Hits: 71

How to create 3D scatter plot in R

A 3D scatter plot is a graphical representation of a dataset that shows the relationship between three variables. It is a useful tool for visualizing the correlation between variables, identifying patterns, and comparing the distribution of different groups of data in three-dimensional space. In this blog post, we will discuss how to create 3D scatter plots in R.

To create a 3D scatter plot in R, you can use the scatterplot3d package. This package provides several functions that can be used to create 3D scatter plots, including scatterplot3d() and scatterplot3d.default(). These functions take three vectors of data as arguments, representing the x, y and z-axis of the plot. The functions also take several other arguments that can be used to customize the appearance of the plot, such as the color of the dots, the size of the dots, and the main title.

Another way to create a 3D scatter plot in R is by using the plotly package. This package provides a powerful and flexible way to create 3D scatter plots and other types of plots. To create a 3D scatter plot using plotly, you can use plot_ly() function and specify the x, y, and z variables, and also set the type of plot as scatter, and also set the mode as markers. The plotly package also provides several other arguments that can be used to customize the appearance of the plot, such as the color of the dots, the size of the dots, and the main title.

In summary, 3D scatter plots are a useful tool for visualizing the relationship between three variables and identifying patterns in data. They can be easily created in R using the scatterplot3d package or the plotly package. Both methods allow you to customize the appearance of the plot, such as the color of the dots, the size of the dots, and the main title.

In this Applied Machine Learning Recipe, you will learn: How to create 3D scatter plot in R.

How to create 3D scatter plot in R

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.