# macro

The macro package provides macro commands and a pre-processor for R programs. The package can be useful in several ways. Before illustrating the functionality, let’s first discuss the macro concept at a high level.

# Macro Concept

A macro language is like a meta-language for code. The meta-language runs first, generates the regular code, and then the regular code is executed to produce some sort of output. It is, at heart, a text manipulation system.

The SAS® macro language is commonly used in SAS programs. The R macro package was inspired by SAS. Specifically, the macro package aims to mimic SAS macro with “mprint” and “mfile” options turned on. These options generate a resolved code file.

# An R Macro Language

Clearly, a macro language for R will not be the same as the SAS macro language. The SAS macro language is built into the SAS execution environment. The SAS pre-processor is run automatically, and the macro syntax is understood by the SAS syntax checker.

With R, none of the above applies. R does not have a built-in macro language, does not have a built-in pre-processor, and the R syntax checker will not expect any sort of macro language syntax.

Therefore, to build macro-like functionality in R, we must get a little creative.

# Macro Statements

The first thing to note is that macro statements in the macro package are written as code comments. The comments ensure that the R syntax checker will not generate an error when parsing the macro functions.

In SAS, macro functions are prefixed with a percent sign (%). In R, macro statements are therefore prefixed with a comment symbol (#) and a percent sign (%). A simple macro assignment looks like this:

    #%let x <- 1

A simple macro conditional looks like this:

    #%if (&x == 1)
      print("x is one!")
    #%else
      print("x is something else!")
    #%end

Note that the above R macro syntax is not identical to the SAS syntax. The syntax devised for the macro package is a blend of SAS and R syntax.

# Differences Between SAS Macro and R Macro

Here are some of the major difference between the SAS macro language and the R macro language:

- R macro statements do not end with a semicolon (“;”). They are single-line commands, and end with a carriage return.
- R macro commands can be used anywhere in the program. They are not restricted to use inside macro functions.
- Single-quotes will not prevent macro variable resolution. In the R macro language, single and double quotes operate the same.
- All operators in the R macro language use R syntax, including the comparison operator (“==”).
- R macro variables in open code must be surrounded by backticks (“`”) to avoid syntax errors. Inside comments, strings, and other macro commands, they do not need backticks.

Otherwise, the R macro language is similar to SAS in terms of basic functionality.

## Available Functionality

The macro package supports the most basic functionality of a macro system. That includes:

1. Macro Comments: Like regular R comments, but are not emitted to the output file.
2. Macro Variables: Allow you to assign a value to a macro variable, and use the macro variable as a text replacement token.
3. Macro Conditionals: If-Then-Else logic that allows you to conditionally execute a chunk of code.
4. Macro Include: Inserts the contents of an external file into your program.
5. Built-in Macro Functions: A small number of macro functions to make the system more practical.
6. Macro Do Loops: Copies code inside the do block for each iteration of the loop.
7. User-Defined Macro Functions: The ability to create a user-defined text-replacement function, with or without parameters.

The above functionality covers the majority of use cases for a macro language. Now let’s see how the R macro language is actually implemented.

### SAS to R Macro Language Map

The table below maps SAS macro statements to the corresponding statement in R. This table can help SAS programmers quickly understand how the macro package works.

| SAS | R
Comment: | %* Here is a comment; | #% Here is a comment
Assignment: | %let x = 1; | #%let x <- 1
Conditional: | %if (&x = 1) %then %do; %put “First condition”; %end; %else %if (&x = 2) %then %do; %put “Second condition”; %end; %else %do; %put “Default condition”; %end; | #%if (&x == 1) print(“First condition”) #%elseif (&x == 2) print(“Second condition”) #%else print(“Default condition”) #%end
Include: | %include “myfile.sas”; | #%include “myfile.R”
Built in Functions: | %sysfunc() %symexist() %symput() %nrstr() | %sysfunc() %symexist() %symput() %nrstr()
Do Loop: | %do y = 1 %to 3; %put Y is &y; %end; | #%do y = 1 %to 3 print(“Y is &y”) #%end
User-Defined Function: | %macro test(); %put “Hello!”; %mend; %test(); | #%macro test() print(“Hello!”) #%mend #%test()

The table above contains all of the commands in the R macro language. For comprehensive documentation, see the Macro Languge Reference.

