Simple tests for an R kernel

This shows basic R manipulation. No access to Spark yet.

Data manipulation



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num = 200

# Create a normal variable
a = 1:num
b = rnorm(num,6)

# Another vector, by mixing from two random variables
c1 = c( rnorm(num/2,5,3),  rnorm(num/2,9,4) )
c2 = as.factor( c( rep('A',num/2), rep('B',num/2) ) )
c = data.frame( dvalue2=c1, vtype=c2 )

c = c[ sample(num), ]



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summary(c)



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exdf = data.frame( dnum=a, dvalue1=b )
exdf <- cbind( exdf, c )



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class(exdf)



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head(exdf)



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summary(exdf)

Graphics



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# Load the ggplot graphics library
library('ggplot2')

# A density plot
qplot( dvalue1, data=exdf, geom="density" )



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# Change default plot size (values in inches)
options(repr.plot.width = 10, repr.plot.height = 5)

# A scatterplot, discriminating by the factor in the 'vtype' column
qplot( dvalue1, dvalue2, color=vtype, data=exdf )

Documentation

The IRKernel has also autocompletion (use TAB). However it does not have contextual helps (tooltips).

But it does provide acccess to R documentation:



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?Lognormal