On Oct 21, 2011, at 6:17 PM, Rich Shepard wrote:
On Fri, 21 Oct 2011, David Winsemius wrote:
What problem are you trying to solve?
What I need now is to compare TDS (total dissolved solids) with
specific
conductivity and the ions that are normally comprise TDS. Before
running any
regression models I need to look at these data from three points of
view:
all data from all sites within each hydrographic drainage basin
collected
during the past 30 years; average (or total) concentrations (not yet
decided
on what makes the most ecological sense) within a stream having
multiple
collection sites; and by site within certain streams.
Here is the data frame structure:
str(chemdata)
'data.frame': 47244 obs. of 6 variables:
$ site : Factor w/ 143 levels "BC-0.5","BC-1",..: 134 134 134
127 127
$ sampdate: Date, format: "2006-12-06" "2006-12-06" ...
$ param : Factor w/ 66 levels "AGP","ANP","ANP/AGP",..: 58 66 12
24 59 66
$ quant : num 1.08e+04 7.95 1.80e-02 2.80e+02 1.90e+01 8.44 1.62e
+03
$ stream : Factor w/ 24 levels "BCrk","CCrk",..: 4 4 4 21 21 21 4
$ basin : Factor w/ 2 levels "BasinEast","BasinWest": 1 1 1 1 1 1
1 1 1 2 ...
The only variable in that dataframe with what appears to be a
continuous value (which is how I would expect "total dissolved solids"
to be measured) is "quant" Are you saying that the value of quant is
measuring something with different units depending on the value of
'param' and that 'site' and 'date' shoud be used to identify
associated measurements? This would appear to be the case based on
what you are saying below.
If this is so the problem is to break apart the dataframe by type of
measurement ('param') butone way would be to split into separate
dataframes then merge back together by an appropriate linkage on site
and date. I'm guessing that 'stream' and 'basin' are superfluous for
the matching and can be later associated with 'site'?
The goal would be a dataframe with 7 renamed 'param' columns ('TDS',
'Cond', 'Mg', 'SO4', 'Cl', 'Na', and 'Ca') and two identifier columns
('site' and 'sampdate'. For the moment I would think you would want
all the data together an not make any decisions about excluding NA
values until you get an overall picture of the situation.
The first thing I would try would be
with(subset(chemdata, param %in% c('TDS', 'Cond', 'Mg', 'SO4', 'Cl',
'Na', and 'Ca') , 1:4) ,
xtabs(quant ~ site + sampdate + param) )
You would get 7 tables One for each 'param' with up to 143 rows and as
many columns as you have sampdates.
This might be a good use for package reshape2 since it generally
returns a dataframe. The above operation would return an array with 3
dimensions. You might get immediate success with something like:
dcast( subset(chemdata, param %in% c('TDS', 'Cond', 'Mg', 'SO4', 'Cl',
'Na', and 'Ca') , 1:4) ,
site + sampdate ~ param)
# the omitted varialble name should ent up in the values columns
To do your testing it might be wise to apply more selective use of
subset. Perhaps on;u go for a few sites and dates.
--
david.
While all the data sets used in the books I've read are simpler and
well
illustrate the analyses presented, what I've not read is guidance on
how
complex data sets could (or should) be partitioned into smaller but
still
related data sets to facilitate analyses. Or, how I extract the
relevant
rows and columns for specific analyses.
That seems very unlikely. What we need is a clearer description of
that
values that your "param" variable can assume, and what you want to
within
categories of those values. We also need you to stop dropping
context.
There are 66 different chemicals in the param factor. However, for
the
immediate effort, only 7 are needed. They are coded 'TDS', 'Cond',
'Mg',
'SO4', 'Cl', 'Na', and 'Ca'.
From the database table I know the number of non-NULL (non-NA) rows
for
each parameter:
TDS 2181
Cond 820
Mg 1120
SO4 1980
Cl 1971
Na 866
Ca 1110
Not all were required to be measured at all sites from the
beginning in
1981. I do not yet know how many rows have non-NULL values for the 6
pairs
compared with TDS.
If there's more information to provide I'll gladly do so.
Thanks,
Rich
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David Winsemius, MD
West Hartford, CT
______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
