RxClass: R6 Class for generating an OFPE prescription or experiment
In paulhegedus/OFPE: On-Farm Precision Experiments (OFPE) Data Management and Analysis Tools
RxClass R Documentation
R6 Class for generating an OFPE prescription or experiment
Description
R6 class for for creating a prescription or experiment for a
field of interest. The user can create a new experiment with inputs randomly
applied across the field with no stratification, or select data from the database
on which to stratify experimental rates. Alternatively the user can create
a prescription or experimental prescription based on the optimized rates from
the user selected management scenario for given economic and weather conditions.
This process builds off of the simulation R6 class (SimClass), where the
Monte Carlo simulation for a year or year(s) the user thinks the upcoming year
is going to resemble, with the user specified economic data, is used for
identifying the optimum rates for each management strategy at each part.
If the user decides to do an experimental prescription, the optimum rates of the
user specified management scenario from the simulation is used as the base map
for the experimental prescription. Experimental rates can be generated from gaps
in the optimum rates to create a distribution of rates across the field to be used
for fitting crop response models in upcoming years, or from user specified
experimental rates. These are then randomly applied to the field
according to user specified proportions for each rate, stratified on the
optimum rates.
If the user decides to make an OFPE prescription with no experiment, the
optimum rates for each point for the user specified management scenario is
taken as the prescription. Instead of applying experimental rates, a farmer
selected rate representing the rate they would have applied is randomly
placed across the field, stratified on the optimum rates. These are used
as a check to evaluate the performance of the optimized rates or whether
further experimentation is needed.
When creating an experimental prescription or a prescription, the user must pass
a set up SimClass. This implies that the user has initialized and set up
the required DatClass, ModClass, and EconDat R6 objects. The user must pass a
SimClass object that has been executed. This simulation output data is used to
generate the experimental prescriptions and the prescriptions.
If the user allows, the prescriptions or experiments are saved to the 'Outputs' folder
and are saved in the OFPE database in the farmer specific aggregated schema in a table
called 'rx' (farmername_a.rx).
Public fields
dbConDatabase connection object connected to an OFPE formatted
database, see DBCon class.
gen_typeWhat type of output to generate; 'NewExp_NoStrat' for
a new experiment with randomly placed experimental rates with no stratification,
'NewExp_wStrat' for a new experiment with randomly placed experimental rates
stratified on user specified data, 'ExpRx' for a experimental rates randomly
placed, stratified on the optimum rates from the user selected prescription,
or 'Rx' where the prescription is output to the user, with check rates of a
farmer selected base rate randomly placed, stratified on optimum rates.
trt_lengthLength, given in feet and converted to meters, for
which to apply treatments.
trt_widthThe width of the treatment blocks. Should be a multiple of
the sprayer boom or spreader width, given as feet (converted to meters).
headingNumeric, heading in degrees from true north to rotate the
experiment/prescription to. Default is 0 (no rotation). Note that if a
heading is provided, the grid is trimmed based on the buffered boundary but
rotation caused by providing a heading may skew treatment rates so that
they encroach into the cleanup strip.
fld_propThe proportion of the field to apply experimental
rates to (i.e. 0.5 or 1.0), OR, the percent of available cells to
apply check rates to (i.e. 0.05, 0.1).
convThe conversion factor between lbs of the input to the
units of the as-applied input (i.e. lbs N/ac to unit input/ac)
base_rateThe rate to apply between the experimental rates
and the field edge, or as check rates in the prescription selected
option.
rx_yearsThe year or vector of years to base the prescription
off of. If the user passes in multiple years, the average optimum rate
from the simulations will be used. If the user has passed in a SimClass
object where the simulation has been executed, the RxClass will check the
output data to avoid redundantly running a simulation.
rx_for_yearProvide the year that the prescription or experiment
is made for. Used for labeling outputs.
SAVELogical, whether to save figures and the experiment or prescription.
Autofilled to FALSE if a user selects NA in the 'out_path' or is NULL.
Autofilled to TRUE otherwise. If not applied and a SimClass object has
been supplied, the selected option in that class will be used.
out_pathProvide the path to the folder in which to store and
save figures and the prescription Type NA to not create any folders.
You will not be able to save any outputs. (Note, even if a path is provided,
the user can pass FALSE as the sole argument to the 'setupOP' method
to prevent the creation of folders. This will automatically prevent
any plots to be saved.). If not applied and a SimClass object has
been supplied, the selected option in that class will be used.
to_DBLogical, whether to save the prescription or experiment
into the database.
farmernameIf the user is creating a new experiment, provide or
select the name of the farmer that owns or manages the field(s) that
an experiment is going to be generated for.
fieldnameIf the user is creating a new experiment, provide or
select the fieldname of the field to use. The field list is from the
available fields in the database for experimentation.
exp_rate_lengthProvide the length of experimental rates to apply.
This applies to new experiments and experimental prescriptions. This
represents the equipment constraints of the farmer. In the case of the
experimental prescription, this number of rates does not include the
number of rates for the optimized base map, so take your selections for
the management scenario and number of optimum rates into account.
exp_ratesProvide a vector of experimental rates equal to the
number of experimental rates provided in 'exp_rate_length'. This is
required for all new experiments, however can be left to null for
experimental prescriptions if experimental rates should be generated
based on gaps in optimum rates.
exp_rates_propProvide proportions (0 - 1) for the length
of experimental rates provided in 'exp_rate_length'. This is required
for all new experiments and experimental prescriptions.
strat_dat_parmsNamed list by fieldname that contains a list for each field
containing named slots for 'table', 'grid_size', 'path', 'year', and 'col_name'
to define the stratification data to use for randomly applying experimental rates.
