notes

R Notes and Resources

Table of Contents

Manuals, how-to’s and tutorials

The R manual
http://cran.r-project.org/doc/manuals/R-intro.html

http://www.ats.ucla.edu/stat/r/
http://www.ats.ucla.edu/stat/r/faq/

Comparison to other languages
http://www.johndcook.com/R_language_for_programmers.html
http://hyperpolyglot.org/numerical-analysis

Advanced R programming - profiling and optimization
http://adv-r.had.co.nz/ *** list of most useful functions to learn first: http://adv-r.had.co.nz/Vocabulary.html

Making executable reports
Rstudio markdown for making reports
http://www.rstudio.com/ide/docs/authoring/using_markdown

Useful Rants
http://tim-smith.us/arrgh/
http://www.burns-stat.com/pages/Tutor/R_inferno.pdf

Tutorial/introduction here:
http://manuals.bioinformatics.ucr.edu/home/programming-in-r

Knitr/Pandoc/Miktex to make PDFs
http://rprogramming.net/create-html-or-pdf-files-with-r-knitr-miktex-and-pandoc/

Advanced lectures - scoping
http://www.stat.berkeley.edu/~statcur/Workshop2/Presentations/functions.pdf
Statistics tutorials with R
http://ww2.coastal.edu/kingw/statistics/R-tutorials/

Advanced text on probability, R and R graphing and (soon to come ?) statistics and machine learning: http://theanalysisofdata.com/

Statistics help

This page focuses primarily on the R language although some explanations of stat methods has creeped in. If you are looking for an explanation of a statistical test or method (or need to choose one) I usually go to one of these sites/books

Choosing and Using Statistical Tests - hard copy at home

http://www.biostathandbook.com - John H. MacDonald
Various books at home on regression, statistical learning etc.
Various books in e. library.

Other Statistics sites
Good sites for exploring distribution
http://zoonek2.free.fr/UNIX http://www.statmethods.net/advgraphs/probability.html

Based on Quick R and R in Action
https://www.statmethods.net/

Bioconductor workflows
http://bioconductor.org/help/workflows/
See also my walkthrough of one of these in git/analysis

installing

for mac: http://cran.rstudio.com/ - get updates from here too

installing bioconductor
source(“http://bioconductor.org/biocLite.R”)
biocLite()
see http://www.bioconductor.org/install/
to install specific packages:
source(“http://bioconductor.org/biocLite.R”)
biocLite(“limma”)

to install package
use R studio interface which calls
install.packages(“iRefR”)
http://www.statmethods.net/interface/packages.html

library()   # see all packages installed  
search()    # see packages currently loaded  
.libPaths() # where does R store libs?

to manually change where R stores libs do this at the shell prompt

export R_LIBS="/home/your_username/R_libs"

making your own packages and libs
source() or
http://www.r-bloggers.com/creating-your-personal-portable-r-code-library-with-github/
or
make a package -see above

start R:

in windows: double click the icon
in linux: type R at the prompt
in mac: use R studio

run an R script from the command line:
http://stat.ethz.ch/R-manual/R-devel/library/utils/html/Rscript.html
Rscript yourScript.R

or
https://stat.ethz.ch/R-manual/R-devel/library/utils/html/BATCH.html
R CMD BATCH yourScript.R
the output is sent to a file called yourScript.Rout
cat yourScript.Rout

tutorial:
help.start() #starts the help browser start with “An introduction to R”
start with Appendix A of this document for an example session
next if you are interested in using graphng capabilities, go to section 12 on graphical procedures or run
demo(graphics)

sessionInfo() - will give a list of attached packages and libraries

 

debugging

debug(function_name)
#make a call to function name and you're in the debugger  
Browser[1]> help  
#do stuff  
undebug(function_name)

if its been a while, take a look at this video
https://vimeo.com/99375765
and then this article
https://support.rstudio.com/hc/en-us/articles/205612627-Debugging-with-RStudio
and then this article which is more advanced
http://adv-r.had.co.nz/Exceptions-Debugging.html

