You will need to follow the previous Getting ready steps of the Interfacing with R via rpy2 recipe. The Notebook is Chapter01/R_magic.ipynb. The Notebook is more complete than the recipe presented here, and includes more chart examples. For brevity here, we will only concentrate on the fundamental constructs to interact with R using magics.

This recipe is an aggressive simplification of the previous one because it illustrates the conciseness and elegance of R magics:

import rpy2.robjects as robjects
import rpy2.robjects.lib.ggplot2 as ggplot2%load_ext rpy2.ipython

Note that the % starts an IPython-specific directive. Just as a simple example, you can write %R print(c(1, 2)) on a Jupyter cell.

Check out how easy it is to execute the R code without using the robjects package. Actually, rpy2 is being used to look under the hood.

%%R
seq.data <- read.delim('sequence.index', header=TRUE, stringsAsFactors=FALSE)
seq.data$READ_COUNT <-as.integer(seq.data$READ_COUNT)
seq.data$BASE_COUNT <-as.integer(seq.data$BASE_COUNT)

You can then specify that the whole cell should be interpreted as an R code by using %%R (note the double %%).

seq_data = %R seq.data
print(type(seq_data))  # pandas dataframe!

The type of the DataFrame is not a standard Python object, but a pandas DataFrame. This is a departure from previous versions of the R magic interface.

my_col = list(seq_data.columns).index("CENTER_NAME")
seq_data['CENTER_NAME'] = seq_data['CENTER_NAME'].apply(lambda x: x.upper())

%R -i seq_data
%R print(colnames(seq_data))

The -i argument informs the magic system that the variable that follows on the Python space is to be copied in the R namespace. The second line just shows that the DataFrame is indeed available in R. The name that we are using is different from the original—it's seq_data instead of seq.data.

%%R
bar <- ggplot(seq_data) +  aes(factor(CENTER_NAME)) + geom_bar() + theme(axis.text.x = element_text(angle = 90, hjust = 1))
print(bar)

The R magic system also allows you to reduce code, as it changes the behavior of the interaction of R with IPython. For example, in the ggplot2 code of the previous recipe, you do not need to use the .png and dev.off R functions, as the magic system will take care of this for you. When you tell R to print a chart, it will magically appear in your Notebook or graphical console.

The R magics have seemed to have changed quite a lot over time in terms of interface. For example, I updated the R code for the first edition of this book a few times. The current version of DataFrame assignment returns pandas objects, which is a major change. Be careful with the version of Jupyter that you use as the %R code can be quite different. If this code does not work and you are using an older version, consult the Notebooks of the first edition of this book, as they might help.