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

GAMS Transfer R is available on CRAN and can be installed as follows from the R console.

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

When building from source, GAMS Transfer R also requires the library zlib. The user can point to zlib by adding the directory containing zlib to the environment variable PATH or by using configure.vars argument of install.packages().

Examples

GDX Read

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

All symbols are internally stored in the Container data field (dictionary). The users should never have to access or modify the data field. The symbols can be accessed via m[<symbol_name>] and the records can be accessed via m[<symbol_name>]$records. Symbol records are stored in the data frame format.

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 and linking our data. 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:

Container fields

Symbols are organized in the Container under the data field. The users should never have to access the data field. Symbols in the container can be accessed via m[<symbol_name>].

Container methods

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.

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
   library(gamstransfer)

   m = Container$new()

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

   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

Set Fields

Set Methods

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

library(gamstransfer)

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

> i$records

uni

1 seattle

2 san-diego

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

library(gamstransfer)

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

> k$records

uni_1 uni_2

1 seattle san-diego

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

library(gamstransfer)

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

> l$records

uni

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).

Note

One can also use the genereateRecords() method to automatically populate randomly generated symbol records in the standard format.

Parameter

Parameter Constructor

Parameter Fields

Parameter Methods

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)

Note

One can also use the genereateRecords() method to automatically populate randomly generated symbol records in the standard format.

Variable

Variable Constructor

Variable Fields

Variable Methods

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.

Note

One can also use the genereateRecords() method to automatically populate randomly generated symbol records in the standard format.

Equation

Equation Constructor

Equation Fields

Equation Methods

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.

Note

One can also use the genereateRecords() method to automatically populate randomly generated symbol records in the standard format.

Alias

Alias Constructor

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 container, 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

Alias Methods

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.

One can also use the genereateRecords() method to automatically populate randomly generated symbol records in the standard format.

UniverseAlias

UniverseAlias Constructor

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

UniverseAlias Methods

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)

library(gamstransfer)

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:

• If the symbol belongs to a Container
• If all domain set symbols exist in the Container
• If all domain set symbols objects are valid
• If all domain sets are one dimensional and not singleton sets
• If records are a data frame (or NULL)
• If the records domainLabels are unique
• The shape of the records is congruent with the dimensionality of the symbol
• That all data columns are type numeric
• To make sure that all domain categories are type string

Note

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

Comparing GAMS Transfer objects

Equivalence between two symbols or between two containers can be tested using equals method. Since the order of records in the records data frame is not important from a GDX point of view, equals method compares symbol records independently from the order in which they are stored in the records data frame. As this requires a merge operation over the domain columns, equals is a computationally expensive call.

Attention

We do not recommend using equals method inside large loops or when performance is critical. It is, however, very useful for data debugging.

A quick example shows the syntax of equals:

One can debug the reason for inequality using the option verbose.

By default, equals takes the strictest view of symbol "equality", i.e., everything must be equal. In this case, the symbol names and descriptions differ between the two sets i and j. We can relax this with a combination of argument flags. Comparing the two symbols again, but ignoring the meta data (i.e., ignoring the symbol name, description and type (if a Variable or Equation)):

The checkUELs argument will ensure that the symbol "universe" is the same (in order and content) between two symbols, as illustrated in the following example:

Numerical comparisons are enabled for Parameters, Variables and Equations. Equality can be flexibly defined through the equals method arguments. Again, the strictest view of equality is taken as the default behavior of equals.

In the case of variables and equations, it is possible for the user to confine the numerical comparison to certain attributes (level, marginal, lower, upper and scale) by specifying the columns argument as the following example illustrates:

Similar to symbols, one can compare two Container objects using the equals method. When comparing Containers, the data fields are compared and if the same symbol keys exist in the Containers under comparison, symbol equals method is used to compare the symbols. Here is a brief example:

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 to forward domain across all dimensions or as a logical vector domainForwarding=c(TRUE, FALSE,....) to forward domain across selected dimensions.

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.

We can also selectively use domainForwarding for part of the domain by passing a logical vector to the domainForwarding argument as shown in the following example. The domain records are forwarded only to the domain set i but not to the domain set j.