** you can’t set breakpoints inside .Rmd files so stop trying **
and if you are using Rstudio and stepping through an .Rmd file, you are essentially already in a debug mode for that environment

if you want to debug one of the function calls from the .Rmd file (say to some function in some package) then do this:

type
debug(function_name)
in the console
then the next time this function is called, you will see your console prompt change to
Browser[1]>
then at this point type help to see the following list of commands

n          next  
s          step into  
f          finish  
c or cont  continue  
Q          quit  
where      show stack  
help       show help  
<expr>     evaluate expression

use these commands to step through the code or evaluate the values of variables in the current environment (i.e. the function you are debugging)
when you are done, type
undebug(function_name)
to prevent the debugger from starting when encuntering that function

for more, try
?debugger
?browser

correcting functions after you have debugged them
you have found an error in a function in some package and want to correct it, see this link
https://stackoverflow.com/questions/2458013/what-ways-are-there-to-edit-a-function-in-r

you can use
fix(function_name)

foo <- function(x)  
{  
    line1 <- x  
    line2 <- 0  
    line3 <- line1 + line2  
    return(line3)  
}

and you want to change the fourth line to
line2 <- 2

you can start by retrieving the line numbers using
as.list(body(foo))

then do

body(foo)[[3]] <- substitute(line2 <- 2)  
foo  
# function (x) {  
#     line1 <- x  
#     line2 <- 2  
#     line3 <- line1 + line2  
#     return(line3)  
# }

working_function <- function() {...} 

source("fix.R")  
body(broken_function) <- body(working_function)

you can use a similar strategy to above in order to change the parameters of a function using
formals(some_function)
see http://adv-r.had.co.nz/Functions.html

basic commands for working with R

quit R:
q()

get help:

?  
? mean

you can also use this syntax

help(mean)  
help.search  
RSiteSearch  
apropos

command history: use the up and down arrows.

set working directory

setwd("I:/myR") # note the quotes  
getwd() #to find what wd is currently set to  
list.files() #  
list.dirs() #

listing data objects in current workspace:
ls() removing data objects:
rm(somedataObjectName)

downloading data from a url

if (!file.exists("data")){ dir.create("data") }  
  
fileUrl <- "http://cancer.sanger.ac.uk/cancergenome/assets/cancer_gene_census.tsv"  
  
download.file(fileUrl,   
destfile="cancer_gene_census.tsv",   
method="curl", quiet = FALSE, mode = "w",  
              cacheOK = TRUE)  
dateDownloaded <- date()

reading in data from a tab-delimited file

data.df <- read.table(  
"cancer_gene_census.tsv",  
        header=TRUE,  
        sep="\t",  
        stringsAsFactors=FALSE, #unless you want  
        quote="" #means no quotes - can solve lots of problems  
)

note:

sometimes files can contain quotes or comment characters where you might not expect them. using quote="" and comment.char=# (after removing comments you know about at the top of a file) can solve problems and save hours of fun. Comment characters (like #) in the middle of a line will cause an error like “line 12344 does not have the expected number of elements”. The problem is that the problematic line number reported by R’s scan function may be incorrect.

note:
the fastest way to remove the first line of a large file might be
csplit -k fileName 1 '{1}'
but for readability (even though its a little slower) use
tail -n +2

reading data a line at a time

fh<-file(description="final/en_US/en_US.blogs.txt", open="r")
thisLine <- readLines(con=fh, n=1)  # can read into list if n > 1 or unspecified
thisLine <- gsub(pattern="[^[:alnum:] ]", replacement="", x=thisLine)  # remove non-word chars
close(fh)

reading data from a file into a single string

fileName <- 'foo.R'
x <- readChar(fileName, file.info(fileName)$size)

reading data from mySql

reading/writing data to HDF5 files

reading and writing from excel files

reading and extracting information from XML

reading and writing JSON

reading and writing large tables

web-scraping

CONTINUE FORMATTING HERE

browse a table loaded from a file

head(cgcensus)      # look at first 10 rows
tail(cgcensus)      # look at last ten rows
cgcensus[1]         # first column
cgcensus[,1]        # first row
t(cgcensus)         # transpose a data frame

saving and loading data from a session:

save(file="variabs.Rdata", list="a","vec", "mat")
load(file="variabs.Rdata")

but the above you have to specify the variable to save

save an entire session

save.image(file="mysession.RData")
# 'save.image()' is just a short-cut for 'save my current workspace',
# i.e., save(list = ls(all=TRUE), file = ".RData").
# It is also what happens with q("yes").
# to restore this use
load(file="mysession.Rdata")

basic math: +,-,*, / and ^, grouped with parentheses

functions:

sqrt(2)
sin(pi)
exp(1)
log(10)

assignment:

a<-5+2
a=2

list variables: ls()

getting information about a variable (data-type, structure etc):

class(x)
typeof(x) # for vectors
str(x)
attributes()

conditional operations, boolean logic, comparison operations

a <- 1
b <- 2
if (a > 0) cat("correct")
if (a > 1 | b > a) cat("correct")
# note that ¦ is not the same as |

strings splitting strings

x <- "taxid:559292(Saccharomyces cerevisiae S288c)"
strsplit(x, split="[:(]")  # split is a regex; use fixed=TRUE to interpret literally
unlist(strsplit(x, split="[:(]"))[2]  # "559292"
# also see stringr::str_split

concatenating (collapsing) a vector into a string using paste

x <- c("a","b","c")
paste(x, sep=" ")          # not what you want
paste(x, collapse=" ")     # "a b c"

vectors:

vec1 <- NULL
vec1 <- c()
vec1 = c(2,3,4)
vec2 = c(2,3,4)
vec3 = c(vec1,vec2)
vec3[2:4]       # elements 2..4
vec3[vec3<4]    # elements less than 4
vec3[-3]        # all elements except 3rd

lists:

obj = list(1,2,"cat")                 # mixed types
obj = list(a=1,b=2,name="sam")        # named list
obj$name
obj['name']
obj[['name']]

hashes (requires the hash package)
http://opendatagroup.wordpress.com/2009/07/26/hash-package-for-r/

hash assignment

hash()
hash( keys=c('foo','bar','baz'), values=1:3 )
hash( foo=1, bar=2, baz=3 )
hash( c( foo=1, bar=2, baz=3 ) )
hash( list( foo=1, bar=2, baz=3 ) )
hash( c('foo','bar','baz'), 1:3 )

hash accession The standard accessors: [, [[, $

h <- hash( c('foo','bar','baz'), 1:3 )
h[ c('foo','bar') ]
h[[ 'foo' ]]
h$foo

hash replacement operation

h <- hash( c('foo','bar','baz'), 1:3 )
h[ c('foo','bar') ] <- c( 'fred', 'wilma' )
h[[ 'foo' ]] <- 'dino'
h$foo <- 'bam bam'

hashes implemented using env (environments)

hash = new.env(hash=TRUE, parent=emptyenv(), size=100L)
assign(key, value, hash)
get(key, hash)
ls(env=hash)  # keys

how to remove NAs from a vector

v <- v[-which(is.na(v))]

how to replace NULLs in a list

l <- list(a=c(1:3), b=NULL, c=c(4:6))
lapply(l, function(x) if(is.null(x)){x<-"NA"} else {x})
# or
replace(l, which(is.na(l)), "NA")

replacing NULLs in a list of lists

l <- list(list(a=1,b=NULL), list(a=2,b=2)) 
lapply(l, function(x) lapply(x, function(x) ifelse(is.null(x), NA, x)))

cut - making bins from a continuous variable

matrix:

mat=matrix(1:9,nrow=3,ncol=3) 
mat[1,3]
mat[,2]
mat[2,]
mat[mat$V1=="3",]
for(i in 1:3){print(mat[i,3])}
mat=cbind(c(1,2,3),c(4,5,6),c(7,8,9))
mat=rbind(c(1,4,7),c(2,5,8),c(3,6,9))
rownames(mat)=c("Obs1", "Obs2", "Obs3")
colnames(mat)=c("Var1", "Var2", "Var3")

converting a data frame to a vector by first converting to a matrix (matrices)

thisVector = as.vector(as.matrix(unname(thisDataFrame)))

