Reorganize response curve data for analysis and plotting
organize_response_curve_data.RdPrepares a set of response curves for future processing and analysis by numbering and reordering the points, (optionally) removing recovery points, and (optionally) calculating average values of key variables across each curve.
Usage
organize_response_curve_data(
licor_exdf,
identifier_columns,
measurement_numbers_to_remove,
column_for_ordering,
ordering_column_tolerance = Inf,
columns_to_average = c(),
print_information = TRUE
)Arguments
- licor_exdf
An
exdfobject representing response curve data from a Licor gas exchange measurement system.- identifier_columns
A vector or list of strings representing the names of columns in
licor_exdfthat, taken together, uniquely identify each curve. This often includes names likeplot,event,replicate, etc.- measurement_numbers_to_remove
A vector of integers specifying which points to remove from each curve; for example, if each curve has 16 points and the 10^th^ and 11^th^ points along the sequence should not be included in subsequent analysis,
measurement_numbers_to_removecould be specified asc(10, 11). Ifmeasurement_numbers_to_removeis set toc(), no points will be removed.- column_for_ordering
The name of a column that is systematically varied to produce each curve; for example, in a light response curve, this would typically by
Qin.- ordering_column_tolerance
To be passed to
check_response_curve_dataas thedriving_column_toleranceinput argument.- columns_to_average
A list of columns whose average values should be calculated; see below for details.
- print_information
To be passed to
check_response_curve_data.
Details
For an exdf object consisting of multiple response curves that can be
identified using the values of its identifier_columns, this function
performs the following actions:
Assigns a sequential number to each measurement in each curve, beginning with 1. In other words, the first point in the curve is given number 1, the second is given number 2, etc. These numbers are stored as a new column called
seq_num.(Optionally) extracts a subset of the data. If
measurement_numbers_to_removeisc(), then this step will be skipped; otherwise, values ofseq_numspecified bymeasurement_numbers_to_removewill be removed, and thencheck_response_curve_datawill be called to make sure the remaining points all follow the same sequence of setpoint values (within the tolerance set byordering_column_tolerance), treating thecolumn_for_orderingas thedriving_column.Reorders the data according to ascending values of the
column_for_ordering.(Optionally) calculates average values of important columns. If
columns_to_averageisc(), then this step will be skipped; otherwise, for each curve, the mean value of each column specified incolumns_to_averagewill be stored in a new column whose name is based on the original column name, but with'_avg'added at the end. For example, the average value of theQincolumn would be stored inQin_avg.
Removing certain points is often helpful for A-Ci curves, where the CO~2~ concentration begins at the ambient value, is decreased to a low value, is reset to atmospheric for several measurements to allow the plant to reacclimate, and then is increased to higher values. In this case, only the first measurement at ambient CO~2~ is used for plotting or additional analysis, and the "recovery" points should be removed.
Reordering the points is often helpful for plotting. For example, the points in an A-Ci curve would not be ordered according to their Ci values in a curve measured using a sequence as described above. This can cause issues when making line plots, so it may be convenient to reorder them according to their Ci values.
Calculating average values of certain columns is especially useful for
estimating Jmax values using calculate_jmax, since this
operation requires average values of leaf temperature and incident photon flux
across each curve.
Examples
# Read an example Licor file included in the PhotoGEA package and organize it.
# This file includes several 7-point light-response curves that can be uniquely
# identified by the values of its 'species' and 'plot' columns. Since these are
# light-response curves, each one follows a pre-set sequence of `Qin` values.
licor_file <- read_gasex_file(
PhotoGEA_example_file_path('ball_berry_1.xlsx')
)
# Split the data into individual curves, keep all seven measurement points in
# each curve, and order them by their incident light values (since these are
# light response curves). The curves were measured from high to low values of
# `Qin`, so after organizing the curves, their order will be reversed from the
# original version. Also add the average value of TleafCnd and Qin for each
# curve.
licor_file <- organize_response_curve_data(
licor_file,
c('species', 'plot'),
c(),
'Qin',
columns_to_average = c('TleafCnd', 'Qin')
)
# View a subset of the data, including the new `seq_num` column
print(licor_file[, c('species', 'plot', 'seq_num', 'Qin', 'A', 'Qin_avg'), TRUE])
#>
#> Converting an `exdf` object to a `data.frame` before printing
#>
#> species [UserDefCon] (NA) plot [UserDefCon] (NA) seq_num [NA] (NA)
#> 1 soybean 1a 7
#> 2 soybean 1a 6
#> 3 soybean 1a 5
#> 4 soybean 1a 4
#> 5 soybean 1a 3
#> 6 soybean 1a 2
#> 7 soybean 1a 1
#> 8 soybean 1b 7
#> 9 soybean 1b 6
#> 10 soybean 1b 5
#> 11 soybean 1b 4
#> 12 soybean 1b 3
#> 13 soybean 1b 2
#> 14 soybean 1b 1
#> 15 tobacco 2 7
#> 16 tobacco 2 6
#> 17 tobacco 2 5
#> 18 tobacco 2 4
#> 19 tobacco 2 3
#> 20 tobacco 2 2
#> 21 tobacco 2 1
#> 22 soybean 5 7
#> 23 soybean 5 6
#> 24 soybean 5 5
#> 25 soybean 5 4
#> 26 soybean 5 3
#> 27 soybean 5 2
#> 28 soybean 5 1
#> Qin [LeafQ] (micromol m^(-2) s^(-1)) A [GasEx] (micromol m^(-2) s^(-1))
#> 1 200.141 5.679174
#> 2 350.106 12.240074
#> 3 500.135 16.617929
#> 4 649.951 23.290614
#> 5 799.983 25.430106
#> 6 1099.800 22.996635
#> 7 2000.030 35.400473
#> 8 200.070 9.276031
#> 9 350.060 14.666531
#> 10 499.982 17.009070
#> 11 650.070 26.383035
#> 12 799.873 25.472460
#> 13 1099.890 30.250627
#> 14 1999.850 47.950460
#> 15 200.236 7.849870
#> 16 350.073 12.044692
#> 17 500.036 12.106347
#> 18 650.001 16.345888
#> 19 800.046 22.218165
#> 20 1099.980 25.492278
#> 21 2000.020 29.614535
#> 22 200.029 10.768250
#> 23 349.991 17.091593
#> 24 499.921 22.068194
#> 25 650.108 22.924484
#> 26 799.910 32.769396
#> 27 1099.960 36.218245
#> 28 1999.980 45.780746
#> Qin_avg [organize_response_curve_data] (micromol m^(-2) s^(-1))
#> 1 800.0209
#> 2 800.0209
#> 3 800.0209
#> 4 800.0209
#> 5 800.0209
#> 6 800.0209
#> 7 800.0209
#> 8 799.9707
#> 9 799.9707
#> 10 799.9707
#> 11 799.9707
#> 12 799.9707
#> 13 799.9707
#> 14 799.9707
#> 15 800.0560
#> 16 800.0560
#> 17 800.0560
#> 18 800.0560
#> 19 800.0560
#> 20 800.0560
#> 21 800.0560
#> 22 799.9856
#> 23 799.9856
#> 24 799.9856
#> 25 799.9856
#> 26 799.9856
#> 27 799.9856
#> 28 799.9856