Poetry with R -- Dissecting the code

introduction()
# Poetry is considered a form of literary art in which
# language is used for its aesthetic and evocative qualities. It
# contains multiple interpretations and therefore resonates
# differently in each reader.
#
# Code is the language used to communicate with computers. It has its
# own rules (syntax) and meaning (semantics). Like literature writers
# or poets, coders also have their own style that include - strategies
# for optimizing the code being read by a computer, and facilitating
# its understanding through visual organization and comments for other
# coders.
#
# Code can speak literature, logic, maths. It contains different
# layers of abstraction and it links them to the physical world of
# processors and memory chips. All these resources can contribute in
# expanding the boundaries of contemporary poetry by using code as a
# new language. Code to speak about life or death, love or hate. Code
# meant to be read, not run.
url("http://code-poems.com")

R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
Natural language support but running in an English locale
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
>

R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
> print("Hello World")
[1] "Hello World"
>
Character vector of length 1 (mode
and type (typeof) comes with it)

[1] "Hello World"
> print
function (x, ...)
UseMethod("print")
<bytecode: 0x0000000010a7a2c8>
<environment: namespace:base>
>

[1] "Hello World"
> print
function (x, ...)
UseMethod("print")
>
This means that code
Is compiled, not interpreted
-- thus faster, but not for today…

[1] "Hello World"
> print
function (x, ...)
UseMethod("print")
>
Environment where it is
defined. Not for now…

[1] "Hello World"
> print
function (x, ...)
UseMethod("print")
> formals("print")
$x
$...
> ?print
starting httpd help server ... done
>
Arglist, help also helps.
x is an input object.
print prints its argument and returns it invisibly (via invisible(x)).
It is a generic function which means that new printing methods can
be easily added for new classes.

[1] "Hello World"
> print
function (x, ...)
UseMethod("print")
> formals("print")
$x
$...
> ?print
starting httpd help server ... done
>
print prints its argument and returns it invisibly (via invisible(x)).
It is a generic function which means that new printing methods can
be easily added for new classes.
Generic function.

summary(so_far)
# o R is interpreted language, but
# bite compiling is possible (see
# compiler package).
# o In the background, everything is
# about environments (which are
# similar to lists), but luckily, this
# is hidden from average user.
# o Everything is an object -- OO.
# o Objects come in classes.
# o Methods can be defined for objects.

> set.seed(1234)
> x <- runif(10)
> y <- 2 + 5 * x + rnorm(10)
> plot(x, y)
>
Random numbers are important.
r unif != run if, uniform
normal
Another generic function.

> set.seed(1234)
> x <- runif(10)
> y <- 2 + 5 * x + rnorm(10)
> plot(x, y)
> (n <- cor.test(x, y))
Pearson's product-moment correlation
data: x and y
t = 5.9327, df = 8, p-value = 0.0003487
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.6325328 0.9770136
sample estimates:
cor
0.9026646
>
Parenthesis: print in short.

> (n <- cor.test(x, y))
Pearson's product-moment correlation
data: x and y
t = 5.9327, df = 8, p-value = 0.0003487
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.6325328 0.9770136
sample estimates:
cor
0.9026646
> class(n)
[1] "htest"
>

> class(n)
[1] "htest"
> str(n)
List of 9
$ statistic : Named num 5.93
..- attr(*, "names")= chr "t"
$ parameter : Named int 8
..- attr(*, "names")= chr "df"
$ p.value : num 0.000349
$ estimate : Named num 0.903
..- attr(*, "names")= chr "cor"
$ null.value : Named num 0
..- attr(*, "names")= chr "correlation"
$ alternative: chr "two.sided"
$ method : chr "Pearson's product-moment correlation"
$ data.name : chr "x and y"
$ conf.int : atomic [1:2] 0.633 0.977
..- attr(*, "conf.level")= num 0.95
- attr(*, "class")= chr "htest"
>
It is a list, nothing more.
Attributes are important
Class is an attribute.

> methods(class="htest")
[1] print.htest*
Non-visible functions are asterisked
>

[1] print.htest*
> print.htest
Error: object 'print.htest' not found
>

[1] print.htest*
> print.htest
Error: object 'print.htest' not found
> getAnywhere("print.htest")
A single object matching ‘print.htest’ was found
It was found in the following places
registered S3 method for print from namespace stats
namespace:stats
with value
function (x, digits = 4, quote = TRUE, prefix = "", ...)
{
cat("n")
Gimme that damn thing!

