Notes for "computing for data analysis" [Coursera course]
- colClass.
Specifying this option instead of using default can make ‘read.table’ run MUCH faster, often twice as faster. In order to use this option, you have to know the class of each column in your data frame. If all of the columns are ‘numberic’, for example, then you can just set colClass="numeric". A quick and dirty way to figure out classes for each column is the following:
initial <- read.table("data",nrows=100)
classes <- sapply(initial,class)
tabAll <- read.table("data",colClass=classes)
- …
The...represents argument list when put in the end. All additional parameters will be saved to...and passed to inner functions.
The...argument is also necessary when the number of arguments passed to the functions can not be known in advance like:
args(paste)
function (..., sep=" ", collapse=NULL)
Once catch with ... is that any arguments that appear after ... on the argument list must be named explicitly and cannot be partially matched.
3.split function
Divide into Groups and Reassemble
Description:
‘split’ divides the data in the vector ‘x’ into the groups defined
by ‘f’. The replacement forms replace values corresponding to
such a division. ‘unsplit’ reverses the effect of ‘split’.Usage:
split(x, f, drop = FALSE, …)
split(x, f, drop = FALSE, …) <- value
unsplit(value, f, drop = FALSE)
> x <- c(rnorm(10), runif(10), rnorm(10,1))
> f <- gl(3,10)
> split(x,f)
$`1`
[1] 0.04568824 1.43881538 1.20726247 0.89607034 -1.82028884 0.41100988
[7] -0.89489825 -1.91846889 -1.61372116 1.12882772
$`2`
[1] 0.82255701 0.71847213 0.58078540 0.72514585 0.02594232 0.93178561
[7] 0.85934484 0.10371824 0.99216828 0.45048527
$`3`
[1] 1.1336430 1.4560381 0.5475995 1.9924938 -1.8635712 0.4090884
[7] 2.0350326 -0.2822986 -0.1865551 -1.1014422
> a <- split(x,f)
> a$1
Error: unexpected numeric constant in "a$1"
> a[1]
$`1`
[1] 0.04568824 1.43881538 1.20726247 0.89607034 -1.82028884 0.41100988
[7] -0.89489825 -1.91846889 -1.61372116 1.12882772
> x
[1] 0.04568824 1.43881538 1.20726247 0.89607034 -1.82028884 0.41100988
[7] -0.89489825 -1.91846889 -1.61372116 1.12882772 0.82255701 0.71847213
[13] 0.58078540 0.72514585 0.02594232 0.93178561 0.85934484 0.10371824
[19] 0.99216828 0.45048527 1.13364297 1.45603806 0.54759947 1.99249377
[25] -1.86357122 0.40908842 2.03503258 -0.28229863 -0.18655514 -1.10144215
> f
[1] 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3
Levels: 1 2 3
#split by two factors
> f1 <- gl(2,5)
> f2 <- gl(5,2)
> f1
[1] 1 1 1 1 1 2 2 2 2 2
Levels: 1 2
> f2
[1] 1 1 2 2 3 3 4 4 5 5
Levels: 1 2 3 4 5
> interaction(f1,f2)
[1] 1.1 1.1 1.2 1.2 1.3 2.3 2.4 2.4 2.5 2.5
Levels: 1.1 2.1 1.2 2.2 1.3 2.3 1.4 2.4 1.5 2.5
> x <- rnorm(10)
> str(split(x, list(f1,f2)))
List of 10
$ 1.1: num [1:2] -0.855 -0.852
$ 2.1: num(0)
$ 1.2: num [1:2] -0.629 -0.494
$ 2.2: num(0)
$ 1.3: num -0.0677
$ 2.3: num -0.296
$ 1.4: num(0)
$ 2.4: num [1:2] 0.403 -0.791
$ 1.5: num(0)
$ 2.5: num [1:2] -1.35 -1.01
> str(split(x, interaction(f1,f2)))
List of 10
$ 1.1: num [1:2] -0.855 -0.852
$ 2.1: num(0)
$ 1.2: num [1:2] -0.629 -0.494
$ 2.2: num(0)
$ 1.3: num -0.0677
$ 2.3: num -0.296
$ 1.4: num(0)
$ 2.4: num [1:2] 0.403 -0.791
$ 1.5: num(0)
$ 2.5: num [1:2] -1.35 -1.01
4.apply, sapply, tapply, mapply
apply
Apply Functions Over Array Margins
Description:
Returns a vector or array or list of values obtained by applying a
function to margins of an array or matrix.Usage:
apply(X, MARGIN, FUN, …)
Arguments:
X: an array, including a matrix.
MARGIN: a vector giving the subscripts which the function will be
applied over. E.g., for a matrix ‘1’ indicates rows, ‘2’
indicates columns, ‘c(1, 2)’ indicates rows and columns.
Where ‘X’ has named dimnames, it can be a character vector
selecting dimension names.