# The msource() Function

In the macro package, pre-processing of the macro code is accomplished by a special function: `msource()`. The `msource()` function is similar to the `source()` function in R. The `msource()` function will execute your code, just like the `source()` function. First, however, it will execute the macro pre-processor.

The steps can be summarized as follows:

Step 1: Pre-process the code and resolve all macro language syntax.
Step 2: Generate a new code file with the resolved code.
Step 3: Execute the resolved code with the `source()` function.

Notice that the above steps map directly to the way the SAS macro processor works.

In addition, the R macro language is a text-replacement language, just like SAS. Macro variables are swapped out with real values as text replacements. Macro conditions are evaluated, and only the code in the TRUE macro conditions is executed.

All of the above is handled by the `msource()` function. `msource()` is the core function in the macro package.

### Parameters

The `msource()` function has several parameters. Here is a short summary:

- pth: The path to the macro program.
- file_out: An optional path to store the generated code.
- envir: An optional environment to use for program execution.
- exec: A TRUE or FALSE value indicating whether to execute the generated code.
- debug: If TRUE, the function will generate additional debug information. By default, this information is sent to the console.
- debug_out: An optional file path to send the debug information.
- symbolgen: If TRUE, macro variable values are added to the debug information.
- echo: Echos the generated code to the console. TRUE by default.
- clear: Clears the macro symbol table and macro function list at the beginning of every call to `msource()`. TRUE by default.

Additional information about the `msource()` parameters is available in the function reference.

## How to Use

To use the macro package, you will first write your macro-enabled R code in a regular “.R” code file. You can use RStudio® or another code editor. The macro statements are written as special comments, as described above. You can execute a macro-enabled program in three different ways:

- From the console
- From a script
- From a keyboard shortcut

The below sections will elaborate on these different approaches.

### Execute From the Console

To execute a macro-enabled program, simply call the `msource()` function in the console and pass the program path. If you want to save the generated code, or run in debug mode, pass the appropriate parameters to `msource()`.

The `msource()` function provides some convenience for those people working in RStudio. If you are working in RStudio, the function will use the path of the currently active program by default. In that case, you don’t even need to pass the program path. Just call `msource()` in the console with no parameters.

A simple example of executing a macro program from the console is provided below. For a more extensive example, please see Example 1.

### Execute From a Script

The `msource()` function can also be sourced from a regular R program. You can create a driver script to set up some macro parameters, and then call the `msource()` function from the driver.

For an example of a driver script, see Example 2 or Example 3.

### Execute From a Keyboard Shortcut

The core features of the macro package are also available via the RStudio Addin menu and keyboard shortcuts. These features allow you to use the macro package interactively in RStudio. They make writing and debugging macro-enabled programs much easier.

See the Installation and Setup vignette for instructions on how to enable and use the macro keyboard shortcuts.

## Example Macro Program

Here is what a simple macro-enabled program looks like:

### Console Output

If you execute the `msource()` function in the console, the above program will run and display the following:

    > msource()
    ---------
    library(procs)
     library(macro)

     # Analysis of 'mpg'
     anl_mpg <- proc_means(mtcars, mpg, class = cyl,
                           output = report)

     # Analysis of 'disp'
     anl_disp <- proc_means(mtcars, disp, class = cyl,
                           output = report)

     # Analysis of 'drat'
     anl_drat <- proc_means(mtcars, drat, class = cyl,
                           output = report)

     # Combine analysis
     final <- list(anl_mpg, anl_disp, anl_drat)

     # Print to console
     print(final)

     # Print to viewer
     proc_print(final,
                titles = c("Analysis of Selected MTCARS Variables",
                           "by cylinders"))

    ---------
    [[1]]
      CLASS VAR  N     MEAN      STD  MIN  MAX
    1     4 mpg 11 26.66364 4.509828 21.4 33.9
    2     6 mpg  7 19.74286 1.453567 17.8 21.4
    3     8 mpg 14 15.10000 2.560048 10.4 19.2

