Rui,
Indeed it does help. Also very happy to see eval() and parse() employed
and demystified here.
In Rdulation,
Karl
On 06/12/12 15:48, Rui Barradas wrote:
Hello,
Something like this?
func <- function(data, A, B, C){
f <- function(a)
function(x) eval(parse(text = paste("x", a)))
iA <- if(is.na(A)) TRUE else f(A)(data$A)
iB <- if(is.na(B)) TRUE else f(B)(data$B)
iC <- if(is.na(C)) TRUE else f(C)(data$C)
data[iA & iB & iC, ]
}
func(eg, "> 0", NA, NA)
func(data=eg, A="< 0", B="< 1", C="> 0")
Hope this helps,
Rui Barradas
Em 06-12-2012 13:49, Karl Brand escreveu:
Esteemed UseRs,
I've got many biggish data frames which need a lot subsetting, like in
this example:
# example
eg <- data.frame(A = rnorm(10), B = rnorm(10), C = rnorm(10), D =
rnorm(10))
egsub <- eg[eg$A < 0 & eg$B < 1 & eg$C > 0, ]
egsub
egsub2 <- eg[eg$A > 1 & eg$B > 0, ]
egsub2
# To make this clearer than 1000s of lines of extractions with []
# I tried to make a function like this:
# func(data="eg", A="< 0", B="< 1", C="> 0")
# Which would also need to be run as
# func(data="eg", A="> 1", B="> 0", C=NA)
#end
Noteably:
-the signs* "<" and ">" need to be flexible _and_ optional
-the quantities also need to be flexible
-column header names i.e, A, B and C don't need flexibility,
i.e., can remain fixed
* "less than" and "greater than" so google picks up this thread
Once again i find just how limited my grasp of R is...Is do.call() the
best way to call binary operators like < & > in a function? Is an
ifelse statement needed for each column to make filtering on it
optional? etc....
Any one with the patience to show their working version of such a
funciton would receive my undying Rdulation. With thanks in advance,
Karl
--
Karl Brand
Dept of Cardiology and Dept of Bioinformatics
Erasmus MC
Dr Molewaterplein 50
3015 GE Rotterdam
T +31 (0)10 703 2460 |M +31 (0)642 777 268 |F +31 (0)10 704 4161
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