**Modern Data » R**, and kindly contributed to R-bloggers)

Recently you may have seen how to build a 3d surface plot with Plotly and IPython notebook. Here you can learn the basics of creating a 3d surface plot with Plotly in RStudio.

Just add the Plotly library into your RStudio environment then add a Plotly username and key:

`install.packages("plotly")`

library(plotly)

py <- plotly()

set_credentials_file(username = 'your_username', key = 'your_key')

To create a surface plot, use two vectors: x and y of length m and n, and a matrix: z of size m*n. In this example, x and y both consist of 100 points ranging from -5 to 4.9.

`x_vec = c(seq(-5, 4.9, 0.1))`

*note this results in the same dimensions (1 column X 100 rows) as specifying:

`x_matrix = matrix(c(x_vec), nrow = 100, ncol = 1)`

The size of x is 1 column with 100 rows. In order to multiply x * y to create matrix z with 100 columns and 100 rows, y should be 100 columns with 1 row.

`y_matrix = matrix(c(x_vec), nrow = 1, ncol = 100)`

To multiply the vertical and horizontal vectors to create matrix z in RStudio, the basic syntax is `z = x %*% y`

. In this example, a function is applied to z to create waves. Below, dimensions x, y, and z are defined. `y`

used here is different than `y1`

used above because y should be the default, 1 column vector, not 1 row x 100 columns. Type is defined as “surface”.

`data <- list(`

x = x_vec,

y = x_vec,

z = matrix(c(cos(x_matrix %*% y_matrix) + sin(x_matrix %*% y_matrix)), nrow = 100, ncol = 100),

type = "surface")

Finally, specify layout information and filename:

`layout <- list(`

title = "Waaaves in r",

scene = list(bgcolor = "rgb(244, 244, 248)"))

```
```

`response <- py$plotly(data,`

kwargs = list(

layout = layout,

filename = "waves example",

fileopt = "overwrite"))

The result will be similar to the interactive 3d plot displayed below:

Creating dashboards or visualizations at your company? Consider Plotly Enterprise for modern intracompany graph and data sharing.

**leave a comment**for the author, please follow the link and comment on their blog:

**Modern Data » R**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...