adding zeros to dataframe

Greetings,
I am new to R and am hoping to get some tips from experienced
R-programmers.
 
I have a dataset that I've read into R as a dataframe. There are 5
columns: Plot location,species name, a species number code (unique
to each species name), abundance, and treatment. There are 272 plots in
each treatment, but only the plots in which the species was recorded
have an abundance value.  For all species in the dataset, I would like
to add zeros to the abundance column for any plots in which the species
was not recorded, so that each species has 272 rows.  The data are
sorted by species and then abundance, so all of the zeros can
presumably just be tacked on to the last (272-occupied plots) row for
each species.
 
My programming skills are still somewhat rudimentary (and biased toward
VBA-style looping...which seems to be leading me astray). Though I have
searched, I have not yet seen this particular problem addressed in the
help files.
 
Many thanks for any suggestions,
Cathy
<mailto:ccollins at ku.edu>

Suppose we call your dataframe "abun.df". Then its columns will be
something like abun.df$location, abun.df$name, abun.df$abundance,
abun.df$trtmt (depending on what you called them in the first place).

From your description, I am presuming that abundence values where

a species was not recorded have no value entered. In that case,
presumably they have gone into abun.df$abundance as "NA". You can
check this with a command like

  sum(is.na(abun.df$abundance))

If you get a positive result, then that is likely to be the case.
As a cross-check:

  sum(abun.df$abundance > 0, na.rm=TRUE)

should give another number which, together with the first, should
add up to the total number of rows in the dataframe.

Assuming, then, that this is the case, the simplest method to set
the non-recorded values to 0 is on the lines of

  ix <- (is.na(abun.df$abundance))
  abun.df$abundance[ix] <- 0

Then you can run the check

  sum(abun.df$abundance == 0)

and you should get a number which is the same as you got from

  sum(is.na(abun.df$abundance))

Hoping this helps,
Ted.

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Date: 01-May-09                                       Time: 18:39:58
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