Set named slots to NA for any fields that you don't want to make stratified
rates on. You can stratify on multiple variables per field, with priority
given by order. Each of the sublist slots for each field must have the same
dimensions. Note that more stratification variables increases processing times.
The table ('table') indicates the location within the database that the stratification
data is stored in. This can either be from an aggregated table ('yld', 'pro',
or 'sat') or can be from a raw table. Simply specify the table name and the
schema will be derived from the farmername. For data from an aggregated table,
the user must also provide the size ('grid_size') of the grid cells used to
aggregate the data the user desires in the aggregated dataset (i.e. 10, 30 meters).
This is a numeric variable and if stratifying on raw data, this parameter
can be left NA. Conversely, if you are stratifying on raw data, an additional
parameter called 'path' needs to be supplied in a named slot of each field's
sublist to specify the original filename of the data imported into the database.
If the desired data is from an aggregated table than enter NA for the 'path'. The year of
the desired data must also be provided ('year'). This is to specify which data in
the aggregated table to use. If using raw data, the year is automatically
derived from the data specified by the filename. Finally, the user must
supply the column name ('col_name') of the variable to stratify on.
This must be supplied for both raw and aggregated data.
simClassIf the user is creating an experimental prescription or
prescription an R6 class SimClass object must be supplied. This has to be
set up. The RxClass will check the SimClass for simulation output data
from which years(s) can be selected from.
exp_rate_genLogical, for the experimental prescription provide
TRUE or FALSE for whether to create experimentla rates from gaps in the
optimized rates. These experimental rates are placed between optimum rates
to establish a range of rates for which to improve model predictions in future
years.
opt_rate_lengthProvide the number of oprimum rates to use.
This applies to prescriptions and experimental prescriptions. This
represents the equipment constraints of the farmer. In the case of the
experimental prescription, this number of rates does not include the
number of experimental rates, so take your selections for
the management scenario and number of experimental rates into account.
mgmt_scenIf the user is creating a prescription or experimental
prescription, they must provide the management scenario to use for their
prescription. The user can choose from the management options listed in
the SimClass. The options are 'SSOPT': site-specific optimized rates,
'FFOPT': full-field optimum uniform rate, 'FS': farmer selected uniform
rate, 'Min': applying the least intensive input rates (i.e. 0 lbs N/ac, or
25 lbs seed/ac), 'Opp' is omitted because this strategy is the least intensive
input rate in conventional system types, and the farmer selected rate for
organic systems, both of which are already provided.
expvarExperimental variable to optimize, select/input
'As-Applied Nitrogen' or 'As-Applied Seed Rate'. This is the type of
input that is experimentally varied across the field as part of the
on-farm experimentation.
min_app_rateThe minimum as-applied rate that the equipment can
apply. This must be in the units of the input applied to the field,
which is not necessarily the same as the experimental input. This is
an equipment restriction specific to the farmer.
buffer_widthThe width of the buffer from the field edge within which
to place experiments or prescriptions. Provided by the user in feet and
converted to meters internally.
min_rate_jumpsOptional, supply either 'N/S' or 'E/W' to indicate
direction in which to minimize rate jumps. This is the predominant direction
that the experimental input is applied across the field. This function minimizes
the difference in rates between adjacent treatments for easier use on the
equipment. Note that this will eradicate any randomization/optimization of
rates and will partially or completely remove stratification. This function makes
sure that the rates do not vary by more than 2 rate levels in the direction
specified. Default is NULL, which prevents execution. This will not guarantee
eradication of all rate jumps, but will reduce the amount.
out_genAn output generator R6 class (i.e. ExpGen or RxGen), that produces
the output specified by the user. There are different classes with different
required arguments based on whether the user is setting up a new experiment
or creating a prescription. These are automatically called by this 'SetupGen'
method.
Methods
Public methods
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Method new()
Initializing this class can be performed independent or
after running the analysis and simulation step of the OFPE data cycle.
If the user is creating a new experiment for a field, they should
initialize the class with the arguments specified in the documentation
for that method. It is recommended that the user uses the interactive
setup method rather than passing in arguments, unless an experienced
user. For new experiments, the user will not require a set up
SimClass object, however if creating a prescription or experimental
prescription the user will need to pass in a set up and/or executed
SimClass R6 class, EVEN IF using the interactive methods.
Usage
RxClass$new(
dbCon,
simClass = NULL,
gen_type = NULL,
trt_length = NULL,
trt_width = NULL,
heading = 0,
fld_prop = NULL,
conv = NULL,
base_rate = NULL,
rx_years = NULL,
rx_for_year = NULL,
out_path = NULL,
SAVE = NULL,
to_DB = NULL,
fieldname = NULL,
farmername = NULL,
exp_rate_length = NULL,
exp_rates = NULL,
exp_rates_prop = NULL,
strat_dat_parms = NULL,
exp_rate_gen = NULL,
opt_rate_length = NULL,
mgmt_scen = NULL,
expvar = NULL,
min_app_rate = NULL,
buffer_width = 0,
min_rate_jumps = NULL
)
Arguments
dbConDatabase connection object connected to an OFPE formatted
database, see DBCon class.
simClassIf the user is creating an experimental prescription or
prescription an R6 class SimClass object must be supplied. This has to be
set up and simulations from years that the user expects the upcoming year
will be like must be executed.
