Embedding R Graphs in Jupyter Notebooks

This tutorial should help you get up and running with embedding R charts inside a Jupyter notebook.

Install Python

Head on over to https://www.python.org/downloads/ and install Python.

Install Jupyter

Simply run the following command in your console:

pip install jupyter
 
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Use pip3 for python 3.x. See here for more details.

Install IRKernel

Next we'll install a R Kernel so that we can use R commands inside a Jupyter notebook. This is similar to installing a R package. Run the following code in your R session:

install.packages(c('repr', 'IRdisplay', 'pbdZMQ', 'devtools'))
devtools::install_github('IRkernel/IRkernel')
IRkernel::installspec()
 
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See here for details.

Install Pandoc

Pandoc is required to successfully render an R chart in a Jupyter notebook. You could either:

• Download and install Pandoc from here.
• Or use the *.exe files in \bin\pandoc from your R-Studio installation folder.

Make sure that both pandoc.exe and pandoc-citeproc are available in your local python installation folder (or Jupyter environment if you have setup a separate environment).

Run Jupyter

Run this in the terminal / console:

jupyter notebook
 
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You should see something like this pop up in a new browser window:

Create a notebook

Click on New >> R to create a new Jupyter notebook using the R kernel.

You should now have something like this:

Examples:

Here are some examples on how to use Plotly's R graphing library inside of a Jupyter notebook.

# Scatter Plot
library(plotly)

set.seed(123)

x <- rnorm(1000)
y <- rchisq(1000, df = 1, ncp = 0)
group <- sample(LETTERS[1:5], size = 1000, replace = T)
size <- sample(1:5, size = 1000, replace = T)

ds <- data.frame(x, y, group, size)

p <- plot_ly(ds, x = x, y = y, mode = "markers", split = group, size = size) %>%
  layout(title = "Scatter Plot")
embed_notebook(p)

# Filled Line Chart
library(plotly)
library(PerformanceAnalytics)

#Load data
data(managers)

# Convert to data.frame
managers.df <- as.data.frame(managers)
managers.df$Dates <- index(managers)

# See first few rows
head(managers.df)

# Plot
p <- plot_ly(managers.df, x = ~Dates, y = ~HAM1, type = "scatter", mode = "lines", name = "Manager 1", fill = "tonexty") %>%
  layout(title = "Time Series plot")
embed_notebook(p)

# Heatmap
library(plotly)
library(mlbench)

# Get Sonar data
data(Sonar)

# Use only numeric data
rock <- as.matrix(subset(Sonar, Class == "R")[,1:59])
mine <- as.matrix(subset(Sonar, Class == "M")[,1:59])

# For rocks
p1 <- plot_ly(z = rock, type = "heatmap", showscale = F)

# For mines
p2 <- plot_ly(z = mine, type = "heatmap", name = "test") %>%
  layout(title = "Mine vs Rock")

# Plot together
p3 <- subplot(p1, p2)
embed_notebook(p3)