Overview of R

Installing r-base (language) and R-Studio (environment)

# Installing R language
sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9
sudo add-apt-repository 'deb [arch=amd64,i386] https://cran.rstudio.com/bin/linux/ubuntu xenial/'
sudo apt-get update
sudo apt-get install r-base

Get the R-Studio from here.

If you want to code in terminal type R. Now you are in a R environment ("console"). q() to exit R in terminal. But use R Studio for scripting not the terminal.

Installing packages in R

Packages are organized in repositories: CRAN, Bioconductor, R-forge, Github or * Googlecode

Installing a package in R and first time source location

# 1 establish bioconductor as a source, first time only
source("https://bioconductor.org/biocLite.R")

# 2 install package methylKit from biocLite
biocLite("methylKit")

# CRAN
# install from CRAN
install.packages("fortunes")

Basics

"file" -> "new project" -> "new directory" and save it to a location

"file" -> "new file" -> "R Script"

Help functions Get help in R here or you use help commands:

help(read.table) # help on a function
?read.table # is the same

help.search("deviation")
??deviation # is the same

Variables

x <- 4+3 # adds sum of 4+3 to the variable x
y <- 9 # also shown in the environment window or with ls()
x+y
[1] 16 # result of x+y

Operators

More tutorials about operators

# Arithmetic Operators
    +, -, *, /, %%, %/%, ^
# Relational Operators, TRUE or FALSE as a result
    >, <, >=, <=, ==, !=
# Logical Operators, & = AND, | = OR
    &, | , !, &&, ||      #&& = searches if the value is somewhere in the vectors
    4<3 | 4>3             #4<3 = FALSE, 4>3 = TRUE
    [1] TRUE
# Assignment Operators
    <-, =, <<-, ->, ->>
# Miscellaneous Operators
    %in%, %*%
# check help for more

Value/data types

# Numeric – A floating point number (default data type of R):
    2.4, 9, 888
# Integer – A number with L suffix:
    2L, 25L, 0L
# Character - Any amount of text:
    "a", "school is a house", "66"
# Logical values:
    TRUE, FALSE
# Complex – defined via imaginary value i:
    2+3i, 5+8i

class() and typeof() functions

# class( ) to determine the class of the R object
# typeof( ) to determine the internal type (storage mode) of the object
    typeof(5.67)
    [1] "double"
    class(5)
    [1] "numeric"
# To create integer variable in R, you invoke as.integer() function.
    x <- as.integer(3)
    x
    [1] 3
    class(var_name)
    [1] "integer"

print("I am at home") #prints its argument in R console
[1] "I am at home"
x <- 6
print(x) #prints also variables
[1] 6
#paste() combines arguments and variables
print(paste("I went there", x, "times"))
[1] "I went there 6 times"

Data storage (Vector, Matrix, Data frame, List)

Vectors

x <- c(2,3,5) #Vector containing three numeric values
x
[1] 2 3 5
class(x)
[1] "numeric" #numeric vector storage

Vector commands

length() function gives length of vector class() function gives value type typeof() also does this somehow

Combining vectors * again with the c() command.

x <- c(1,2,3)
y <- c("a","b") #character vector
z <- c(x,y) #new vector will contain elements of both vectors
z
[1] "1" "2" "3" "a" "b"
class(z)
[1] "character" #numeric values will be converted into character string. As a vector contains same data type

Vector arithmetic's

#each member of vector will be multiplied by 5
    a <- c(1,3,5)
    5*a
    [1] 5 15 25
#Addition or multiplication will be performed element by element
    b <- c(2,4,6)
    a + b
    [1] 3 7 11
    a * b
    [1] 2 12 30
#multiplication as "power to" will be performed element by element
    a^2
    [1]1 9 25  # power 2 for each member
#if different length of vectors: then shorter will recycle - Recycling Rule.
    x <- c(2,4)
    y <- c(3,5,7,9)
    x + y
    [1] 5 9

Accessing Vector data The values of a vector can be retrieved <variable>[<position>] or removed <variable>[-<position>] Use c() function to retrieve more then one value * Use <- to redirect the values in new variables

    x <- c("a","b","c","d")
    x[3] #retrive command
    [1] c
    x[-3] # remove command
    [1] "a" "b" "d"
# retrieving more values frome one vector
    x[c(2,3)] #use the combine argument
    [1] "b" "c"
    x[c(2,3,3)]
    [1] "b" "c" "c" #Duplicate indexes
    x[c(2,1,3)]
    [1] "b" "a" "c" #out of order indexes
    x[2:4]
    [1] "b" "c" "d" #Range index with colon ":" operator