cat(strwrap(x$method, prefix = "t"), sep = "n")
cat("n")
cat("data: ", x$data.name, "n")
out <- character()
if (!is.null(x$statistic))
out <- c(out, paste(names(x$statistic), "=", format(round(x$statistic,
4))))
if (!is.null(x$parameter))
out <- c(out, paste(names(x$parameter), "=", format(round(x$parameter,
3))))
if (!is.null(x$p.value)) {
fp <- format.pval(x$p.value, digits = digits)
out <- c(out, paste("p-value", if (substr(fp, 1L, 1L) ==
"<") fp else paste("=", fp)))
}
cat(strwrap(paste(out, collapse = ", ")), sep = "n")
if (!is.null(x$alternative)) {
cat("alternative hypothesis: ")
if (!is.null(x$null.value)) {
if (length(x$null.value) == 1L) {
alt.char <- switch(x$alternative, two.sided = "not equal to",
less = "less than", greater = "greater than")
cat("true", names(x$null.value), "is", alt.char,
x$null.value, "n")
}
else {
cat(x$alternative, "nnull values:n")
print(x$null.value, ...)
}
}
else cat(x$alternative, "n")
}

if (!is.null(x$conf.int)) {
cat(format(100 * attr(x$conf.int, "conf.level")), "percent confidence interval:n",
format(c(x$conf.int[1L], x$conf.int[2L])), "n")
}
if (!is.null(x$estimate)) {
cat("sample estimates:n")
print(x$estimate, ...)
}
cat("n")
invisible(x)
}
<bytecode: 0x0000000010f7a3e0>
<environment: namespace:stats>
>
Return the value invisibly.
Defined in stats pkg.

> m <- lm(y ~ x)
>
lm linear regression
y ~ x formula, let’s get back to
this later

> m <- lm(y ~ x)
> m
Call:
lm(formula = y ~ x)
Coefficients:
(Intercept) x
1.740 4.857
>
this later
This makes life so much easier,
talk about it next year.

> m <- lm(y ~ x)
> m
Call:
lm(formula = y ~ x)
Coefficients:
(Intercept) x
1.740 4.857
> names(m)
[1] "coefficients" "residuals" "effects" "rank"
[5] "fitted.values" "assign" "qr" "df.residual"
[9] "xlevels" "call" "terms" "model"
>
this later
This makes life so much easier,
talk about it next year.

> print.lm
function (x, digits = max(3, getOption("digits") - 3), ...)
{
cat("nCall:n", paste(deparse(x$call), sep = "n", collapse = "n"),
"nn", sep = "")
if (length(coef(x))) {
cat("Coefficients:n")
print.default(format(coef(x), digits = digits), print.gap = 2,
quote = FALSE)
}
else cat("No coefficientsn")
cat("n")
invisible(x)
}
<bytecode: 0x0000000010542380>
<environment: namespace:stats>
>

> (s <- summary(m))
Call:
lm(formula = y ~ x)
Residuals:
Min 1Q Median 3Q Max
-0.7372 -0.4189 -0.2076 0.2832 1.2928
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.7401 0.4539 3.834 0.004990 **
x 4.8571 0.8187 5.933 0.000349 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.6751 on 8 degrees of freedom
Multiple R-squared: 0.8148, Adjusted R-squared: 0.7917
F-statistic: 35.2 on 1 and 8 DF, p-value: 0.0003487
>

> coef(m)
(Intercept) x
1.740131 4.857122
>

> coef(m)
(Intercept) x
1.740131 4.857122
> coef(s)
(Intercept) 1.740131 0.4538773 3.833924 0.0049900329
x 4.857122 0.8186989 5.932733 0.0003486669
>

> coef(m)
(Intercept) x
1.740131 4.857122
> coef(s)
(Intercept) 1.740131 0.4538773 3.833924 0.0049900329
x 4.857122 0.8186989 5.932733 0.0003486669
> class(s)
[1] "summary.lm"
>

> coef(m)
(Intercept) x
1.740131 4.857122
> coef(s)
(Intercept) 1.740131 0.4538773 3.833924 0.0049900329
x 4.857122 0.8186989 5.932733 0.0003486669
> class(s)
[1] "summary.lm"
> getAnywhere("print.summary.lm")
A single object matching ‘print.summary.lm’ was found
It was found in the following places
registered S3 method for print from namespace stats
namespace:stats
with value
function (x, digits = max(3, getOption("digits") - 3), symbolic.cor =
x$symbolic.cor,
signif.stars = getOption("show.signif.stars"), ...)
{
cat("nCall:n", paste(deparse(x$call), sep = "n", collapse = "n"),
"nn", sep = "")
resid <- x$residuals
df <- x$df
rdf <- df[2L]
cat(if (!is.null(x$weights) && diff(range(x$weights)))

print(summay(presentation))
# o Print method is simplest.
# o It conveys meaning to user.
# o Results are usually structures in
# different ways,
# o need methods to access information:
> methods(class="lm")
[1] add1.lm* alias.lm* anova.lm
[4] case.names.lm* confint.lm* cooks.distance.lm*
[7] deviance.lm* dfbeta.lm* dfbetas.lm*
[10] drop1.lm* dummy.coef.lm* effects.lm*
[13] extractAIC.lm* family.lm* formula.lm*
[16] hatvalues.lm influence.lm* kappa.lm
[19] labels.lm* logLik.lm* model.frame.lm
[22] model.matrix.lm nobs.lm* plot.lm
[25] predict.lm print.lm proj.lm*
[28] qr.lm* residuals.lm rstandard.lm
[31] rstudent.lm simulate.lm* summary.lm
[34] variable.names.lm* vcov.lm*

> if (questions) {
+ answer(questions)
+ } else q("no")
# ERUG meeting -- December 11, 2012
# Poetry with R -- Dissecting the code
# P. Solymos

Poetry with R -- Dissecting the code

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