I have a For loop that is quite slow and am wondering if there is a faster
option:
df - data.frame(TreeID=rep(1:500,each=20), Age=rep(seq(1,20,1),500))
df$Height - exp(-0.1 + 0.2*df$Age)
df$HeightGrowth - NA #intialize with NA
for (i in 2:nrow(df))
{if(df$TreeID[i]==df$TreeID[i-1])
Hello,
One way to speed it up is to use a matrix instead of a data.frame. Since
data.frames can hold data of all classes, the access to their elements
is slow. And your data is all numeric so it can be hold in a matrix. The
second way below gave me a speed up by a factor of 50.
How about
for (ir in unique(df$TreeID)) {
in.ir - df$TreeID == ir
df$HeightGrowth[in.ir] - cumsum(df$Height[in.ir])
}
Seemed fast enough to me.
In R, it is generally good to look for ways to operate on entire vectors
or arrays, rather than element by element within them. The cumsum()
On Jun 10, 2013, at 10:28 AM, Trevor Walker wrote:
I have a For loop that is quite slow and am wondering if there is a faster
option:
df - data.frame(TreeID=rep(1:500,each=20), Age=rep(seq(1,20,1),500))
df$Height - exp(-0.1 + 0.2*df$Age)
df$HeightGrowth - NA #intialize with NA
for (i
Sorry, it looks like I was hasty.
Absent another dumb mistake, the following should do it.
The request was for differences, i.e., the amount of growth from one
period to the next, separately for each tree.
for (ir in unique(df$TreeID)) {
in.ir - df$TreeID == ir
df$HeightGrowth[in.ir] - c(NA,
Well, speaking of hasty...
This will also do it, provided that each tree's initial height is less
than the previous tree's final height. In principle, not a safe
assumption, but might be ok depending on where the data came from.
df$delta - c(NA,diff(df$Height))
df$delta[df$delta 0] - NA
-Don
Walker trevordaviswal...@gmail.com
To: r-help@r-project.org
Cc:
Sent: Monday, June 10, 2013 1:28 PM
Subject: [R] Speed up or alternative to 'For' loop
I have a For loop that is quite slow and am wondering if there is a faster
option:
df - data.frame(TreeID=rep(1:500,each=20), Age=rep(seq(1,20,1
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