R for Bioinformatic Analyses

Hannah Tavalire and Bill Cresko - University of Oregon

January 2019 - Cesky Krumlov

Lecture 1 - Using R for Biostatistical Analyses

But first a beautiful chair

Before we talk about how amazing `R` is…

navigate into this directory: ~/workshop_materials/evomics_stats_2019/
type ‘git pull’

Why use `R`?

R is a statistical programming language (derived from S)
Superb data management & graphics capabilities
You can write your own functions
Powerful and flexible
Runs on all computer platforms
Well established system of packages and documentation
Active development and dedicated community
Can use a nice GUI front end such as Rstudio
Reproducibility
- keep your scripts to see exactly what was done
- distribute these with your data
- embed your R analyses in polished RMarkdown files
FREE

`R` resources

The R Project Homepage: http://www.r-project.org
Quick R Homepage: http://www.statmethods.net
Bioconductor: http://www.bioconductor.org
An Introduction to R (long!): http://cran.r-project.org/doc/manuals/R-intro.html
R for Data Science: https://r4ds.had.co.nz
Google - tutorials, guides, demos, packages and more

Running `R`

Need to make sure that you have R installed
- locally or on a server
- https://www.r-project.org
Run R from the command line
- just type R
- can run it locally as well as on clusters
Install a R Integrated Development Environment (IDE)
- RStudio: http://www.rstudio.com
- Makes working with R much easier, particularly for a new R user
- Run on Windows, Mac or Linux OS
- We’re running as a server on the AWS instances

`RStudio`

Exercise 1.1 - Exploring `RStudio`

Open RStudio by adding :8787 to your AMI url
Take a few minutes to familiarize yourself with the Rstudio environment by locating the following features:
- See what types of new files can be made in Rstudio by clicking the top left icon- open a new R script.
- The windows clockwise from top left are: the code editor, the workspace and history, the plots and files window, and the R console.
- In the plots and files window, click on the packages and help tabs to see what they offer.
Now open the file called Exercises_for_R_Lectures.Rmd in /workshop_materials/evomics_stat_2019/03.Exercises/
- This file will serve as your digital notebook for parts of the workshop and contains the other exercises.

Introduction to `RMarkdown`

`RMarkdown`

A great way to embed R code into descriptive files to keep your life organized
You can insert R chunks into Rmarkdown documents
You will be doing more with markdown on Thursday

Exercise 1.2 - Intro to `RMarkdown` Files

Take a few minutes to familiarize yourself with RMarkdown files by completing exercise 1.2 in your exercises document.

BASICS of `R`

Commands can be submitted through
- terminal, console or scripts
- can be embedded as code chunks in RMarkdown
On these slides evaluating code chunks and showing output
- shown here after the two # symbols
- the number of output items is in []
R follows the normal priority of mathematical evaluation (PEDMAS)

BASICS of `R`

Input code chunk and then output

4 * 4

## [1] 16

Input code chunk and then output

(4 + 3 * 2^2)

## [1] 16

Assigning Variables

A better way to do this is to assign variables
Variables are assigned values using the <- operator.
Variable names must begin with a letter, but other than that, just about anything goes.
Do keep in mind that R is case sensitive.

Assigning Variables

x <- 2
x * 3

## [1] 6

y <- x * 3
y - 2

## [1] 4

These do not work

3y <- 3
3*y <- 3

Arithmetic operations on functions

Arithmetic operations can be performed easily on functions as well as numbers.

x <- 12
x + 2

## [1] 14

x^2

## [1] 144

log(x)

## [1] 2.484907

Arithmetic operations on functions

Note that the last of these - log - is a built in function of R, and therefore the object of the function needs to be put in parentheses
These parentheses will be important, and we’ll come back to them later when we add arguments after the object in the parentheses
The outcome of calculations can be assigned to new variables as well, and the results can be checked using the print command

Arithmetic operations on functions

y <- 67
print(y)

## [1] 67

x <- 124
z <- (x * y)^2
print(z)

## [1] 69022864

STRINGS

Operations can be performed on character variables as well
Note that “characters” need to be set off by quotation marks to differentiate them from numbers
The c stands for concatenate
Note that we are using the same variable names as we did previously, which means that we’re overwriting our previous assignment
A good rule of thumb is to use new names for each variable, and make them short but still descriptive

STRINGS

x <- "I Love"
print(x)

## [1] "I Love"

y <- "Biostatistics"
print(y)

## [1] "Biostatistics"

z <- c(x, y)
print(z)

## [1] "I Love"        "Biostatistics"

VECTORS

In general R thinks in terms of vectors
- a list of characters, factors or numerical values (“I Love”)
- it will benefit any R user to try to write scripts with that in mind
- it will simplify most things
Vectors can be assigned directly using the ‘c()’ function and then entering the exact values.