using names and multidimensional arrays as hashes

a<-array(0,dim=c(2,3))
dimnames(a)<-list(intA=c("P001","P002"), intB=c("P003","P004","P005"))
a["P001","P004"]<-1
a["P001","P004"]
dimnames(a)["intA"]
a["P001",]
a[c("P001","P002"),]
theseRows<-c("P001","P002")
a[theseRows,]
a[c(TRUE,FALSE),]
L = a["P001",]==1
y=(dimnames(a)[["intB"]])
y[L]  # "P004"

dates
http://www.statmethods.net/input/dates.html

today <- Sys.Date()
format(today, format="%B %d %Y")
date()

times

rightNow <-Sys.time()
format(rightNow, format="%H:%M:%S")

means - arithmetic, geometric and harmonic
http://stats.stackexchange.com/questions/23117/which-mean-to-use-and-when

(Excellent explanatory section retained as-is.)

data frames:

thisDataFrame1 = data.frame(cbind(x=1,y=1:10))
df = data.frame(matrix(vector(), 0, 3, dimnames=list(c(), c("Date", "File", "User"))), stringsAsFactors=F)
df <- data.frame(Date=as.Date(character()), File=character(), User=character(), stringsAsFactors=FALSE)
cancerNames2geneids <- data.frame(cancerName=character(), geneid=integer(), stringsAsFactors=FALSE)

deleting rows using logical indices / numeric indices:

airquality2 <- subset(airquality, !(Day %in% c(5, 7) & Month == 5))
airquality3 <- airquality[-c(2, 7), ]
df = df[-1,]

delete a column:

df$x = NULL

Using rbind overwrites column labels — notes preserved.

R Grouping functions: sapply/lapply/apply/tapply/by/aggregate
http://stackoverflow.com/questions/3505701/r-grouping-functions-sapply-vs-lapply-vs-apply-vs-tapply-vs-by-vs-aggrega

using apply

sapply(1:3, function(x) x^2)
lapply(1:3, function(x) x^2)

tapply

library(datasets)
data(iris)
tapply(iris$Sepal.Length, iris$Species, mean)

aggregate examples (ToothGrowth) — formulas shown:

aggregate(len ~ supp, data = ToothGrowth, mean)
aggregate(len ~ ., data = ToothGrowth, mean)
aggregate(. ~ supp, data = ToothGrowth, mean)

useful function for finding out about functions

isGeneric("print")
getOption("defaultPackages")
args(mean)
str(lm)
methods(predict)
search()

managing and working with packages and libraries
http://www.ats.ucla.edu/stat/r/faq/packages.htm

installed.packages()
available.packages()
install.packages("packageName")
search()
sessionInfo()
detach("package:pkg", unload=TRUE)

Example of detaching packages to resolve conflicts preserved.

printing to screen / variables:

vec1 = c(2,3,4); print(vec1); print("hello")

person <-"Grover"; action <-"flying"
cat("cat-Dimension of the data frame is", person,"\n", sep='\t')
message(sprintf("On %s I realized %s was...\n%s by the street", Sys.Date(), person, action))

print to a file

cat("Hello",file="outfile.txt",sep="\n")
cat("World",file="outfile.txt",append=TRUE)
write(thisSubjectName, file="outFile", append=TRUE, sep='')
sink("outfile.txt"); print("hello"); cat("\n"); cat("world"); sink()

printing inside a loop or a function — notes preserved.

make an eps figure

postscript(file="./fig2.eps", horizontal=F)
pie(c(1,2,3))
dev.off()

Getting/setting working dir, read/write tables

getwd(); setwd()
mat=matrix(1:9,nrow=3,ncol=3) 
write.table(mat, "mytable.txt")
thisTable = read.table("mytable.txt")

Basic plotting — references and links preserved.

Legends / factors example preserved.

Saving plots

par(mar=c(5,4,4,2)+0.1)
dev.copy(pdf, 'filename.pdf'); dev.off()
png(file="./fig2.png", width=500, height=500); plot(1:10); dev.off()

Histogram

x <- mtcars$mpg
hist(x, breaks=12, col="red"); rug(x)
hist(x, breaks=12, col="red", freq=FALSE); lines(density(x))