Re: [R] Applying function with separate dataframe (calibration file) supplying some inputs
Thanks Josh,
I appreciate your comments. Merge is quite easy and I guess I should just go
that route. I don't have so many data points so the speed isn't really much
of an issue. I was just curious whether there was anything more elegant.
Yes, the function is not clear or ideal for troubleshooting. I adapted this
from an excel sheet in an effort to speed my data analysis and in the excel
file the final calculation was simply a long string of calculations. I
appreciate your attempt to make it clearer and to chunk it into fewer easier
to read pieces. I plan to go back and do that as well.
Thanks again.
Nate
On Wed, Oct 19, 2011 at 10:33 PM, Joshua Wiley jwiley.ps...@gmail.comwrote:
Hi Nathan,
I honestly do not think that anything else will be much better than
merging the two datasets. If the datasets are not merged, you
essentially have to apply your optode function to each vial, store the
results, then combine them all together. This is innefficient.
Merging the datasets may be innefficient in a way, but once it is
done, your function can be applied to the entire dataset in one step.
If you have big data and the merge is slow, take a look at the
data.table package. I have recently (not that it was bad before, I
just never really knew how much it could do) been quite impressed with
it. As a whole other note, your optode function was quite difficult
to read, and I highly doubt you can confidently look at the code and
ensure there is not a typo, missed operator, etc. somewhere in that
block of formula code. I attempted to clean it up some, though
perhaps not with 100% success.
###
optode2 - function(cal0, T0, cal100, T100, phase, temp) {
dr - pi/180
f1 - 0.801
deltaPsiK - (-0.08)
deltaKsvK - 0.000383
m - 22.9
tan0T100 - tan(((cal0 + deltaPsiK * (T100 - T0))) * dr)
tan0Tm - tan((cal0 + (deltaPsiK * (temp - T0))) * dr)
tan100T100 - tan(cal100 * dr)
tanmTm - tan(phase * dr)
A - tan100T100 / tan0T100 / m * 100^2
B - tan100T100 / tan0T100 * 100 + tan100T100 / tan0T100 / m *100 -
f1 / m * 100 - 100 + f1 * 100
C - tan100T100 / tan0T100 - 1
KsvT100 - (- B + (sqrt(B^2 - 4 * A * C))) / (2 * A)
KsvTm - KsvT100 + (deltaKsvK * (temp - T100))
a - tanmTm / tan0Tm / m * KsvTm^2
b - tanmTm / tan0Tm * KsvTm + tanmTm / tan0Tm / m * KsvTm - f1 / m
* KsvTm - KsvTm + f1 * KsvTm
c - tanmTm / tan0Tm - 1
tot - tanmTm / tan0T100
big - tot * KsvTm + tanmTm / tan0T100 / m * KsvTm - f1 / m * KsvTm
- KsvTm + f1 * KsvTm
saturation - (-big + (sqrt((big)^2-4 * (tanmTm / tan0T100 / m *
KsvTm^2) * (tot - 1 / (2 * (tot / m * KsvTm^2))
return(saturation)
}
## Read in your example data
d1 - read.table(textConnection(
vial cal0T0 cal100 T100
1 61 1828 18
2 60.81827.118
3 60.21828.3 18
4 59.818 27.2 18), header = TRUE, stringsAsFactors =
FALSE)
d2 - read.table(textConnection(
vial phasetemp time
1 3117.510
1 31.5 17.420
1 32.8 17.530
2 29.0 17.5 10
2 29.7 17.5 20
2 30.9 17.5 30
3 27.1 17.4 10
3 27.6 17.4 20
3 28.1 17.5 30
4 31.0 17.6 10
4 33.3 17.6 20
4 35.617.6 30), header = TRUE, stringsAsFactors =
FALSE)
closeAllConnections()
dat - merge(d1, d2, by = vial)
## optode wrapper
f - function(d) optode2(d$cal0, d$T0, d$cal100, d$T100, d$phase, d$temp)
dat$oxygen - f(dat)
dat
###
Cheers,
Josh
On Wed, Oct 19, 2011 at 8:38 PM, Nathan Miller natemille...@gmail.com
wrote:
Hello,
I am not entirely sure the subject line captures what I am trying to do,
but
hopefully this description of the problem will help folks to see my
challenge and hopefully offer constructive assistance.
I have an experimental setup where I measure the decrease in oxygen in
small
vials as an organism, such as an oyster, consumes the oxygen. Each vial
is
calibrated before the experiment and these calibrations are used to
convert
the raw data after the experiment into oxygen values. I end up with two
dataframes. One has the calibration data and for example could look like
this
vial cal0T0 cal100 T100
1 61 1828 18
2 60.81827.118
3 60.21828.3 18
4 59.818 27.2 18
The second is a data file which could look like this
vial phasetemp time
1 3117.510
1 31.5 17.420
1 32.8 17.530
2 29.0 17.5 10
2 29.7 17.5 20
2 30.9 17.5 30
3 27.1 17.4 10
3 27.6 17.4 20
3 28.1 17.5 30
4
Re: [R] Applying function with separate dataframe (calibration file) supplying some inputs
Hi Nathan,
I honestly do not think that anything else will be much better than
merging the two datasets. If the datasets are not merged, you
essentially have to apply your optode function to each vial, store the
results, then combine them all together. This is innefficient.