Name Vector Members * Names can be assigned to vector members by using names( )

    x <- c("Christian", "Brandt")
    names(x)<- c("first_name", "last_name") #naming each value entry of vector x
    x
     first_name    last_name
    "Christian"     "Brandt"
# data retrival works also with names
    x[c("last_name","first_name")]
    last_name     first_name
    "Christian"     "Brandt"

Other commands * such commands can all be combined like excel functions

#Create a sequential vector from 10 to 20 steps by 2
    seq(from=10,to=20,by=2)
    [1] 10 12 14 16 18 20
#Sequential vector of vector_length 5 which starts from 1 and incremented by 3
    seq(from=1,by=3,length.out=5)
    [1] 1 4 7 10 13
#replication command
    rep("hello",5)
    [1] "hello" "hello" "hello" "hello" "hello"
#combine rep( ) functions
    c(rep(1,2),rep(2,3),rep(3,4))
    [1] 1 1 2 2 2 3 3 3 3
#advanced examples of vector h    
    x<- 1:10
    x
    [1] 1 2 3 4 5 6 7 8 9 10
    y <- x[x<8 & x>3]
    y
    [1] 4 5 6 7

Matrices

#Create a matrix of 2 rows and 3 columns; byrow=FALSE fills columns first
    z <- matrix ( c(1,2,3,4,5,6), nrow=2, ncol=3, byrow= TRUE)
    z
    [,1] [,2] [,3]
    [1,]  1    2    3
    [2,]  4    5    6
#Accessing the data
    x[2,3]
    [1] 6 # value of 2nd row and 3th column
    x[2,]
    [1] 2 4 6 # values of 2nd row
    x[,c(1,3)]
        [,1] [,3]
    [1,]  1    5
    [2,]  2    6

Naming a matrix using dimnames then also retrivable by name e.g. x["row1", c("col1","col2")]

dimnames(x)<- list( c("row1", "row2"), c("col1", "col2", "col3") )
x
     col1 col2 col3
row1    1    2    3
row2    4    5    6

Show properties of matrix

class(x)
typeof(x)
nrow(x)
ncol(x)
length(x)

Dataframe - This is the Excel of R

a<- c(1,2,3) # numeric vector
b<- c("x","y","z") # character vector
c<- c(TRUE,FALSE,TRUE) # logical vector
# creating data frame
df<- data.frame (a,b,c)
# accessing data frame
df
      a b     c # header contains vector names
    1 1 x TRUE
    2 2 y FALSE
    3 3 z TRUE
#extracting data
df["1","b"]
[1] x

Adding and removing data * using rbind for rows and cbind for columns

#creating a dataframe    
    df <- data.frame(first = c('Christian'), last = c('Brandt'), lucky_number = c(5), stringsAsFactors = FALSE)
    df
        first   last lucky_number
    Christian Brandt            5
#adding a row
    df <- rbind(df, list('Erik', 'Bongcam', 10 ))
    df
            first    last lucky_number
    1   Christian  Brandt            5
    2        Erik Bongcam           10
#adding a column
    df <-cbind(df, coffeetime =c(TRUE,TRUE))
# removal of data with
    df1<- df[-2,] # remove 2nd row
    df2<- df[,-3] # remove 3rd column

Assigning names

# You can assign names to rows and columns as:
    dimnames(df)<- list ( c("row1","row2","row3"), c("col1","col2","col3") )
    df
    col1 col2  col3
    row1    1  TRUE
    row2    2 FALSE
    row3    3  TRUE
# possible ways to get data (all with the same results)
    df[[3]]
    df[["col3"]]
    df$col3
    df[,"col3"]

    [1] TRUE FALSE TRUE #result for all 4 commands
#retrieve multiple columns
    df[c("col2","col3")]

List

    my_list <- list(a,b,33) #a and b is a vector, 33 is only a value
    my_list
    [[1]]
    [1] 1 2 3

    [[2]]
    [1] "SLU" "Uppsala"

    [[3]]
    [1] 33
#Accessing data
    my_list[2]
    [[2]]
    [1] "SLU" "Uppsala"

    my_list[[2]][1]
    [1] "SLU"
#Modify the contents of list members directly with [[ ]]
    my_list[[2]][1] = "new_SLU"