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

Returns: data frame

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

Example #1

library(gamstransfer)

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

> m$describeSets()

name isSingleton domain domainType dimension numberRecords sparsity

1 i FALSE * none 1 2 NA

2 j FALSE * none 1 3 NA

Example #2 – with aliases

describeParameters

Returns: data frame

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

describeVariables

Returns: data frame

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

describeEquations

Returns: data frame

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

describeAliases

Returns: data frame

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

Example #1

library(gamstransfer)

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 dimension numberRecords sparsity

1 ip i FALSE * none 1 5 NA

2 ipp i FALSE * none 1 5 NA

3 jp j FALSE * none 1 10 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)

Note

If there are unused UELs in the domain symbol, toDense() requires that the unused UELs are at the end of UEL list. One can achieve this by calling the reorderUELs() method for the domain symbol. Similarly, if the symbol records are in a different order than that of domain symbol UEL, UELs should be reordered to follow the record order. This can also be achived using reorderUELs() symbol method.

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.

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.

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:

When reorderUELs() is used without the uels argument, the UELs are rearranged based on the records order as illustrated in the following example.

Moreover, if there are unused UELs, they are moved to the end as shown below. Here, i4 is the unused UELs that gets moved to the end after using reorderUELs.

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.

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 or n+1 number of columns, where n is the dimensionality of the symbol. The first n columns include the domain elements while the last column, if present, includes the set element text. Records are organized such that there is one record per row.

The names of the domain columns (domain labels) are unique. If the domain labels are non-unique, they are converted to follow a pattern of <user_domain_label>_<index_position> by domainLabels setter. If the records are passed in a non-data frame format (vector, matrix etc.), domain labels follow a pattern of <domain_name>_<index_position>. A symbol dimension that is referenced to the universe is labeled uni. 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 or n + 1 number of columns, where n is the dimensionality of the symbol. The first n columns include the domain elements while the last column, if present, 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. In cases where the value columns is not present for a parameter, it is assumed that the value is the default parameter value (i.e., 0). Scalar parameters with an empty data frame as records is assumed to contain the default value of 0.

The names of the domain columns (domain labels) are unique. If the domain labels are non-unique, they are converted to follow a pattern of <user_domain_label>_<index_position> by domainLabels setter. If the records are passed in a non-data frame format (vector, matrix etc.), domain labels follow a pattern of <domain_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 to n + 5 number of columns, where n is the dimensionality of the symbol. The first n columns include the domain elements while the remaining 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 one row. In cases where the records data frame has missing attribute columns, it is assumed that the column contains the default value. Similarly, if the records data frame for a scalar is empty, it is assumed that all the attribute values are at their default. Default values for variables and equations depend on their type.

The names of the domain columns (domain labels) are unique. If the domain labels are non-unique, they are converted to follow a pattern of <user_domain_label>_<index_position> by domainLabels setter. If the records are passed in a non-data frame format (vector, matrix etc.), domain labels follow a pattern of <domain_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:

Generate Records

Container symbol records in standard format can also be generated using the convenience method generateRecords(). This method generates records with the cartesian product of domain all sets. If the argument density is less than 1, randomly selected records are removed. For symbols that are not scalar, using this method requires that the symbol domain type is "regular" (i.e., <symbol_name>$domainType = "regular"). A few examples using the method generateRecords() for each type of Container symbol are provided below.

Set

Example #1 Create a large (dense) 4D set

Generating the initial data.frame could be difficult for Set symbols that have a large number of records and a small number of UELs. These higher dimensional symbols will benefit from the generateRecords convenience method.

Example #2 Create a large (sparse) 4D set

It is also possible to generate a sparse set (randomly selected rows are removed from the dense dataframe) with the density argument to generateRecords.

Example #3 Create a large 4D set with 1 sparse dimension

Parameter

Example #1 Create a large (dense) 4D Parameter

Generating the initial data.frame could be difficult for Parameter symbols that have a large number of records and a small number of UELs. These higher dimensional symbols will benefit from the generateRecords convenience method.

Note

In Example #1 a large 4D parameter was generated. by default, the value of these records are randomly drawn numbers from the interval [0,1] (uniform distribution).