Merging the datasets may be innefficient in a way, but once it is
done, your function can be applied to the entire dataset in one step.
If you have big data and the merge is slow, take a look at the
data.table package. I have recently (not that it was bad before, I
just never really knew how much it could do) been quite impressed with
it. As a whole other note, your optode function was quite difficult
to read, and I highly doubt you can confidently look at the code and
ensure there is not a typo, missed operator, etc. somewhere in that
block of formula code. I attempted to clean it up some, though
perhaps not with 100% success.
###
optode2 - function(cal0, T0, cal100, T100, phase, temp) {
dr - pi/180
f1 - 0.801
deltaPsiK - (-0.08)
deltaKsvK - 0.000383
m - 22.9
tan0T100 - tan(((cal0 + deltaPsiK * (T100 - T0))) * dr)
tan0Tm - tan((cal0 + (deltaPsiK * (temp - T0))) * dr)
tan100T100 - tan(cal100 * dr)
tanmTm - tan(phase * dr)
A - tan100T100 / tan0T100 / m * 100^2
B - tan100T100 / tan0T100 * 100 + tan100T100 / tan0T100 / m *100 -
f1 / m * 100 - 100 + f1 * 100
C - tan100T100 / tan0T100 - 1
KsvT100 - (- B + (sqrt(B^2 - 4 * A * C))) / (2 * A)
KsvTm - KsvT100 + (deltaKsvK * (temp - T100))
a - tanmTm / tan0Tm / m * KsvTm^2
b - tanmTm / tan0Tm * KsvTm + tanmTm / tan0Tm / m * KsvTm - f1 / m
* KsvTm - KsvTm + f1 * KsvTm
c - tanmTm / tan0Tm - 1
tot - tanmTm / tan0T100
big - tot * KsvTm + tanmTm / tan0T100 / m * KsvTm - f1 / m * KsvTm
- KsvTm + f1 * KsvTm
saturation - (-big + (sqrt((big)^2-4 * (tanmTm / tan0T100 / m *
KsvTm^2) * (tot - 1 / (2 * (tot / m * KsvTm^2))
return(saturation)
}
## Read in your example data
d1 - read.table(textConnection(
vial cal0T0 cal100 T100
1 61 1828 18
2 60.81827.118
3 60.21828.3 18
4 59.818 27.2 18), header = TRUE, stringsAsFactors = FALSE)
d2 - read.table(textConnection(
vial phasetemp time
1 3117.510
1 31.5 17.420
1 32.8 17.530
2 29.0 17.5 10
2 29.7 17.5 20
2 30.9 17.5 30
3 27.1 17.4 10
3 27.6 17.4 20
3 28.1 17.5 30
4 31.0 17.6 10
4 33.3 17.6 20
4 35.617.6 30), header = TRUE, stringsAsFactors = FALSE)
closeAllConnections()
dat - merge(d1, d2, by = vial)
## optode wrapper
f - function(d) optode2(d$cal0, d$T0, d$cal100, d$T100, d$phase, d$temp)
dat$oxygen - f(dat)
dat
###
Cheers,
Josh
On Wed, Oct 19, 2011 at 8:38 PM, Nathan Miller natemille...@gmail.com wrote:
Hello,
I am not entirely sure the subject line captures what I am trying to do, but
hopefully this description of the problem will help folks to see my
challenge and hopefully offer constructive assistance.
I have an experimental setup where I measure the decrease in oxygen in small
vials as an organism, such as an oyster, consumes the oxygen. Each vial is
calibrated before the experiment and these calibrations are used to convert
the raw data after the experiment into oxygen values. I end up with two
dataframes. One has the calibration data and for example could look like
this
vial cal0 T0 cal100 T100
1 61 18 28 18
2 60.8 18 27.1 18
3 60.2 18 28.3 18
4 59.8 18 27.2 18
The second is a data file which could look like this
vial phase temp time
1 31 17.5 10
1 31.5 17.4 20
1 32.8 17.5 30
2 29.0 17.5 10
2 29.7 17.5 20
2 30.9 17.5 30
3 27.1 17.4 10
3 27.6 17.4 20
3 28.1 17.5 30
4 31.0 17.6 10
4 33.3 17.6 20
4 35.6 17.6 30
I have a complicated function (included at the bottom) that uses the
calibration values and the raw data to calculate actual oxygen levels. It
works great, but as its currently written it requires that each calibration
be entered individually. I would rather apply the function based upon the
vial number (applying the calibration for vial 1 to all vial 1 data,
calibration for vial 2 to all vial 2 data).
I have managed to do this by combining the two dataframes into one that
looks like this
data
vial phase temp time cal0 T0 cal100 T100
1 31 17.5 10 61 18 28 18
1 31.5 17.4 20 61 18
