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Last active Dec 17, 2018

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R Markdown to IPython Notebook

This is a proof-of-concept of how an R Markdown Notebook can be converted into an IPython Notebook. The idea is 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.

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 it out, install the notedown module and run make example.ipynb.

R Markdown to IPython Notebook

You can view the converted IPython Notebook on nbviewer

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%load_ext rmagic

Introduction to ggplot2

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

Adding a Python Chunk

def f(x):
  return x + 2
f(2)

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)

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()

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))

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)

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)
## Introduction to ggplot2
This is a short demo on how to convert an R Markdown Notebook into an IPython Notebook using knitr and notedown.
Adding a Python Chunk
```{r engine="python"}
def f(x):
return x + 2
f(2)
```
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)
rmd = paste("```\n%load_ext rmagic\n```\n\n",
paste(readLines('example.Rmd'), collapse = '\n'),
collapse = '\n'
)
knit(text = rmd, output = 'example.md')
example.md: example.Rmd
./knit
example.ipynb: example.md
notedown example.md | sed 's/%%r/%%R/' > example.ipynb
@aaren

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commented Mar 7, 2014

I can get this executing without error in the ipython notebook but I can't get the plots to appear without using print.

I get no output with this:

%%R
head(iris)

But I do get output with this:

%%R
out <- head(iris)
print(out)

Any ideas?

This is the first time I've used R.

@ramnathv

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Owner Author

commented Apr 6, 2014

I am using IPython 2.0. I had the same problems with the earlier version on IPython. Can you update and check if the problem still persists?

@aaren

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commented Apr 11, 2014

That fixed it. Now working for me with IPython 2.0

@lecy

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commented Mar 7, 2016

Very nice! Building on this, here is a simple R function to call the conversion from within the R environment:

https://github.com/lecy/RMD-to-Jupyter/blob/master/README.md

library(tools)

rmd2jupyter <- function( filename, path=getwd() ) 
{
  path_in <- paste( path, "/", filename, " ", sep="" )
  path_out <- paste(path, "/", file_path_sans_ext(filename), ".ipynb", sep="")
  full_shell <- paste("notedown ", path_in, " --rmagic --run > ", path_out, sep="")
  shell(full_shell)
}

# download the example.Rmd file from this repository
# setwd(...) to where the example.Rmd file is located

rmd2jupyter( "example.Rmd" )
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