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# AAK: Sat 15 Aug 2014 00:48:44 PDT
# Loop operators in R
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x <- list(a=1:10, b=rnorm(20))
#Applies the function mean on each element of the list:
y <- lapply(x,mean)

x <- 1:4
y <- lapply(x,runif)
# Passing arguments to runif
y <- lapply(x,runif,min=0,max=10)

# sapply is the same as lapply but the result is simplified (in its type)

x <- matrix(rnorm(200),20,10)
# returns the mean of the columns:
y <- apply(x,2,mean)
# returns the sum of the rows:
y <- apply(x,1,sum)
# in apply(Matrix,I,FUNC), I is the index that is preserved

# mapply is multivariable apply,

noise <-function(n,mean,std){
rnorm(n,mean,std)
}

# Imagine we want to create all the combination of
# n=(1,2,3,4,5) and means=(1,1.5,2,2.5,3)

y <- mapply(noise,1:5,seq(1,3.0,0.5),1.0)

# tapply applies the function on portions of a vector
x <- c(rnorm(100), runif(100), rnorm(100,2))
# Creates a factor with 3 levels  1x100, 2x100, 3x100
indx <- gl(3,100)
means <- tapply(x,indx,mean)
# returns the mean of each group (levels) i.e here 3 numbers:
print(means)

# Using factor you can also split a vector:

y <- split(x,indx)
print(summary(y))

library(datasets)

# Example using airquality dataset, splitting data frame:

s <- split(airquality, airquality$Month)

# Note the on the fly function definition:
res <- sapply(s, function(x) colMeans(x[,c("Ozone", "Solar.R","Wind")], na.rm=TRUE))
print(res)

# Example using data frames:
library(datasets)

# See mtcars data frame

# Returns the average of car's mpg for each cyl group (4, 5, 6 cyldender)

cylmean <- tapply(mtcars$mpg,mtcars$cyl,mean)
print(cylmean)