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Rmd to IPython Notebook and HTML

I had blogged earlier about a proof-of-concept for how to convert an R Markdown Notebook into an IPython Notebook. The idea was to allow R users to author reproducible documents in R Markdown, but be able to seamlessly convert into HTML or an IPython Notebook, which allows greater interactivity.

This is an update to the same post that makes use of the native R kernel for the IPython Notebook, being developed by @takluyver.

As noted earlier, the bulk of the work here is done by notedown, a python module that helps create IPython notebooks from markdown. The approach I have taken here is to convert the RMarkdown file into a Markdown file using a custom knitr script, and then using notedown to render it as an IPython notebook.

If you want to test this out, you will first need to install notedown

$ pip install notedown

The next step is to convert the Rmd source into html and ipynb. For the sake of convenience, I have added a Makefile that will do it all. Just run make all and it will create example.html using knit2html and example.ipynb using knitr and notedown. So you get two birds with one stone!

Now, to open the ipython notebook, you will need to install the native R kernel. Installation instructions can be found here. Once you have installed all dependencies, you can open the notebook from the command line using:

ipython notebook --KernelManager.kernel_cmd="['R', '-e', 'IRkernel::main(\'{connection_file}\')']"

The notebook will not contain any output, since I set eval = F while converting it. You will need to select Cell > Run All from the toolbar to automatically execute all cells and embed output. I am presuming there might be a mechanism in the IPython Notebook architecture to trigger this execution from the command line.

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<h2>Introduction to ggplot2</h2>
<a href="example.Rmd">Download</a>
<p>This is a short demo on how to convert an R Markdown Notebook into an IPython Notebook using knitr and notedown.</p>
<p>This is an introduction to <a href="http://github.com/hadley/ggplot2">ggplot2</a>. You can view the source as an R Markdown document, if you are using an IDE like RStudio, or as an IPython notebook, thanks to <a href="https://github.com/aaren/notedown">notedown</a>.</p>
<p>We need to first make sure that we have <code>ggplot2</code> and its dependencies installed, using the <code>install.packages</code> function.</p>
<p>Now that we have it installed, we can get started by loading it into our workspace</p>
<pre><code class="r">library(ggplot2)
</code></pre>
<p>We are now fully set to try and create some amazing plots. </p>
<h4>Data</h4>
<p>We will use the ubiqutous <a href="http://stat.ethz.ch/R-manual/R-patched/library/datasets/html/iris.html">iris</a> dataset.</p>
<pre><code class="r">head(iris)
</code></pre>
<pre><code>## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
</code></pre>
<h4>Simple Plot</h4>
<p>Let us create a simple scatterplot of <code>Sepal.Length</code> with <code>Petal.Length</code>.</p>
<pre><code class="r">ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) + geom_point()
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-3"/> </p>
<p>The basic idea in <code>ggplot2</code> is to map different plot aesthetics to variables in the dataset. In this plot, we map the x-axis to the variable <code>Sepal.Length</code> and the y-axis to the variable <code>Petal.Length</code>.</p>
<h4>Add Color</h4>
<p>Now suppose, we want to color the points based on the <code>Species</code>. <code>ggplot2</code> makes it really easy, since all you need to do is map the aesthetic <code>color</code> to the variable <code>Species</code>.</p>
<pre><code class="r">ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) + geom_point(aes(color = Species))
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-4"/> </p>
<p>Note that I could have included the color mapping right inside the <code>ggplot</code> line, in which case this mapping would have been applicable globally through all layers. If that doesn&#39;t make any sense to you right now, don&#39;t worry, as we will get there by the end of this tutorial.</p>
<h4>Add Line</h4>
<p>We are interested in the relationship between <code>Petal.Length</code> and <code>Sepal.Length</code>. So, let us fit a regression line through the scatterplot. Now, before you start thinking you need to run a <code>lm</code> command and gather the predictions using <code>predict</code>, I will ask you to stop right there and read the next line of code.</p>
<pre><code class="r">ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) + geom_point() + geom_smooth(method = &quot;lm&quot;,
se = F)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-5"/> </p>
<p>If you are like me when the first time I ran this, you might be thinking this is voodoo! I thought so too, but apparently it is not. It is the beauty of <code>ggplot2</code> and the underlying notion of grammar of graphics.</p>
<p>You can extend this idea further and have a regression line plotted for each <code>Species</code>.</p>
<pre><code class="r">ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) + geom_point() +
geom_smooth(method = &quot;lm&quot;, se = F)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-6"/> </p>
</body>
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Introduction to ggplot2

This is a short demo on how to convert an R Markdown Notebook into an IPython Notebook using knitr and notedown.

This is an introduction to ggplot2. You can view the source as an R Markdown document, if you are using an IDE like RStudio, or as an IPython notebook, thanks to notedown.

