What is R?

R is a language and environment for statistical computing and graphics developed in 1993. It provides a wide variety of statistical and graphical techniques (linear and nonlinear modeling, statistical tests, time series analysis, classification, clustering, …), and is highly extensible, meaning that the user community can write new R tools. It is a GNU project (Free and Open Source).

The R language has its roots in the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and now, R is developed by the R Development Core Team, of which Chambers is a member. R is named partly after the first names of the first two R authors (Robert Gentleman and Ross Ihaka), and partly as a play on the name of S. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.

Some of R’s strengths:

• The ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.
• It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.
• R can be extended (easily) via packages.
• R has its own LaTeX-like documentation format, which is used to supply comprehensive documentation, both on-line in a number of formats and in hardcopy.
• It has a vast community both in academia and in business.
• It’s FREE!

The R environment

R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes

• an effective data handling and storage facility,
• a suite of operators for calculations on arrays, in particular matrices,
• a large, coherent, integrated collection of intermediate tools for data analysis,
• graphical facilities for data analysis and display either on-screen or on hardcopy, and
• a well-developed, and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.

The term “environment” is intended to characterize it as a fully planned and coherent system, rather than an incremental accretion of very specific and inflexible tools, as is frequently the case with other data analysis software.

R, like S, is designed around a true computer language, and it allows users to add additional functionality by defining new functions. Much of the system is itself written in the R dialect of S, which makes it easy for users to follow the algorithmic choices made. For computationally-intensive tasks, C, C++ and Fortran code can be linked and called at run time. Advanced users can write C code to manipulate R objects directly.

Many users think of R as a statistics system. The R group prefers to think of it of an environment within which statistical techniques are implemented.

The R Homepage

The R homepage has a wealth of information on it,

R-project.org

On the homepage you can:

• Learn more about R
• Download R
• Get Documentation (official and user supplied)
• Get access to CRAN ‘Comprehensive R archival network’

Interface for R

There are many ways one can interface with R language. Here are a few popular ones:

• RStudio
• RGui
• Jupyter and R notebooks
• text editors, such as vi(m), Emacs…

RStudio

RStudio started in 2010, to offer R a more full featured integrated development environment (IDE) and modeled after matlab’s IDE.

RStudio has many features:

• syntax highlighting
• code completion
• smart indentation
• “Projects”
• workspace browser and data viewer
• embedded plots
• Markdown notebooks, Sweave authoring and knitr with one click pdf or html
• runs on all platforms and over the web
• etc. etc. etc.

RStudio and its team have contributed to many R packages.[13] These include:

• Tidyverse – R packages for data science, including ggplot2, dplyr, tidyr, and purrr
• Shiny – An interactive web technology
• RMarkdown – Insert R code into markdown documents
• knitr – Dynamic reports combining R, TeX, Markdown & HTML
• packrat – Package dependency tool
• devtools – Package development tool

RStudio Cheat Sheets: rstudio-ide.pdf

Simple variables: variables that have a numeric value, a character value (such as a string), or a logical value (True or False)

Examples of numeric values.

# assign number 150 to variable a.
a <- 150
a

## [1] 150

# assign a number in scientific format to variable b.
b <- 3e-2
b

## [1] 0.03

Examples of character values.

# assign a string "Professor" to variable title
title <- "Professor"
title

## [1] "Professor"

# assign a string "Hello World" to variable hello
hello <- "Hello World"
hello

## [1] "Hello World"

Examples of logical values.

# assign logical value "TRUE" to variable is_female
is_female <- TRUE
is_female

## [1] TRUE

# assign logical value "FALSE" to variable is_male
is_male <- FALSE
is_male

## [1] FALSE

# assign logical value to a variable by logical operation
age <- 20
is_adult <- age > 18
is_adult

## [1] TRUE

To find out the type of variable.

class(is_female)

## [1] "logical"

# To check whether the variable is a specific type
is.numeric(hello)

## [1] FALSE

is.numeric(a)

## [1] TRUE

is.character(hello)

## [1] TRUE

The rule to convert a logical variable to numeric: TRUE > 1, FALSE > 0

as.numeric(is_female)

## [1] 1

as.numeric(is_male)

## [1] 0

R does not know how to convert a numeric variable to a character variable.

b

## [1] 0.03

as.character(b)

## [1] "0.03"

Vectors: a vector is a combination of multiple values(numeric, character or logical) in the same object. A vector is created using the function c() (for concatenate).

friend_ages <- c(21, 27, 26, 32)
friend_ages

## [1] 21 27 26 32

friend_names <- c("Mina", "Ella", "Anna", "Cora")
friend_names

## [1] "Mina" "Ella" "Anna" "Cora"

One can give names to the elements of a vector.