gen_typeWhat type of output to generate; 'NewExp_NoStrat' for
a new experiment with randomly placed experimental rates with no stratification,
'NewExp_wStrat' for a new experiment with randomly placed experimental rates
stratified on user specified data, 'ExpRx' for a experimental rates randomly
placed, stratified on the optimum rates from the user selected prescription,
or 'Rx' where the prescription is output to the user, with check rates of a
farmer selected base rate randomly placed, stratified on optimum rates.
trt_lengthLength, in feet, for which to apply treatments.
trt_widthThe width, in feet, for which to apply treatments.
headingNumeric, heading in degrees from true north to rotate the
experiment/prescription to. Default is 0 (no rotation). Note that if a
heading is provided, the grid is trimmed based on the buffered boundary but
rotation caused by providing a heading may skew treatment rates so that
they encroach into the cleanup strip.
fld_propThe proportion of the field to apply experimental
rates to (i.e. 0.5 or 1.0), OR, the percent of available cells to
apply check rates to (i.e. 0.05, 0.1).
convThe conversion factor between lbs of the input to the
units of the as-applied input (i.e. lbs N/ac to unit input/ac)
base_rateThe rate to apply between the experimental rates
and the field edge, or as check rates in the prescription selected
option.
rx_yearsThe year or vector of years to base the prescription
off of. If the user passes in multiple years, the average optimum rate
from the simulations will be used. If the user has passed in a SimClass
object where the simulation has been executed, the RxClass will check the
output data to avoid redundantly running a simulation.
rx_for_yearProvide the year that the prescription or experiment
is made for. Used for labeling outputs.
out_pathProvide the path to the folder in which to store and
save figures and the prescription Type NA to not create any folders.
You will not be able to save any outputs. (Note, even if a path is provided,
the user can pass FALSE as the sole argument to the 'setupOP' method
to prevent the creation of folders. This will automatically prevent
any plots to be saved.). If not applied and a SimClass object has
been supplied, the selected option in that class will be used.
SAVELogical, whether to save figures and the experiment or prescription.
Autofilled to FALSE if a user selects NA in the 'out_path' or is NULL.
Autofilled to TRUE otherwise. If not applied and a SimClass object has
been supplied, the selected option in that class will be used.
to_DBLogical, whether to save the prescription or experiment
into the database.
fieldnameIf the user is creating a new experiment, provide or
select the fieldname of the field to use. The field list is from the
available fields in the database for experimentation.
farmernameIf the user is creating a new experiment, provide or
select the name of the farmer that owns or manages the field(s) that
an experiment is going to be generated for.
exp_rate_lengthProvide the length of experimental rates to apply.
This applies to new experiments and experimental prescriptions. This
represents the equipment constraints of the farmer. In the case of the
experimental prescription, this number of rates does not include the
number of rates for the optimized base map, so take your selections for
the management scenario and number of optimum rates into account.
exp_ratesProvide a vector of experimental rates equal to the
number of experimental rates provided in 'exp_rate_length'. This is
required for all new experiments, however can be left to null for
experimental prescriptions if experimental rates should be generated
based on gaps in optimum rates.
exp_rates_propProvide proportions (0 - 1) for the length
of experimental rates provided in 'exp_rate_length'. This is required
for all new experiments and experimental prescriptions.
strat_dat_parmsNamed list by fieldname that contains a list for each field
containing named slots for 'table', 'grid_size', 'path', 'year', and 'col_name'
to define the stratification data to use for randomly applying experimental rates.
You can stratify on multiple variables per field, with priority given by order.
Each of the sublist slots for each field must have the same dimensions. Note
that more stratification variables increases processing times.
The table ('table') indicates the location within the database that the stratification
data is stored in. This can either be from an aggregated table ('yld', 'pro',
or 'sat') or can be from a raw table. Simply specify the table name and the
schema will be derived from the farmername. For data from an aggregated table,
the user must also provide the size ('grid_size') of the grid cells used to
aggregate the data the user desires in the aggregated dataset (i.e. 10, 30 meters).
This is a numeric variable and if stratifying on raw data, this parameter
can be left NA. Conversely, if you are stratifying on raw data, an additional
parameter called 'path' needs to be supplied in a named slot of each field's
sublist to specify the original filename of the data imported into the database.
If the desired data is from an aggregated table than enter NA for the 'path'. The year of
the desired data must also be provided ('year'). This is to specify which data in
the aggregated table to use. If using raw data, the year is automatically
derived from the data specified by the filename. Finally, the user must
supply the column name ('col_name') of the variable to stratify on.
This must be supplied for both raw and aggregated data.
exp_rate_genLogical, for the experimental prescription provide
TRUE or FALSE for whether to create experimental rates from gaps in the
optimized rates. These experimental rates are placed between optimum rates
to establish a range of rates for which to improve model predictions in future
years.
opt_rate_lengthProvide the number of optimum rates to use.
This applies to prescriptions and experimental prescriptions. This
represents the equipment constraints of the farmer. In the case of the
experimental prescription, this number of rates does not include the
number of experimental rates, so take your selections for
the management scenario and number of experimental rates into account.
mgmt_scenIf the user is creating a prescription or experimental
prescription, they must provide the management scenario to use for their
prescription. The user can choose from the management options listed in
the SimClass. The options are 'SSOPT': site-specific optimized rates,
'FFOPT': full-field optimum uniform rate, 'FS': farmer selected uniform
rate, 'Min': applying the least intensive input rates (i.e. 0 lbs N/ac, or
25 lbs seed/ac), 'Opp' is omitted because this strategy is the least intensive
input rate in conventional system types, and the farmer selected rate for
organic systems, both of which are already provided.
expvarExperimental variable to optimize, select/input
'As-Applied Nitrogen' or 'As-Applied Seed Rate'. This is the type of
input that is experimentally varied across the field as part of the
on-farm experimentation.
