R is an open source language for data analysis and graphics that allows users to load various packages for effective and better data interpretation. Its popularity has soared in recent years because of its powerful capabilities when it comes to turning different kinds of data into intuitive visualization solutions.
This book is an update to our earlier R data visualization cookbook with 100 percent fresh content and covering all the cutting edge R data visualization tools. This book is packed with practical recipes, designed to provide you with all the guidance needed to get to grips with data visualization using R. It starts off with the basics of ggplot2, ggvis, and plotly visualization packages, along with an introduction to creating maps and customizing them, before progressively taking you through various ggplot2 extensions, such as ggforce, ggrepel, and gganimate. Using real-world datasets, you will analyze and visualize your data as histograms, bar graphs, and scatterplots, and customize your plots with various themes and coloring options. The book also covers advanced visualization aspects such as creating interactive dashboards using Shiny
By the end of the book, you will be equipped with key techniques to create impressive data visualizations with professional efficiency and precision.
Vitor Bianchi Lanzetta is an R enthusiast who uses R both for his thesis and in his spare time. Vitor fitted several neural networks models to predict commodity prices. As a graduate student he was called to join the university's team in the CFA Challenge; here, among other things he programmed a Monte Carlo simulation from his team's model by using R.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Vitor Bianchi Lanzetta is an R enthusiast who uses R both for his thesis and in his spare time. Vitor fitted several neural networks models to predict commodity prices. As a graduate student he was called to join the university's team in the CFA Challenge; here, among other things he programmed a Monte Carlo simulation from his team's model by using R.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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