GAMS Transfer is a package to maintain GAMS data outside a GAMS script in a programming language like Python, Matlab, or R. It allows the user to add GAMS symbols (Sets, Aliases, Parameters, Variables and Equations), to manipulate GAMS symbols, as well as read/write symbols to different data endpoints. GAMS Transfer's main focus is the highly efficient transfer of data between GAMS and the target programming language, while keeping those operations as simple as possible for the user. In order to achieve this, symbol records - the actual and potentially large-scale data sets - are stored in native data structures of the corresponding programming languages. The benefits of this approach are threefold: (1) The user is usually very familiar with these data structures, (2) these data structures come with a large tool box for various data operations, and (3) optimized methods for reading from and writing to GAMS can transfer the data as a bulk - resulting in the high performance of this package.

Getting Started

Install

The user must download and install the latest version of GAMS in order to install GAMS Transfer R. GAMS Transfer R can then be installed from either the source package or from the binary package.

The installation from the source will install gamstransfer and all of its dependencies.

The users can also install gamstransfer without compiling the package source code using gamstransfer binary package. The binary packages are platform dependent and the instructions for each supported platform are shown below.

Windows

Linux

macOS

GAMS Transfer R depends on packages R6, R.utils, and Rcpp.

Examples

GDX Read

Reading in all symbols can be accomplished with one line of code (we reference data from the trnsport.gms example).

All symbol data is organized in the data field (a named list) which can be accessed via m$data. The records are stored as data frames and can be accessed via m$data$<symbol_name>$records or m$data[[symbol_name]]$records.

GDX Write

There are five symbol classes within GAMS Transfer R: Sets, Parameters, Variables, Equations, and Aliases. For purposes of this quick start, we show how to recreate the distance data structure from the trnsport.gms model (the parameter d). This brief example shows how users can achieve "GAMS-like" functionality, but within an R environment. GAMS Transfer R leverages the object oriented programming to simplify the syntax.

This example shows a few fundamental features of GAMS Transfer R:

1. A Container is analogous to a GDX file
2. Symbols will always be linked to a Container (notice that we always pass the Container reference m to the symbol constructor)
3. Records can be added to a symbol with the setRecords() method, through the records constructor argument (internally calls setRecords()), or through directly setting the records field. GAMS Transfer R will convert many common R data structures into a standard format.
4. Domain linking is possible by passing domain set objects to other symbols.
5. Writing a GDX file can be accomplished in one line with the write() method.

Full Example

It is possible to use GAMS Transfer R to recreate the trnsport.gms results in GDX form. As part of this example, we also introduce the write() method (and generate new.gdx). We will discuss it in more detail in the following section: Data Exchange with GDX.

It can be observed from the above example that a typical work flow for writing using GAMS Transfer R is creating a container, filling it with symbols (Sets, Parameters, Variables, Equations, and Aliases), and write it to a GDX file. To read a GDX file, a Container can simply be initialized with the GDX file name as an argument.

These examples introduced the reader to the GAMS Transfer R syntax. In the remaining sections, we will present details about the core functionality and dig further into the syntax.

Manual

Container

Storing, manipulating, and transforming sparse data requires that it lives within an environment. This data can then be linked together to enable various operations. In GAMS Transfer R, we refer to this "environment" as the Container, it is the main repository for storing our data. GAMS Transfer R offers two types of containers: Container and ConstContainer. By default, the user should choose Container. For even higher efficiency, one can choose a ConstContainer. However, it is important to know that it is a constant container and the symbols cannot be updated once added.

Container

In GAMS Transfer R, the main object class for storing and linking our data is called Container. Linking data enables data operations such as implicit set growth, domain checking, and data format transformations (to dense/sparse matrix formats). A Container also enables different read/write operations.

Creating a Container is a simple matter of initializing an object. For example:

This new Container object, here called m, contains a number of convenient fields and methods that allow the user to interact with the symbols that are in the Container. Some of these methods are simply used to filter out different types of symbols, other methods are used to numerically characterize the data within each symbol.

The Container constructor arguments are:

Argument	Type	Description	Required	Default
loadFrom	string	Name of the GDX file being read into the Container	No	NULL
systemDirectory	string	Absolute path to GAMS system directory	No	Attempts to find the GAMS installation by checking path system variable and looking for the GAMS executable.

Container fields

Field	Description	Type	Special Setter Behavior
data	main list that is used to store all symbol data	list	-

Symbols are organized in the Container under the data field. The $ notation (m$data) is used to access the underlying named list; symbols in this named list can then be retrieved with the standard bracket (or double bracket) notation (m$data[<symbol_name>]).

Container methods

Method	Description	Arguments/Defaults	Returns
addAlias	add an Alias	name (string) aliasWith (Set,Alias)	Alias object
addUniverseAlias	add a UniverseAlias	name (string)	UniverseAlias object
addEquation	add an Equation	name (string) type (string) domain=NULL (string,list) records=NULL (data frame,vector,NULL) domainForwarding=FALSE (logical) description="" (string)	Equation object
addParameter	add a Parameter	name (string) domain=NULL (string,list) records=NULL (data frame,array, matrix) domainForwarding=FALSE (logical) description="" (string)	Parameter object
addSet	add a Set	name (string) domain="*" (string,list) isSingleton=FALSE (logical) records=NULL (data frame,array,matrix) domainForwarding=FALSE (logical) description="" (string)	Set object
addVariable	add a Variable	name (string) type="free" (string) domain=NULL (string,list) records=NULL (data frame,array,matrix) domainForwarding=FALSE (logical) description="" (string)	Variable object
countDomainViolations	returns a named list containing number of domain violations symbols with non-zero domain violations	-	list
countDuplicateRecords	returns a named list containing number of duplicate records for symbols with non-zero duplicate records	-	list
describeAliases	create a summary table with descriptive statistics for Aliases	symbols=NULL (string,list) - if NULL, assumes all Aliases	data frame
describeEquations	create a summary table with descriptive statistics for Equations	symbols=NULL (string,list) - if NULL, assumes all equations	data frame
describeParameters	create a summary table with descriptive statistics for Parameters	symbols=NULL (string,list) - if NULL, assumes all parameters	data frame
describeSets	create a summary table with descriptive statistics for Sets	symbols=NULL (string,list) - if NULL, assumes all sets	data frame
describeVariables	create a summary table with descriptive statistics for Variables	symbols=NULL (string,list) - if NULL, assumes all variables	data frame
dropDomainViolations	drops domain violations for symbols in the Container	-	-
dropDuplicateRecords	drops duplicate records for symbols in the Container	keep= first/last	-
getDomainViolations	gets domain violations for symbols in the Container	-	list of DomainViolation
getSymbolNames	returns the original symbol names for a vector names of any case	names (character)	character
getSymbols	returns a list of object references for symbols	symbols (character, vector, list)	Symbol (for one symbol) or a list
getUELs	returns the UELs used in the Container	symbols=NULL (character), ignoreUnused = FALSE	character vector
getUniverseSet	provides a universe for all symbols	-	list
hasDomainViolations	returns TRUE if any symbol in the container has domain violations, FALSE otherwise	-	logical
hasDuplicateRecords	returns TRUE if any symbol in the container has duplicate records, FALSE otherwise	-	logical
hasSymbols	checks if symbol names for a vector names exists in the refContainer	names (character)	logical
isValid	TRUE if all symbols in the Container are valid	verbose=FALSE (logical) force=FALSE (logical)	logical
listAliases	list all aliases (isValid=NULL), list all valid aliases (isValid=TRUE), list all invalid aliases (isValid=FALSE) in the container	isValid=NULL (logical)	vector
listEquations	list all equations (isValid=NULL), list all valid equations (isValid=TRUE), list all invalid equations (isValid=FALSE) in the container	isValid=NULL (logical) types=NULL (list or vector of eqaution types) - if NULL, assumes all types	vector
listParameters	list all parameters (isValid=NULL), list all valid parameters (isValid=TRUE), list all invalid parameters (isValid=FALSE) in the container	isValid=NULL (logical)	vector
listSets	list all sets (isValid=NULL), list all valid sets (isValid=TRUE), list all invalid sets (isValid=FALSE) in the container	isValid=NULL (logical)	vector
listSymbols	list all symbols (isValid=NULL), list all valid symbols (isValid=TRUE), list all invalid symbols (isValid=FALSE) in the container	isValid=NULL (logical)	vector
listVariables	list all variables (isValid=NULL), list all valid variables (isValid=TRUE), list all invalid variables (isValid=FALSE) in the container	isValid=NULL (logical) types=NULL (list or vector of variable types) - if NULL, assumes all types	vector
read	main method to read loadFrom, can be provided with a list of symbols to read in subsets, records controls if symbol records are loaded or just metadata	loadFrom (string, Container, ConstContainer) symbols="all" (string, list) records=TRUE (logical)	NULL
removeSymbols	symbols to remove from the Container, also sets the symbols' refContainer to NULL	symbols (string,list)	NULL
removeUELs	removes uels from all symbols the Container	uels=NULL (character)	NULL
renameSymbol	rename a symbol oldName in the Container	oldName (string), newName (string)	NULL
renameUELs	renames uels in the Container	uels (named character), allowMerge=FALSE (logical). If allowMerge = TRUE, the underlying integer mapping of a factor is allowed to change to offer additional data flexibility	NULL
reorderSymbols	reorder symbols in order to avoid domain violations	-	NULL
write	main bulk write method to a writeTo target	writeTo (string) compress=FALSE (logical) uelPriority=NULL (string,list)	NULL

ConstContainer

While many GAMS Transfer users can rely on the Container for building their data pipeline, some users may only be interested in post-processing data from a GAMS model run. This one-directional flow of data means that these users do not need some of the advanced Container features such as domain linking, matrix generation, domain checking, etc. The ConstContainer (i.e., a Constant Container) object class is a data-focused read-only object that will provide a snapshot of the data target (a GDX file) being read.