min_app_rateThe minimum as-applied rate that the equipment can
apply. This must be in the units of the input applied to the field,
which is not necessarily the same as the experimental input. This is
an equipment restriction specific to the farmer.
buffer_widthThe width of the buffer from field edge for
experiments or prescriptions (feet).
min_rate_jumpsOptional, supply either 'N/S' or 'E/W' to indicate
direction in which to minimize rate jumps. This is the predominant direction
that the experimental input is applied across the field. This function minimizes
the difference in rates between adjacent treatments for easier use on the
equipment. Note that this will eradicate any randomization/optimization of
rates and will partially or completely remove stratification. This function makes
sure that the rates do not vary by more than 2 rate levels in the direction
specified. Default is NULL, which prevents execution. This will not guarantee
eradication of all rate jumps, but will reduce the amount.
Returns
An initialized RxClass R6 class object.
Method selectInputs()
Interactive method for selecting inputs related to the prescription or
experiment generation process. It is recommended to use this interactive
method rather than passing in inputs as arguments unless an experienced
user. If the user is creating an experimental prescription or prescription,
they must have set up this class with a SimClass object, even if using
this interactive method for selecting other inputs.
The user first selects whether to make a prescription or experiment. If
the user specifies a type and has not passed in a required input upon
initialization, an error will be thrown. Based on the user specification
different selections are required for the user to set up the generation
method.
The user will have to select the treatment length and width, the heading
to align the treatments relative to N/S, the base rate to apply to around
experiments or for checks in the prescription, the year(s) the farmer
thinks the upcoming year will resemble and to make the prescription for,
and whether to save the prescription or experiment in the database.
Usage
RxClass$selectInputs()
Arguments
NoneNo arguments needed because passed in during class
instantiation.
Returns
A 'RxClass' object with complete user selections.
Method setupOP()
Method used to setup the output location for the prescription and related
figures.
Usage
RxClass$setupOP(create = TRUE)
Arguments
createLogical, whether to create folders for output. If not,
no plots will be saved by default.
Returns
A folder created in the path for model output figures.
Method setupGen()
Method used to setup the experiment or prescription generator based on the
user select 'gen_type'. This initializes the output generator selected by the
user with the arguments needed for that generator.
Usage
RxClass$setupGen()
Arguments
rxClassrxClass R6 class object. This method passes it's own class
instantiation into the generator class.
Returns
An instantiated output generator class.
Method executeOutGen()
Method for calling the execution method of the experiment or prescription
generator. These call the methods in the generator R6 classes.
Usage
RxClass$executeOutGen()
Arguments
NoneAll parametes supplied upon initialization.
Returns
A completed experiment or prescription.
Method saveOutputs()
Method for saving prescriptions or experiments and related plots. This only
save plots if the user has supplied a folder path to save the
plots to, and if the user does not select SAVE == FALSE. If the user passes in
an argument to SAVE, this replaces any previously selected SAVE option (e.g.
if SAVE was set to TRUE in the class initialization and the user passes FALSE
as an argument here, the class' selection for SAVE is set to FALSE from TRUE).
Usage
RxClass$saveOutputs(SAVE = NULL, to_DB = NULL)
Arguments
SAVEWhether to save diagnostic plots. If NULL uses the user selected
choice. If not NULL and is logical, argument replaces previously set SAVE
options for the entire class.
to_DBLogical, whether to save the prescription or experiment
into the database. If NULL uses the user selected choice. If not NULL
and is logical, argument replaces previously set to_DB options for the
entire class.
Returns
Shapefile and map saved in 'Outputs' folder and in database.
Method plotRxMap()
This method is for plotting maps of simulation outcomes. These
include the net-returns from the management strategies, predicted
responses, and optimized rates. Is a wrapper to a private plotRxMap
method.
Usage
RxClass$plotRxMap(SAVE = TRUE)
Arguments
SAVELogical, whether to save figure.
Returns
Maps saved in 'Outputs/Rx/' and/or returned.
Method plotCellTypeMap()
This method is for plotting a map showing the distribution of cell
types. This shows which cells are the optimized or management scenario
rates, which are experimental rates, and where base rates are applied.
Usage
RxClass$plotCellTypeMap(SAVE = TRUE)
Arguments
SAVELogical, whether to save figure.
Returns
Maps saved in 'Outputs/Rx/' and/or returned.
Method uploadRxFun()
This method is for uploading the experiment or prescription to the
database.
Usage
RxClass$uploadRxFun(
RX,
db,
farmername,
fieldname,
rx_year,
size,
mgmt_scen,
dtype = "rx_poly"
)
Arguments
RXData frame with the net-returns and experimental optimums
for every point for every simulation iteration.
dbDatabase connection connected to an OFPE formatted
database.
farmernameThe name of the farmer that manages the field.
fieldnameUnique field name corresponding to all fields used
in the simulation.
rx_yearProvide the year that the prescription or experiment
is made for. Used for labeling outputs.
sizeThe size of the treatment zones, which is the treatment width x
the treatment length.
mgmt_scenIf the user is creating a prescription or experimental
prescription, they must provide the management scenario to use for their
prescription. The user can choose from the management options listed in
the SimClass. The options are 'SSOPT': site-specific optimized rates,
'FFOPT': full-field optimum uniform rate, 'FS': farmer selected uniform
rate, 'Min': applying the least intensive input rates (i.e. 0 lbs N/ac, or
25 lbs seed/ac), 'Opp' is omitted because this strategy is the least intensive
input rate in conventional system types, and the farmer selected rate for
organic systems, both of which are already provided. This is set to 'exp'
for all new experiments.
dtypeThe data type of the data for upload to the database, default
to 'rx'.
