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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.
- Welcome
This is the online version of “Modern Data Visualization...
- Preface
This is an illustrated guide for creating data...
- 1 Introduction
1.1 How to use this book. I hope that this book will provide...
- 2 Data Preparation
Basically, for each case with a missing value, the k most...
- 4 Univariate Graphs
The majority of participants are white, followed by black,...
- 5 Bivariate Graphs
5.1 Categorical vs. Categorical. When plotting the...
- 6 Multivariate Graphs
6.1 Grouping. In grouping, the values of the first two...
- 7 Maps
Let’s plot US states by Hispanic and Latino populations,...
- Welcome
• 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
These basic plots can be enhanced in many ways to be more informative. A corrgram (“correlation diagram”) allows the data to be rendered in a variety of ways, specified by panel functions. For even larger data sets, more abstract visual summaries are necessary to see the patterns of relationships.
There are a number of fantastic R/Data Science books and resources available online for free from top most creators and scientists. Here are such 13 free 21 free (so far) online data science books and resources for learning data analytics online from people like Hadley Wickham, Winston Chang, Garrett Grolemund and Johns Hopkins University ...
This cookbook contains more than 150 recipes to help scientists, engineers, programmers, and data analysts generate high-quality graphs quickly—without having to comb through all the details of R’s graphing systems.
20 Φεβ 2024 · In this section, we give an example of bar plots of the average value of mpg in different groups, with error bars. The use of error bars can indicate the variability or uncertainty in the data.
Bar charts can be displayed horizontally or vertically. The height or length of the bars are proportional to the values they represent. Use the barplot() function to draw a vertical bar chart: Use the col parameter to change the color of the bars: To change the bar texture, use the density parameter:
