# [R] How to pre-filter large amounts of data effectively

From: Torsten Schindler <Torsten.Schindler_at_chello.at>
Date: Tue 09 Aug 2005 - 20:22:24 EST

Hi,

I'm a R newbie and want to accelerate the following pre-filtering step of a data set with more than 115,000 rows :

#-----------------
# Function to filter out constant data columns
filter.const<-function(X, vectors=c('column', 'row'), tol=0){

realdata=c()
filteredX<-matrix()
if( vectors == 'row' ){

```     for( row in (1:nrow(X)) ){
if( length(which(X[row,]!=median(X[row,])))>tol ){
realdata[length(realdata)+1]=row
}
}
filteredX=X[realdata,]
} else if( vectors == 'column' ){
for( col in (1:ncol(X)) ){
if( length(which(X[,col]!=median(X[,col])))>tol ){
realdata[length(realdata)+1]=col
}
}
filteredX=X[,realdata]
```

}
return(list(x=filteredX, ix=realdata)) }

#-----------------
# Filter out all all-constant columns in my training data set
#
# Read training data set with class information in the first column
training <- read.csv('training_data.txt') dim(training) # => 49 rows and 525 columns

# Prepare column names by stripping the underline and the number at
the end
colnames(training) <- sub('_\\d+\$', '', colnames(training), perl=TRUE)

# Filter out the all-constant columns, exclude column 1, the class
column called myclass
training.filter <- filter.const(training[,-1])

# The filtered data frame is

training.filtered <- cbind(myclass=training[,1], training.filter\$x) dim(training.filtered) # => 49 rows and 250 columns

# Save the filtered training set for later use in classification
filtered.data <- 'training_set_filtered.Rdata' save(training.filtered, file=filtered.data)

#-----------------
# THE FOLLOWING FILTERING STEP TAKES 3 HOUR ON MY PowerBook
# AND CONSUMES ABOUT 600 Mb MEMORY.
#
# I WOULD BE HAPPY ABOUT ANY HINT HOW TO IMPROVE THIS.

# Pre-filter the big data set (more than 115,000 rows and 524
columns) for later class predictions.
# The big data set contains the same column names as the training
set, but in a different order.

input.file <- 'big_data_set.txt'
filtered.file <- 'big_data_set_filtered.txt'

# Prepare column names by stripping the underline and the number at
the end
colnames(prediction.set) <- sub('_\\d+\$', '', colnames (prediction.set), perl=TRUE)

# Get descriptor columns of the training data set without the
Activity_Class column
training.filtered.property.colnames <- colnames(training.filtered)[-1]

# Filter out the all-constant columns from the training set
prediction.set.filtered <- prediction.set [training.filtered.property.colnames]
dim(prediction.set.filtered) # => 1 row and 249 columns

# Write header and the first filtered row
write.csv(prediction.set.filtered, file=filtered.file,

append=FALSE,
col.names=training.filtered.property.colnames)

blocksize <- 1000
for (lineid in (0:120)*blocksize) {

cat('lineid: ', lineid, '\n')

# We have to add an dummy colname "x" in the col.names, when the header is not read!

```                         col.names=c('x',prediction.set.header),
row.names=1,
skip=lineid+2, nrow=blocksize))
```
if (class(prediction.set) == "try-error") break

# Filter out all-constant training set columns from the block    prediction.set.filtered <- prediction.set [training.filtered.property.colnames]

# Append the data
# (I know this function is slow, but I couldn't figure out how to do it faster, so far.)

write.table(prediction.set.filtered, file=filtered.file,

```                         append=TRUE, col.names=FALSE, sep=",")
```
}

#-------------
# Now read in the filtered data set and save it for later use in
classification
filtered.data <- 'prediction_set_filtered.Rdata' save(prediction.set.filtered, file=filtered.data)

I would be very happy about any hints how to improve the code above!!!

Best regards,

Torsten

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