Scott at 00:39, 28 February 2015

2015-02-28T00:39:57Z

← Older revision		Revision as of 00:39, 28 February 2015
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	add this line to /etc/apt/sources.list:		add this line to /etc/apt/sources.list:
	<pre>		<pre>
	deb http://cran.cnr.Berkeley.edu/bin/linux/ubuntu~~/ lucid~~/		deb http://cran.cnr.Berkeley.edu/bin/linux/ubuntu trusty/
	</pre>		</pre>
	then run <code>apt-get update</code>		then run <code>apt-get update</code>
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	If you get an error message about GPG keys:		If you get an error message about GPG keys:
	<pre>		<pre>
	~~gpg~~ --keyserver keyserver.ubuntu.com --recv-~~key E2A11821~~		sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E084DAB9
	~~gpg -a --export E2A11821 > foo~~
	~~apt-key add foo~~
	</pre>		</pre>
	Repeat this for additional error signatures.		Repeat this for additional error signatures.

Scott at 19:44, 2 February 2011

2011-02-02T19:44:09Z

New page

==Install on Ubuntu==
add this line to /etc/apt/sources.list:
<pre>
deb http://cran.cnr.Berkeley.edu/bin/linux/ubuntu/ lucid/
</pre>
then run <code>apt-get update</code>

If you get an error message about GPG keys:
<pre>
gpg --keyserver keyserver.ubuntu.com --recv-key E2A11821
gpg -a --export E2A11821 > foo
apt-key add foo
</pre>
Repeat this for additional error signatures.

install basic R packages:
<pre>
apt-get install r-base
</pre>
To get third-party packages from CRAN, start R as root. Then run a command like this:
<pre>
install.packages(c("iterators", "foreach"))
</pre>

==Help System==
<pre>
help(glm) # get help on a function
?glm # same thing
example(glm) # see an example
help.search("regression") # search all help files
??regression # same thing
vignette("affy") # how to use a package
</pre>

==Running R==

Start an interactive console with "<code>R</code>". Use the "<code>q()</code>" command to quit.

To run an R script from the command line:
<pre>
R CMD BATCH generate_graphs.R
</pre>

To run an R script from an interactive console:
<pre>
source generate_graphs.R
</pre>

==Syntax==
* Comments are anything after a pound sign.
* Semicolons may be used to separate expressions.
* Curly braces may group a sequence of expressions and return only the last expression.

==Control==
<pre>
if (x < y) x else y

repeat {if (i > 25) break else {print(i); i <- i + 5;}}

while (i <= 25) {print(i); i <- i + 5}

for (i in seq(from=5,to=25,by=5)) print(i)
</pre>

=====iterators package (from CRAN)=====
<pre>
> library(iterators)
> onetofive = iter(1:5)
> nextElem(onetofive) # 1
</pre>

=====foreach package (from CRAN)=====
<pre>
> foreach(i=1:5) %do% sqrt(i) # 1, 1.414214, 1.732051, 2, 2.236068
</pre>

==Assignment==
<pre>
x = 34
x <- 34
34 -> x
</pre>

Values are generally copied on assignment.

==Special Values==
<code>NA</code> (for missing data), <code>Inf</code> (infinity), <code>NaN</code> (bad computation), <code>NULL</code>, <code>TRUE</code> (=1), <code>FALSE</code> (=0)

==Data Types and Classes==
<pre>
x = 22
class(x) # "numeric", class refers to how an object may be used
class("hi") # "character"
typeof(x) # "double", type refers to how an object is stored
x = as(22, "integer") # type-casting
typeof(x) # "integer"
</pre>

==Strings (or character vectors)==
<pre>
paste("a", "b") # "a b", default separator is single space
paste("a", "b", sep="") # "ab"
</pre>

==Vectors==
A vector is a one-dimensional sequence of same-typed elements
<pre>
c(0, 1, 1, 2, <span class="docTextHighlight">3</span>, 5, 8)
x = 1:10 # same as x = c(1,2,3,4,5,6,7,8,9,10)
x[3] # 3
x[[3]] # 3
x[3:6] # same as c(3,4,5,6)
x[-1:-5] # c(6,7,8,9,10)
x[x>5] # c(6,7,8,9,10)
x[x%%3 == 0] # c(3, 6, 9)
x[c(1,5,10)] # c(1,5,10)
x[12] = 12 # now x is c(1,2,3,4,5,6,7,8,9,10,NA,12)
seq(from=5,to=25,by=5) # c(5,10,15,20,25)
sqrt(1:7) # many built-in functions will automatically apply themselves to every element of a vector
sapply(1:7) # apply the function sequentially to each element of the vector
</pre>

==Arrays==
An array is a multidimensional vector of same-typed elements.
<pre>
> x = array(1:12, dim=c(3,4))
> x
[,1] [,2] [,3] [,4]
[1,] 1 4 7 10
[2,] 2 5 8 11
[3,] 3 6 9 12
> x[1,3] # 7
> x[,3] # c(7,8,9)> x[2:3,3] # c(8,9)
</pre>

