What is the most popular visualization package used in R?

Top 10 R Packages For Data Visualisation

• 1| Colourpicker. About: Colourpicker is a tool for Shiny framework and for selecting colours in plots.
• 2| Esquisse.
• 3| ggplot2.
• 4| ggvis.
• 5| ggforce.
• 6| Lattice.
• 7| Plotly.
• 8| patchwork.

Which R package should you use for data visualization?

My favorite R packages for data visualization and munging

Is R used for data visualization?

R is an amazing platform for data analysis, capable of creating almost any type of graph. This book helps you create the most popular visualizations – from quick and dirty plots to publication-ready graphs. The text relies heavily on the ggplot2 package for graphics, but other approaches are covered as well.

Which packages are used to manipulate a data set?

List of Packages

• dplyr.
• data. table.
• ggplot2.
• reshape2.
• readr.
• tidyr.
• lubridate.

Why is R good for data visualization?

It helps tremendously in doing any exploratory data analysis as well as feature engineering. This is where R offers incredible help. R Programming offers a satisfactory set of inbuilt function and libraries (such as ggplot2, leaflet, lattice) to build visualizations and present data.

What are the common types of data visualization?

Common general types of data visualization:

• Charts.
• Tables.
• Graphs.
• Maps.
• Infographics.
• Dashboards.

What are the top your libraries for data visualization?

So let’s check out some of these Top R Libraries for Data Visualization that are commonly used these days. ggplot2 is an R data visualization library that is based on The Grammar of Graphics. ggplot2 can create data visualizations such as bar charts, pie charts, histograms, scatterplots, error charts, etc. using high-level API.

What is the most important package in your for data science?

The 10 Most Important Packages in R for Data Science. 1 1. ggplot2. ggplot2 is based on the ‘Grammar of Graphics”, which is a popular data visualization library. Graphs with one variable, two variables, and 2 2. data.table. 3 3. dplyr. 4 4. tidyr. 5 5. Shiny.

What are the best your packages for predictive models?

Following is the list of 60 or so R packages which help take care of different aspects when working to create predictive models: caret: Stands for Classification And REgression Training. Provides a set of functions which could be used to do some of the following when working with classification and regression problems.

What are the tools used for data visualization?

Visualization: Represent packages used for visualization. ggplot2: One of the best tools for data visualization, ggplot2 could be used to create plots, layer-by-layer, using data from different data sources. knitr: An alternative tool to Sweave, Knitr provides methods for dynamic report generation.