calculate average over multiple data frames

Question

I would like to use R for plotting performance evaluation results of distinct DB systems. For each system I loaded the same data and execute the same queries in several iterations.

The data for a single systems looks like this:

"iteration", "lines", "loadTime", "query1", "query2", "query3"
1, 100000, 120.4, 0.5, 6.4, 1.2
1, 100000, 110.1, 0.1, 5.2, 2.1
1, 50000, 130.3, 0.2, 4.3, 2.2

2, 100000, 120.4, 0.1, 2.4, 1.2
2, 100000, 300.2, 0.2, 4.5, 1.4
2, 50000, 235.3, 0.4, 4.2, 0.5

3, 100000, 233.5, 0.7, 8.3, 6.7
3, 100000, 300.1, 0.9, 0.5, 4.4
3, 50000, 100.2, 0.4, 9.2, 1.2

What I need now (for plotting) is a matrix or data frame containing the average of these measurements.

At the moment I am doing this:

# read the file
all_results <- read.csv(file="file.csv", head=TRUE, sep=",")

# split the results by iteration
results <- split(all_results, all_results$iteration)

# convert each result into a data frane
r1 = as.data.frame(results[1])
r2 = as.data.frame(results[2])
r3 = as.data.frame(results[3])

# calculate the average
(r1 + r2 +r3) / 3

I could put all this into a function and calculate the average matrix in a for loop, but I have the vague feeling that there must be a more elegant solution. Any ideas?

What can I do for cases when I have incomplete results, e.g., when one iteration has less rows than the others?

Thanks!

Answer 1

If I understand you correctly, on a given DB system, in each "iteration" (1...N) you are loading a sequence of DataSets (1,2,3) and running queries on them. It seems like at the end you want to calculate the average time across all iterations, for each DataSet. If so, you actually need to have an additional column DataSet in your all_results table that identifies the DataSet. We can add this column as follows:

all_results <- cbind( data.frame( DataSet = rep(1:3,3) ), all_results )
> all_results
  DataSet iteration  lines loadTime query1 query2 query3
1       1         1 100000    120.4    0.5    6.4    1.2
2       2         1 100000    110.1    0.1    5.2    2.1
3       3         1  50000    130.3    0.2    4.3    2.2
4       1         2 100000    120.4    0.1    2.4    1.2
5       2         2 100000    300.2    0.2    4.5    1.4
6       3         2  50000    235.3    0.4    4.2    0.5
7       1         3 100000    233.5    0.7    8.3    6.7
8       2         3 100000    300.1    0.9    0.5    4.4
9       3         3  50000    100.2    0.4    9.2    1.2

Now you can use the ddply function from the plyr package to easily extract the averages for the load and query times for each DataSet.

> ddply(all_results, .(DataSet), colwise(mean, .(loadTime, query1, query2)))
  DataSet loadTime    query1 query2
1       1 158.1000 0.4333333    5.7
2       2 236.8000 0.4000000    3.4
3       3 155.2667 0.3333333    5.9

Incidentally, I highly recommend you look at Hadley Wickham's plyr package for a rich set of data-manipulation functions

Answer 2

I don't see why you need to split all_results by iteration. You can just use aggregate on all_results. There's no need for all iterations to have the same number of observations.

Lines <- "iteration, lines, loadTime, query1, query2, query3
1, 100000, 120.4, 0.5, 6.4, 1.2
1, 100000, 110.1, 0.1, 5.2, 2.1
1, 50000, 130.3, 0.2, 4.3, 2.2
2, 100000, 120.4, 0.1, 2.4, 1.2
2, 100000, 300.2, 0.2, 4.5, 1.4
2, 50000, 235.3, 0.4, 4.2, 0.5
3, 100000, 233.5, 0.7, 8.3, 6.7
3, 100000, 300.1, 0.9, 0.5, 4.4
3, 50000, 100.2, 0.4, 9.2, 1.2"

all_results <- read.csv(textConnection(Lines))

aggregate(all_results[,-1], by=all_results[,"iteration",drop=FALSE], mean)