# assign names to a vector by specifying them
names(friend_ages) <- c("Mina", "Ella", "Anna", "Carla")
friend_ages

##  Mina  Ella  Anna Carla 
##    21    27    26    32

# assign names to a vector using another vector
names(friend_ages) <- friend_names
friend_ages

## Mina Ella Anna Cora 
##   21   27   26   32

Or One may create a vector with named elements from scratch.

friend_ages <- c(Mina=21, Ella=27, Anna=26, Cora=32)
friend_ages

## Mina Ella Anna Cora 
##   21   27   26   32

To find out the length of a vector:

length(friend_ages)

## [1] 4

To access elements of a vector: by index, or by name if it is a named vector.

friend_ages[2]

## Ella 
##   27

friend_ages["Ella"]

## Ella 
##   27

friend_ages[c(1,3)]

## Mina Anna 
##   21   26

friend_ages[c("Mina", "Anna")]

## Mina Anna 
##   21   26

# selecting elements of a vector by excluding some of them.
friend_ages[-3]

## Mina Ella Cora 
##   21   27   32

To select a subset of a vector can be done by logical vector.

my_friends <- c("Mina", "Ella", "Anna", "Cora")
my_friends

## [1] "Mina" "Ella" "Anna" "Cora"

has_child <- c("TRUE", "TRUE", "FALSE", "TRUE")
has_child

## [1] "TRUE"  "TRUE"  "FALSE" "TRUE"

my_friends[has_child == "TRUE"]

## [1] "Mina" "Ella" "Cora"

Matrices: A matrix is like an Excel sheet containing multiple rows and columns. It is used to combine vectors of the same type.

col1 <- c(1,3,8,9)
col2 <- c(2,18,27,10)
col3 <- c(8,37,267,19)

my_matrix <- cbind(col1, col2, col3)
my_matrix

##      col1 col2 col3
## [1,]    1    2    8
## [2,]    3   18   37
## [3,]    8   27  267
## [4,]    9   10   19

rownames(my_matrix) <- c("row1", "row2", "row3", "row4")
my_matrix

##      col1 col2 col3
## row1    1    2    8
## row2    3   18   37
## row3    8   27  267
## row4    9   10   19

t(my_matrix)

##      row1 row2 row3 row4
## col1    1    3    8    9
## col2    2   18   27   10
## col3    8   37  267   19

To find out the dimension of a matrix:

ncol(my_matrix)

## [1] 3

nrow(my_matrix)

## [1] 4

dim(my_matrix)

## [1] 4 3

Accessing elements of a matrix is done in similar ways to accessing elements of a vector.

my_matrix[1,3]

## [1] 8

my_matrix["row1", "col3"]

## [1] 8

my_matrix[1,]

## col1 col2 col3 
##    1    2    8

my_matrix[,3]

## row1 row2 row3 row4 
##    8   37  267   19

my_matrix[col3 > 20,]

##      col1 col2 col3
## row2    3   18   37
## row3    8   27  267

Calculations with matrices.

my_matrix * 3

##      col1 col2 col3
## row1    3    6   24
## row2    9   54  111
## row3   24   81  801
## row4   27   30   57

log10(my_matrix)

##           col1     col2     col3
## row1 0.0000000 0.301030 0.903090
## row2 0.4771213 1.255273 1.568202
## row3 0.9030900 1.431364 2.426511
## row4 0.9542425 1.000000 1.278754

Total of each row.

rowSums(my_matrix)

## row1 row2 row3 row4 
##   11   58  302   38

Total of each column.

colSums(my_matrix)

## col1 col2 col3 
##   21   57  331

It is also possible to use the function apply() to apply any statistical functions to rows/columns of matrices. The advantage of using apply() is that it can take a function created by user.

