Data Visualization

To create a simple plot, run a console in your favorite language and paste in the following code sample:

R

# A standard R plot 
plot(rnorm(1000)) 

# A ggplot2 plot 
library("ggplot2") 
qplot(hp, mpg, data=mtcars, color=am, 
facets=gear~cyl, size=I(3), 
xlab="Horsepower", ylab="Miles per Gallon")

Python 2

import matplotlib.pyplot as plt
import random
plt.plot([random.normalvariate(0,1) for i in xrange(1,1000)])

Cloudera Data Science Workbench processes each line of code individually (unlike notebooks that process code per-cell). This means if your plot requires multiple commands, you will see incomplete plots in the workbench as each line is processed.

To get around this behavior, wrap all your plotting commands in one Python function. Cloudera Data Science Workbench will then process the function as a whole, and not as individual lines. You should then see your plots as expected.

You can also display images, using a command in the following format:

R

library("cdsw") 

download.file("https://upload.wikimedia.org/wikipedia/commons/2/29/Minard.png", "/cdn/Minard.png") 
image("Minard.png")

Python 2

import urllib
from IPython.display import Image
urllib.urlretrieve("http://upload.wikimedia.org/wikipedia/commons/2/29/Minard.png", "Minard.png")

Image(filename="Minard.png")

Your code can generate and display HTML. To create an HTML widget, paste in the following:

R

library("cdsw") 
html('<svg><circle cx="50" cy="50" r="50" fill="red" /></svg>')

Python 2

from IPython.display import HTML
HTML('<svg><circle cx="50" cy="50" r="50" fill="red" /></svg>')

Scala

Cloudera Data Science Workbench allows you to build visualization libraries for Scala using jvm-repr. The following example demonstrates how to register a custom HTML representation with the "text/html" mimetype in Cloudera Data Science Workbench. This output will render as HTML in your workbench session.

//HTML representation
case class HTML(html: String)

//Register a displayer to render html 
Displayers.register(classOf[HTML],
  new Displayer[HTML] {
    override def display(html: HTML): java.util.Map[String, String] = {
      Map(
        "text/html" -> html.html
      ).asJava
    }
  })

val helloHTML = HTML("<h1> <em> Hello World </em> </h1>")
  
display(helloHTML)

Most visualizations require more than basic HTML. Embedding HTML directly in your console also risks conflicts between different parts of your code. The most flexible way to embed a web resource is using an IFrame:

R

library("cdsw")
iframe(src="https://www.youtube.com/embed/8pHzROP1D-w", width="854px", height="510px")

Python 2

from IPython.display import HTML
HTML('<iframe width="854" height="510" src="https://www.youtube.com/embed/8pHzROP1D-w"></iframe>')

You can generate HTML files within your console and display them in IFrames using the /cdn folder. The cdn folder persists and services static assets generated by your engine runs. For instance, you can embed a full HTML file with IFrames.

R

library("cdsw") 
f <- file("/cdn/index.html") 
html.content <- paste("<p>Here is a normal random variate:", rnorm(1), "</p>") 
writeLines(c(html.content), f) 
close(f) 
iframe("index.html")

Python 2

from IPython.display import HTML
import random

html_content  = "<p>Here is a normal random variate: %f </p>" % random.normalvariate(0,1)

file("/cdn/index.html", "w").write(html_content)
HTML("<iframe src=index.html>")

Cloudera Data Science Workbench uses this feature to support many rich plotting libraries such as htmlwidgets, Bokeh, and Plotly.

Cloudera Data Science Workbench supports native grid displays of DataFrames across several languages.

Python 3

import pandas as pd
pd.DataFrame(data=[range(1,100)])

For PySpark DataFrames, use pandas and run df.toPandas() on a PySpark DataFrame. This will bring the DataFrame into local memory as a pandas DataFrame.

R

In R, DataFrames will display as grids by default. For example, to view the Iris data set, you would just use:

iris

sparkly_df %>% as.data.frame

Scala

Calling the display() function on an existing dataframe will trigger a collect, much like df.show().

val df = sc.parallelize(1 to 100).toDF()
display(df)

Cloudera Data Science Workbench supports Markdown documentation of your code written in comments. This allows you to generate reports directly from valid Python and R code that runs anywhere, even outside Cloudera Data Science Workbench. To add documentation to your analysis, create comments in Markdown format:

R

# Heading
# -------
#
# This documentation is **important.**
#
# Inline math: $e^ x$
#
# Display math: $$y = \Sigma x + \epsilon$$

print("Now the code!")

Python

# Heading
# -------
#
# This documentation is **important.**
#
# Inline math: $e^ x$
#
# Display math: $$y = \Sigma x + \epsilon$$

print("Now the code!")