Returns
Output experiment or prescription saved in database.
Method clone()
The objects of this class are cloneable with this method.
Usage
RxClass$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
DBCon for the database connection class,
SimClass for the class that contains simulation outputs,
ExpGen for the class for creating new experiments, and
RxGen for the class that creates experimental prescriptions
or pure prescriptions.
paulhegedus/OFPE documentation built on Nov. 23, 2022, 5:09 a.m.
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| RxClass | R Documentation |
R6 Class for generating an OFPE prescription or experiment
Description
R6 class for for creating a prescription or experiment for a field of interest. The user can create a new experiment with inputs randomly applied across the field with no stratification, or select data from the database on which to stratify experimental rates. Alternatively the user can create a prescription or experimental prescription based on the optimized rates from the user selected management scenario for given economic and weather conditions. This process builds off of the simulation R6 class (SimClass), where the Monte Carlo simulation for a year or year(s) the user thinks the upcoming year is going to resemble, with the user specified economic data, is used for identifying the optimum rates for each management strategy at each part.
If the user decides to do an experimental prescription, the optimum rates of the user specified management scenario from the simulation is used as the base map for the experimental prescription. Experimental rates can be generated from gaps in the optimum rates to create a distribution of rates across the field to be used for fitting crop response models in upcoming years, or from user specified experimental rates. These are then randomly applied to the field according to user specified proportions for each rate, stratified on the optimum rates.
If the user decides to make an OFPE prescription with no experiment, the optimum rates for each point for the user specified management scenario is taken as the prescription. Instead of applying experimental rates, a farmer selected rate representing the rate they would have applied is randomly placed across the field, stratified on the optimum rates. These are used as a check to evaluate the performance of the optimized rates or whether further experimentation is needed.
When creating an experimental prescription or a prescription, the user must pass a set up SimClass. This implies that the user has initialized and set up the required DatClass, ModClass, and EconDat R6 objects. The user must pass a SimClass object that has been executed. This simulation output data is used to generate the experimental prescriptions and the prescriptions.
If the user allows, the prescriptions or experiments are saved to the 'Outputs' folder and are saved in the OFPE database in the farmer specific aggregated schema in a table called 'rx' (farmername_a.rx).
Public fields
dbConDatabase connection object connected to an OFPE formatted database, see DBCon class.
gen_typeWhat type of output to generate; 'NewExp_NoStrat' for a new experiment with randomly placed experimental rates with no stratification, 'NewExp_wStrat' for a new experiment with randomly placed experimental rates stratified on user specified data, 'ExpRx' for a experimental rates randomly placed, stratified on the optimum rates from the user selected prescription, or 'Rx' where the prescription is output to the user, with check rates of a farmer selected base rate randomly placed, stratified on optimum rates.
trt_lengthLength, given in feet and converted to meters, for which to apply treatments.
trt_widthThe width of the treatment blocks. Should be a multiple of the sprayer boom or spreader width, given as feet (converted to meters).
headingNumeric, heading in degrees from true north to rotate the experiment/prescription to. Default is 0 (no rotation). Note that if a heading is provided, the grid is trimmed based on the buffered boundary but rotation caused by providing a heading may skew treatment rates so that they encroach into the cleanup strip.
fld_propThe proportion of the field to apply experimental rates to (i.e. 0.5 or 1.0), OR, the percent of available cells to apply check rates to (i.e. 0.05, 0.1).
convThe conversion factor between lbs of the input to the units of the as-applied input (i.e. lbs N/ac to unit input/ac)
base_rateThe rate to apply between the experimental rates and the field edge, or as check rates in the prescription selected option.
rx_yearsThe year or vector of years to base the prescription off of. If the user passes in multiple years, the average optimum rate from the simulations will be used. If the user has passed in a SimClass object where the simulation has been executed, the RxClass will check the output data to avoid redundantly running a simulation.
rx_for_yearProvide the year that the prescription or experiment is made for. Used for labeling outputs.
SAVELogical, whether to save figures and the experiment or prescription. Autofilled to FALSE if a user selects NA in the 'out_path' or is NULL. Autofilled to TRUE otherwise. If not applied and a SimClass object has been supplied, the selected option in that class will be used.
out_pathProvide the path to the folder in which to store and save figures and the prescription Type NA to not create any folders. You will not be able to save any outputs. (Note, even if a path is provided, the user can pass FALSE as the sole argument to the 'setupOP' method to prevent the creation of folders. This will automatically prevent any plots to be saved.). If not applied and a SimClass object has been supplied, the selected option in that class will be used.
to_DBLogical, whether to save the prescription or experiment into the database.
farmernameIf the user is creating a new experiment, provide or select the name of the farmer that owns or manages the field(s) that an experiment is going to be generated for.
fieldnameIf the user is creating a new experiment, provide or select the fieldname of the field to use. The field list is from the available fields in the database for experimentation.
exp_rate_lengthProvide the length of experimental rates to apply. This applies to new experiments and experimental prescriptions. This represents the equipment constraints of the farmer. In the case of the experimental prescription, this number of rates does not include the number of rates for the optimized base map, so take your selections for the management scenario and number of optimum rates into account.
exp_ratesProvide a vector of experimental rates equal to the number of experimental rates provided in 'exp_rate_length'. This is required for all new experiments, however can be left to null for experimental prescriptions if experimental rates should be generated based on gaps in optimum rates.
exp_rates_propProvide proportions (0 - 1) for the length of experimental rates provided in 'exp_rate_length'. This is required for all new experiments and experimental prescriptions.
strat_dat_parmsNamed list by fieldname that contains a list for each field containing named slots for 'table', 'grid_size', 'path', 'year', and 'col_name' to define the stratification data to use for randomly applying experimental rates. Set named slots to NA for any fields that you don't want to make stratified rates on. You can stratify on multiple variables per field, with priority given by order. Each of the sublist slots for each field must have the same dimensions. Note that more stratification variables increases processing times.