The simplified format of apply() is as following:

apply(X, MARGIN, FUN)

X: data matrix MARGIN: possible values are 1 (for rows) and 2 (for columns) FUN: the function to apply on rows/columns

To calculate the mean of each row.

apply(my_matrix, 1, mean)

##       row1       row2       row3       row4 
##   3.666667  19.333333 100.666667  12.666667

To calculate the median of each row

apply(my_matrix, 1, median)

## row1 row2 row3 row4 
##    2   18   27   10

Factors: a factor represents categorical or groups in data. The function factor() can be used to create a factor variable.

friend_groups <- factor(c(1,2,1,2))
friend_groups

## [1] 1 2 1 2
## Levels: 1 2

In R, categories are called factor levels. The function levels() can be used to access the factor levels.

levels(friend_groups)

## [1] "1" "2"

Change the factor levels.

levels(friend_groups) <- c("best_friend", "not_best_friend")
friend_groups

## [1] best_friend     not_best_friend best_friend     not_best_friend
## Levels: best_friend not_best_friend

Change the order of levels.

levels(friend_groups) <- c("not_best_friend", "best_friend")
friend_groups

## [1] not_best_friend best_friend     not_best_friend best_friend    
## Levels: not_best_friend best_friend

By default, the order of factor levels is taken in the order of numeric or alphabetic.

friend_groups <- factor(c("not_best_friend", "best_friend", "not_best_friend", "best_friend"))
friend_groups

## [1] not_best_friend best_friend     not_best_friend best_friend    
## Levels: best_friend not_best_friend

The factor levels can be specified when creating the factor, if the order does not follow the default rule.

friend_groups <- factor(c("not_best_friend", "best_friend", "not_best_friend", "best_friend"), levels=c("not_best_friend", "best_friend"))
friend_groups

## [1] not_best_friend best_friend     not_best_friend best_friend    
## Levels: not_best_friend best_friend

If you want to know the number of individuals at each levels, there are two functions.

summary(friend_groups)

## not_best_friend     best_friend 
##               2               2

table(friend_groups)

## friend_groups
## not_best_friend     best_friend 
##               2               2

Data frames: a data frame is like a matrix but can have columns with different types (numeric, character, logical).

A data frame can be created using the function data.frame().

# creating a data frame using previously defined vectors
friends <- data.frame(name=friend_names, age=friend_ages, child=has_child)
friends

##      name age child
## Mina Mina  21  TRUE
## Ella Ella  27  TRUE
## Anna Anna  26 FALSE
## Cora Cora  32  TRUE

To check whether a data is a data frame, use the function is.data.frame().

is.data.frame(friends)

## [1] TRUE

is.data.frame(my_matrix)

## [1] FALSE

One can convert a object to a data frame using the function as.data.frame().

class(my_matrix)

## [1] "matrix"

my_data <- as.data.frame(my_matrix)
class(my_data)

## [1] "data.frame"

A data frame can be transposed in the similar way as a matrix.

my_data

##      col1 col2 col3
## row1    1    2    8
## row2    3   18   37
## row3    8   27  267
## row4    9   10   19

t(my_data)

##      row1 row2 row3 row4
## col1    1    3    8    9
## col2    2   18   27   10
## col3    8   37  267   19

To obtain a subset of a data frame can be done in similar ways as we have discussed: by index, by row/column names, or by logical values.

friends["Mina",]

##      name age child
## Mina Mina  21  TRUE

# The columns of a data frame can be referred to by the names of the columns
friends

##      name age child
## Mina Mina  21  TRUE
## Ella Ella  27  TRUE
## Anna Anna  26 FALSE
## Cora Cora  32  TRUE

friends$age

## [1] 21 27 26 32

friends[friends$age > 26,]

##      name age child
## Ella Ella  27  TRUE
## Cora Cora  32  TRUE

friends[friends$child == "TRUE",]

##      name age child
## Mina Mina  21  TRUE
## Ella Ella  27  TRUE
## Cora Cora  32  TRUE

Function subset() can also be used to get a subset of a data frame.

# select friends that are older than 26
subset(friends, age > 26)

##      name age child
## Ella Ella  27  TRUE
## Cora Cora  32  TRUE

# select the information of the ages of friends
subset(friends, select=age)

##      age
## Mina  21
## Ella  27
## Anna  26
## Cora  32

A data frame can be extended.

