HI,
May be this helps:
set.seed(24)
mat1<- matrix(sample(1:60,30*24,replace=TRUE),ncol=24)
colnames(mat1)<- rep(c("O","H","L","C"),6)
indx<-seq_along(colnames(mat1))
n<- length(unique(colnames(mat1)))
res<- lapply(split(indx,(indx-1)%%n+1),function(i) mat1[,i])
lapply(res,head,2)
#$`1`
# O O O O O O
#[1,] 18 56 51 24 24 52
#[2,] 14 31 60 12 43 34
#
#$`2`
# H H H H H H
#[1,] 20 6 4 23 10 2
#[2,] 15 37 22 52 30 42
#
#$`3`
# L L L L L L
#[1,] 30 25 29 1 57 16
#[2,] 15 23 15 10 44 60
#
#$`4`
# C C C C C C
#[1,] 20 13 8 44 5 13
#[2,] 45 17 35 8 25 12
A.K.
Motivation:
Bring in data containing a number of columns divisable by 4.
This data contains several different assets and the columns correspond
to Open,High,Low,Close, ....Open,High,Low,Close, etc (thus divisible by
4). From where I am getting this data, the header is not labled as
Open,High,Low,Close, but rather just has the asset symbol.
The end goal is to have each Open,High,Low,Close, as its own
OHLC object, to be run through different volatility functions (via
QuantMod )
I believe i am best served by first grouping the original data
so that each asset is its own object, with 4 columns. Then i can rename
the columns to be:
colnames(function$asset) <-c("Open", "High","Low", "Close")
I've attempted to use split, but am having trouble with split along the
columns.
Obviously I could manipulate the indexing, with something like
data[i:i+4] and use a loop. Maybe this indexing approach would work with
use of apply().
Previously, I've been using Mathematica for most of my data
manipulation, and there I would partition the entire data set i.e.
Matrix, into column# / 4 separate objects. So, in that case I have a 3
dimensional object. I'd then call the object by its 3rd dimension index
# [][#].
I'm having trouble doing that here. Any thoughts, or at the least helping me
to group the data by column.
For the sake of possible examples, lets say the dimensions of my data is n.rows
= 30, n.col = 24
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