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In a previous post-Google Analytics, R & Plot.ly, I shared an example of how to create a heatmap throught Plot.ly using Google Analytics data.
Recently, the Plot.ly team has released another function called 'ggplotly'. It a super-simple-yet-amazing way to render a ggplot graph (built locally in R) in Plot.ly. But before I share how to do this, I'd like to also show in more detail how to built and embedd a Plot.ly plot as an iframe within a single page using RMarkdown and Plot.ly's R API. That way, you'll be able to more fully understand the process and be ready to customize this dashboard-style template for your own use.
I. Plot.ly + R Markdown Example
First, let's set the RMarkdown options and load all the packages we need. The nitty-gritty details behind what's needed and why are beyond the scope of this post. If you have any questions or need some further help, I'd be more than happy to lend a hand in any way I can. Just leave a comment below or shoot me an email at my Contact page
Once we have all the R packages we need loaded, we can then setup our plot.ly R API connection:
Paste the following below the previous r code chunk (i.e. - add it to your R Markdown document in the inline r code syntax so it's rendered as html by knitr).
Then, paste the iframe code in between a pair of backticks outside of an r code chuck so it's rendered within as html within the RMarkdown file once knitted:
`r I(plotly_iframe)`
II. R Markdown, Plot.ly + ggplotly Example
In this second example, we will use the data from above (x, y1, y2) to first create a standard plot with ggplot2 then show how easy it is to re-create it with Plot.ly.
Send ggplot plot to Plot.ly using ggplotly. It seems too easy to be true, doesn't it? ;-)
I'd like to personally thank Matt Sundquist for inspiring this post and the Plot.ly team for their hardwork in putting together a great foundation for an open source data visualization and collaboration. I'd also like to thank all those involved and supporting the underlying packages and software required to make all this possible.
Once again, I'd be more than happy to lend a hand in any way I can. Just leave a comment below or shoot me an email at my Contact page
Thanks for stopping by,
Justin
P.S. - Check back soon for another post that details the use of this format in a real world e-commerce analysis example.
Credits:
Edit, 3/28/18: RStudio just announced Python interoperability through the reticulate package. Rmd Notebooks are unbeatable, in my opinion.
Original Post:
I started using Jupyter Notebooks back when they were called IPython. I even remember having to set up a virtual Linux environment because they were not available on Windows. As much as I have enjoyed their functionality, I recently switched entirely to R Markdown in an RStudio environment. Here’s why.
Exporting to Github
The github_document option in R Markdown is the perfect way to share your analysis over the web. Just write the markdown with R chunks, “knit” it, and commit everything to a repository, and RStudio will create a directory with all plots and outputs ready to view. One advantage Jupyter Notebooks used to hold was the aesthetic formatting of tables and dataframes on Github. But with the kable function in the knitr package, I have been able to output HTML tables that look just as clean.
I updated a previous Gist to show how this works on Github. Compare that finished product with the raw code here.
Exporting to Word is a breeze
Rmarkdown Equivalent In Python
A lot of my analysis is for non-technical clients, many of whom are most comfortable with a Word doc. I have found the process of translating code to .docx to be exquisitely easy. The hardest part was inserting page breaks, but thankfully someone come up with a clever hack.
Version control
As others have pointed out, version control of a Jupyther Notebook is not a straightforward process. R Markdown is all plain text, so changes are easy to track in Git. For the first time, I feel in control of changes made to my Word documents.
RStudio
I had the R kernel working nicely in Jupyter Notebooks. I did not encounter a lot of errors. But the entire time I was developing, I missed all of the features of RStudio. In particular, I missed having tab completion and the correct indentation on dplyr chains. But I also missed the layout with my environment, history, console, etc., not to mention the View() function on dataframes, and running code line-by-line. RStudio has become an amazing, feature-rich IDE, which I am not anxious to trade for another environment.
Python is here (sort of)
At least since 2004, it is possible to run Python scripts in RStudio. There is even syntax highlighting and other IDE features. The nice thing is, the knitr library has support for other languages, so you can include Python chunks in, for example, your Word doc.
There are problems with this, as u/_Wintermute noted. Apparently, the variables do not transfer between “chunks,” let alone languages. The docs for the Python language engine describe a data exchange, which is exciting, but it looks like you have to write the dataframe in one language and then read it in the other. These are not exactly seamless transitions.
Unknown Unknowns
Python Markdown Code
I’m certain there are a lot of great features of both platforms that I am totally unaware of. And I still think Jupyter is doing more to integrate the various data science languages. It’s possible I will switch back, but for now I seem to be getting more locked in to RStudio. At the moment I am researching how to convert my website into R Markdown. It just works really well.