The table ('table') indicates the location within the database that the stratification data is stored in. This can either be from an aggregated table ('yld', 'pro', or 'sat') or can be from a raw table. Simply specify the table name and the schema will be derived from the farmername. For data from an aggregated table, the user must also provide the size ('grid_size') of the grid cells used to aggregate the data the user desires in the aggregated dataset (i.e. 10, 30 meters). This is a numeric variable and if stratifying on raw data, this parameter can be left NA. Conversely, if you are stratifying on raw data, an additional parameter called 'path' needs to be supplied in a named slot of each field's sublist to specify the original filename of the data imported into the database. If the desired data is from an aggregated table than enter NA for the 'path'. The year of the desired data must also be provided ('year'). This is to specify which data in the aggregated table to use. If using raw data, the year is automatically derived from the data specified by the filename. Finally, the user must supply the column name ('col_name') of the variable to stratify on. This must be supplied for both raw and aggregated data.
simClassIf the user is creating an experimental prescription or prescription an R6 class SimClass object must be supplied. This has to be set up. The RxClass will check the SimClass for simulation output data from which years(s) can be selected from.
exp_rate_genLogical, for the experimental prescription provide TRUE or FALSE for whether to create experimentla rates from gaps in the optimized rates. These experimental rates are placed between optimum rates to establish a range of rates for which to improve model predictions in future years.
opt_rate_lengthProvide the number of oprimum rates to use. This applies to prescriptions and experimental prescriptions. This represents the equipment constraints of the farmer. In the case of the experimental prescription, this number of rates does not include the number of experimental rates, so take your selections for the management scenario and number of experimental rates into account.
mgmt_scenIf the user is creating a prescription or experimental prescription, they must provide the management scenario to use for their prescription. The user can choose from the management options listed in the SimClass. The options are 'SSOPT': site-specific optimized rates, 'FFOPT': full-field optimum uniform rate, 'FS': farmer selected uniform rate, 'Min': applying the least intensive input rates (i.e. 0 lbs N/ac, or 25 lbs seed/ac), 'Opp' is omitted because this strategy is the least intensive input rate in conventional system types, and the farmer selected rate for organic systems, both of which are already provided.
expvarExperimental variable to optimize, select/input 'As-Applied Nitrogen' or 'As-Applied Seed Rate'. This is the type of input that is experimentally varied across the field as part of the on-farm experimentation.
min_app_rateThe minimum as-applied rate that the equipment can apply. This must be in the units of the input applied to the field, which is not necessarily the same as the experimental input. This is an equipment restriction specific to the farmer.
buffer_widthThe width of the buffer from the field edge within which to place experiments or prescriptions. Provided by the user in feet and converted to meters internally.
min_rate_jumpsOptional, supply either 'N/S' or 'E/W' to indicate direction in which to minimize rate jumps. This is the predominant direction that the experimental input is applied across the field. This function minimizes the difference in rates between adjacent treatments for easier use on the equipment. Note that this will eradicate any randomization/optimization of rates and will partially or completely remove stratification. This function makes sure that the rates do not vary by more than 2 rate levels in the direction specified. Default is NULL, which prevents execution. This will not guarantee eradication of all rate jumps, but will reduce the amount.
out_genAn output generator R6 class (i.e. ExpGen or RxGen), that produces the output specified by the user. There are different classes with different required arguments based on whether the user is setting up a new experiment or creating a prescription. These are automatically called by this 'SetupGen' method.
Methods
Public methods
Method new()
Initializing this class can be performed independent or after running the analysis and simulation step of the OFPE data cycle. If the user is creating a new experiment for a field, they should initialize the class with the arguments specified in the documentation for that method. It is recommended that the user uses the interactive setup method rather than passing in arguments, unless an experienced user. For new experiments, the user will not require a set up SimClass object, however if creating a prescription or experimental prescription the user will need to pass in a set up and/or executed SimClass R6 class, EVEN IF using the interactive methods.
Usage
RxClass$new( dbCon, simClass = NULL, gen_type = NULL, trt_length = NULL, trt_width = NULL, heading = 0, fld_prop = NULL, conv = NULL, base_rate = NULL, rx_years = NULL, rx_for_year = NULL, out_path = NULL, SAVE = NULL, to_DB = NULL, fieldname = NULL, farmername = NULL, exp_rate_length = NULL, exp_rates = NULL, exp_rates_prop = NULL, strat_dat_parms = NULL, exp_rate_gen = NULL, opt_rate_length = NULL, mgmt_scen = NULL, expvar = NULL, min_app_rate = NULL, buffer_width = 0, min_rate_jumps = NULL )
Arguments
dbConDatabase connection object connected to an OFPE formatted database, see DBCon class.
simClassIf the user is creating an experimental prescription or prescription an R6 class SimClass object must be supplied. This has to be set up and simulations from years that the user expects the upcoming year will be like must be executed.
gen_typeWhat type of output to generate; 'NewExp_NoStrat' for a new experiment with randomly placed experimental rates with no stratification, 'NewExp_wStrat' for a new experiment with randomly placed experimental rates stratified on user specified data, 'ExpRx' for a experimental rates randomly placed, stratified on the optimum rates from the user selected prescription, or 'Rx' where the prescription is output to the user, with check rates of a farmer selected base rate randomly placed, stratified on optimum rates.