    [[2]]
      CLASS  VAR  N     MEAN      STD   MIN   MAX
    1     4 disp 11 105.1364 26.87159  71.1 146.7
    2     6 disp  7 183.3143 41.56246 145.0 258.0
    3     8 disp 14 353.1000 67.77132 275.8 472.0

    [[3]]
      CLASS  VAR  N     MEAN       STD  MIN  MAX
    1     4 drat 11 4.070909 0.3654711 3.69 4.93
    2     6 drat  7 3.585714 0.4760552 2.76 3.92
    3     8 drat 14 3.229286 0.3723618 2.76 4.22

The `msource()` console output contains two sections. The first section is the generated code. The second section is any printed output from the generated code. In this way, you can see what code was actually sourced, and the output of that code.

If you do not wish to see the generated code, set the “echo” parameter on `msource()` to FALSE.

### Viewer Output

The above macro program also displayed the analysis in the RStudio viewer. It looks like this:

See the procs package documentation for more information about the `proc_means()` and `proc_print()` functions. Both the macro package and the procs package are part of the sassy system of packages.

# Additional Features

## Symbol Table Functions

The functions in the macro package operate via an internal memory register called the macro symbol table. The symbol table stores the names and values for any macro variables, and the definition of any macro functions. The symbol table can be accessed from regular R code using a special set of functions. The symbol table functions are as follows:

- `symtable()`: Returns an object containing the macro symbol table and macro function list. By default, this object is printed to the console.
- `symclear()`: Clears the macro symbol table and deletes any macro function definitions.
- `symget()`: Gets a macro variable value from the macro symbol table.
- `symput()`: Sets the value of a macro variable.

These functions can be used to observe and manipulate the macro symbol table from regular R code or from the console.

### Example Symtable Function

For instance, we can follow up on the `msource()` example from above by calling the `symtable()` function in the console. Like this:

    > symtable()
    # Macro Symbol Table: 5 macro variables
         Name                       Value
    1    &dat                      mtcars
    2    &idx                           3
    3    &var                        drat
    4 &varlst anl_mpg, anl_disp, anl_drat
    5   &vars    c("mpg", "disp", "drat")
    # Macro Function List: 1 functions
    # Function '%get_means': 2 parameters
    - dat
    - var

The above console output shows the macro symbol table and the macro function list. This information can be useful for ensuring the program ran correctly and for debugging.

For a full discussion on the symbol table functions, see the Symbol Table Functions vignette.

## Debugging

The macro package provides extensive debugging capabilities. These capabilities can be enabled by activating the “debug” parameter on the `msource()` function. When activated, the debug information will be sent to the console.

Let’s illustrate the debug feature by running `msource()` again for the above sample program, but this time set the “debug” parameter to TRUE:

