Continuous Subgroups with ddply

Question

I would like to summarize my experimental data every time a state changes.

For instance:

> df=data.frame(tos=1:9, temp=rep(c(25,50,25), each=3), response=c(3.2,3.3,3.3, 6.5, 6.5, 6.5, 3.5,3.6,3.5))
> df
    time temp response
1   1   25      3.2
2   2   25      3.3
3   3   25      3.3
4   4   50      6.5
5   5   50      6.5
6   6   50      6.5
7   7   25      3.5
8   8   25      3.6
9   9   25      3.5

I would like to summarize this as follows:

temp response.mean
25      3.3
50      6.5
25      3.5

If ddply is used:

Library (plyr)
ddply (df, c ("temp"), summarize, reponse.mean = mean (response)

conclusion:

  temp response.mean
1   25           3.4
2   50           6.5

Is there any way to do this?

Answer 1

Here is one way to achieve this.

# find how many observations in each experiment
tmp1    = rle(df$temp)$lengths

# create a column referring to experiment number
df$expt = rep(1:length(tmp1), tmp1)

# compute means for each combination of temp and expt
ddply(df, .(expt, temp), summarize, response.mean = mean(response))

It leads to exit

   expt temp response.mean
1    1   25      3.266667
2    2   50      6.500000
3    3   25      3.533333