==Lists==
A list is a heterogeneous collection of objects, each of which may be named.
<pre>
x = list(thing="hat", size="8.25")
x[1] # "hat"
x$thing # "hat"
x[["thing"]] # "hat"
x[["thin"]] # NULL
x[["thin", exact=FALSE]] # "hat", search on substring match
> nl = list(first=x,second=y)
> nl$first$thing # "hat"
> nl[[c("first", "thing")]] # "hat"
</pre>

==Factors and Levels==
<pre>
eye.colors = factor(c("brown", "blue", "blue", "green", "brown", "brown", "brown"))
levels(eye.colors) # "blue" "brown" "green"
</pre>

==Shingles==
A shingle is a generalization of a factor to a continuous variable. A shingle consists of a numeric vector and a set of intervals. The intervals are allowed to overlap (much like roof shingles, hence the name shingles).

==Data Frames==
A ''data frame'' is a list that contains multiple named vectors that are the same length, like a database table.

<pre>
> teams <- c("PHI","NYM","FLA","ATL","WSN")
> w <- c(92, 89, 94, 72, 59)
> l <- c(70, 73, 77, 90, 102)
> nleast <- data.frame(teams,w,l)
> nleast
teams w l
1 PHI 92 70
2 NYM 89 73
3 FLA 94 77
4 ATL 72 90
5 WSN 59 102
nleast$w # c(92, 89, 94, 72, 59)
nleast$w[nleast$teams == "FLA"] # 94
cbind (df1, df2) # combine two dataframes into one by adding columns
rbind(df1, df2) # combine two dataframes into one by adding rows
</pre>

==Models and Formulas==
A linear model may be expressed as
<pre>
y ~ x1 + x2 + ... + xn
</pre>
which is a formula object. Example of linear model using built-in "cars" data set:
<pre>
> cars.lm <- lm(formula=dist~speed,data=cars)
> cars.lm
Call:
lm(formula = dist ~ speed, data = cars)
Coefficients:
(Intercept) speed
-17.579 3.932
> summary(cars.lm) # shows lots more details
</pre>

<pre>
y ~ x1+x2+x3 # y is a linear function of x1, x2, and x3
y ~ x1+x2+x3+0 # y is a linear combination of x1, x2, and x3 with no intercept
y ~ x1+x2 | x3 # y is a linear function of x1 and x2 conditioned on x3
y ~ I(x1+x2) # y is a linear function of a single variable (x1+x2)
y ~ (x1+x2)*x3 # equivalent to y ~ x1+x2+x3+I(x1*x2)+I(x2*x3)
y ~ (x1+x2)^2 # equivalent to y ~ (x1+x2)*(x1+x2)
y ~ log(x1)+sin(x2) # can incorporate simple functions
</pre>

== Functions ==
<pre>
myFunc = function(x,y) { c(x+1, y+1) }
myFunc(2,3) # c(3,4)
myFunc # function(x,y){c(x+1,y+1)}
timethis <- function(...) { # the "..." takes an arbitrary list of arguments and passes them on
start.time <- Sys.time();
eval.parent(...); # accesses a function from the parent environment
print(Sys.time() - start.time);

}

> addemup <- function(x,...) {
+ args <- list(...)
+ for (a in args) x <- x + a
+ return(x)
+ }
> addemup(1,1) # 2
> addemup(1,2,3,4,5) # 15

assigns.x <- function(i) {x <<- i} # assigns the variable x in the parent environment
</pre>

==Dates==
<pre>
as.Date("2009-09-08") + 7 # "2009-09-15"
today = Sys.Date()
format(today, "%m/%d/%Y")
</pre>

==Time Series==
<pre>
> ts(1:8,start=c(2008,2),frequency=4)
Qtr1 Qtr2 Qtr3 Qtr4
2008 1 2 3
2009 4 5 6 7
2010 8
</pre>

==Environments==
An environment consists of the set of symbols that have been defined. A function body, list, or data frame contains its own environment.
<pre>
example.list <- list(a=1, b=2, c=3)
with(example.list, a+b+c) # 6
</pre>

== Classes ==
<pre>
> setClass("TimeSeries",
+ representation(
+ data="numeric",
+ start="POSIXct",
+ end="POSIXct"
+ )
+ )

> my.TimeSeries <- new("TimeSeries",
+ data=c(1,2,3,4,5,6),
+ start=as.POSIXct("07/01/2009 0:00:00",tz="GMT", format="%m/%d/%Y %H:%M:%S"),
+ end=as.POSIXct("07/01/2009 0:05:00",tz="GMT", format="%m/%d/%Y %H:%M:%S")
+ )

> setValidity("TimeSeries",
+ function(object) {
+ object@start <= object@end &&
+ length(object@start) == 1 &&
+ length(object@end) == 1
+ }
+ )
</pre>

R Basics - Revision history

Scott at 00:39, 28 February 2015

Scott at 19:44, 2 February 2011