VECTORS

n <- c(2, 3, 4, 2, 1, 2, 4, 5, 10, 8, 9)
print(n)

##  [1]  2  3  4  2  1  2  4  5 10  8  9

z <- n + 3
print(z)

##  [1]  5  6  7  5  4  5  7  8 13 11 12

FACTORS

The vector x is now what is called a list of character values (“I Love”).
Sometimes we would like to treat the characters as if they were units for subsequent calculations.
These are called factors, and we can redefine our character variables as factors.
This might seem a bit strange, but it’s important for statistical analyses where we might want to see the mean or variance for two different treatments.

FACTORS

x_factor <- as.factor(x)
print(x_factor)

## [1] I Love
## Levels: I Love

Note that factor levels are reported alphabetically

FACTORS

We can also determine how R “sees” a variable using str() or class() functions.
This is a useful check when importing datasets or verifying that you assigned a class correctly

str(x)

##  chr "I Love"

class(x)

## [1] "character"

Types or ‘classes’ of vectors of data

Types of vectors of data

int stands for integers
dbl stands for doubles, or real numbers
chr stands for character vectors, or strings
dttm stands for date-times (a date + a time)
lgl stands for logical, vectors that contain only TRUE or FALSE
fctr stands for factors, which R uses to represent categorical variables with fixed possible values
date stands for dates

Types of vectors of data

Logical vectors can take only three possible values:
- FALSE
- TRUE
- NA which is ‘not available’.
Integer and double vectors are known collectively as numeric vectors.
- In R numbers are doubles by default.
Integers have one special value: NA, while doubles have four:
- NA
- NaN which is ‘not a number’
- Inf
- -Inf

Basic Statistics

Many functions exist to operate on vectors.

mean(n)
median(n)
var(n)
log(n)
exp(n)
sqrt(n)
sum(n)
length(n)
sample(n, replace = T)  #has an additional argument (replace=T)

Arguments modify or direct the function in some way
- There are many arguments for each function, some of which are defaults
- Tab complete is helpful to view argument options

Getting Help

Getting Help on any function is very easy - just type a question mark and the name of the function.
There are functions for just about anything within R and it is easy enough to write your own functions if none already exist to do what you want to do.
In general, function calls have a simple structure: a function name, a set of parentheses and an optional set of parameters/arguments to send to the function.
Help pages exist for all functions that, at a minimum, explain what parameters exist for the function.

Getting Help

-help(mean)
-`?`(mean)
-example(mean)
-help.search("mean")
-apropos("mean")
-args(mean)

Creating vectors

Creating a vector of new data by entering it by hand can be a drag
However, it is also very easy to use functions such as
- seq
- sample

Creating vectors

What do the arguments mean?

seq_1 <- seq(0, 10, by = 0.1)
print(seq_1)

##   [1]  0.0  0.1  0.2  0.3  0.4  0.5  0.6  0.7  0.8  0.9  1.0  1.1  1.2  1.3
##  [15]  1.4  1.5  1.6  1.7  1.8  1.9  2.0  2.1  2.2  2.3  2.4  2.5  2.6  2.7
##  [29]  2.8  2.9  3.0  3.1  3.2  3.3  3.4  3.5  3.6  3.7  3.8  3.9  4.0  4.1
##  [43]  4.2  4.3  4.4  4.5  4.6  4.7  4.8  4.9  5.0  5.1  5.2  5.3  5.4  5.5
##  [57]  5.6  5.7  5.8  5.9  6.0  6.1  6.2  6.3  6.4  6.5  6.6  6.7  6.8  6.9
##  [71]  7.0  7.1  7.2  7.3  7.4  7.5  7.6  7.7  7.8  7.9  8.0  8.1  8.2  8.3
##  [85]  8.4  8.5  8.6  8.7  8.8  8.9  9.0  9.1  9.2  9.3  9.4  9.5  9.6  9.7
##  [99]  9.8  9.9 10.0

Creating vectors

seq_2 <- seq(10, 0, by = -0.1)
print(seq_2)

##   [1] 10.0  9.9  9.8  9.7  9.6  9.5  9.4  9.3  9.2  9.1  9.0  8.9  8.8  8.7
##  [15]  8.6  8.5  8.4  8.3  8.2  8.1  8.0  7.9  7.8  7.7  7.6  7.5  7.4  7.3
##  [29]  7.2  7.1  7.0  6.9  6.8  6.7  6.6  6.5  6.4  6.3  6.2  6.1  6.0  5.9
##  [43]  5.8  5.7  5.6  5.5  5.4  5.3  5.2  5.1  5.0  4.9  4.8  4.7  4.6  4.5
##  [57]  4.4  4.3  4.2  4.1  4.0  3.9  3.8  3.7  3.6  3.5  3.4  3.3  3.2  3.1
##  [71]  3.0  2.9  2.8  2.7  2.6  2.5  2.4  2.3  2.2  2.1  2.0  1.9  1.8  1.7
##  [85]  1.6  1.5  1.4  1.3  1.2  1.1  1.0  0.9  0.8  0.7  0.6  0.5  0.4  0.3
##  [99]  0.2  0.1  0.0