> x <- c(rnorm(10), runif(10), rnorm(10,1))
> y <- matrix(x,nrow=3)
> y
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
[1,] 0.04568824 0.8960703 -0.8948982 1.1288277 0.58078540 0.9317856 0.9921683
[2,] 1.43881538 -1.8202888 -1.9184689 0.8225570 0.72514585 0.8593448 0.4504853
[3,] 1.20726247 0.4110099 -1.6137212 0.7184721 0.02594232 0.1037182 1.1336430
[,8] [,9] [,10]
[1,] 1.4560381 -1.8635712 -0.2822986
[2,] 0.5475995 0.4090884 -0.1865551
[3,] 1.9924938 2.0350326 -1.1014422
> apply(y,1,mean)
[1] 0.2990596 0.1327723 0.4912411
tapply
Apply a Function Over a Ragged Array
Description:
Apply a function to each cell of a ragged array, that is to each
(non-empty) group of values given by a unique combination of the
levels of certain factors.Usage:
tapply(X, INDEX, FUN = NULL, …, simplify = TRUE)
Arguments:
X: an atomic object, typically a vector.
INDEX: list of one or more factors, each of same length as ‘X’. The
elements are coerced to factors by ‘as.factor’.
> x <- c(rnorm(10), runif(10), rnorm(10,1))
> f <- gl(3,10)
> tapply(x,f,range)
$`1`
[1] -1.918469 1.438815
$`2`
[1] 0.02594232 0.99216828
$`3`
[1] -1.863571 2.035033
Apply a Function to Multiple List or Vector Arguments
Description:
‘mapply’ is a multivariate version of ‘sapply’. ‘mapply’ applies
‘FUN’ to the first elements of each … argument, the second
elements, the third elements, and so on. Arguments are recycled
if necessary.Usage:
mapply(FUN, …, MoreArgs = NULL, SIMPLIFY = TRUE,
USE.NAMES = TRUE)Arguments:
FUN: function to apply, found via ‘match.fun’.
…: arguments to vectorize over (vectors or lists of strictly
positive length, or all of zero length). See also ‘Details’.MoreArgs: a list of other arguments to ‘FUN’.
> list(rep(1,4), rep(2,3), rep(3,2), rep(4,1))
[[1]]
[1] 1 1 1 1
[[2]]
[1] 2 2 2
[[3]]
[1] 3 3
[[4]]
[1] 4
> mapply(rep,1:4,4:1)
[[1]]
[1] 1 1 1 1
[[2]]
[1] 2 2 2
[[3]]
[1] 3 3
[[4]]
[1] 4
> noise <- function(n, mean, sd) {
+ rnorm(n, mean, sd)
+ }
> noise(5,1,2)
[1] 2.3318087 -0.5513379 2.1479612 3.6134264 2.3675103
> noise(1:5,1:5,2) #wrong usage
[1] 1.1778732 0.3632799 6.2087896 2.9261086 3.5094597
> mapply(noise,1:5,1:5,2)
[[1]]
[1] -0.1760846
[[2]]
[1] -0.7810509 -1.4659350
[[3]]
[1] 3.0684418 0.7133993 4.5190991
[[4]]
[1] 1.946463 3.025599 2.906996 5.319200
[[5]]
[1] 5.443579 5.280245 2.844177 4.538039 6.393585
> list(noise(1,1,2), noise(2,2,2), noise(3,3,2), noise(4,4,2), noise(5,5,2))
[[1]]
[1] 1.524288
[[2]]
[1] 1.547129 5.261838
[[3]]
[1] 3.150581 4.107338 2.906161
[[4]]
[1] 3.280658 2.605533 3.905065 5.884222
[[5]]
[1] 5.200394 5.541272 4.189657 5.596289 5.019267
5.interaction
Compute Factor Interactions
Description:
‘interaction’ computes a factor which represents the interaction
of the given factors. The result of ‘interaction’ is always
unordered.Usage:
interaction(…, drop = FALSE, sep = “.”, lex.order = FALSE)
> f1 <- gl(2,5)
> f2 <- gl(5,2)
> f1
[1] 1 1 1 1 1 2 2 2 2 2
Levels: 1 2
> f2
[1] 1 1 2 2 3 3 4 4 5 5
Levels: 1 2 3 4 5
> interaction(f1,f2)
[1] 1.1 1.1 1.2 1.2 1.3 2.3 2.4 2.4 2.5 2.5
Levels: 1.1 2.1 1.2 2.2 1.3 2.3 1.4 2.4 1.5 2.5
6.plot
#plot two types of points in a scatter plot
> x <- rnorm(100)
> str(x)
num [1:100] 1.128 -1.144 0.523 1.179 -0.602 ...
> y <- x + rnorm(100)
> str(y)
num [1:100] 1.814 -2.452 0.271 1.844 -1.471 ...
> g <- gl(2,50, labels=c("Male", "Female"))
> str(g)
Factor w/ 2 levels "Male","Female": 1 1 1 1 1 1 1 1 1 1 ...
> plot(x, y, type="n")
> points(x[g=="Male"], y[g=="Male"], col="green")
> points(x[g=="Female"], y[g=="Female"], col="blue")
>
