![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](https://image.slidesharecdn.com/day1d-rstructuresobjects-matricesanddataframes-171210221231/85/Day-1d-R-structures-objects-matrices-and-data-frames-pptx-2-320.jpg)
![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](https://image.slidesharecdn.com/day1d-rstructuresobjects-matricesanddataframes-171210221231/85/Day-1d-R-structures-objects-matrices-and-data-frames-pptx-3-320.jpg)
![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](https://image.slidesharecdn.com/day1d-rstructuresobjects-matricesanddataframes-171210221231/85/Day-1d-R-structures-objects-matrices-and-data-frames-pptx-5-320.jpg)
![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](https://image.slidesharecdn.com/day1d-rstructuresobjects-matricesanddataframes-171210221231/85/Day-1d-R-structures-objects-matrices-and-data-frames-pptx-7-320.jpg)
![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](https://image.slidesharecdn.com/day1d-rstructuresobjects-matricesanddataframes-171210221231/85/Day-1d-R-structures-objects-matrices-and-data-frames-pptx-8-320.jpg)
![Data Frames
data.frames
> mydata[ , "id"]
> mydata$id # does the same thing](https://image.slidesharecdn.com/day1d-rstructuresobjects-matricesanddataframes-171210221231/85/Day-1d-R-structures-objects-matrices-and-data-frames-pptx-9-320.jpg)
![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](https://image.slidesharecdn.com/day1d-rstructuresobjects-matricesanddataframes-171210221231/85/Day-1d-R-structures-objects-matrices-and-data-frames-pptx-10-320.jpg)
![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](https://image.slidesharecdn.com/day1d-rstructuresobjects-matricesanddataframes-171210221231/85/Day-1d-R-structures-objects-matrices-and-data-frames-pptx-11-320.jpg)
![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](https://image.slidesharecdn.com/day1d-rstructuresobjects-matricesanddataframes-171210221231/85/Day-1d-R-structures-objects-matrices-and-data-frames-pptx-12-320.jpg)
![Operators
delA delB delC
geneA 1 2 3
geneB 4 5 6
> ind <- x["geneA", ] > 1 & x["geneA", ] < 3
> x["geneA", ind]
[1] 2](https://image.slidesharecdn.com/day1d-rstructuresobjects-matricesanddataframes-171210221231/85/Day-1d-R-structures-objects-matrices-and-data-frames-pptx-13-320.jpg)
![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
$age
[1] 38](https://image.slidesharecdn.com/day1d-rstructuresobjects-matricesanddataframes-171210221231/85/Day-1d-R-structures-objects-matrices-and-data-frames-pptx-14-320.jpg)
![Specifics for lists
lists
>mylist <- list(analysis=”GSEA”, genes=c(“Foxo3a”, “TP53”), cutoff=0.05)
> mylist$analysis
> mylist$genes[2]](https://image.slidesharecdn.com/day1d-rstructuresobjects-matricesanddataframes-171210221231/85/Day-1d-R-structures-objects-matrices-and-data-frames-pptx-15-320.jpg)
![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](https://image.slidesharecdn.com/day1d-rstructuresobjects-matricesanddataframes-171210221231/85/Day-1d-R-structures-objects-matrices-and-data-frames-pptx-16-320.jpg)
![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](https://image.slidesharecdn.com/day1d-rstructuresobjects-matricesanddataframes-171210221231/85/Day-1d-R-structures-objects-matrices-and-data-frames-pptx-17-320.jpg)
Day 1d R structures & objects: matrices and data frames.pptx
- 1. R structures & objects: matrices and data frames Day 1 - Introduction to R for Life Sciences
- 2. 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
- 3. 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
- 4. 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
- 5. 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
- 6. 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!
- 7. 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
- 8. 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
- 9. Data Frames data.frames > mydata[ , "id"] > mydata$id # does the same thing
- 10. 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
- 11. 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
- 12. 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
- 13. Operators delA delB delC geneA 1 2 3 geneB 4 5 6 > ind <- x["geneA", ] > 1 & x["geneA", ] < 3 > x["geneA", ind] [1] 2
- 14. 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 $age [1] 38
- 15. Specifics for lists lists >mylist <- list(analysis=”GSEA”, genes=c(“Foxo3a”, “TP53”), cutoff=0.05) > mylist$analysis > mylist$genes[2]
- 16. 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
- 17. 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