Creating vectors

seq_square <- (seq_2) * (seq_2)
print(seq_square)

##   [1] 100.00  98.01  96.04  94.09  92.16  90.25  88.36  86.49  84.64  82.81
##  [11]  81.00  79.21  77.44  75.69  73.96  72.25  70.56  68.89  67.24  65.61
##  [21]  64.00  62.41  60.84  59.29  57.76  56.25  54.76  53.29  51.84  50.41
##  [31]  49.00  47.61  46.24  44.89  43.56  42.25  40.96  39.69  38.44  37.21
##  [41]  36.00  34.81  33.64  32.49  31.36  30.25  29.16  28.09  27.04  26.01
##  [51]  25.00  24.01  23.04  22.09  21.16  20.25  19.36  18.49  17.64  16.81
##  [61]  16.00  15.21  14.44  13.69  12.96  12.25  11.56  10.89  10.24   9.61
##  [71]   9.00   8.41   7.84   7.29   6.76   6.25   5.76   5.29   4.84   4.41
##  [81]   4.00   3.61   3.24   2.89   2.56   2.25   1.96   1.69   1.44   1.21
##  [91]   1.00   0.81   0.64   0.49   0.36   0.25   0.16   0.09   0.04   0.01
## [101]   0.00

Creating vectors

seq_square_new <- (seq_2)^2
print(seq_square_new)

##   [1] 100.00  98.01  96.04  94.09  92.16  90.25  88.36  86.49  84.64  82.81
##  [11]  81.00  79.21  77.44  75.69  73.96  72.25  70.56  68.89  67.24  65.61
##  [21]  64.00  62.41  60.84  59.29  57.76  56.25  54.76  53.29  51.84  50.41
##  [31]  49.00  47.61  46.24  44.89  43.56  42.25  40.96  39.69  38.44  37.21
##  [41]  36.00  34.81  33.64  32.49  31.36  30.25  29.16  28.09  27.04  26.01
##  [51]  25.00  24.01  23.04  22.09  21.16  20.25  19.36  18.49  17.64  16.81
##  [61]  16.00  15.21  14.44  13.69  12.96  12.25  11.56  10.89  10.24   9.61
##  [71]   9.00   8.41   7.84   7.29   6.76   6.25   5.76   5.29   4.84   4.41
##  [81]   4.00   3.61   3.24   2.89   2.56   2.25   1.96   1.69   1.44   1.21
##  [91]   1.00   0.81   0.64   0.49   0.36   0.25   0.16   0.09   0.04   0.01
## [101]   0.00

R Interlude

Complete Exercises 1.3-1.6

Drawing samples from distributions

Here is a way to create your own data sets that are random samples…

x <- rnorm(n = 10000, mean = 0, sd = 10)
y <- sample(1:10000, 10000, replace = T)
xy <- cbind(x, y)
plot(xy)

Drawing samples from distributions

x <- rnorm(10000, 0, 10)
y <- sample(1:10000, 10000, replace = T)
xy <- cbind(x, y)
hist(x)

Drawing samples from distributions

You’ve probably figured out that y from the last example is drawing numbers with equal probability.
What if you want to draw from a distribution?
Again, play around with the arguments in the parentheses to see what happens.

x <- rnorm (10000, 0, 10)
y <- sample (???, 10000, replace = ???)

R for Bioinformatic Analyses

Hannah Tavalire and Bill Cresko - University of Oregon

January 2019 - Cesky Krumlov

Lecture 1 - Using R for Biostatistical Analyses

But first a beautiful chair

Before we talk about how amazing R is…

Why use R?

R resources

Running R

RStudio

Exercise 1.1 - Exploring RStudio

Introduction to RMarkdown

RMarkdown

Exercise 1.2 - Intro to RMarkdown Files

BASICS of R

BASICS of R

BASICS of R

Assigning Variables

Assigning Variables

Arithmetic operations on functions

Arithmetic operations on functions

Arithmetic operations on functions

STRINGS

STRINGS

VECTORS

VECTORS

FACTORS

FACTORS

FACTORS

Types or ‘classes’ of vectors of data

Types of vectors of data

Types of vectors of data

Basic Statistics

Getting Help

Getting Help

Creating vectors

Creating vectors

Creating vectors

Creating vectors

Creating vectors

R Interlude

Drawing samples from distributions

Drawing samples from distributions

Drawing samples from distributions

Drawing samples from distributions<

Before we talk about how amazing `R` is…

Why use `R`?

`R` resources

Running `R`

`RStudio`

Exercise 1.1 - Exploring `RStudio`

Introduction to `RMarkdown`

`RMarkdown`

Exercise 1.2 - Intro to `RMarkdown` Files

BASICS of `R`

BASICS of `R`

BASICS of `R`