The ConstContainer shares many of the same methods and attributes that are in the Container Container class, which makes moving between the ConstContainer and the Container easy. Some important differences should be kept in mind:

• The ConstContainer does not link any symbol data
• The ConstContainer can only read from one source at a time. Every new call of $read() will clear the data list.
• The ConstContainer constructor does not read in any symbol records. This enables users to browse an unknown data source quickly (similar behavior to gdxdump).
• The ConstContainer does not have a $write() method. A ConstContainer can be read by a Container to enable data writing (however, a copy of the data will be generated).
• The user will never need to instantiate a symbol object and add it to the ConstContainer. The ConstContainer internally generates its own set of (simplified) symbol classes and holds them in the $data field.

All of these differences were inspired by users that want to read the data as fast as possible and probe unknown data files without worrying about memory issues. ConstContainer provides users with a high level view of the data quickly.

Creating a ConstContainer is a simple matter of initializing an object. For example:

This new ConstContainer object, here called h, will load all the symbol data from out.gdx but it will not load any of the records. To load records, users must use the $read() method.

The ConstContainer constructor arguments are:

Argument	Type	Description	Required	Default
loadFrom	string	Name of the GDX file being read into the Container	No	NULL
systemDirectory	string	Absolute path to GAMS system directory	No	Attempts to find the GAMS installation by checking path system variable and looking for the GAMS executable.

ConstContainer fields

Field	Description	Type	Special Setter Behavior
data	main list that is used to store all symbol data	list	-

Symbols are organized in the Container under the data field. The $ notation (m$data) is used to access the underlying named list; symbols in this named list can then be retrieved with the standard bracket (or double bracket) notation (m$data[<symbol_name>]).

ConstContainer methods

Method	Description	Arguments/Defaults	Returns
describeAliases	create a summary table with descriptive statistics for Aliases	symbols=NULL (string,list) - if NULL, assumes all Aliases	data frame
describeEquations	create a summary table with descriptive statistics for Equations	symbols=NULL (string,list) - if NULL, assumes all equations	data frame
describeParameters	create a summary table with descriptive statistics for Parameters	symbols=NULL (string,list) - if NULL, assumes all parameters	data frame
describeSets	create a summary table with descriptive statistics for Sets	symbols=NULL (string,list) - if NULL, assumes all sets	data frame
describeVariables	create a summary table with descriptive statistics for Variables	symbols=NULL (string,list) - if NULL, assumes all variables	data frame
getSymbolNames	returns the original symbol names for a vector names of any case	names (character)	character
hasSymbols	checks (case insensitive) if symbol names for a vector names exists in the refContainer	names (character)	logical
listAliases	list all aliases in the container	-	vector
listEquations	list all equations in the container	types=NULL (list or vector of equation types) - if NULL, assumes all types	vector
listParameters	list all parameters in the container	-	vector
listSets	list all sets in the container	-	vector
listSymbols	list all symbols in the container	-	vector
listVariables	list all variables in the container	types=NULL (list or vector of variable types) - if NULL, assumes all types	vector
read	main method to read loadFrom, can be provided with a list of symbols to read in subsets, records controls if symbol records are loaded or just metadata	loadFrom (string) symbols="all" (string, list) records=TRUE (logical)	NULL

The structure of the DataFrames that are returned from the describe* methods mirrors that in the Container; the user should reference Describing Data for detailed descriptions of the columns.

Symbols

In GAMS Transfer R, a symbol is either a GAMS Set, Parameter, Variable, Equation, Alias. In GAMS Transfer R, a symbol cannot live on its own, but is always part of a Container or a ConstContainer.

Create a symbol

There are two different ways to create a GAMS set and add it to a Container.

1. Use symbol constructor for Set, Parameter, Variable, Equation, Alias
   m = Container$new()

   p = Parameter$new(m, "p")
2. Use the Container methods addSet, addParameter, addVariable, addEquation, addAlias (which internally calls the symbol constructor)
   m = Container$new()

   p = m$addParameter("p")

Add Symbol Records

Three possibilities exist to assign symbol records:

1. Setting the argument records in the symbol constructor/Container method (internally calls setRecords)
2. Using the symbol method setRecords
3. Setting the field records directly

If the data is in a convenient format, a user may want to pass the records directly within the set constructor. This is an optional keyword argument and internally the symbol constructor will simply call the setRecords method. The symbol method setRecords is a convenience method that transforms the given data into an approved data frame format (see Standard Data Formats). Many native R data types can be easily transformed into data frames, so the setRecords method will accept a number of different types for input. The setRecords method is called internally on any data structure that is passed through the records argument.

The examples of setting records using the above-mentioned methods can be found in the respective documentation for each symbol ( Set, Parameter, Variable, Equation, and Alias ).

Set

Set Constructor

Argument	Type	Description	Required	Default
container	Container	A reference to the Container object that the symbol is being added to	Yes	-
name	string	Name of symbol	Yes	-
domain	list	String, List of domains given either as a string ("*" for universe set) or as a reference to a Set object	No	"*"
isSingleton	logical	Indicates if set is a singleton set (TRUE) or not (FALSE)	No	FALSE
records	string vector, data frame	Symbol records	No	NULL
domainForwarding	logical	Flag that forces set elements to be recursively included in all parent sets (i.e., implicit set growth)	No	FALSE
description	string	Description of symbol	No	""

Set Fields

Field	Description	Type
description	description of symbol	string
dimension	dimension of symbol, setting dimension is a shorthand notation to set domain to a list of size n containing "*"	integer
domain	list of domains given either as string (* for universe set) or as reference to the Set/Alias object	list
domainForwarding	flag that forces set elements to be recursively included in all parent sets (i.e., implicit set growth)	logical
domainLabels	column headings for the records data frame	list of string
domainNames	string version of domain names	list of string
domainType	none, relaxed or regular depending on state of domain links	string
isSingleton	logical if symbol is a singleton set	logical
name	name of symbol	string
numberRecords	number of symbol records (i.e., returns nrow(self$records) if not NULL)	integer
records	the main symbol records	data frame
refContainer	reference to the Container that the symbol belongs to	Container
summary	output a list of only the metadata	list

Set Methods

Method	Description	Arguments/Defaults	Returns
addUELs	adds UELs to the symbol	uels (character), dimension=NULL	NULL
countDomainViolations	returns the number of domain violations for the symbol	-	numeric
countDuplicateRecords	returns the number of duplicate records for the symbol	-	numeric
findDomainViolations	get the index of records that contain any domain violations	-	data.frame
getCardinality	get the full cartesian product of the domain	-	integer
dropDomainViolations	drops domain violations for the symbol	-	-
dropDuplicateRecords	drops duplicate records for the Symbol	keep= first/last	-
getDomainViolations	gets domain violations for the symbol	-	list of DomainViolation
getSparsity	get the sparsity of the symbol w.r.t the cardinality	-	numeric
getUELs	returns the UELs used in the Symbol	dimension = NULL, codes = NULL, ignoreUnused=FALSE	character vector
hasDomainViolations	returns TRUE if the symbol contains domain violations, FALSE otherwise	-	logical
hasDuplicateRecords	returns TRUE if the symbol contains duplicate records, FALSE otherwise	-	logical
isValid	checks if the symbol is in a valid format, throw exceptions if verbose=TRUE, recheck a symbol if force=TRUE	verbose=FALSE force=FALSE	logical
removeUELs	removes UELs from the symbol	uels=NULL (character), dimension=NULL	NULL
renameUELs	renames UELs in the symbol	uels(character of same length as current UELs or named character vector), dimension=NULL, allowMerge=FALSE (logical). If allowMerge = TRUE, the underlying integer mapping of a factor is allowed to change to offer additional data flexibility	NULL
setRecords	main convenience method to set standard data frame formatted records	records (string vector, list, data frame)	NULL
setUELs	sets UELs for the Symbol	uels(character), dimension, rename=FALSE	NULL

Adding Set Records

Three possibilities exist to assign symbol records to a set: We show a few examples of ways to create differently structured sets.