trt_lengthLength, in feet, for which to apply treatments.
trt_widthThe width, in feet, for which to apply treatments.
headingNumeric, heading in degrees from true north to rotate the experiment/prescription to. Default is 0 (no rotation). Note that if a heading is provided, the grid is trimmed based on the buffered boundary but rotation caused by providing a heading may skew treatment rates so that they encroach into the cleanup strip.
fld_propThe proportion of the field to apply experimental rates to (i.e. 0.5 or 1.0), OR, the percent of available cells to apply check rates to (i.e. 0.05, 0.1).
convThe conversion factor between lbs of the input to the units of the as-applied input (i.e. lbs N/ac to unit input/ac)
base_rateThe rate to apply between the experimental rates and the field edge, or as check rates in the prescription selected option.
rx_yearsThe year or vector of years to base the prescription off of. If the user passes in multiple years, the average optimum rate from the simulations will be used. If the user has passed in a SimClass object where the simulation has been executed, the RxClass will check the output data to avoid redundantly running a simulation.
rx_for_yearProvide the year that the prescription or experiment is made for. Used for labeling outputs.
out_pathProvide the path to the folder in which to store and save figures and the prescription Type NA to not create any folders. You will not be able to save any outputs. (Note, even if a path is provided, the user can pass FALSE as the sole argument to the 'setupOP' method to prevent the creation of folders. This will automatically prevent any plots to be saved.). If not applied and a SimClass object has been supplied, the selected option in that class will be used.
SAVELogical, whether to save figures and the experiment or prescription. Autofilled to FALSE if a user selects NA in the 'out_path' or is NULL. Autofilled to TRUE otherwise. If not applied and a SimClass object has been supplied, the selected option in that class will be used.
to_DBLogical, whether to save the prescription or experiment into the database.
fieldnameIf the user is creating a new experiment, provide or select the fieldname of the field to use. The field list is from the available fields in the database for experimentation.
farmernameIf the user is creating a new experiment, provide or select the name of the farmer that owns or manages the field(s) that an experiment is going to be generated for.
exp_rate_lengthProvide the length of experimental rates to apply. This applies to new experiments and experimental prescriptions. This represents the equipment constraints of the farmer. In the case of the experimental prescription, this number of rates does not include the number of rates for the optimized base map, so take your selections for the management scenario and number of optimum rates into account.
exp_ratesProvide a vector of experimental rates equal to the number of experimental rates provided in 'exp_rate_length'. This is required for all new experiments, however can be left to null for experimental prescriptions if experimental rates should be generated based on gaps in optimum rates.
exp_rates_propProvide proportions (0 - 1) for the length of experimental rates provided in 'exp_rate_length'. This is required for all new experiments and experimental prescriptions.
strat_dat_parmsNamed list by fieldname that contains a list for each field containing named slots for 'table', 'grid_size', 'path', 'year', and 'col_name' to define the stratification data to use for randomly applying experimental rates. You can stratify on multiple variables per field, with priority given by order. Each of the sublist slots for each field must have the same dimensions. Note that more stratification variables increases processing times.
The table ('table') indicates the location within the database that the stratification data is stored in. This can either be from an aggregated table ('yld', 'pro', or 'sat') or can be from a raw table. Simply specify the table name and the schema will be derived from the farmername. For data from an aggregated table, the user must also provide the size ('grid_size') of the grid cells used to aggregate the data the user desires in the aggregated dataset (i.e. 10, 30 meters). This is a numeric variable and if stratifying on raw data, this parameter can be left NA. Conversely, if you are stratifying on raw data, an additional parameter called 'path' needs to be supplied in a named slot of each field's sublist to specify the original filename of the data imported into the database. If the desired data is from an aggregated table than enter NA for the 'path'. The year of the desired data must also be provided ('year'). This is to specify which data in the aggregated table to use. If using raw data, the year is automatically derived from the data specified by the filename. Finally, the user must supply the column name ('col_name') of the variable to stratify on. This must be supplied for both raw and aggregated data.
exp_rate_genLogical, for the experimental prescription provide TRUE or FALSE for whether to create experimental rates from gaps in the optimized rates. These experimental rates are placed between optimum rates to establish a range of rates for which to improve model predictions in future years.
opt_rate_lengthProvide the number of optimum rates to use. This applies to prescriptions and experimental prescriptions. This represents the equipment constraints of the farmer. In the case of the experimental prescription, this number of rates does not include the number of experimental rates, so take your selections for the management scenario and number of experimental rates into account.
mgmt_scenIf the user is creating a prescription or experimental prescription, they must provide the management scenario to use for their prescription. The user can choose from the management options listed in the SimClass. The options are 'SSOPT': site-specific optimized rates, 'FFOPT': full-field optimum uniform rate, 'FS': farmer selected uniform rate, 'Min': applying the least intensive input rates (i.e. 0 lbs N/ac, or 25 lbs seed/ac), 'Opp' is omitted because this strategy is the least intensive input rate in conventional system types, and the farmer selected rate for organic systems, both of which are already provided.
expvarExperimental variable to optimize, select/input 'As-Applied Nitrogen' or 'As-Applied Seed Rate'. This is the type of input that is experimentally varied across the field as part of the on-farm experimentation.
min_app_rateThe minimum as-applied rate that the equipment can apply. This must be in the units of the input applied to the field, which is not necessarily the same as the experimental input. This is an equipment restriction specific to the farmer.
buffer_widthThe width of the buffer from field edge for experiments or prescriptions (feet).
min_rate_jumpsOptional, supply either 'N/S' or 'E/W' to indicate direction in which to minimize rate jumps. This is the predominant direction that the experimental input is applied across the field. This function minimizes the difference in rates between adjacent treatments for easier use on the equipment. Note that this will eradicate any randomization/optimization of rates and will partially or completely remove stratification. This function makes sure that the rates do not vary by more than 2 rate levels in the direction specified. Default is NULL, which prevents execution. This will not guarantee eradication of all rate jumps, but will reduce the amount.
