rmagic.ipynb

Kernel: Python 2 (SageMath)

Rmagic in IPython

NOTE: When running in CoCalc, this notebook must be run using the Classical Jupyter notebook.

This file is part of the examples collection of the Sagemath Cloud.

Rmagic is an extension for IPython. Check out the full IPython notebook for additional details! It is based on RPy2 and allows to seamlessly talk to an underlying R session via an IPython notebook.

To activate it, see the cell below. There are basically only a few core commands:

• %R runs a line of R code, return values can be assigned via var = %R ....

• %%R -i <input> -o <output> ... runs the entire cell in R and -i and -o specify the variables for input and output.

• %Rpush ... sends the data of a given variable to R.

• %Rpull ... retrieves the variable (namespace is populated) and the data of a variable inside R.

• %Rget ... is similar to Rpull, but only retrieves the actual data.

%pylab inline
%load_ext rpy2.ipython

Populating the interactive namespace from numpy and matplotlib

import numpy as np

%R print(seq(10))
%R print(summary(factor(c("a", "b", "b", "a", "c", "a", "c"))))

 [1]  1  2  3  4  5  6  7  8  9 10

a b c 
3 2 2 

%%R
v <- 5.5
a <- seq(10) + v
print(summary(a))
print(sd(a))

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   6.50    8.75   11.00   11.00   13.25   15.50 
[1] 3.02765

a only exists in R, hence the following error:

a

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-5-60b725f10c9c> in <module>()
----> 1 a

NameError: name 'a' is not defined

%Rget pulls and converts the data into Python:

a = %Rget a

a

array([  6.5,   7.5,   8.5,   9.5,  10.5,  11.5,  12.5,  13.5,  14.5,  15.5])

type(a)

numpy.ndarray

%Rpull is similar, and defines the variable, too:

%Rpull v

v

array([ 5.5])

%%R runs the given cell in R and -o [variable] "outputs" it to Python

%%R -o b
b <- c(2,3,4,3,5,6,5,6,7,8)
print(paste(length(a), "==", length(b), "?"))

[1] "10 == 10 ?"

print(b)

[ 2.  3.  4.  3.  5.  6.  5.  6.  7.  8.]

Plots

Basically, they are stright forward. Multiple simultaneous plots are displayed accordingly.

%R plot(a, b, 'b-')

%%R -o lmod
lmod <- lm(b ~ a)
print(lmod)

Call:
lm(formula = b ~ a)

Coefficients:
(Intercept)            a  
       -1.7          0.6  

Interested in the coefficients?

Use R's slot accessor $ via a call to R in %R to retrieve the coefficients as a NumPy array.

coeffs = %R lmod$coefficients
coeffs

array([-1.7,  0.6])

%R print(summary(lmod))

Call:
lm(formula = b ~ a)

Residuals:
   Min     1Q Median     3Q    Max 
 -1.00  -0.35   0.10   0.40   0.80 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.70000    0.79162  -2.147    0.064 .  
a            0.60000    0.06963   8.617 2.55e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.6325 on 8 degrees of freedom
Multiple R-squared:  0.9027,	Adjusted R-squared:  0.8906 
F-statistic: 74.25 on 1 and 8 DF,  p-value: 2.549e-05

Plotting in a 2x2 grid via R's par command and setting the output canvas size to 800x600 pixels.

%%R -w 800 -h 600
par(mfrow=c(2,2))
plot(lmod)

%%R -o faithful
library(datasets)
print(summary(faithful))

   eruptions        waiting    
 Min.   :1.600   Min.   :43.0  
 1st Qu.:2.163   1st Qu.:58.0  
 Median :4.000   Median :76.0  
 Mean   :3.488   Mean   :70.9  
 3rd Qu.:4.454   3rd Qu.:82.0  
 Max.   :5.100   Max.   :96.0  

%%R
library(lattice)
print(
    wireframe(volcano,
              shade = TRUE,
              zlab = "",
              aspect = c(61.0/87, 0.5),
              light_source = c(10,0,10)))