Example #1 - Create a 1D set from a vector

m = Container$new()

i = Set$new(m, "i", records = c("seattle", "san-diego"))

# NOTE: the above syntax is equivalent to -

# i = Set$new(m, "i")

# i$setRecords(c("seattle", "san-diego"))

# NOTE: the above syntax is also equivalent to -

# m$addSet("i", records= c("seattle", "san-diego"))

# NOTE: the above syntax is also equivalent to -

# i = m$addSet("i")

# i$setRecords(c("seattle", "san-diego"))

# NOTE: the above syntax is also equivalent to -

# m$addSet("i")

# m$data[["i"]]$setRecords(c("seattle", "san-diego"))

> i$records

uni_1 element_text

1 seattle

2 san-diego

Example #2 - Create a 2D set from a list

m = Container$new()

k = Set$new(m, "k", c("*", "*"), records=list("seattle", "san-diego"))

# NOTE: the above syntax is equivalent to -

# k = Set$new(m, "k", c("*", "*"))

# k$setRecords(list("seattle", "san-diego"))

# NOTE: the above syntax is also equivalent to -

# m$addSet("k", c("*","*"), records=list("seattle", "san-diego"))

# NOTE: the above syntax is also equivalent to -

# k = m$addSet("k", c("*","*"))

# k$setRecords(list("seattle", "san-diego"))

# NOTE: the above syntax is also equivalent to -

# m$addSet("k", c("*", "*"))

# m$data[["k"]]$setRecords(list("seattle", "san-diego"))

> k$records

uni_1 uni_2 element_text

1 seattle san-diego

Example #3 - Create a 1D set from a data frame slice

m = Container$new()

dist = data.frame(

from = c("seattle", "seattle", "seattle",

"san-diego", "san-diego", "san-diego"),

to = c("new-york", "chicago", "topeka",

"new-york", "chicago", "topeka"),

thousand_miles = c(2.5, 1.7, 1.8, 2.5, 1.8, 1.4)

)

l = Set$new(m, "l", records = unique(dist[["from"]]))

# NOTE: the above syntax is equivalent to -

# l = Set$new(m, "l")

# l$setRecords(unique(dist[["from"]]))

# NOTE: the above syntax is also equivalent to -

# m$addSet("l", records=unique(dist[["from"]]))

# NOTE: the above syntax is also equivalent to -

# l = m$addSet("l")

# l$setRecords(unique(dist[["from"]]))

# NOTE: the above syntax is also equivalent to -

# m$addSet("l")

# m$data[["l"]]$setRecords(unique(dist[["from"]]))

> l$records

uni_1 element_text

1 seattle

2 san-diego

Set element text is very handy when labeling specific set elements within a set. A user can add a set element text directly with a set element. Note that it is not required to label all set elements, as can be seen in the following example.

Example #4 - Add set element text

The primary advantage of the setRecords method is that GAMS Transfer R will convert many different (and convenient) data types into the standard data format (a data frame). Users that require higher performance will want to directly pass the Container a reference to a valid data frame, thereby skipping some of these computational steps. This places more burden on the user to pass the data in a valid standard form, but it speeds the records setting process.
In this section, we walk the user through an example of how to set records directly.

Example #5 - Directly set records (1D set)

Stepping through this example we take the following steps:

1. Create an empty Container
2. Create a GAMS set i in the Container, but do not set the records
3. Create a data frame (manually, in this example) taking care to follow the standard format
4. The data frame has the right shape and column labels so we can proceed to set the records.
5. We need to cast the uni_1 column as a factor, so we create a custom ordered category type using factor
6. Finally, we set the records directly by passing a reference to df_i into the symbol records attribute. The setter function of records checks that a data frame is being set, but does not check validity. Thus, as a final step, we call the $isValid() method to verify that the symbol is valid.

Note

Users can debug their data frames by running <symbol_name>$isValid(verbose=TRUE) to get feedback about their data.

Example #6 - Directly set records (1D subset)

This example is more subtle in that we want to create a set j that is a subset of i. We create the set i using the setRecords method but then set the records directly for j. There are two important details to note: 1) the column labels in df_j now reflect the standard format for a symbol with a domain set (as opposed to the universe) and 2) we create the factors by referencing the parent set (i) for the levels (instead of referencing itself).

Parameter

Parameter Constructor

Argument	Type	Description	Required	Default
container	Container	A reference to the Container object that the symbol is being added to	Yes	-
name	string	Name of symbol	Yes	-
domain	list	List of domains given either as a string ("*" for universe set) or as a reference to a Set object, an empty domain list will create a scalar parameter	No	NULL
records	many	Symbol records	No	NULL
domainForwarding	logical	Flag that forces set elements to be recursively included in all parent sets (i.e., implicit set growth)	No	FALSE
description	string	Description of symbol	No	""

Parameter Fields

Field	Description	Type
description	description of symbol	character
dimension	dimension of symbol, setting dimension is a shorthand notation to set domain to a list of size n containing "*"	numeric,integer
domain	list of domains given either as a string ("*" for universe set) or as a reference to the Set/Alias object	list
domainForwarding	flag that forces set elements to be recursively included in all parent sets (i.e., implicit set growth)	logical
domainLabels	column headings for the records data frame	list of string
domainNames	string version of domain names	list of string
domainType	none, relaxed or regular depending on state of domain links	string
isScalar	TRUE if the length(self$domain) = 0	logical
name	name of symbol	string
numberRecords	number of symbol records (i.e., returns nrow(self$records) if not NULL)	integer
records	the main symbol records	data frame
refContainer	reference to the Container that the symbol belongs to	Container
shape	a vector describing the array dimensions if records were converted with $toDense()	vector
summary	output a list of only the metadata	list

Parameter Methods

Method	Description	Arguments/Defaults	Returns
addUELs	adds UELs to the symbol	uels (character), dimension=NULL	NULL
countDomainViolations	returns the number of domain violations for the symbol	-	numeric
countDuplicateRecords	returns the number of duplicate records for the symbol	-	numeric
countEps	total number of SpecialValues$EPS in value column	-	integer
countNA	total number of SpecialValues[["NA"]] in value column	-	integer
countNegInf	total number of SpecialValues$NEGINF in value column	-	integer
countPosInf	total number of SpecialValues$POSINF in value column	-	integer
countUndef	total number of SpecialValues$UNDEF in value column	-	integer
dropDomainViolations	drops domain violations for the symbol	-	-
dropDuplicateRecords	drops duplicate records for the Symbol	keep= first/last	-
findDomainViolations	get the index of records that contain any domain violations	-	data.frame
getCardinality	get the full cartesian product of the domain	-	integer
getDomainViolations	gets domain violations for the symbol	-	list of DomainViolation
getMaxValue	get the maximum value in value column	-	numeric
getMaxAbsValue	get the maximum absolute value in value column	-	numeric
getMeanValue	get the mean value in value column	-	numeric
getMinValue	get the minimum value in value column	-	numeric
getSparsity	get the sparsity of the symbol w.r.t the cardinality	-	numeric
getUELs	returns the UELs used in the Symbol	dimension = NULL, codes = NULL, ignoreUnused=FALSE	character vector
hasDomainViolations	returns TRUE if the symbol contains domain violations, FALSE otherwise	-	logical
hasDuplicateRecords	returns TRUE if the symbol contains duplicate records, FALSE otherwise	-	logical
isValid	checks if the symbol is in a valid format, throw exceptions if verbose=TRUE, recheck a symbol if force=TRUE	verbose=FALSE force=FALSE	logical
removeUELs	removes UELs from the symbol	uels=NULL (character), dimension=NULL	NULL
renameUELs	renames UELs in the symbol	uels(character of same length as current UELs or named character vector), dimension=NULL, allowMerge=FALSE (logical). If allowMerge = TRUE, the underlying integer mapping of a factor is allowed to change to offer additional data flexibility	NULL
setRecords	main convenience method to set standard data frame records	records (many types)	NULL
setUELs	sets UELs for the Symbol	uels(character), dimension, rename=FALSE	NULL
toDense	convert value column of a symbol to a dense matrix or array format	-	array or matrix
whereMax	find the row number in records data frame with a maximum value (return the first instance only)	-	integer
whereMaxAbs	find the row number in records data frame with a maximum absolute value (return the first instance only)	-	integer
whereMin	find the row number in records data frame with a minimum value (return the first instance only)	-	integer

Adding Parameter Records

Three possibilities exist to assign symbol records to a parameter: We show a few examples of ways to create differently structured parameters:

Example #1 - Create a GAMS scalar

Note

GAMS Transfer R will still convert scalar values to a standard format (i.e., a data frame with a single row and column).

Example #2 - Create a 2D parameter (defined over a set) from a

data frame slice

Example #3 - Create a 2D parameter from an array using setRecords

As with Sets, the primary advantage of the setRecords method is that GAMS Transfer will convert many different (and convenient) data types into the standard data format (data frame). Users that require higher performance will want to directly pass the Container a reference to a valid data frame, thereby skipping some of these computational steps. This places more burden on the user to pass the data in a valid standard form, but it speeds the records setting process. In this section, we walk the user through an example of how to set records directly.

Example #4 - Correctly set records (directly)

In this example, we create a large parameter (31,536,000 records and 8880 unique domain elements. We mimic data that is labeled for every second in one year) and assign it to a parameter with a$records. GAMS Transfer R requires that all domain columns must be ordered factors. The records setter function does very little work other than checking if the object being set is a data frame. This places more responsibility on the user to create a data frame that complies with the standard format. In Example #1, we take care to properly reference the factor from the domain sets, and ensure that the symbol a is valid with a$isValid() = TRUE.

Users will need to use the $isValid(verbose=TRUE) method to debug any structural issues. As an example, we incorrectly generate categorical data types by passing the data frame constructor the generic factor argument. This creates factor columns, but they are not ordered and they do not reference the underlying domain set. These errors result in a being invalid.

