SciCom also allows for implementing R scripts in a “language” that is just like Ruby, so that the developer does not need to know that she is actually writing an R script. All R methods are accessible through an R namespace.
The next script is the same baseball model done in R above using SciCom ‘language’:
require ‘scicom’
# This dataset comes from Baseball-Reference.com.
baseball = R.read__csv("baseball.csv")
# Lets look at the data available for Momeyball.
moneyball = baseball.subset(baseball.Year < 2002)
# Let's see if we can predict the number of wins, by looking at
# runs allowed (RA) and runs scored (RS). RD is the runs difference.
# We are making a linear model for predicting wins (W) based on RD
moneyball.RD = moneyball.RS - moneyball.RA
wins_reg = R.lm("W ~ RD", data: moneyball)
wins_reg.summary.pp
We show bellow an example of calculating the correlation matrix without using the build-in functions. First this is done in an R script and then using SciCom:
# Create a matrix and give it rownames and colnames
set.seed(42)
Xij <- matrix(sample(seq(0, 9), 40, replace = TRUE), ncol = 4)
rownames(Xij) <- paste("S", seq(1, dim(Xij)[1]), sep = "")
colnames(Xij) <- paste("V", seq(1, dim(Xij)[2]), sep = "")
# find the means of the columns
n <- dim(Xij)[1]
one <- rep(1, n)
X.means <- t(one) %*% Xij/n
# find the covariance of the matrix
X.diff <- Xij - one %*% X.means
X.cov <- t(X.diff) %*% X.diff/(n - 1)
round(X.cov, 2)
# find the correlation
sdi <- diag(1/sqrt(diag(X.cov)))
rownames(sdi) <- colnames(sdi) <- colnames(X.cov)
round(sdi, 2)
X.cor <- sdi %*% X.cov %*% sdi
rownames(X.cor) <- colnames(X.cor) <- colnames(X.cov)
round(X.cor, 2)
Now the same code using SciCom
require ‘scicom’
# Create a matrix and give it rownames and colnames
R.set__seed(42)
R.seq(0,9).sample(40, replace: TRUE).matrix(ncol: 4)
.fassign(:rownames, R.paste("S", R.seq(1, xij.attr.dim[1]), sep: ""))
.fassign(:colnames, R.paste("V", R.seq(1, xij.attr.dim[2]), sep: ""))
# find the means of the columns
n = xij.dim[1]
one = R.rep(1, n)
x_means = one.t._ :*, xij/n
# find the covariance of the matrix
x_diff = xij - (one._ :*, x_means)
x_cov = (x_diff.t._ :*, x_diff/(n - 1)).round(2)
# find the correlation
sdi = (1 / x_cov.diag.sqrt).diag.round(2)
sdi.fassign(:rownames, x_cov.rownames)
sdi.fassign(:colnames, x_cov.colnames)
x_cor = ((sdi._ :*, x_cov)._ :*, sdi)
.round(2)
.fassign(:rownames, x_cov.rownames)
.fassign(:colnames, x_cov.colnames)
As another example, here is a SciCom script to print the number of days for every month is 2005:
require ‘scicom’
everyday = R.seq(from: R.as__Date('2005-1-1'), to: R.as__Date('2005-12-31'), by: 'day')
cmonth = everyday.format('%b')
cmonth
.factor(levels: cmonth.unique, ordered: TRUE)
.table
.pp
As can be seen from these examples, R methods can be accessed through the R namespace in SciCom, so, R method ‘seq’ is called in SciCom as ‘R.seq’. R methods that are applied on objects can be called in two ways, either using the R namespace as in ‘R.factor’ or directly on the object, as in this case we did ‘cmonth.factor’. This last example shows how SciCom allows method chaining, which is not possible in an R script.
SciCom allows programmers to access any R function in the R namespace. For instance, as shown above, function c in R can be access in SciCom by a call to R.c. As another example, method seq in R is accessed in SciCom by a call to R.seq.
