1) The document discusses various ways to access and select elements from vectors, matrices, and data frames in R, including using integers to specify positions, logical vectors to specify TRUE/FALSE elements, and character vectors to specify names. 2) It provides examples of accessing elements using these different methods, as well as examples using logical and mathematical operators like <, >, &, | for element selection. 3) The document discusses how R automatically recycles (repeats) values in shorter vectors to match the length of longer vectors during operations, and how this works for logical operators.

1. Access, select & ordering
Day 1 - Introduction to R for Life Sciences

2. Accessing vectors, matrices, data.frames
Positions within vectors, matrices and data.frames are accessed
using [ ]:
> v <- c(10, 3, 5, 10)
> v[2]
3
[] can also be used to assign (write) new values, e.g: v[2] <- 10
( ) are used for function calls (or grouping operators, more later) !!!
for instance: myvector <- c( ), mymatrix <- matrix( ), mydata <- data.frame( )

3. Three ways to access values from vectors, matrices
and data.frames
Integers: specify the positions of the elements you mean
Logical: specify (using TRUE/FALSE) which elements you want
Character: specify their names
only if your vector/matrix/data.frame has (unique) names!
All these selections are made with vectors.
They are sometimes called indexes.

4. Examples:
chromlength <- c(230218, 813184, 316620, 1531933)
Integer:
chromlength[ c(4, 2) ] => 1531933, 813184
Logical:
chromlength[ c(FALSE, FALSE, TRUE, FALSE) ] => 316620
Character:
names(chromlength) <- c("chrI", "chrII", "chrIII", "chrIV")
chromlength[ c("chrIII", "chrI") ] => 316620, 230218

5. Specifics for lists & data.frames
lists
>mylist <- list(analysis=”GSEA”, genes=c(“Foxo3a”, “TP53”), cutoff=0.05)
> mylist$analysis
> mylist$genes[2]
data.frames
> mydata[ , "id"]
> mydata$id # does the same thing

6. Dimensions of data.frames and matrices
> x <- matrix(1:6, nrow=2, byrow=TRUE)
[,1] [,2] [,3]
[1,] 1 2 3
[2,] 4 5 6
x[ i, j ]
index before the comma: indicates the row(s). If missing: all rows
index after the comma: indicates column(s). If missing: all columns

7. Example
> x
[,1] [,2] [,3]
[1,] 1 2 3
[2,] 4 5 6
Using integers:
> x[2, 3] # the value on the second row, third column
> x[ , 2] # all rows, second column. So: the whole 2nd column
> x[ , c(1,3)] # the first and third column (new data.frame or matrix!)
> x[ , -2] # everything but the second column
> x[ , 1:3] # first up to and including third column

8. Using logicals:
delA delB delC
geneA 1 2 3
geneB 4 5 6
> ind <- c(FALSE, TRUE, TRUE)
> x[ 1 , ind] # first row; first column:no, 2nd, 3rd column: yes
[1] 2 3

9. Using characters:
> 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
> x["geneB", "delA"] # selects the value of geneB in delA
> x[, c("delA", "delC")] # selects columns delA and delC

10. Logical vector and selection
Often (implicitly) used in combination with select statements
delA delB delC
geneA 1 2 3
geneB 4 5 6
> ind <- x["geneA", ] > 1
[1] FALSE TRUE TRUE
> x["geneA", ind]
[1] 2 3
> x["geneA", x["geneA", ] > 1] # same as above, but implicit

11. Operators
< # Less than
> # Greater than
== # Equal to. Note: don’t confuse with = (assignment)
>= # Greater than or equal to
<= # Less than or equal to
& # AND
| # OR
Note: x <- 2 is an assignment
x < -2 is a comparison! Use extra spaces or parentheses

12. AND (&), OR (|) , NOT (!)
a b a & b
FALSE FALSE FALSE
FALSE TRUE FALSE
TRUE FALSE FALSE
TRUE TRUE TRUE
FALSE NA FALSE
TRUE NA NA
a b a | b
FALSE FALSE FALSE
FALSE TRUE TRUE
TRUE FALSE TRUE
TRUE TRUE TRUE
FALSE NA NA
TRUE NA TRUE
a ! a
FALSE TRUE
TRUE FALSE
NA NA

13. Auto-recycling of vector content
If you combine vectors of different length, R will automatically
‘recycle’ the content of the shortest vector to become the length of
the longest:
> mynumbers <- c(10.4, 5, 8.4, 3)
> mynumbers2 <- mynumbers + 1
> mynumbers2
11.4, 6, 9.4, 4 # In fact, mynumbers + c(1, 1, 1, 1) is done
But also:
> mynumbers2 + c(2, 30)
13.4, 36, 11.4, 34 # Here, mynumbers2 + c(2, 30, 2, 30) is done.

14. Recycling also works with logical operators
Comparison of equal length vectors (no recycling needed) :
> v1 <- c(10, 5, 5, 1)
> v2 <- c(10, 3, 5, 2)
> v1 == v2
TRUE, FALSE, TRUE, FALSE
Comparison of unequal length vectors:
> v1 == 5 # The value 5 is recycled to get an equal length vector.
# So in fact, v1 == c(5,5,5,5) is done
FALSE, TRUE, TRUE, FALSE

15. Operators
delA delB delC
geneA 1 2 3
geneB 4 5 6
> ind <- x["geneA", ] > 1 & x["geneA", ] < 3
> x["geneA", ind]
[1] 2

16. Combining logical operators
AND-operator has precedence over OR-operator
(like in mathematics: *, / have precedence over -, +)
Group them with parentheses if needed, or for clarity
> ind <- ( x < -1.7 | x > 2 ) & !is.na(x)

17. Select statements
Special (common) functions, all return a logical vector
is.na()
is.numeric() (and also is.character(), is.factor(), is.matrix(), is.data.frame() )
duplicated()
! # (exclamation mark): logical NOT, i.e. negation
Used a lot in checking the consistency of your data or arguments
for a function

18. Ordering
(Re)order a data.frame or matrix using the values from a single
column using order()
> mydata <- data.frame( id=c(1,3,4,2), name=c("geneB", "geneA", "geneD",
"geneC"), value=c(-0.2, 1.5, -3, 3))
> mydata[order(mydata[, "id"]), ] # sort on id
> mydata[order(mydata[, "name"]), ] # sort on name