Example #5 - Incorrectly set records (directly)

Variable

Variable Constructor

Argument	Type	Description	Required	Default
container	Container	A reference to the Container object that the symbol is being added to	Yes	-
name	string	Name of symbol	Yes	-
type	string	Type of variable being created [binary, integer, positive, negative, free, sos1, sos2, semicont, semiint]	No	free
domain	list, vector	List, vector of domains given either as a string ("*" for universe set) or as a reference to a Set object, an empty domain list will create a scalar variable	No	NULL
records	many	Symbol records	No	NULL
domainForwarding	logical	Flag that forces set elements to be recursively included in all parent sets (i.e., implicit set growth)	No	FALSE
description	string	Description of symbol	No	""

Variable Fields

Field	Description	Type
description	description of symbol	string
dimension	dimension of symbol, setting dimension is a shorthand notation to set domain to a list of size n containing "*"	integer
domain	list of domains given either as string (* for universe set) or as reference to the Set/Alias object	list
domainForwarding	flag that forces set elements to be recursively included in all parent sets (i.e., implicit set growth)	logical
domainLabels	column headings for the records data frame	list of string
domainNames	string version of domain names	list of string
domainType	none, relaxed or regular depending on state of domain links	string
name	name of symbol	string
numberRecords	number of symbol records (i.e., returns nrow(self$records) if not NULL)	integer
records	the main symbol records	data frame
refContainer	reference to the Container that the symbol belongs to	Container
shape	a vector describing the array dimensions if records were converted with $toDense()	vector
summary	output a list of only the metadata	list
type	string type of variable	list

Variable Methods

Method	Description	Arguments/Defaults	Returns
addUELs	adds UELs to the symbol	uels (character), dimension=NULL	NULL
countDomainViolations	returns the number of domain violations for the symbol	-	numeric
countDuplicateRecords	returns the number of duplicate records for the symbol	-	numeric
countEps	total number of SpecialValues$EPS across all columns	columns = "level"	integer
countNA	total number of SpecialValues[["NA"]] across all columns	columns = "level"	integer
countNegInf	total number of SpecialValues$NEGINF across all columns	columns = "level"	integer
countPosInf	total number of SpecialValues$POSINF across all columns	columns = "level"	integer
countUndef	total number of SpecialValues$UNDEF across all columns	columns = "level"	integer
dropDomainViolations	drops domain violations for the symbol	-	-
dropDuplicateRecords	drops duplicate records for the Symbol	keep= first/last	-
findDomainViolations	get the index of records that contain any domain violations	-	data.frame
getCardinality	get the full cartesian product of the domain	-	integer
getDomainViolations	gets domain violations for the symbol	-	list of DomainViolation
getMaxValue	get the maximum value across all columns	columns = "level"	numeric
getMaxAbsValue	get the maximum absolute value across all columns	columns = "level"	numeric
getMinValue	get the minimum value across all columns	columns = "level"	numeric
getMeanValue	get the mean value across all columns	columns = "level"	numeric
getSparsity	get the sparsity of the symbol w.r.t the cardinality	-	numeric
getUELs	returns the UELs used in the Symbol	dimension = NULL, codes = NULL, ignoreUnused=FALSE	character vector
hasDomainViolations	returns TRUE if the symbol contains domain violations, FALSE otherwise	-	logical
hasDuplicateRecords	returns TRUE if the symbol contains duplicate records, FALSE otherwise	-	logical
isValid	checks if the symbol is in a valid format, throw exceptions if verbose=TRUE, recheck a symbol if force=TRUE	verbose=FALSE force=FALSE	logical
removeUELs	removes UELs from the symbol	uels=NULL (character), dimension=NULL	NULL
renameUELs	renames UELs in the symbol	uels(character of same length as current UELs or named character vector), dimension=NULL, allowMerge=FALSE (logical). If allowMerge = TRUE, the underlying integer mapping of a factor is allowed to change to offer additional data flexibility	NULL
setRecords	main convenience method to set standard data frame records	records (many types)	NULL
setUELs	sets UELs for the Symbol	uels(character), dimension, rename=FALSE	NULL
toDense	convert symbol to a dense matrix or array format	column = level	array or matrix
whereMax	find the row number in records data frame with a maximum value (return the first instance only)	column = "level"	integer
whereMaxAbs	find the row number in records data frame with a maximum absolute value (return the first instance only)	column = "level"	integer
whereMin	find the row number in records data frame with a minimum value (return the first instance only)	column = "level"	integer

Adding Variable Records

Three possibilities exist to assign symbol records to a variable: We show a few examples of ways to create differently structured variables:

Example #1 - Create a GAMS scalar variable

Example #2 - Create a 2D positive variable, specifying no numerical data

Example #3 - Create a 2D variable (defined over a set) from a matrix

As with Sets, the primary advantage of the setRecords method is that GAMS Transfer will convert many different (and convenient) data types into the standard data format (data frame). Users that require higher performance will want to directly pass the Container a reference to a valid data frame, thereby skipping some of these computational steps. This places more burden on the user to pass the data in a valid standard form, but it speeds the records setting process. In this section, we walk the user through an example of how to set records directly.

Example #4 - Correctly set records (directly)

In this example, we create a large variable (31,536,000 records and 8880 unique domain elements. We mimic data that is labeled for every second in one year) and assign it to a variable with a$records. GAMS Transfer R requires that all domain columns must be ordered factors. The records setter function does very little work other than checking if the object being set is a data frame. This places more responsibility on the user to create a data frame that complies with the standard format. In Example #1, we take care to properly reference the factor from the domain sets and ensure that the symbol a is valid with a$isValid() = TRUE.

As with Set and Parameters, users can use the $isValid(verbose=TRUE) method to debug any structural issues.

Equation

Equation Constructor

Argument	Type	Description	Required	Default
container	Container	A reference to the Container object that the symbol is being added to	Yes	-
name	string	Name of symbol	Yes	-
type	string	Type of equation being created [eq (or E/e), geq (or G/g), leq (or L/l), nonbinding (or N/n), external (or X/x)]	No	free
domain	list, vector	List, vector of domains given either as a string ("*" for universe set) or as a reference to a Set object, an empty domain list will create a scalar equation	No	NULL
records	many	Symbol records	No	NULL
domainForwarding	logical	Flag that forces set elements to be recursively included in all parent sets (i.e., implicit set growth)	No	FALSE
description	string	Description of symbol	No	""

Equation Fields

Field	Description	Type
description	description of symbol	string
dimension	dimension of symbol, setting dimension is a shorthand notation to set domain to a list of size n containing "*"	integer
domain	list of domains given either as string (* for universe set) or as reference to the Set/Alias object	list
domainForwarding	flag that forces set elements to be recursively included in all parent sets (i.e., implicit set growth)	logical
domainLabels	column headings for the records data frame	list of string
domainNames	string version of domain names	list of string
domainType	none, relaxed or regular depending on state of domain links	string
name	name of symbol	string
numberRecords	number of symbol records (i.e., returns nrow(self$records) if not NULL)	integer
records	the main symbol records	data frame
refContainer	reference to the Container that the symbol belongs to	Container
shape	a vector describing the array dimensions if records were converted with $toDense()	vector
summary	output a list of only the metadata	list
type	string type of equation	list

Equation Methods

Method	Description	Arguments/Defaults	Returns
addUELs	adds UELs to the symbol	uels (character), dimension=NULL	NULL
countDomainViolations	returns the number of domain violations for the symbol	-	numeric
countDuplicateRecords	returns the number of duplicate records for the symbol	-	numeric
countEps	total number of SpecialValues$EPS across all columns	columns = "level"	integer
countNA	total number of SpecialValues[["NA"]] across all columns	columns = "level"	integer
countNegInf	total number of SpecialValues$NEGINF across all columns	columns = "level"	integer
countPosInf	total number of SpecialValues$POSINF across all columns	columns = "level"	integer
countUndef	total number of SpecialValues$UNDEF across all columns	columns = "level"	integer
dropDomainViolations	drops domain violations for the symbol	-	-
dropDuplicateRecords	drops duplicate records for the Symbol	keep= first/last	-
findDomainViolations	get the index of records that contain any domain violations	-	data.frame
getCardinality	get the full cartesian product of the domain	-	integer
getDomainViolations	gets domain violations for the symbol	-	list of DomainViolation
getMaxValue	get the maximum value across all columns	columns = "level"	numeric
getMaxAbsValue	get the maximum absolute value across all columns	columns = "level"	numeric
getMinValue	get the minimum value iacross all columns	columns = "level"	numeric
getMeanValue	get the mean value across all columns	columns = "level"	numeric
getSparsity	get the sparsity of the symbol w.r.t the cardinality	-	numeric
getUELs	returns the UELs used in the Symbol	dimension = NULL, codes = NULL, ignoreUnused=FALSE	character vector
hasDomainViolations	returns TRUE if the symbol contains domain violations, FALSE otherwise	-	logical
hasDuplicateRecords	returns TRUE if the symbol contains duplicate records, FALSE otherwise	-	logical
isValid	checks if the symbol is in a valid format, throw exceptions if verbose=TRUE, recheck a symbol if force=TRUE	verbose=FALSE force=FALSE	logical
removeUELs	removes UELs from the symbol	uels=NULL (character), dimension=NULL	NULL
renameUELs	renames UELs in the symbol	uels(character of same length as current UELs or named character vector), dimension=NULL, allowMerge=FALSE (logical). If allowMerge = TRUE, the underlying integer mapping of a factor is allowed to change to offer additional data flexibility	NULL
setRecords	main convenience method to set standard data frame records	records (many types)	NULL
setUELs	sets UELs for the Symbol	uels(character), dimension, rename=FALSE	NULL
toDense	convert symbol to a dense matrix or array format	column = "value"	array or matrix
whereMax	find the row number in records data frame with a maximum value (return the first instance only)	column="level"	integer
whereMaxAbs	find the row number in records data frame with a maximum absolute value (return the first instance only)	column="level"	integer
whereMin	find the row number in records data frame with a minimum value (return the first instance only)	column="level"	integer

Adding Equation Records

Three possibilities exist to assign symbol records to an equation: We show a few examples of ways to create differently structured equations:

Example #1 - Create a GAMS scalar equation

Example #2 - Create a 2D positive equation, specifying no numerical data

Example #3 - Create a 2D equation (defined over a set) from a matrix

As with sets, parameters, and variables the primary advantage of the setRecords method is that GAMS Transfer will convert many different (and convenient) data types into the standard data format (data frame). Users that require higher performance will want to directly pass the Container a reference to a valid data frame, thereby skipping some of these computational steps. This places more burden on the user to pass the data in a valid standard form, but it speeds the records setting process. In this section, we walk the user through an example of how to set records directly.

