# Data visualization examples in r

## R Graphics Essentials for Great Data Visualization: +200 Practical Examples You Want to Know for Data Science by Alboukadel Kassambara

Data visualization is one of the most important part of data science. Many books and courses present a catalogue of graphics but they dont teach you which charts to use according to the type of the data. In this book, we start by presenting the key graphic systems and packages available in R, including R base graphs, lattice and ggplot2 plotting systems. Next, we provide more than 200 practical examples to create great graphics for the right data using either the ggplot2 package and extensions or the traditional R graphics.With this book, you ll learn:

- How to quickly create beautiful graphics using ggplot2 packages

- How to properly customize and annotate the plots

- Type of graphics for visualizing categorical and continuous variables

- How to add automatically p-values to box plots, bar plots and alternatives

- How to add marginal density plots and correlation coefficients to scatter plots

- Key methods for analyzing and visualizing multivariate data

- R functions and packages for plotting time series data

- How to combine multiple plots on one page to create production-quality figures.

## Easy way of Data visualization in R

## Data Visualization in R

With ever increasing volume of data, it is impossible to tell stories without visualizations. Data visualization is an art of how to turn numbers into useful knowledge. R Programming lets you learn this art by offering a set of inbuilt functions and libraries to build visualizations and present data. To determine which amongst these is best suited for your data, I suggest you should answer a few questions like,. Below is a great explanation on selecting a right chart type by Dr.

The world today is filled with data and it becomes imperative that we analyse it properly to gain meaningful insights.

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This chapter will teach you how to visualise your data using ggplot2. R has several systems for making graphs, but ggplot2 is one of the most elegant and most versatile. With ggplot2, you can do more faster by learning one system and applying it in many places. This chapter focusses on ggplot2, one of the core members of the tidyverse. To access the datasets, help pages, and functions that we will use in this chapter, load the tidyverse by running this code:.

If you've visited the CRAN repository of R packages lately, you might have noticed that the number of available packages has now topped a dizzying 12, This means there are packages for practically any data visualization task you can imagine, from visualizing cancer genomes to graphing the action of a book. For new R coders, or anyone looking to hone their R data viz chops, CRAN's repository may seem like an embarrassment of riches—there are so many data viz packages out there, it's hard to know where to start. While it's relatively easy to create standard plots in R, if you need to make a custom plot, things can get hairy fast. That's why ggplot2 was born: to make building custom plots easier.

## 5 thoughts on “R Graphics Essentials for Great Data Visualization: +200 Practical Examples You Want to Know for Data Science by Alboukadel Kassambara”

Data Visualisation is a vital tool that can unearth possible crucial insights The plot() function is a kind of a generic function for plotting of R objects.

This post presents a selection of 7 essential data visualizations, and how to recreate them using a mix of base R functions and a few common packages.

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If we need to be explicit about where a function (or dataset) comes from, we'll use the special form package::function(). For example, ggplotggplot() tells you.

What type of visualization to use for what sort of problem? This tutorial helps you .. Below example uses the same data prepared in the diverging bars example.