[R] For loop gets exponentially slower as dataset gets larger...

From: r user <ruser2006_at_yahoo.com>
Date: Wed 04 Jan 2006 - 03:59:19 EST

I am running R 2.1.1 in a Microsoft Windows XP environment.

I have a matrix with three vectors (“columns”) and ~2 million “rows”. The three vectors are date_, id, and price. The data is ordered (sorted) by code and date_.

(The matrix contains daily prices for several thousand stocks, and has ~2 million “rows”. If a stock did not trade on a particular date, its price is set to “NA”)

I wish to add a fourth vector that is “next_price”. (“Next price” is the current price as long as the current price is not “NA”. If the current price is NA, the “next_price” is the next price that the security with this same ID trades. If the stock does not trade again, “next_price” is set to NA.)

I wrote the following loop to calculate next_price. It works as intended, but I have one problem. When I have only 10,000 rows of data, the calculations are very fast. However, when I run the loop on the full 2 million rows, it seems to take ~ 1 second per row.

Why is this happening? What can I do to speed the calculations when running the loop on the full 2 million rows?

(I am not running low on memory, but I am maxing out my CPU at 100%)

Here is my code and some sample data:

data<- data[order(data\$code,data\$date_),]   l<-dim(data)[1]
w<-3
data[l,w+1]<-NA

for (i in (l-1):(1)){
data[i,w+1]<-ifelse(is.na(data[i,w])==F,data[i,w],ifelse(data[i,2]==data[i+1,2],data[i+1,w+1],NA))   }

```  date      id         price     next_price
6/24/2005        1635    444.7838         444.7838
6/27/2005        1635    448.4756         448.4756
6/28/2005        1635    455.4161         455.4161
6/29/2005        1635    454.6658         454.6658
6/30/2005        1635    453.9155         453.9155
7/1/2005          1635    453.3153         453.3153
7/4/2005          1635    NA      453.9155
7/5/2005          1635    453.9155         453.9155
7/6/2005          1635    453.0152         453.0152
7/7/2005          1635    452.8651         452.8651
7/8/2005          1635    456.0163         456.0163
12/19/2005      1635    442.6982         442.6982
12/20/2005      1635    446.5159         446.5159
12/21/2005      1635    452.4714         452.4714
12/22/2005      1635    451.074           451.074
12/23/2005      1635    454.6453         454.6453
12/27/2005      1635    NA      NA
12/28/2005      1635    NA      NA
12/1/2003        1881    66.1562           66.1562
12/2/2003        1881    64.9192           64.9192
12/3/2003        1881    66.0078           66.0078
12/4/2003        1881    65.8098           65.8098
12/5/2003        1881    64.1275           64.1275
12/8/2003        1881    64.8697           64.8697
12/9/2003        1881    63.5337           63.5337
12/10/2003      1881    62.9399           62.9399

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