ploty is a well-known online platform which is used for data visualization. But many developers and coders might not know that they can access its capabilities from any R notebook.

Similar to highcharter library, Ploty helps in designing interactive plot and graphs. However, some of the charts and figures such as 3D charts, candlestick, and contour charts are only available in the Plotty library.

# sunburstR

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Sunburst charts are widely used for data visualization. It helps to describe the form of events from user flows to sports data through a product.

With the help of this library, you can build several R charts including sequence visualizations which was designed by Kerry Rodden in the website d3.js.

The graphs and diagrams are very interactive and engaging which help users to explore the patterns and behavior of data themselves.


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RGl is mostly used to create interactive and engaging plots of R. It is modeled over the base R graphics in three dimensions instead of two.

Similar to the Lattice library, it is inspired by the grid package, but technically it is not compatible with it. Hence, expert R developers find the methods very similar to the Lattice library.

RGL has several popular features including lighting effects and a wide range of 3D shapes. It also can make an animation of any 3D scene.

# dygraphs

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Dygraphs is a powerful, fast and flexible library which provides an R interface for exploring time-series data sets.

It is very interactive and comes with many features such as mouse-over labels, panning, and zooming. Othe popular features of dygraphs include synchronization and range selector.

It also does not slow while handling huge datasets with millions of points. You can also use dygraphs along with RColorBrewer to select a wide range of color palette.

# Data.table

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Data.table is one of the most iconic libraries which is used for heavy-duty data wrangling. It helps to perform many operations with just a few lines of code.

It is also very fast, and doe not come with any performance or memory limitations.

The general structure of data.table is very concise – DT[i, j, by], where i and j represent respective columns and rows.

The Last Say

These are the top R libraries and packages which are used for data visualization and data import. The libraries have proven to be very helpful and efficient by several expert data science professionals and programmers. Apart from the list above, there are many other valuable R libraries available which are used for various data science and data analysis purposes.