Advanced data manipulation

if -else, for, while function

IF ELSE functions * example:

x <- 9
if (x >= 10)
{
print("x is greater than or equal to 10")
}
else
{
print("x is less than 10")
}

Loop functions * for(i in <variable>). Thats the counter. A interactive tutorial on loops can be found here (includes coding, while functions).

for(i in 1:10) {print(i)} #output goes from 1 to 10
# or do stuff like this:
m<- c(1:10) #a vector from 1 to 10
for(i in m){print(paste("5*",i,"=",5*i))}
#results is 5*3=15 for one of the 10 rows

Working dir

Advanced folder creation with IF loop:

mainDir <- "/Users/naat0001/Desktop"
subDir <- "outputDirectory"
if (file.exists(subDir)) #TRUE or FALSE
{
setwd(file.path(mainDir, subDir))
}
else
{
dir.create(file.path(mainDir, subDir)) #creates dir
setwd(file.path(mainDir, subDir))
}
# file.path(mainDir, subDir) says basically
# /Users/naat0001/Desktop/outputDirectory

File import and download

x <- read.table("path_to_file/file_name.csv", sep=",", header = TRUE)
# read.table needs sep="" and header =TRUE

Download and read a file download.file does store the file in your working dir with destfile=

download.file("https://raw.githubusercontent.com/swcarpentry/r-novice-gapminder/gh-pages/_episodes_rmd/data/gapminder-FiveYearData.csv", destfile = "gapminder-FiveYearData.csv")
# read the file to a varible
x <- read.csv("gapminder-FiveYearData.csv")
# you can also directly read the online file to a variable
x <- read.table("https://raw.githubusercontent.com/swcarpentry/r-novice-gapminder/gh-pages/_episodes_rmd/data/gapminder-FiveYearData.csv", sep=",", header = TRUE)

Useful commands

#x is a dataframe
str(x) # tells you what x is, e.g. a dataframe and what class in each column is stored (e.g. numerical)
head(x) #To view the first few rows of the data
tail(x) #To view the last few rows of the data
summary(x) #shows for each coloum various stuff like minimum, lower quartile, median, mean, upper quartile...
nrow(x) #number of rows
#nrow example with build in functions:
nrow(x[x$BPressure=="high" | x$BPressure=="pre-high",]) #counts rows in colum BPressure with the value high oder pre-high
#like SVERWEIS if value in BCHolesterol >190 show value of column Name in the same row  
x[x$BCholesterol>190,"Name"]

Export data frames into a file You do this with the write.table() function If you don’t have any row names set up for your data frame use this:

write.table(data,file="out.txt",sep="\t",quote=FALSE,row.names=FALSE)

write.table(data,file="out.txt",sep="\t",quote=FALSE,col.names=NA)`

Merging two files

# lets say i have 2 dataframes:
df1 <- data.frame(Chr=paste0('chr',1:9), Gene=paste0('gene',19:11))
df2 <- data.frame(Chr=paste0('chr',2:10),Position=paste0('pos',22:30))
# they look like this, shortend it with (...)
> df1
   Chr   Gene
1 chr1 gene19
2 chr2 gene18
3 chr3 gene17
4 chr4 gene16
(...)
> df2
    Chr Position
1  chr2    pos22
2  chr3    pos23
3  chr4    pos24
4  chr5    pos25
(...)
# you need two uniq column names, one in each dataframe
# we merge them based on the uniq "Chr" column (our "ID")
df3 <- merge(df1, df2, by="Chr")
# it only works if the column names are the same
# so use this to name them (changes the name of df2 column 1 to "Chr_ID":
colnames(df2)[1] <- "Chr_ID"
> df3
 Chr   Gene Position
1 chr2 gene18    pos22
2 chr3 gene17    pos23
3 chr4 gene16    pos24
4 chr5 gene15    pos25
(...)

# alternatively say which columns should be used for the merging:
df3 <- merge(df1, df2, by.x="Chr", by.y="Chr_ID")

Graphs in R

x <- rnorm(50) # 50 random values from normal distribution.
hist(x) # plot histogram of values of x
hist(x,main="title of histogram") # change title
hist(x, main = "Title of histogram", col = "orange") # with color bars

# How to save / export graphs from console in pdf format.
pdf(file="my_histogram.pdf")
hist(x, main = "Title of histogram", col = "orange")
dev.off()