Example #4 - Correctly set records (directly)

In this example, we create a large equation (31,536,000 records and 8880 unique domain elements. We mimic data that is labeled for every second in one year) and assign it to an equation with a$records. GAMS Transfer R requires that all domain columns must be ordered factors. The records setter function does very little work other than checking if the object being set is a data frame. This places more responsibility on the user to create a data frame that complies with the standard format. In Example #1, we take care to properly reference the factor from the domain sets and ensure that the symbol a is valid in the end with a$isValid() = TRUE.

As with sets, parameters, and variables, users can use the $isValid(verbose=TRUE) method to debug any structural issues.

Alias

Alias Constructor

Argument	Type	Description	Required	Default
container	Container	A reference to the Container object that the symbol is being added to	Yes	-
name	string	Name of symbol	Yes	-
aliasWith	Set object	set object to create an alias for	Yes	-

Example - Creating an alias from a set

GAMS Transfer R only stores the reference to the parent set as part of the alias structure. Most properties that are called from an alias object simply point to the properties of the parent set (with the exception of refContainer, name, and aliasWith). It is possible to create an alias from another alias object. In this case, a recursive search will be performed to find the root parent set. This is the set that will ultimately be stored as the aliasWith field. We can see this behavior in the following example:

Alias Fields

Field	Description	Type
aliasWith	aliased object	Set
description	description of symbol	string
dimension	dimension of symbol, setting dimension is a shorthand notation to set domain to a list of size n containing "*"	integer
domainForwarding	flag that forces set elements to be recursively included in all parent sets (i.e., implicit set growth)	logical
domainLabels	column headings for the records data frame	list of string
domainNames	string version of domain names	list of string
domainType	none, relaxed or regular depending on state of domain links	string
isSingleton	logical if symbol is a singleton set	logical
name	name of symbol	string
numberRecords	number of symbol records (i.e., returns nrow(self$records) if not NULL)	integer
records	the main symbol records	data frame
refContainer	reference to the Container that the symbol belongs to	Container
summary	output a list of only the metadata	list

Alias Methods

Method	Description	Arguments/Defaults	Returns
addUELs	adds UELs to the symbol	uels (character), dimension=NULL	NULL
countDomainViolations	returns the number of domain violations for the symbol	-	numeric
countDuplicateRecords	returns the number of duplicate records for the symbol	-	numeric
dropDomainViolations	drops domain violations for the symbol	-	-
dropDuplicateRecords	drops duplicate records for the Symbol	keep= first/last	-
findDomainViolations	get the index of records that contain any domain violations	-	data.frame
getCardinality	get the full cartesian product of the domain	-	integer
getDomainViolations	gets domain violations for the symbol	-	list of DomainViolation
getSparsity	get the sparsity of the symbol w.r.t the cardinality	-	numeric
getUELs	returns the UELs used in the Symbol	dimension = NULL, codes = NULL, ignoreUnused=FALSE	character vector
hasDomainViolations	returns TRUE if the symbol contains domain violations, FALSE otherwise	-	logical
hasDuplicateRecords	returns TRUE if the symbol contains duplicate records, FALSE otherwise	-	logical
isValid	checks if the symbol is in a valid format, throw exceptions if verbose=TRUE, recheck a symbol if force=TRUE	verbose=FALSE force=FALSE	logical
removeUELs	removes UELs from the symbol	uels=NULL (character), dimension=NULL	NULL
renameUELs	renames UELs in the symbol	uels(character of same length as current UELs or named character vector), dimension=NULL, allowMerge=FALSE (logical). If allowMerge = TRUE, the underlying integer mapping of a factor is allowed to change to offer additional data flexibility	NULL
setRecords	main convenience method to set standard data frame formatted records	records (string vector, list, data frame)	NULL
setUELs	sets UELs for the Symbol	uels(character), dimension, rename=FALSE	NULL

Adding Alias Records

The linked structure of Aliases offers some unique opportunities to access some of the setter functionality of the parent set. Specifically, GAMS Transfer allows the user to change the domain, description, dimension, and records of the underlying parent set as a shorthand notation. We can see this behavior if we look at a modified Example #1 from adding set records.

Example - Creating set records through an alias link

Note

An alias $isValid()=TRUE when the underlying parent set is also valid. If the parent set is removed from the Container the alias will no longer be valid.

UniverseAlias

UniverseAlias Constructor

Argument	Type	Description	Required	Default
container	Container	A reference to the Container object that the symbol is being added to	Yes	-
name	string	Name of symbol	Yes	-

Example - Creating an alias for the Universe

In GAMS it is possible to create aliases to the universe (i.e., the entire list of UELs) with the syntax:

set i / i1, i2 /;
alias(h,*);
set j / j1, j2 /;

GAMS Transfer R allows creating aliases to the universe set.

In this small example, h would be associated with all four UELs (i1, i2, j1 and j2) even though set j was defined after the alias declaration. GAMS Transfer mimics this behavior with the UniverseAlias class. Internally, the records attribute will always call the <Container>$getUELs(). The UniverseAlias class is fundamentally different from the Alias class because it does not point to a parent set object. It is not possible to perform operations (like setRecords or findDomainViolations) on the parent set through a UniverseAlias (because there is no parent set object). This means that a UniverseAlias can be created by only defining the symbol name. We can see this behavior in the following example:

Note

Unlike other sets, the universe does not hold on to set element_text, thus the returned data frame for the UniverseAlias will only have one column.

UniverseAlias Fields

Field	Description	Type
aliasWith	always "*"	string
description	always Aliased with *	string
dimension	always 1	integer
domainLabels	always "*"	character vector
domainNames	always "*"	character vector
domainType	always none	string
isSingleton	always FALSE	logical
name	name of symbol	string
numberRecords	number of symbol records (i.e., returns nrow(self$records) if not NULL)	integer
records	the main symbol records	data frame
refContainer	reference to the Container that the symbol belongs to	Container
summary	output a list of only the metadata	list

UniverseAlias Methods

Method	Description	Arguments/Defaults	Returns
getCardinality	returns nrow(self$records)	-	integer
getSparsity	always 0	-	numeric
getUELs	returns the UELs from the Container. Returns only UELs in the data if ignoreUnused=TRUE, otherwise returns all UELs	ignoreUnused=FALSE	character vector
isValid	checks if the symbol is in a valid format, throw exceptions if verbose=TRUE, recheck a symbol if force=TRUE	verbose=FALSE force=FALSE	logical

ConstContainer Symbols

The symbols in the ConstContainer do not contain a reference to any other symbols or container and fields of the symbols in the ConstContainer cannot be modified. This results in several key differences between container symbols and ConstContainer symbols. Unlike container symbols, user should never instantiate ConstContainer symbol objects directly.

ConstContainer Set Fields

Field	Description	Type
description	description of symbol	string
dimension	dimension of symbol, setting dimension is a shorthand notation to set domain to a list of size n containing "*"	integer
domainLabels	column headings for the records data frame	list of string
domainNames	string version of domain names	list of string
domainType	none, relaxed or regular depending on state of domain links	string
isSingleton	logical if symbol is a singleton set	logical
name	name of symbol	string
numberRecords	number of symbol records (i.e., returns nrow(self$records) if not NULL)	integer
records	the main symbol records	data frame
summary	output a list of only the metadata	list

ConstContainer Set Methods

Method	Description	Arguments/Defaults	Returns
getCardinality	get the full cartesian product of the domain	-	integer
getSparsity	get the sparsity of the symbol w.r.t the cardinality	-	numeric

ConstContainer Parameter Fields

Field	Description	Type
description	description of symbol	string
dimension	dimension of symbol, setting dimension is a shorthand notation to set domain to a list of size n containing "*"	integer
domainLabels	column headings for the records data frame	list of string
domainNames	string version of domain names	list of string
domainType	none, relaxed or regular depending on state of domain links	string
isScalar	TRUE if the length(self$domain) = 0	logical
name	name of symbol	string
numberRecords	number of symbol records (i.e., returns nrow(self$records) if not NULL)	integer
records	the main symbol records	data frame
summary	output a list of only the metadata	list

