Are you looking to explore your 3D data in R? With the help of the 'rgl' package, you can create interactive and visually stunning 3D plots to better understand your data. Here's a step-by-step guide on how to view 3D data in R.

First, you'll need to install the 'rgl' package, which is used for creating 3D visualizations in R. You can do this by running the following command in your R console:

```R

install.packages('rgl')

```

Once the package is installed, you can load it into your R session using the following command:

```R

library(rgl)

```

Now that you have the 'rgl' package loaded, you can start creating 3D plots. Let's start by creating a simple 3D scatterplot using sample data. We'll use the 'iris' dataset, which is included in R by default. Here's an example of how to create a 3D scatterplot of the 'iris' dataset:

```R

data(iris)

plot3d(iris$Sepal.Length, iris$Sepal.Width, iris$Petal.Length, col = as.numeric(iris$Species))

```

In this example, we used the 'plot3d' function to create a 3D scatterplot of the sepal length, sepal width, and petal length of the iris flowers. We also specified the color of the points based on the species of the flowers.

The 'rgl' package provides various options for customizing your 3D plots. You can rotate, zoom, and interact with the plot to explore the data from different angles. Additionally, you can add labels, titles, and annotations to make your 3D plots more informative and visually appealing.

Furthermore, you can create more complex 3D visualizations, such as 3D surface plots, wireframe plots, and 3D density plots, to gain deeper insights into your data.

In conclusion, the 'rgl' package in R enables you to create interactive and advanced 3D visualizations of your data. By following the steps outlined in this guide, you can effectively view and analyze 3D data in R, allowing for a more comprehensive understanding of your datasets.