Parameters can be passed to R functions normaly. For example, the code bellow creates a vector with with doubles:
> vec = R.c(2.4, 5.55, 10, 18.27, 34.45)
> vec.pp
[1] 2,4 5,55 10 18,27 34,45
Ruby variable can also be passed as arguments to R methods:
> vec2 = R.c(vec, 3.5)
> vec2.pp
[1] 2,4 5,55 10 18,27 34,45 3,5
> dbl = 3.5
> vec3 = R.c(vec2, dbl)
> vec3.pp
[1] 2,4 5,55 10 18,27 34,45 3,5 5,75
More complex Ruby classes, such ar Ruby hashes, of course, cannot be passed as argument to R methods. SciCom, in principle, should support every Ruby method that is available in Renjin. Note that Renjin is still under development and not all methods and libraries are available.
Some methods and variables in R have a '.' in their names. This is standard R notation; however, '.' in Ruby is interpreted as a method call and thus cannot be part of a variable name or function. In order to access names in R that have a '.' on them in SciCom, the '.' is substituted by '__':
> # variable defined in R with a '.' in the name
> R.eval("r.d = 10.35")
> # access the variable 'r.d' in Ruby by using '__' notation
> R.r__d.pp
[1] 10,35
Accessing method as.complex in R is also done by using '__' notation:
# acess R method 'as.complex' using 'as__complex' notation
> comp = R.as__complex(-1)
> p R.Re(comp).gz
-1.0
> p R.Im(comp).gz
0.0
> # now method 'is.complex'
> p R.is__complex(comp).gt
true
SciCom allows methods to be chained. The code above can be written as:
# prints the real part of a complex number by using method chaining
> R.as__complex(-1)
.Re
.pp
[1] -1
We will see more examples of method chaining later in this documentation. In principle, we will try to use method chaining whenever possible, but in some situations normal use of R methods through the 'R.' notation will be used to remind the reader that this notation is also possible.
R allows the use of named parameters in function calls. SciCom also allows for named parameters. Named parameters in SciCom require the use of Ruby hashes in the normal Ruby way.
> # R code to create a complex number
> R.eval("comp = complex(real = 0, imaginary = 1)")
> R.eval("print(comp)")
[0.0+1.0i]
> # Same code as above in SciCom notation, with chaining
> R.complex(real: 0, imaginary: 1)
> .pp
[0.0+1.0i]
SciCom allows R programmers to use R commands inside a Ruby script in a way similar to RinRuby by calling method eval and passing to it an R script:
# Basic integration with R can always be done by calling eval and passing it a valid
# R expression.
> R.eval("r.i = 10L")
> R.eval("print(r.i)")
[1] 10
> R.eval("vec = c(10, 20, 30, 40, 50)")
> R.eval("print(vec)")
[1] 10 20 30 40 50
> R.eval("print(vec[1])")
[1] 10
Programmers can also use here docs to integrate an R script inside a Ruby script. The next example show a model for predicting baseball wins based on runs allowed and runs scored. The data comes from Baseball-Reference.com.
R.eval <<EOF
# This dataset comes from Baseball-Reference.com.
baseball = read.csv("baseball.csv")
# str has a bug in Renjin
# str(data)
# Lets look at the data available for Momeyball.
moneyball = subset(baseball, Year < 2002)
# Let's see if we can predict the number of wins, by lookin at
# runs allowed (RA) and runs scored (RS). RD is the runs difference.
# We are making a linear model from predicting wins (W) based on RD
moneyball$RD = moneyball$RS - moneyball$RA
WinsReg = lm(W ~ RD, data=moneyball)
print(summary(WinsReg))
EOF
The output of the program above is:
Call:
lm(data = moneyball, formula = W ~ RD)
Residuals:
Min 1Q Median 3Q Max
-14,266 -2,651 0,123 2,936 11,657
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 80,881 0,131 616,675 <0 ***
RD 0,106 0,001 81,554 <0 ***
---
Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1
Residual standard error: 3,939 on 900 degrees of freedom
Multiple R-squared: 0,8808, Adjusted R-squared: 0,8807
F-statistic: 6.650,9926 on 1 and 900 DF, p-value: < 0