To recover the runways, try:
levels(df$lrw)[times[, 'runway']]
The 'runway' column has the index into 'levels(df$lrw)'
On Mon, Jul 18, 2011 at 4:35 PM, James Rome wrote:
> There is one problem. No matter what I do, I can't recover the correct
> runway in the final list.
> You had "rw = as.nume
There is one problem. No matter what I do, I can't recover the correct
runway in the final list.
You had "rw = as.numeric(df$lrw) # index into 'levels' "
I have tried
df$lrw = factor(df$lrw, ordered=TRUE)
rwys = factor(unique(df$lrw), ordered=TRUE)# Get the names of
the runways
> rwy
Here is what I did; convert the data to a numeric matrix for faster
processing. You can convert back to a dataframe since you have the
indices into the levels for the flights and runways.
> # read in data
> source('/temp/df/df')
> # convert to matrix
> df.mat <- cbind(pt = as.numeric(df$PredTime)
I thought I had included the data... Here it is again.
What I want to do is to make box and whisker plots with each flight
counted the same number of times in each time bin. Hence the
interpolation to minute time hacks.
On 7/17/2011 4:16 PM, jim holtman wrote:
> It would be nice if you had some
It would be nice if you had some sample data included so that we could
see how the code worked. Have you use Rprof on the code to see where
you are spending your time? You might want to use 'matrix' instead of
'data.frames' since there is a big performance impact with dataframes
when indexing. A
df is a very large data frame with arrival estimates for many flights
(DF$flightfact) at random times (df$PredTime). The error of the estimate
is df$dt.
My problem is that I want to know the prediction error at each minute
before landing. This code works, but is very slow, and dominates
everything.
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