Example #2 - Create a large (sparse) 4D parameter with normally distributed values

Note

The custom function passed to the argument func must expose a size argument. It might be tedious to know the exact number of the records that will be generated, especially if a fractional density is specified; therefore, the generateRecords method will pass in the correct size automatically.

Example #3 - Create a large 4D parameter with a random number seed

Note

The seed is an int that will set the random number generator state (enables reproducible sequences of random numbers).

Variable and Equation

Generating records for the symbol types Variable and Equation is similar to that of previously shown examples of parameters and sets. However, since there are more than one attributes to variables and equations, there are a few differences. By default, the random sampling is done is only for the level attribute with default values being passed to the other attributes. To randomly generate other attributes, one can use the custom func argument. This is shown in the following example.

Example #1 Create a large (sparse) 4D variable and Equation

Alias

The method generateRecords for an alias simply calls the corresponding method for its referenced set.

Example #1 Create a large (dense) 4D set from an Alias

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 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 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)

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 preserve the symbol domain type from the source 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.

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

Data Exchange between Containers

GAMS Transfer R allows data exchange between two Containers with the help of read and copy methods.

Data exchange with read

Similar to reading from a GDX file, a Container can read from another Container object. Following examples demonstrate this with the help of the GDX output generated from trnsport.gms model file.

Example (reading data from Container)

Example (reading data when symbol with the same name already exists)

The container read method does not allow reading from another source (Container, or a GDX file) when a symbol with the same name already exists. The existing symbol must be removed or renamed.

Data exchange with copy

Symbol copy method provides an alternative way to exchange data between Containers. Following examples demonstrate this starting from a Container containing data from GDX output generated from trnsport.gms model file.

Example (copy symbol from one container to another)

> library(gamstransfer)

> c = Container$new("trnsport.gdx")

> c$listSymbols()

[1] "i" "j" "a" "b" "d" "f" "c" "x"

[9] "z" "cost" "supply" "demand"

> m = Container$new()

> c["f"]$copy(m)

> m$listSymbols()

[1] "f"

The above example copies symbol f from Container c to Container m. If one copies a symbol with domain that does not exist in the destination Container, domain is relaxed as shown in the following example.

Example (copy symbol to another container without domain symbols)

> library(gamstransfer)

> c = Container$new("trnsport.gdx")

> c$listSymbols()

[1] "i" "j" "a" "b" "d" "f" "c" "x"

[9] "z" "cost" "supply" "demand"

m = Container$new()

> c["d"]$copy(m)

> m$listSymbols()

[1] "d"

> m["d"]$domain

[1] "i" "j"

> m["d"]$domainType

[1] "relaxed"

Example (copy symbol to another container with overwrite)

> library(gamstransfer)

> c = Container$new()

> i = Set$new(c, "i", records=c("i1","i2"))

> i$records

uni

1 i1

2 i2

> m = Container$new()

> i = Set$new(m, "i", records= c("i3","i4"))

> i$records

uni

1 i3

2 i4

# the following command throws an error

> c["i"]$copy(m)

Error in private$.copy(destination, overwrite) :

Symbol i already exists in `destination`

> c["i"]$copy(m, overwrite = TRUE)

> m["i"]$records

uni

1 i1

2 i2

Example (bulk copy operation via Container copy method)

A bulk operation is also possible via Container copy method as shown in the following example.

> library(gamstransfer)

> c = Container$new("trnsport.gdx")

> c$listSymbols()

[1] "i" "j" "a" "b" "d" "f" "c" "x"

[9] "z" "cost" "supply" "demand"

> m = Container$new()

> c$copy(m) # copy all symbols

> c$listSymbols()

[1] "i" "j" "a" "b" "d" "f" "c" "x"

[9] "z" "cost" "supply" "demand"

> m = Container$new()

> c$copy(m, symbols=c("a","b","d")) # copy a subset of symbols

> m$listSymbols()

[1] "a" "b" "d"

> c$copy(m, symbols="a", overwrite = TRUE) # copy symbols with overwrite

> m$listSymbols()

[1] "a" "b" "d"