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• Functions
• Logical Statements
• Control Structures
• Pseudo-Code

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Functions

Review core concepts on FUNCTIONS:

FUNCTION REVIEW

Make sure you are clear about:

• Arguments
• Object assignment (arrow) versus argument assignment (equals)
• When are quotation marks needed around arguments?
• Single-value arguments (one number or string) versus compound arguments (use c() or pass existing objects)
• Argument defaults
• Explicit and implicit use of argument names and positions
• Return values in R
• Function scope

Logical Statements

Recall that logical operators like EQUALS, NOT EQUAL, AND, OR, and OPPOSITE are used identify sets of cases that belong to a desired group, and sets of cases outside of the desired group.

==    EQUALS 
!=    NOT EQUAL 
&     AND
|     OR
!     OPPOSITE

Recall that logical statements are used to construct groups. Group membership is encoded into logical vectors, where TRUE indicates membership and FALSE indicates cases that do not belong to the group. For example, identifying commercial properties in downtown Syracuse:

# logical statement to identify commercial properties:

these <- downtown$landuse == "Commercial"

# vector "these" contains TRUE and FALSE values,
# TRUE = belongs to the group specified by the logical statement (commercial parcels)
# FALSE = does not belong (anything other than commercial) 

# if the property belongs to the desired group (these == TRUE), 
# assign firebrick red as the color, otherwise assign gray as the property color 

group.colors <- ifelse( these, "firebrick", "gray90" )     
plot( downtown,  border="gray70", col=group.colors )     

Compound logical statements require combining multiple criteria to define a group:

study.group == "TREATMENT"   &   gender == "FEMALE" 

If you need a refresher, review the chapter on LOGICAL STATEMENTS.

Control Structures

Control structures are functions that allow you to implement logic or process in your code. They structure and implement the rules which govern our world. For example:

# pseudocode example
IF 
  the ATM password is correct
  check whether the requested amount is smaller than account balance
  IF
    account balance > requested amount 
    return requested amount 

The most frequent control structures do the following:

• Only execute a chunk of code if certain requirements are met (if-then or if-else functions)
• Implement code as long as a requirement is met (while loops)
• Repeat the same code for each item in a set (for loops)

Skim the following chapters for an introduction to a varietiy of control structures in R:

Quick Reference on Control Structures

Control Structures in R

In this lab we will focus on IF-THEN statements.

Next week we will introduce FOR LOOPS in order to build a simulation.

Pseudo-Code

Planning Your Solution with Pseudo-Code

Typically as you start a specific task in programming there are two things to consider.

(1) What are the steps needed to complete the task? (2) How do I implement each step? How do I translate them into the appropriate functions and syntax?

It will save you a huge amount of time if you separate these tasks. First, take a step back from the problem that think about the steps. Write down each step in order. Make some notes about what data is needed from the previous step, and what the return result will be from the current step.

Think back to the cooking example. If we are going to bake cookies our pseudo-code would look something like this:

1. Preheat the oven.
2. In a large bowl, mix butter with the sugars until well-combined.
3. Stir in vanilla and egg until incorporated.
4. Addflour, baking soda, and salt.
5. Stir in chocolate chips.
6. Bake.

Note that it lacks many necessary details. How much of each ingredient? What temperature does the oven need to be? How long do we bake for?

In computer programming terms butter, sugar, and brown sugar are the inputs or arguments needed for a function. The mixture made by combining the three ingredients in the first step is the return value of the process.

Once we have the big picture down and are comfortable with the process then we can start to fill in these details:

1. Preheat the oven.
   • Preheat to 375 degrees
2. In a large bowl, mix butter with the sugars until well-combined.
   • 1/3 cup butter
   • 1/2 cup sugar
   • 1/4 cup brown sugar
   • mix until the consistency of wet sand

In the final step, we will begin to implement code.

# 1. Preheat the oven. 
#    - preheat to 375 degrees 

preheat_oven <- function( temp=375 )
{
   start_oven( temp )
   return( NULL )
}


# 2. In a large bowl, mix butter with the sugars until well-combined. 
#    - 1/3 cup butter    
#    - 1/2 cup sugar    
#    - 1/4 cup brown sugar   
#    - mix until the consistency of wet sand 

mix_sugar <- function( butter=0.33, sugar=0.5, brown.sugar=0.25 )
{
   sugar.mixture <- mix( butter, sugar, brown.sugar )
   return( sugar.mixture )
}


# 3. Stir in vanilla and egg until incorporated. 
#    - add to sugar mixture 
#    - mix until consistency of jelly 

add_wet_ingredients <- function( sugar=sugar.mixture, eggs=2, vanilla=2 )
{
   # note that the results from the previous step are the inputs into this step
}

We are describing here the process of writing pseudo-code. It it the practice of:

1. Breaking an analytical task into discrete steps.
2. Noting the inputs and logic needed at each step.
3. Implementing code last.

Pseudo-code helps you start the process and work incrementally. It is important because the part of your brain that does general problem-solving (creating the basic recipe) is different than the part that drafts specific syntax in a langauge and de-bugs the code. If you jump right into the code it is easy to get lost or derailed.

More importantly, pseudo-code captures the problem logic, and thus it is independet of any specific computer language. When collaborating on projects one person might generate the system logic, and another might implement. So it is important to practice developing a general overview of your task at hand.

Here are some helpful examples:

• Pseudocode guide
• Khan Academy video

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