ConstContainer Parameter Methods

Method	Description	Arguments/Defaults	Returns
countEps	total number of SpecialValues$EPS in value column	-	integer
countNA	total number of SpecialValues[["NA"]] in value column	-	integer
countNegInf	total number of SpecialValues$NEGINF in value column	-	integer
countPosInf	total number of SpecialValues$POSINF in value column	-	integer
countUndef	total number of SpecialValues$UNDEF in value column	-	integer
getCardinality	get the full cartesian product of the domain	-	integer
getMaxValue	get the maximum value in value column	-	numeric
getMaxAbsValue	get the maximum absolute value in value column	-	numeric
getMeanValue	get the mean value in value column	-	numeric
getMinValue	get the minimum value in value column	-	numeric
getSparsity	get the sparsity of the symbol w.r.t the cardinality	-	numeric
whereMax	find the row number in records data frame with a maximum value (return the first instance only)	-	integer
whereMaxAbs	find the row number in records data frame with a maximum absolute value (return the first instance only)	-	integer
whereMin	find the row number in records data frame with a minimum value (return the first instance only)	-	integer

ConstContainer Variable Fields

Field	Description	Type
description	description of symbol	string
dimension	dimension of symbol, setting dimension is a shorthand notation to set domain to a list of size n containing "*"	integer
domainLabels	column headings for the records data frame	list of string
domainNames	string version of domain names	list of string
domainType	none, relaxed or regular depending on state of domain links	string
name	name of symbol	string
numberRecords	number of symbol records (i.e., returns nrow(self$records) if not NULL)	integer
records	the main symbol records	data frame
summary	output a list of only the metadata	list
type	string type of variable	list

ConstContainer Variable Methods

Method	Description	Arguments/Defaults	Returns
countEps	total number of SpecialValues$EPS across all columns	columns = "level"	integer
countNA	total number of SpecialValues[["NA"]] across all columns	columns = "level"	integer
countNegInf	total number of SpecialValues$NEGINF across all columns	columns = "level"	integer
countPosInf	total number of SpecialValues$POSINF across all columns	columns = "level"	integer
countUndef	total number of SpecialValues$UNDEF across all columns	columns = "level"	integer
getCardinality	get the full cartesian product of the domain	-	integer
getMaxValue	get the maximum value across all columns	columns = "level"	numeric
getMaxAbsValue	get the maximum absolute value across all columns	columns = "level"	numeric
getMeanValue	get the mean value across all columns	columns = "level"	numeric
getMinValue	get the minimum value across all columns	columns = "level"	numeric
getSparsity	get the sparsity of the symbol w.r.t the cardinality	-	numeric
whereMax	find the row number in records data frame with a maximum value (return the first instance only)	column="level"	integer
whereMaxAbs	find the row number in records data frame with a maximum absolute value (return the first instance only)	column="level"	integer
whereMin	find the row number in records data frame with a minimum value (return the first instance only)	column="level"	integer

ConstContainer Equation Fields

Field	Description	Type
description	description of symbol	string
dimension	dimension of symbol, setting dimension is a shorthand notation to set domain to a list of size n containing "*"	integer
domainLabels	column headings for the records data frame	list of string
domainNames	string version of domain names	list of string
domainType	none, relaxed or regular depending on state of domain links	string
name	name of symbol	string
numberRecords	number of symbol records (i.e., returns nrow(self$records) if not NULL)	integer
records	the main symbol records	data frame
summary	output a list of only the metadata	list
type	string type of equation	list

ConstContainer Equation Methods

Method	Description	Arguments/Defaults	Returns
countEps	total number of SpecialValues$EPS across all columns	columns = "level"	integer
countNA	total number of SpecialValues[["NA"]] across all columns	columns = "level"	integer
countNegInf	total number of SpecialValues$NEGINF across all columns	columns = "level"	integer
countPosInf	total number of SpecialValues$POSINF across all columns	columns = "level"	integer
countUndef	total number of SpecialValues$UNDEF across all columns	columns = "level"	integer
getCardinality	get the full cartesian product of the domain	-	integer
getMaxValue	get the maximum value across all columns	columns = "level"	numeric
getMaxAbsValue	get the maximum absolute value across all columns	columns = "level"	numeric
getMeanValue	get the mean value across all columns	columns = "level"	numeric
getMinValue	get the minimum value iacross all columns	columns = "level"	numeric
getSparsity	get the sparsity of the symbol w.r.t the cardinality	-	numeric
whereMax	find the row number in records data frame with a maximum value (return the first instance only)	column="level"	integer
whereMaxAbs	find the row number in records data frame with a maximum absolute value (return the first instance only)	column="level"	integer
whereMin	find the row number in records data frame with a minimum value (return the first instance only)	column="level"	integer

ConstContainer Alias Fields

Field	Description	Type
aliasWith	aliased object or name	Set or string
description	description of symbol	string
dimension	dimension of symbol, setting dimension is a shorthand notation to set domain to a list of size n containing "*"	integer
domainLabels	column headings for the records data frame	list of string
domainNames	string version of domain names	list of string
domainType	none, relaxed or regular depending on state of domain links	string
isSingleton	logical if symbol is a singleton set	logical
name	name of symbol	string
numberRecords	number of symbol records (i.e., returns nrow(self$records) if not NULL)	integer
records	the main symbol records	data frame
summary	output a list of only the metadata	list

ConstContainer Alias Methods

Method	Description	Arguments/Defaults	Returns
getCardinality	get the full cartesian product of the domain	-	integer
getSparsity	get the sparsity of the symbol w.r.t the cardinality	-	numeric

ConstContainer UniverseAlias Fields

Field	Description	Type
aliasWith	always "*"	string
description	always Aliased with *	string
dimension	always 1	integer
domainLabels	always "*"	character vector
domainNames	always "*"	character vector
domainType	always none	string
isSingleton	always FALSE	logical
name	name of symbol	string
numberRecords	number of symbol records (i.e., returns nrow(self$records) if not NULL)	integer
records	the main symbol records	data frame
summary	output a list of only the metadata	list

ConstContainer UniverseAlias Methods

Method	Description	Arguments/Defaults	Returns
getCardinality	returns nrow(self$records)	-	integer
getSparsity	always 0	-	numeric

Additional Features

Validating Data

GAMS Transfer R requires that the records for all symbols exist in a standard format (Standard Data Formats) in order for them to be understood by the Container. It is possible that the data could end up in a state that is inconsistent with the standard format (especially if setting symbol attributes directly). GAMS Transfer R includes the $isValid() method in order to determine if a symbol is valid. This method returns a logical. This method does not guarantee that a symbol will be successfully written to GDX. Other data errors (duplicate records, long UEL names, or domain violations) could exist that are not tested in $isValid(). For example, we create two valid sets and then check them with $isValid() to be sure.

It is possible to run $isValid() on both the Container as well as the symbol object. The Container method $isValid() will also return FALSE if there are any invalid symbols in the Container object. In addition, the Container$isValid() method also detects broken Container references in symbols and inconsistent symbol naming between the Container$data field and the symbol objects.

Example (valid data)

m = Container$new()

i = Set$new(m, "i", records = c("seattle", "san-diego", "washington_dc"))

j = Set$new(m, "j", i, records = c("san-diego", "washington_dc"))

> i$isValid()

[1] TRUE

> j$isValid()

[1] TRUE

> m$isValid()

[1] TRUE

Now we create some data that is invalid due to incorrect column names in the records for set j.

In this example, we use records field of the symbol j to set the records. As mentioned in setting the records, when setting records directly, users must adhere to Standard Data Formats. In this example, symbol j does not have the correct number of columns. Moreover, the column headings do not follow the convention (should be "i_1"). The user can get more detailed error reporting if the verbose argument is set to TRUE. For example:

The $isValid() method checks:

1. If the symbol belongs to a Container
2. If all domain set symbols exist in the Container
3. If all domain set symbols objects are valid
4. If records are a data frame (or None)
5. The shape of the records is congruent with the dimensionality of the symbol
6. If records column headings are in standard format
7. That all data columns are type numerical
8. To make sure that all domain categories are type string

Note

Calling $isValid() too often may have a significant impact on the performance.

Domain Forwarding

GAMS includes the ability to define sets directly from data using the implicit set notation (see: Implicit Set Definition (or: Domain Defining Symbol Declarations)). This notation has an analogue in GAMS Transfer R called domainForwarding.

Note

It is possible to recursively update a subset tree in GAMS Transfer R.

Domain forwarding is available as an argument to all symbol object constructors; the user would simply need to pass domainForwarding=TRUE.

In this example, we have raw data that is in the dist data frame, and we want to send the domain information into the i and j sets. We take care to pass the set objects as the domain for parameter c.

Note

The element order in the sets i and j mirrors that in the raw data.

In this example, we show that domain forwarding will also work recursively to update the entire set lineage. The domain forwarding occurs at the creation of every symbol object. The correct order of elements in set i is (z, a, b, c) because the records from j are forwarded first, and then the records from k are propagated through (back to i).

Describing Data

The methods describeSets, describeParameters, describeVariables, and describeEquations allow the user to get a summary view of key data statistics. The returned data frame aggregates the output for a number of other methods (depending on symbol type). A description of each Container method is provided in the following subsections:

describeSets

Argument	Type	Description	Required	Default
symbols	list, string	A list of sets in the Container to include in the output. describeSets will include aliases if they are explicitly passed by the user.	No	NULL (if NULL specified, will assume all sets)

Returns: data frame

The following table includes a short description of the column headings in the return.