# add a column that has the information on the marrital status of friends
friends$married <- c("YES", "YES", "NO", "YES")
friends

##      name age child married
## Mina Mina  21  TRUE     YES
## Ella Ella  27  TRUE     YES
## Anna Anna  26 FALSE      NO
## Cora Cora  32  TRUE     YES

A data frame can also be extended using the functions cbind() and rbind().

# add a column that has the information on the salaries of friends
cbind(friends, salary=c(4000, 8000, 2000, 6000))

##      name age child married salary
## Mina Mina  21  TRUE     YES   4000
## Ella Ella  27  TRUE     YES   8000
## Anna Anna  26 FALSE      NO   2000
## Cora Cora  32  TRUE     YES   6000

Lists: a list is an ordered collection of objects, which can be any type of R objects (vectors, matrices, data frames).

A list can be created using the function list().

my_list <- list(mother="Sophia", father="John", sisters=c("Anna", "Emma"), sister_age=c(5, 10))
my_list

## $mother
## [1] "Sophia"
## 
## $father
## [1] "John"
## 
## $sisters
## [1] "Anna" "Emma"
## 
## $sister_age
## [1]  5 10

# names of elements in the list
names(my_list)

## [1] "mother"     "father"     "sisters"    "sister_age"

# number of elements in the list
length(my_list)

## [1] 4

To access elements of a list can be done using its name or index.

my_list$mother

## [1] "Sophia"

my_list[["mother"]]

## [1] "Sophia"

my_list[[1]]

## [1] "Sophia"

my_list[[3]]

## [1] "Anna" "Emma"

my_list[[3]][2]

## [1] "Emma"

Invoking a function by its name, followed by the parenthesis and zero or more arguments.

# to find out the current working directory
getwd()

## [1] "/Users/jli/Jessie/Research/BioInfo/Courses/2019-March-Bioinformatics-Prerequisites/wednesday/Intro2R"

# to set a different working directory, use setwd
setwd("/Users/jli/Desktop")

# to list all variables in the environment
ls()

##  [1] "a"             "actg"          "actgm"         "age"          
##  [5] "b"             "col1"          "col2"          "col3"         
##  [9] "colFmt"        "data"          "data2"         "fq"           
## [13] "fqm"           "friend_ages"   "friend_groups" "friend_names" 
## [17] "friends"       "full"          "g"             "gc"           
## [21] "h"             "has_child"     "hello"         "i"            
## [25] "i2"            "ic"            "id"            "is"           
## [29] "is_adult"      "is_female"     "is_male"       "j"            
## [33] "k"             "l"             "my_data"       "my_friends"   
## [37] "my_list"       "my_matrix"     "new_is"        "rl"           
## [41] "sn"            "title"         "x"             "y"

# to create a vector from 2 to 3, using increment of 0.1
seq(2, 3, by=0.1)

##  [1] 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0

# to create a vector with repeated elements
rep(1:3, times=3)

## [1] 1 2 3 1 2 3 1 2 3

rep(1:3, each=3)

## [1] 1 1 1 2 2 2 3 3 3

# to get help information on a function in R: ?function.name
?seq
?sort
?rep

str(data2)

## 'data.frame':    33602 obs. of  24 variables:
##  $ C61 : int  322 149 15 687 1 1447 2667 297 0 74 ...
##  $ C62 : int  346 87 32 469 1 1032 2472 226 0 79 ...
##  $ C63 : int  256 162 35 568 5 1083 2881 325 0 138 ...
##  $ C64 : int  396 144 22 651 4 1204 2632 341 0 85 ...
##  $ C91 : int  372 189 24 885 5 1413 5120 199 0 68 ...
##  $ C92 : int  506 169 33 978 3 1484 6176 180 0 41 ...
##  $ C93 : int  361 147 21 794 0 1138 7088 195 0 110 ...
##  $ C94 : int  342 108 35 862 2 938 6810 107 0 81 ...
##  $ I561: int  638 163 18 799 4 1247 2258 377 0 72 ...
##  $ I562: int  488 141 8 769 3 1516 1808 534 0 76 ...
##  $ I563: int  440 119 54 725 1 984 2279 300 0 184 ...
##  $ I564: int  479 147 35 715 0 1044 2299 223 0 156 ...
##  $ I591: int  770 182 23 811 2 1374 4755 298 0 96 ...
##  $ I592: int  430 156 8 567 8 1355 3128 318 0 70 ...
##  $ I593: int  656 153 16 831 8 1437 4419 397 0 77 ...
##  $ I594: int  467 177 24 694 1 1577 3726 373 0 77 ...
##  $ I861: int  143 43 42 345 0 412 1452 86 0 174 ...
##  $ I862: int  453 144 17 575 4 1338 1516 266 0 113 ...
##  $ I863: int  429 114 22 605 0 1051 1455 281 0 69 ...
##  $ I864: int  206 50 39 404 3 621 1429 164 0 176 ...
##  $ I891: int  567 161 26 735 5 1434 3867 230 0 69 ...
##  $ I892: int  458 195 28 651 7 1552 4718 270 0 80 ...
##  $ I893: int  520 157 39 725 0 1248 4580 220 0 81 ...
##  $ I894: int  474 144 30 591 5 1186 3575 229 0 62 ...