    > msource(debug = TRUE)
    ********************************************************************************
    **  Pre-Processing
    ********************************************************************************
    -    File In: C:/packages/macro/tests/testthat/programs/sample16.R
    -   File Out: C:\Users\dbosa\AppData\Local\Temp\RtmpQhWV50/sample16.R
    ********************************************************************************
    [ In#][Out#]:
    [   1][   1]:
    [   2][   2]: library(procs)
    [   3][   3]: library(macro)
    [   4][   4]:
    [   5][    ]: #% Assign macro variables
    [   6][    ]: #%let dat <- mtcars
    [   7][    ]: #%let vars <- c("mpg", "disp", "drat")
    [   8][    ]:
    [   9][    ]: #% Macro to run proc_means
    [  10][    ]: #%macro get_means(dat, var)
    [  17][    ]:
    [  18][    ]: #% Loop over variables
    [  19][    ]: #%do idx = 1 %to 3
    [  20][    ]: #%let idx <- 1
    [  21][    ]:   #%let var <- mpg
    [  22][    ]:   #%get_means(&dat, &var)
    [  23][    ]: #%let dat <- mtcars
    [  24][    ]: #%let var <- mpg
    [  25][    ]:
    [  26][   5]: # Analysis of 'mpg'
    [  27][   6]: anl_mpg <- proc_means(mtcars, mpg, class = cyl,
    [  28][   7]:                       output = report)
    [  29][   8]:
    [  30][    ]: #%mend get_means
    [  31][    ]: #%let idx <- 2
    [  32][    ]:   #%let var <- disp
    [  33][    ]:   #%get_means(&dat, &var)
    [  34][    ]: #%let dat <- mtcars
    [  35][    ]: #%let var <- disp
    [  36][    ]:
    [  37][   9]: # Analysis of 'disp'
    [  38][  10]: anl_disp <- proc_means(mtcars, disp, class = cyl,
    [  39][  11]:                       output = report)
    [  40][  12]:
    [  41][    ]: #%mend get_means
    [  42][    ]: #%let idx <- 3
    [  43][    ]:   #%let var <- drat
    [  44][    ]:   #%get_means(&dat, &var)
    [  45][    ]: #%let dat <- mtcars
    [  46][    ]: #%let var <- drat
    [  47][    ]:
    [  48][  13]: # Analysis of 'drat'
    [  49][  14]: anl_drat <- proc_means(mtcars, drat, class = cyl,
    [  50][  15]:                       output = report)
    [  51][  16]:
    [  52][    ]: #%mend get_means
    [  53][    ]: #% end do
    [  54][    ]:
    [  55][    ]: #% Create list of analysis datasets
    [  56][    ]: #%let varlst <- anl_mpg, anl_disp, anl_drat
    [  57][    ]:
    [  58][  17]: # Combine analysis
    [  59][  18]: final <- list(anl_mpg, anl_disp, anl_drat)
    [  60][  19]:
    [  61][  20]: # Print to console
    [  62][  21]: print(final)
    [  63][  22]:
    [  64][  23]: # Print to viewer
    [  65][  24]: proc_print(final,
    [  66][  25]:            titles = c("Analysis of Selected MTCARS Variables",
    [  67][  26]:                       "by cylinders"))
    [  68][  27]:
    ********************************************************************************
    **  Execution
    ********************************************************************************

    > library(procs)

    > library(macro)

    > # Analysis of 'mpg'
    > anl_mpg <- proc_means(mtcars, mpg, class = cyl,
    +                       output = report)

    > # Analysis of 'disp'
    > anl_disp <- proc_means(mtcars, disp, class = cyl,
    +                       output = report)

    > # Analysis of 'drat'
    > anl_drat <- proc_means(mtcars, drat, class = cyl,
    +                       output = report)

    > # Combine analysis
    > final <- list(anl_mpg, anl_disp, anl_drat)

    > # Print to console
    > print(final)
    [[1]]
      CLASS VAR  N     MEAN      STD  MIN  MAX
    1     4 mpg 11 26.66364 4.509828 21.4 33.9
    2     6 mpg  7 19.74286 1.453567 17.8 21.4
    3     8 mpg 14 15.10000 2.560048 10.4 19.2

    [[2]]
      CLASS  VAR  N     MEAN      STD   MIN   MAX
    1     4 disp 11 105.1364 26.87159  71.1 146.7
    2     6 disp  7 183.3143 41.56246 145.0 258.0
    3     8 disp 14 353.1000 67.77132 275.8 472.0

    [[3]]
      CLASS  VAR  N     MEAN       STD  MIN  MAX
    1     4 drat 11 4.070909 0.3654711 3.69 4.93
    2     6 drat  7 3.585714 0.4760552 2.76 3.92
    3     8 drat 14 3.229286 0.3723618 2.76 4.22

    > # Print to viewer
    > proc_print(final,
    +            titles = c("Analysis of Selected MTCARS Variables",
    +                       "by cylinders"))

    ********************************************************************************
    **  End
    ********************************************************************************

The debug information shows how the incoming macro code was processed. For each incoming line, it shows whether that line was emitted to the generated code file, and how it was resolved. Any errors during pre-processing or execution will appear at the point they occurred. In this way, it is very easy to find which line has the error.

For a full explanation of the debugging features in the macro package, see the Debugging vignette.

# Next Steps

Here are some more articles for further exploration:

- Installation and Setup
- Macro Language Reference
- Symbol Table Functions
- Debugging
- Global Options
- Example 1: Listing
- Example 2: Another Listing
- Example 3: Table
- Frequently Asked Questions