Field / Statistic	Description
name	name of the symbol
isAlias	logical if the symbol is an Alias
isSingleton	logical if the set is a singleton set
domain	domain labels for the symbol
domainType	none, relaxed or regular depending on the symbol state
dim	dimension
numberRecs	number of records in the symbol
cardinality	cartesian product of the domain information
sparsity	1 - numberRecs/cardinality

Example #1

m = Container$new("trnsport.gdx")

> m$describeSets()

name isAlias isSingleton domain domainType dim numberRecs cardinality

1 i FALSE FALSE * none 1 2 NA

2 j FALSE FALSE * none 1 3 NA

sparsity

1 NA

2 NA

Example #2 – with aliases

describeParameters

Argument	Type	Description	Required	Default
symbols	list, string	A list of parameters in the Container to include in the output	No	NULL (if NULL specified, will assume all parameters)

Returns: data frame

The following table includes a short description of the column headings in the return.

Field / Statistic	Description
name	name of the symbol
isScalar	logical if the symbol is a scalar (i.e., dimension = 0)
domain	domain labels for the symbol
domainType	none, relaxed or regular depending on the symbol state
dim	dimension
numRecs	number of records in the symbol
minValue	min value in data
meanValue	mean value in data
maxValue	max value in data
whereMin	row number min value (if multiple, returns only first occurrence)
whereMax	row number of max value (if multiple, returns only first occurrence)
countEps	number of SpecialValues$EPS in data
countNa	number of SpecialValues[["NA"]] in data
countUndef	number of SpecialValues$UNDEF in data
cardinality	cartesian product of the domain information
sparsity	1 - numRecs/cardinality

describeVariables

Argument	Type	Description	Required	Default
symbols	list, string	A list of variables in the Container to include in the output	No	NULL (if NULL specified, will assume all variables)

Returns: data frame

The following table includes a short description of the column headings in the return.

Field / Statistic	Description
name	name of the symbol
type	type of variable (i.e., binary,integer,positive,negative,free,sos1,sos2,semicont,semiint)
domain	domain labels for the symbol
domainType	none, relaxed or regular depending on the symbol state
dim	dimension
numRecs	number of records in the symbol
cardinality	cartesian product of the domain information
sparsity	1 - numRecs/cardinality
minLevel	min value in the level
meanLevel	mean value in the level
maxLevel	max value in the level
whereMaxAbsLevel	row number of max(abs(level)) in data
countEpsLevel	number of SpecialValues$EPS in level
minMarginal	min value in the marginal
meanMarginal	mean value in the marginal
maxMarginal	max value in the marginal
whereMaxAbsMarginal	row number of max(abs(marginal)) in data
countEpsMarginal	number of SpecialValues$EPS in marginal

describeEquations

Argument	Type	Description	Required	Default
symbols	list, string	A list of equations in the Container to include in the output	No	NULL (if NULL specified, will assume all variables)

Returns: data frame

The following table includes a short description of the column headings in the return.

Field / Statistic	Description
name	name of the symbol
type	type of variable (i.e., binary,integer,positive,negative,free,sos1,sos2,semicont,semiint)
domain	domain labels for the symbol
domainType	none, relaxed or regular depending on the symbol state
dim	dimension
numRecs	number of records in the symbol
cardinality	cartesian product of the domain information
sparsity	1 - numRecs/cardinality
minLevel	min value in the level
meanLevel	mean value in the level
maxLevel	max value in the level
whereMaxAbsLevel	row number of max(abs(level)) in data
countEpsLevel	number of SpecialValues$EPS in level
minMarginal	min value in the marginal
meanMarginal	mean value in the marginal
maxMarginal	max value in the marginal
whereMaxAbsMarginal	row number of max(abs(marginal)) in data
countEpsMarginal	number of SpecialValues$EPS in marginal

describeAliases

Argument	Type	Description	Required	Default
symbols	list, string	A list of aliases in the Container to include in the output.	No	NULL (if NULL specified, will assume all aliases)

Returns: data frame

The following table includes a short description of the column headings in the return.

Field / Statistic	Description
name	name of the symbol
aliasWith	name of the parent set
isSingleton	logical if an alias of a singleton set
domain	domain labels for the symbol
domainType	none, relaxed or regular depending on the symbol state
dim	dimension
numberRecs	number of records in the symbol
cardinality	cartesian product of the domain information
sparsity	1 - numberRecs/cardinality

Example #1

m = Container$new()

i = Set$new(m, "i", records = paste0("i",1:5))

j = Set$new(m, "j", records = paste0("j",1:10))

ip = Alias$new(m, "ip", i)

ipp = Alias$new(m, "ipp", ip)

jp = Alias$new(m, "jp", j)

> m$describeAliases()

name aliasWith isSingleton domain domainType dim numberRecs cardinality

1 ip i FALSE * none 1 5 NA

2 ipp i FALSE * none 1 5 NA

3 jp j FALSE * none 1 10 NA

sparsity

1 NA

2 NA

3 NA

Matrix Generation

GAMS Transfer R stores data in a "flat" format, that is, one record entry per data frame row. However, it is often necessary to convert this data format into a matrix/array format. GAMS Transfer R enables users to do this with relative ease using the toDense symbol methods This method will return a dense N-dimensional array (matrix for 2-Dimensions) with each dimension corresponding to the GAMS symbol dimension; it is possible to output an array up to 20 dimensions (a GAMS limit).

Example (1D data w/o domain linking (i.e., a relaxed domain))

Note that the parameter a is not linked to another symbol, so when converting to a matrix, the indexing is referenced to the data structure in a$records. Defining a sparse parameter a over a set i allows us to extract information from the i domain and construct a very different dense matrix, as the following example shows:

Example (2D data w/ domain linking)

The Universe Set

A Unique Element (UEL) is an (i,s) pair where i is an identification number for a string s. A list of all UELs is also known as 'the universe' or 'the universe set'. GAMS uses UELs to efficiently store domain entries of a record by storing the UEL ID i of a domain entry instead of the actual string s. This avoids storing the same string multiple times. The concept of UELs also exists in R and is called a "factor". GAMS Transfer R leverages these types in order to efficiently store strings and enable domain checking within the R environment.

Each domain column in a data frame can be a factor, the effect is that each symbol maintains its own list of UELs per dimension. R lets the user choose if levels in a factor should be regarded as ordered. GAMS Transfer R relies exclusively on ordered factors. By using ordered factors, GAMS Transfer R will order the UELs such that elements appear in the order in which they appeared in the data (which is how GAMS defines the UELs). GAMSTransfer allows the user to reorder the UELs with the uelPriority argument in the $write() method.

GAMS Transfer R does not keep track of the UELs separately from other symbols in the Container, it will be created internal to the $write() method and is based on the order in which data is added to the container. The user can access the current state of UELs with the $getUELs() container method. For example, we set a two dimensional set:

Customize The Universe Set

GAMS Transfer R allows the users to customize the Universe Set with the help of Symbol UELs methods shown in the following table.

Method	Description	Arguments/Defaults	Returns
addUELs	adds UELs to the symbol	uels (character), dimension=NULL	NULL
getUELs	returns the UELs used in the Symbol	dimension = NULL, codes = NULL, ignoreUnused=FALSE	character vector
removeUELs	removes UELs from the symbol	uels=NULL (character), dimension=NULL	NULL
renameUELs	renames UELs in the symbol	uels(character of same length as current UELs or named character vector), dimension=NULL, allowMerge=FALSE (logical). If allowMerge = TRUE, the underlying integer mapping of a factor is allowed to change to offer additional data flexibility	NULL
setUELs	sets UELs for the Symbol	uels(character), dimension, rename=FALSE	NULL

Note

Symbols should be valid in order to use these methods.

when dimension argument is optional, the method is applied to all dimensions

Some of these methods are also available as Container methods as shown in the following table. Container methods internally call the corresponding Symbol methods.

Method	Description	Arguments/Defaults	Returns
getUELs	returns the UELs used in the Container	symbols=NULL (character), ignoreUnused = FALSE	character vector
removeUELs	removes uels from all symbols the Container	uels=NULL (character)	NULL
renameUELs	renames uels in the Container	uels (named character), allowMerge=FALSE (logical). If allowMerge = TRUE, the underlying integer mapping of a factor is allowed to change to offer additional data flexibility	NULL

getUELs Examples

addUELs Examples

In this example we have added three new (unused) UELs: ham, and, cheese. These three UELs will now appear in the GAMS universe set (accessible with m$getUELs()). The addition of unused UELs does not impact the validity of the symbols (i.e., unused UELs will not trigger domain violations).

removeUELs Examples

If a user removes a UEL that appears in data, that data will be lost perminately. The domain label will be transformed into an NA as seen in this example:

Attention

A container cannot be written if there are NA entries in any of the domain columns (in any symbol). An error is thrown if there are missing domain labels.

renameUELs Examples

renameUELs is a method of all GAMS Symbol classes as well as the Container class. This method allows the user to rename UELs in a symbol dimension(s), over several symbols, or over the entire container. This method is handy when attempting to harmonize labeling schemes between data structures that originated from different sources. For example:

results in the following records:

However, two different data sources were used to generate the parameters a and b – one data source used the uppercase postal abbreviation of the state name and the other source used a lowercase full state name as the uniqe identifier. With the following syntax the user can harmonize to a mixed case postal code labeling scheme (without losing any of the original UEL ordering).

This results in the following records and the universe set:

reorderUELs Examples

reorderUELs is a method of all GAMS symbol classes. This method allows the user to reorder UELs of a specific symbol dimension. reorderUELs will not add/remove any UELs. For example:

But perhaps we want to reorder the UELs i1, i2, i3 to i3, i2, i1.