Returns
An initialized RxClass R6 class object.
Method selectInputs()
Interactive method for selecting inputs related to the prescription or experiment generation process. It is recommended to use this interactive method rather than passing in inputs as arguments unless an experienced user. If the user is creating an experimental prescription or prescription, they must have set up this class with a SimClass object, even if using this interactive method for selecting other inputs.
The user first selects whether to make a prescription or experiment. If the user specifies a type and has not passed in a required input upon initialization, an error will be thrown. Based on the user specification different selections are required for the user to set up the generation method.
The user will have to select the treatment length and width, the heading to align the treatments relative to N/S, the base rate to apply to around experiments or for checks in the prescription, the year(s) the farmer thinks the upcoming year will resemble and to make the prescription for, and whether to save the prescription or experiment in the database.
Usage
RxClass$selectInputs()
Arguments
NoneNo arguments needed because passed in during class instantiation.
Returns
A 'RxClass' object with complete user selections.
Method setupOP()
Method used to setup the output location for the prescription and related figures.
Usage
RxClass$setupOP(create = TRUE)
Arguments
createLogical, whether to create folders for output. If not, no plots will be saved by default.
Returns
A folder created in the path for model output figures.
Method setupGen()
Method used to setup the experiment or prescription generator based on the user select 'gen_type'. This initializes the output generator selected by the user with the arguments needed for that generator.
Usage
RxClass$setupGen()
Arguments
rxClassrxClass R6 class object. This method passes it's own class instantiation into the generator class.
Returns
An instantiated output generator class.
Method executeOutGen()
Method for calling the execution method of the experiment or prescription generator. These call the methods in the generator R6 classes.
Usage
RxClass$executeOutGen()
Arguments
NoneAll parametes supplied upon initialization.
Returns
A completed experiment or prescription.
Method saveOutputs()
Method for saving prescriptions or experiments and related plots. This only save plots if the user has supplied a folder path to save the plots to, and if the user does not select SAVE == FALSE. If the user passes in an argument to SAVE, this replaces any previously selected SAVE option (e.g. if SAVE was set to TRUE in the class initialization and the user passes FALSE as an argument here, the class' selection for SAVE is set to FALSE from TRUE).
Usage
RxClass$saveOutputs(SAVE = NULL, to_DB = NULL)
Arguments
SAVEWhether to save diagnostic plots. If NULL uses the user selected choice. If not NULL and is logical, argument replaces previously set SAVE options for the entire class.
to_DBLogical, whether to save the prescription or experiment into the database. If NULL uses the user selected choice. If not NULL and is logical, argument replaces previously set to_DB options for the entire class.
Returns
Shapefile and map saved in 'Outputs' folder and in database.
Method plotRxMap()
This method is for plotting maps of simulation outcomes. These include the net-returns from the management strategies, predicted responses, and optimized rates. Is a wrapper to a private plotRxMap method.
Usage
RxClass$plotRxMap(SAVE = TRUE)
Arguments
SAVELogical, whether to save figure.
Returns
Maps saved in 'Outputs/Rx/' and/or returned.
Method plotCellTypeMap()
This method is for plotting a map showing the distribution of cell types. This shows which cells are the optimized or management scenario rates, which are experimental rates, and where base rates are applied.
Usage
RxClass$plotCellTypeMap(SAVE = TRUE)
Arguments
SAVELogical, whether to save figure.
Returns
Maps saved in 'Outputs/Rx/' and/or returned.
Method uploadRxFun()
This method is for uploading the experiment or prescription to the database.
Usage
RxClass$uploadRxFun( RX, db, farmername, fieldname, rx_year, size, mgmt_scen, dtype = "rx_poly" )
Arguments
RXData frame with the net-returns and experimental optimums for every point for every simulation iteration.
dbDatabase connection connected to an OFPE formatted database.
farmernameThe name of the farmer that manages the field.
fieldnameUnique field name corresponding to all fields used in the simulation.
rx_yearProvide the year that the prescription or experiment is made for. Used for labeling outputs.
sizeThe size of the treatment zones, which is the treatment width x the treatment length.
mgmt_scenIf the user is creating a prescription or experimental prescription, they must provide the management scenario to use for their prescription. The user can choose from the management options listed in the SimClass. The options are 'SSOPT': site-specific optimized rates, 'FFOPT': full-field optimum uniform rate, 'FS': farmer selected uniform rate, 'Min': applying the least intensive input rates (i.e. 0 lbs N/ac, or 25 lbs seed/ac), 'Opp' is omitted because this strategy is the least intensive input rate in conventional system types, and the farmer selected rate for organic systems, both of which are already provided. This is set to 'exp' for all new experiments.
dtypeThe data type of the data for upload to the database, default to 'rx'.
Returns
Output experiment or prescription saved in database.
Method clone()
The objects of this class are cloneable with this method.
Usage
RxClass$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
DBCon for the database connection class,
SimClass for the class that contains simulation outputs,
ExpGen for the class for creating new experiments, and
RxGen for the class that creates experimental prescriptions
or pure prescriptions.
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