Two special functions: lapply() and sapply()

#?lapply()

data <- as.data.frame(matrix(rnorm(49), ncol=7), stringsAsFactors=F)
dim(data)

## [1] 7 7

lapply(1:dim(data)[1], function(x){sum(data[x,])})

## [[1]]
## [1] -0.245014
## 
## [[2]]
## [1] -0.6737587
## 
## [[3]]
## [1] 0.8720167
## 
## [[4]]
## [1] -6.901058
## 
## [[5]]
## [1] -1.824077
## 
## [[6]]
## [1] 4.803424
## 
## [[7]]
## [1] -2.255819

apply(data, MARGIN=1, sum)

## [1] -0.2450140 -0.6737587  0.8720167 -6.9010579 -1.8240773  4.8034239
## [7] -2.2558193

lapply(1:dim(data)[1], function(x){log10(sum(data[x,]))})

## Warning in FUN(X[[i]], ...): NaNs produced

## Warning in FUN(X[[i]], ...): NaNs produced

## Warning in FUN(X[[i]], ...): NaNs produced

## Warning in FUN(X[[i]], ...): NaNs produced

## Warning in FUN(X[[i]], ...): NaNs produced

## [[1]]
## [1] NaN
## 
## [[2]]
## [1] NaN
## 
## [[3]]
## [1] -0.0594752
## 
## [[4]]
## [1] NaN
## 
## [[5]]
## [1] NaN
## 
## [[6]]
## [1] 0.6815509
## 
## [[7]]
## [1] NaN

# To check the syntax of using sapply():
#?sapply()

sapply(1:dim(data)[1], function(x){log10(sum(data[x,]))})

## Warning in FUN(X[[i]], ...): NaNs produced

## Warning in FUN(X[[i]], ...): NaNs produced

## Warning in FUN(X[[i]], ...): NaNs produced

## Warning in FUN(X[[i]], ...): NaNs produced

## Warning in FUN(X[[i]], ...): NaNs produced

## [1]        NaN        NaN -0.0594752        NaN        NaN  0.6815509
## [7]        NaN

sapply(1:dim(data)[1], function(x){log10(sum(data[x,]))}, simplify=FALSE)

## Warning in FUN(X[[i]], ...): NaNs produced

## Warning in FUN(X[[i]], ...): NaNs produced

## Warning in FUN(X[[i]], ...): NaNs produced

## Warning in FUN(X[[i]], ...): NaNs produced

## Warning in FUN(X[[i]], ...): NaNs produced

## [[1]]
## [1] NaN
## 
## [[2]]
## [1] NaN
## 
## [[3]]
## [1] -0.0594752
## 
## [[4]]
## [1] NaN
## 
## [[5]]
## [1] NaN
## 
## [[6]]
## [1] 0.6815509
## 
## [[7]]
## [1] NaN

It is always recommended to update to the most current version of R and Bioconductor. If it is not possible and R < 3.5.0, please use the legacy approach to install Bioconductor packages

source("http://bioconductor.org/biocLite.R")
## install core packages
biocLite()
## install specific packages
biocLite("RCircos")
biocLite(c("IdeoViz", "devtools"))

The R function install.packages() can be used to install packages that are not part of Bioconductor.

install.packages("ggplot2", repos="http://cran.us.r-project.org")

Install from source of github.

library(devtools)
install_github("stephenturner/qqman")

To save history in R session

savehistory(file="March27.history")

#loadhistory(file="March27.history")

To save objects in R session

save(list=c("x", "data"), file="March27.RData")

#load("March27.RData")