Note

This example does not change the indexing scheme of the data frame. It only changes the underlying integer numbering scheme for the factor levels.
We can see this by looking at the levels:

setUELs Examples

setUELs is a method of all GAMS symbol classes. This method allows the user to create new UELs, rename UELs, and reorder UELs all in one method. For example:

A user could accomplish a UEL reorder operation with setUELs:

A user could accomplish a UEL reorder + add UELs operation with setUELs:

A user could accomplish a UEL reorder + add + rename with setUELs:

Note

This example does not change the indexing scheme of the data frame, but the rename=TRUE flag means that the records will get updated as if a renameUELs call had been made.

If a user wanted to set new UELs on top of this data, without renaming, they would need to be careful to include the current UELs in the UELs being set. It is possible to lose these labels if they are not included (which will prevent the data from being written to GDX).

Removing Symbols

Removing symbols from a Container is easy when using the removeSymbols container method; this method accepts either a string or a list of string.

Attention

Once a symbol has been removed, it is possible to have hanging references as domain links in other symbols. The user will need to repair these other symbols with the proper domain links in order to avoid validity errors.

Reordering Symbols

In order to write the contents of the Container, it is required that the symbols are sorted such that, for example, a Set used as a domain of another symbol appears before that symbol. The Container will try to establish a valid ordering when writing the data. This type of situation could be encountered if the user is adding and removing many symbols (and perhaps rewriting symbols with the same name). Users should attempt to only add symbols to a Container once, and care must be taken when naming. The method reorderSymbols attempts to fix symbol ordering problems. The following example shows how this can occur:

Example Symbol reordering

Since the link to i is broken, Set j is now invalid. The user has to manually set the domain again.

Now even though j is valid, the symbols are out of order. The order can be fixed as follows.

Domain Violations

Domain violations occur when a symbol uses other Sets as domain(s) – and is thus of domain type regular, see Symbol Domain – and uses a domain entry in its records that is not present in the corresponding referenced domain set. Such a domain violation will lead to a GDX error when writing the data.

For example, the symbol j in the following example contains domain violations because j is defined over domain set i and the records entry "grayslake" is not present in i.

Trying to write this container to a GDX file will fail. To ask for domain violations, call the method Symbol$getDomainViolations(). It returns a list of DomainViolation objects with respect to each dimension of the symbol. This list can then be used to resolve the domain violations.

The GAMS Transfer R feature of domain forwarding offers an automatic expansion of the domain set with the violated entries in order to eliminate domain violations.

Note

Checking for domain violations is not a part of Symbol$isValid() for performance reasons.

The method for automatically resolving the domain violations can be convenient, but it effectively disables domain checking, which is a valuable tool for error detection. We encourage to use domain forwarding as rarely as possible. The same holds for using relaxed domain informaiton when regular domain information would be possible.

GAMS Special Values

The GAMS system contains five special values: UNDEF (undefined), NA (not available), EPS (epsilon), +INF (positive infinity), -INF (negative infinity). These special values must be mapped to their R equivalents. GAMS Transfer R follows the following convention to generate the 1:1 mapping:

• +INF is mapped to Inf
• -INF is mapped to -Inf
• EPS is mapped to -0.0 (mathematically identical to zero)
• NA is mapped to a NA
• UNDEF is mapped to NaN

GAMS Transfer R syntax is designed to quickly get data into a form that is usable in further analyses or visualization. The user does not need to remember these constants as they are provided within the class SpecialValues as SpecialValues$POSINF, SpecialValues$NEGINF, SpecialValues$EPS, SpecialValues[["NA"]], and SpecialValues$UNDEF. Some examples are shown below.

The following data frame for x would look like:

The user can now easily test for specific special values in the value column of the DataFrame (returns a logical vector):

Note

The syntax SpecialValues$NA is not allowed in R. Therefore, to access NA, one has to use SpecialValues[["NA"]]. As shown in the example above, double bracket syntax works for other special values too.

Standard Data Formats

This section is meant to introduce the standard format that GAMS Transfer R expects for symbol records. It has already been mentioned that we store data as a data frame, but there is an assumed structure to the column headings and column types that will be important to understand. GAMS Transfer R includes convenience functions in order to ease the burden of converting data from a user-centric format to one that is understood by GAMS Transfer R. However, advanced users will want to convert their data first and add it directly to the Container.

Set Records Standard Format

All set records (including singleton sets) are stored as a data frame with n number of columns, where n is the dimensionality of the symbol + 1. The first n-1 columns include the domain elements while the last column includes the set element explanatory text. Records are organized such that there is one record per row.

The names of the domain columns follow a pattern of <domain_set_name>_<index_position>; a symbol dimension that is referenced to the universe is labeled uni_<index position>. The explanatory text column is called element_text and must take the last position in the data frame.

All domain columns must be factors and the element_text column must be a string type.

Some examples:

Parameter Records Standard Format

All parameter records (including scalars) are stored as a data frame with n number of columns, where n is the dimensionality of the symbol + 1. The first n-1 columns include the domain elements while the last column includes the numerical value of the records. Records are organized such that there is one record per row. Scalar parameters have zero dimension, therefore they only have one column and one row.

The names of the domain columns follow a pattern of <domain_set_name>_<index_position>; a symbol dimension that is referenced to the universe is labeled uni_<index_position>. The value column is called value and must take the last position in the data frame.

All domain columns must be factors and the value column must be a numeric type. GAMS Transfer R requires that all the factor levels are of type string.

Some examples:

Variable/Equation Records Standard Format

Variables and equations share the same standard data format. All records (including scalar variables/equations) are stored as a data frame with n number of columns, where n is the dimensionality of the symbol + 5. The first n-5 columns include the domain elements while the last five columns include the numerical values for different attributes of the records. Records are organized such that there is one record per row. Scalar variables/equations have zero dimension, therefore they have five columns and one row.

The names of the domain columns follow a pattern of <domain_set_name>_<index position>; a symbol dimension that is referenced to the universe is labeled uni_<index_position>. The attribute columns are called level, marginal, lower, upper, and scale. These attribute columns must appear in this order. Attributes that are not supplied by the user will be assigned the default GAMS values for that variable/equation type. It is possible to not pass any attributes, GAMS Transfer R would then simply assign default values to all attributes.

All domain columns must be factors and the attribute columns must be a numeric type. GAMS Transfer R requires that all the factor levels are of type string.

Some examples:

Data Exchange with GDX

Up until now, we have been focused on using GAMS Transfer R to create symbols in an empty Container using the symbol constructors (or their corresponding container methods). These tools will enable users to ingest data from many different formats and add them to a Container. However, it is also possible to read in symbol data directly from GDX files using the read container method. In the following sections, we will discuss this method in detail as well as the write method, which allows users to write out to new GDX files.

Reading from GDX

There are two main ways to read in GDX based data.

• Pass the file path directly to the Container or ConstContainer constructor (will read all symbols and records)
• Pass the file path directly to the read method (default read all symbols, but can read partial files)

The first option here is provided for convenience and will, internally, call the read method. For a Container, this method will read in all symbols as well as their records. For a ConstContainer, this method will read only symbol metadata. This is the easiest and fastest way to get data out of a GDX file and into your R environment. For the following examples, we leverage the GDX output generated from the trnsport.gms model file.

Example (reading full data into a Container using the constructor)

m = Container$new("trnsport.gdx")

A user could also read in data with the read method as shown in the following example.

Example (reading full data into a Container with read method)

It is also possible to read in a partial GDX file with the read method, as shown in the following example:

This syntax assumes that the user will always want to read in both the metadata as well as the actual data records, but it is possible to skip the reading of the records by passing the argument records=FALSE.

Attention

The read method attempts to link the domain objects together (in order to have a "regular" domainType) but if domain sets are not part of the read operation there is no choice but to default to a "relaxed" domainType. This can be seen in the last example where we only read in the variable x and not the domain sets (i and j) that the variable is defined over. All the data will be available to the user, but domain checking is no longer possible. The symbol x will remain with "relaxed" domain type even if the user were to read in sets i and j in a second read call.

Example (reading only metadata into a ConstContainer using the constructor)

Note that in this example we make use of the convenience notation contained in the constructor to read in only the metadata of the trnsport.gdx file. This allows users to quickly explore the symbols contained in a file and it also explains why there are many NA values in the columns of the $describeParameters() method.

Example (reading full data with ConstContainer$read() method)

Writing to GDX

A user can write data to a GDX file by simply passing a file path (as a string). The write method will then create the GDX and write all data in the Container.

Note

It is not possible to write the Container when any of its symbols are invalid. If any symbols are invalid an error will be raised and the user will need to inspect the problematic symbols (perhaps using a combination of the listSymbols(isValid=FALSE) and isValid(verbose=TRUE) methods).

Example

Example (write a compressed GDX file)

Advanced users might want to specify an order to their UELs (i.e., the universe set); recall that the UEL ordering follows that dictated by the data. As a convenience, it is possible to prepend the list of UELs with a user specified order using the uelPriority argument.

Example (change the order of the UEL)

The original UEL order for this GDX file would have been c("a", "b", "c"), but since this example reorders the UELs with uelPriority, the positions of b and c have been swapped. This can be verified with the gdxdump utility (using the uelTable argument):

gdxdump foo.gdx ueltable=foo

Set foo /
  'a' ,
  'c' ,
  'b' /;
$onEmpty

Set i(*) /
'a',
'c',
'b' /;

$offEmpty