We need to first make sure that we have ggplot2 and its dependencies installed, using the install.packages function.

Now that we have it installed, we can get started by loading it into our workspace

library(ggplot2)

We are now fully set to try and create some amazing plots.

Data

We will use the ubiqutous iris dataset.

head(iris)
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

Simple Plot

Let us create a simple scatterplot of Sepal.Length with Petal.Length.

ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) + geom_point()

plot of chunk unnamed-chunk-3

The basic idea in ggplot2 is to map different plot aesthetics to variables in the dataset. In this plot, we map the x-axis to the variable Sepal.Length and the y-axis to the variable Petal.Length.

Add Color

Now suppose, we want to color the points based on the Species. ggplot2 makes it really easy, since all you need to do is map the aesthetic color to the variable Species.

ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) + geom_point(aes(color = Species))

plot of chunk unnamed-chunk-4

Note that I could have included the color mapping right inside the ggplot line, in which case this mapping would have been applicable globally through all layers. If that doesn't make any sense to you right now, don't worry, as we will get there by the end of this tutorial.

Add Line

We are interested in the relationship between Petal.Length and Sepal.Length. So, let us fit a regression line through the scatterplot. Now, before you start thinking you need to run a lm command and gather the predictions using predict, I will ask you to stop right there and read the next line of code.

ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) + geom_point() + geom_smooth(method = "lm", 
    se = F)

plot of chunk unnamed-chunk-5

If you are like me when the first time I ran this, you might be thinking this is voodoo! I thought so too, but apparently it is not. It is the beauty of ggplot2 and the underlying notion of grammar of graphics.

You can extend this idea further and have a regression line plotted for each Species.

ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) + geom_point() + 
    geom_smooth(method = "lm", se = F)

plot of chunk unnamed-chunk-6

## Introduction to ggplot2
[Download](example.Rmd)
This is a short demo on how to convert an R Markdown Notebook into an IPython Notebook using knitr and notedown.
This is an introduction to [ggplot2](http://github.com/hadley/ggplot2). You can view the source as an R Markdown document, if you are using an IDE like RStudio, or as an IPython notebook, thanks to [notedown](https://github.com/aaren/notedown).
We need to first make sure that we have `ggplot2` and its dependencies installed, using the `install.packages` function.
Now that we have it installed, we can get started by loading it into our workspace
```{r}
library(ggplot2)
```
We are now fully set to try and create some amazing plots.
#### Data
We will use the ubiqutous [iris](http://stat.ethz.ch/R-manual/R-patched/library/datasets/html/iris.html) dataset.
```{r}
head(iris)
```
#### Simple Plot
Let us create a simple scatterplot of `Sepal.Length` with `Petal.Length`.
```{r}
ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) +
geom_point()
```
The basic idea in `ggplot2` is to map different plot aesthetics to variables in the dataset. In this plot, we map the x-axis to the variable `Sepal.Length` and the y-axis to the variable `Petal.Length`.
#### Add Color
Now suppose, we want to color the points based on the `Species`. `ggplot2` makes it really easy, since all you need to do is map the aesthetic `color` to the variable `Species`.
```{r}
ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) +
geom_point(aes(color = Species))
```
Note that I could have included the color mapping right inside the `ggplot` line, in which case this mapping would have been applicable globally through all layers. If that doesn't make any sense to you right now, don't worry, as we will get there by the end of this tutorial.
#### Add Line
We are interested in the relationship between `Petal.Length` and `Sepal.Length`. So, let us fit a regression line through the scatterplot. Now, before you start thinking you need to run a `lm` command and gather the predictions using `predict`, I will ask you to stop right there and read the next line of code.
```{r}
ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) +
geom_point() +
geom_smooth(method = 'lm', se = F)
```
If you are like me when the first time I ran this, you might be thinking this is voodoo! I thought so too, but apparently it is not. It is the beauty of `ggplot2` and the underlying notion of grammar of graphics.
You can extend this idea further and have a regression line plotted for each `Species`.
```{r}
ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
geom_point() +
geom_smooth(method = 'lm', se = F)
```
#!/usr/bin/Rscript
library(knitr)
opts_chunk$set(eval = FALSE, tidy = FALSE)
knit('example.Rmd', output = 'example.md')
all: example.ipynb example.html
example.md: example.Rmd
./knit
example.ipynb: example.md
notedown example.md | sed '/%%r/d' > example.ipynb
example.html: example.Rmd
R -e "knitr::knit2html('example.Rmd')"
@lwaldron
Copy link

Thanks, this approach worked for me! Although I had to make a couple changes:

  1. rmarkdown::render('example.Rmd') instead of knitr, and
  2. %%R (capital R) in the notedown command

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