R can be used as a calculator for basic arithmetic but also allows working with different data types like numeric, logical, and character vectors. Variables are created by assigning values and can contain single items or collections of items. Common data structures in R include vectors, matrices, data frames, and lists which allow organizing multiple values and combining different data types. Factors are a special data type for categorical variables.

1. R structures & objects
Day 1 - Introduction to R for Life Sciences

2. R: a calculator on steroids
The most basic use of R, is that as a calculator:
> 3+2
5
> 5*12
60
But it can do much more

3. Data types
numeric # integers & floating point numbers, e.g. 3; -4.4; 1.36e-9
logical # truth values: only TRUE and FALSE
chr # strings, e.g. "hello", "yes", ""
factors # categories (a bit like chr)
All types have special value NA, representing "not available" or
“illegal value”
There is a special case called NULL, which means: “nothing at all”
# Note the double quotes!!!

4. Variables and assignment
Variables contain values. This can also be more than a single item.
Variables are created as soon as you assign a value
Previous value, if any is, overwritten
> mynumber <- 24.3 # mnemonic: 24.3 ‘goes into’ mynumber
> mycharacter <- "foobar" # (also called a ‘value’, even though not numerical)

5. Data types & variables
Variable names are case-sensitive!
The following variables are all different:
> mynumber <- 24.3
> MyNumber <- 24.3
> my.number <- 24.3 # dot (‘.’) and underscore (‘_’) are often used to make variable names
> my_number <- 24.3 # more readable. They have no special significance
> "foobar" # is a literal value
> foobar # is a variable

6. Data structures
Vectors
Matrices
Data.frames
Lists

7. Data structures - vectors
Vectors are built with the c()-operator
> a <- c(1, 2, 4, 4.3, -3, 6) # numeric vector, assigned using the “<-” operator
> b <- c("red", "green", "blue") # character vector
> c <- c(TRUE, FALSE, TRUE, TRUE, FALSE) # logical vector
Single values don’t really exist:
x <- 1 # x is a vector of length 1
All values in one vector have the same type:
c(1, "red", TRUE) # can not exist, is converted to c("1", "red", "TRUE")

8. Creating vectors
> a <- c(1, 10, 11)
> b <- c(20, 30)
> d <- c(a, 12)
> e <- c(12, a) # note: different from d!
> f <- c(b, d)
> b <- c(b, 40)

9. > a <- seq(1, 5, 1) # shorthand for this: a <- 1:5
[1] 1 2 3 4 5
> b <- rep(1:3, 2)
[1] 1 2 3 1 2 3
> c <- runif(10) # generate 10 random values
When in doubt, consult the function’s documentation, e.g. ?runif
Convenience functions for creating vectors

10. Matrices
A matrix is a “vector in the shape of a table”
All items in the matrix are the same data type
Can be built from rows using rbind(), or from columns using cbind(),
or using matrix()
> rbind( 1:3, 11:13)
[,1] [,2] [,3]
[1,] 1 2 3
[2,] 11 12 13
> cbind(11:13, 23:25)
[,1] [,2]
[1,] 11 23
[2,] 12 24
[3,] 13 25

11. Using the matrix function
> x <- matrix(1:6, nrow=2, byrow=TRUE)
[,1] [,2] [,3]
[1,] 1 2 3
[2,] 4 5 6
Row and column names make life easier!
> x <- matrix(1:6, nrow=2, byrow=TRUE,
dimnames=list( c(“geneA”, “geneB”), c(“delA”, “delB”, “delC”))
delA delB delC
geneA 1 2 3
geneB 4 5 6

12. Data structures - data.frames
Data.frame: a more general form of a matrix, its columns can be
different type
> id <- c(1, 2, 3, 4)
> color <- c("red", "green", "blue", NA)
> passed <- c(TRUE, TRUE, TRUE, FALSE)
> mydata <- data.frame(id, color, passed)
id color passed
1 1 red TRUE
2 2 green TRUE
3 3 blue TRUE
4 4 <NA> FALSE

13. Data structures - lists
An ordered collection of "things"
> a <- c(1, 2, 3, 4)
> mylist <- list(name="Patrick", numbers=a, age=38)
$name
[1] "Patrick"
$numbers
[1] 1 2 3 4
$age
[1] 38

14. Data types - factors
Factors deal with categorical variables
> gender <- factor(c(rep("male", 2), rep("female", 3)))
> gender
[1] male male female female female the actual values
Levels: female male allowed values
> str(gender)
Factor w/ 2 levels "female","male": 2 2 1 1 1

15. Operations are always element-wise
> a <- 1:3
> b <- 4:6
> a + b
5 7 9
> b^a # ‘raised to power’
4 25 216
> p <- matrix(1:4, ncol=2,
byrow=TRUE)
> q <- cbind(c(10, 10), c(100,100))
> p*q
[,1] [,2]
[1,] 10 200
[2,] 30 400

16. Auto-recycling of vector content
If you combine vectors of different length, R will automatically
recycle vector content to the longest of the two:
> mynumbers <- c(10.4, 5, 8.4, 3)
> mynumbers2 <- mynumbers + 1
> mynumbers2
11.4, 6, 9.4, 4
What in fact happens is
> mynumbers2 <- mynumbers + c(1, 1, 1, 1)

17. Useful functions
str() # display the data structure
summary() # display a summary of the data
length() # get the length of a vector or list (data.frame: nrow!)
dim() # get the dimensions of a data.frame or matrix
head() # show the first part of a data structure
You can also explore your data in the Environment window!