Improvement in Process time

Hi

Basically you can define your data frame before the cycle begins (specify total size, populate rows and columns with NA) and only populate it with your results during a cycle.

Or you can use a list for storing results and use do.call to transform it to data frame.

something like this

lll<- vector("list", 10)
for(i in 1:10) lll[[i]] <- rnorm(12)
do.call(cbind, lll)


I wonder if you get better answer without toy example, because there can be other reasons for slow computation.

Cheers
Petr

-----Original Message-----
From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Amelia
Marsh via R-help
Sent: Tuesday, February 02, 2016 1:03 PM
To: R Help R
Subject: [R] Improvement in Process time

Dear R forum,

I am running a Particular process 1000 times for different rates. Each
time the result of the process is getting stored (appended) in a
data.frame. However, the process is taking unsual time at times more
than 2 hours. When I had tried to find out the reason for such a long
process time, I have realized that writing a data.frame is consuming
lot of time.

Here is an extract of my code

# ---------------------------------------------------------------

tx_discounted <- read.csv('transaction_discounted.csv', na.strings='')
tx_discounted$id <- as.character(tx_discounted$id)

n             <- max(unique(simulated_exchange$id))

result         <- NULL
current  <- 1
rcount   <- 0
current1 <- 1
rcount1  <- 0
current2 <- 1
rcount2  <- 0
for (env in 0:n) {

if (rcount == 0) rcount <- nrow(subset(simulated_interest, id==env))
temp           <- current+rcount-1
env_rates  <- simulated_interest[current:temp,] env_rates  <-
env_rates[order(env_rates$curve, env_rates$day_count), ]
if (rcount1 == 0)     rcount1 <- nrow(subset(simulated_exchange, id==env))
temp           <- current1+rcount1-1
exch_rates <- simulated_exchange[current1:temp,]
if (rcount2 == 0)     rcount2 <- nrow(subset(simulated_instruments,
id==env))
temp           <- current2+rcount2-1
instr_rates<- simulated_instruments[current2:temp,]
current        <- current+rcount
current1       <- current1+rcount1
current2       <- current2+rcount2

curve       <- daply(env_rates, 'curve', function(x) {
return(approxfun(x$day_count, x$rate, rule = 2))
})

# ____________________________________________________

## Actual time consumtion begins from following part

# ____________________________________________________

result <- rbind(result, ddply(tx_discounted, 'id', function(x) {

if(!is.na(x$curve) && x$curve != '') {
intrate <- curve[[x$curve]](x$maturity_period)
} else {
intrate <- subset(instr_rates,
instrument==as.character(x$instrument))$value
}

cross_rate <- subset(exch_rates, key==paste(x$currency,
x$currency_base, sep='_'))$rate
mtm_bc     <- cross_rate *
(x$amount/(1+((intrate/100)*(x$maturity_period/x$intbasis))))

return(data.frame(env=env, id=x$id, instrument=x$instrument,
currency=x$currency, intrate = intrate, maturity_period =
x$maturity_period,  intbasis = x$intbasis, cross_rate = cross_rate,
amount=x$amount, mtm_bc=mtm_bc))
}))
}


# ---------------------------------------------------------------------
------

Unfortuantely I can't share the input files. Is there any way I can
improve the process time.

Regards and thanking in advance

Amelia

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