Advanced Example: PCA

%%R -o pca_usarrest
library("stats")
pca_usarrest <- princomp(USArrests, cor=TRUE)
print(summary(pca_usarrest))

Importance of components:
                          Comp.1    Comp.2    Comp.3     Comp.4
Standard deviation     1.5748783 0.9948694 0.5971291 0.41644938
Proportion of Variance 0.6200604 0.2474413 0.0891408 0.04335752
Cumulative Proportion  0.6200604 0.8675017 0.9566425 1.00000000

%%R
print(summary(pca_usarrest))

Importance of components:
                          Comp.1    Comp.2    Comp.3     Comp.4
Standard deviation     1.5748783 0.9948694 0.5971291 0.41644938
Proportion of Variance 0.6200604 0.2474413 0.0891408 0.04335752
Cumulative Proportion  0.6200604 0.8675017 0.9566425 1.00000000

%%R
biplot(pca_usarrest)

The pca_usarrest variable references a datastructure from R. Applying R functions via RPy2 directly is no problem.

from rpy2 import robjects as ro
print(ro.r.summary(pca_usarrest))

Importance of components:
                          Comp.1    Comp.2    Comp.3     Comp.4
Standard deviation     1.5748783 0.9948694 0.5971291 0.41644938
Proportion of Variance 0.6200604 0.2474413 0.0891408 0.04335752
Cumulative Proportion  0.6200604 0.8675017 0.9566425 1.00000000

%R print(help(sum))

R Help on ‘sum’sum                    package:base                    R Documentation

Sum of Vector Elements

Description:

     ‘sum’ returns the sum of all the values present in its arguments.

Usage:

     sum(..., na.rm = FALSE)
     
Arguments:

     ...: numeric or complex or logical vectors.

   na.rm: logical.  Should missing values (including ‘NaN’) be removed?

Details:

     This is a generic function: methods can be defined for it directly
     or via the ‘Summary’ group generic.  For this to work properly,
     the arguments ‘...’ should be unnamed, and dispatch is on the
     first argument.

     If ‘na.rm’ is ‘FALSE’ an ‘NA’ or ‘NaN’ value in any of the
     arguments will cause a value of ‘NA’ or ‘NaN’ to be returned,
     otherwise ‘NA’ and ‘NaN’ values are ignored.

     Logical true values are regarded as one, false values as zero.
     For historical reasons, ‘NULL’ is accepted and treated as if it
     were ‘integer(0)’.

     Loss of accuracy can occur when summing values of different signs:
     this can even occur for sufficiently long integer inputs if the
     partial sums would cause integer overflow.  Where possible
     extended-precision accumulators are used, but this is
     platform-dependent.

Value:

     The sum. If all of ‘...’ are of type integer or logical, then the
     sum is integer, and in that case the result will be ‘NA’ (with a
     warning) if integer overflow occurs.  Otherwise it is a length-one
     numeric or complex vector.

     *NB:* the sum of an empty set is zero, by definition.

S4 methods:

     This is part of the S4 ‘Summary’ group generic.  Methods for it
     must use the signature ‘x, ..., na.rm’.

     ‘plotmath’ for the use of ‘sum’ in plot annotation.

References:

     Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) _The New S
     Language_.  Wadsworth & Brooks/Cole.

See Also:

     ‘colSums’ for row and column sums.

Examples:

     ## Pass a vector to sum, and it will add the elements together.
     sum(1:5)
     
     ## Pass several numbers to sum, and it also adds the elements.
     sum(1, 2, 3, 4, 5)
     
     ## In fact, you can pass vectors into several arguments, and everything gets added.
     sum(1:2, 3:5)
     
     ## If there are missing values, the sum is unknown, i.e., also missing, ....
     sum(1:5, NA)
     ## ... unless  we exclude missing values explicitly:
     sum(1:5, NA, na.rm = TRUE)
     

array(['/projects/sage/sage/local/lib/R//library/base/help/sum'], 
      dtype='|S54')