Check Yourself: A Little Bit More on Functions

At a very high-up level of abstraction, a function in R is a distinct and defined set of code that performs some operation or procedure. Last time, we wrote a kernel in terms of a function:

nexp_kernel = function(distance, bandwidth=1500){
                       u = distance / bandwidth
                       return(exp(-u))
}

In abstract, functions are descriptions of what you want to do to a set of inputs. Breaking it down, all functions are created using the same methods:

name_of_function = function(argument_1, argument_2, argument_with_default = TRUE)
                           {
                            if(argument_with_default){
                              return(argument_1 * argument_2)
                            }
                            else{
                              return(argument_1 / argument_2)
                            }
                    }

• The name_of_function is an arbitrary name, and should be something that clearly describes what the function does (like, load_data(), or simulate_next_year()).
• Then, the words inside of function(), the argument_1, argument_2, argument_with_default, are called arguments. Basically, an argument is an input provided to a function. Functions can have an unlimited number of arguments, but it’s best to only take arguments you need to use later in the function. The actual words used to describe argument_1 can be anything. They should be descriptive, clear, and helpful (such as distance and bandwidth when we were defining nexp_kernel).

• Finally, the body of the function comes between the braces. In our example, this is where the if/else stuff is. Otherwise, you can think of it like: function(){This is the body of the function}. The body is where you describe the operations you want to use on the arguments. Above, our example function will:

  1. check if the argument_with_default is true
  2. if it is, then it will compute argument_1 * argument_2.
  3. if it’s not, then it will compute argument_1 / argument_2.

• You send values outside of the function into the rest of your code using return(). Once you return(something), the function stops. For instance, in our example code, if we return(argument_1 * argument_2), we never even check the other branch of code after the else{}.

Functions are very helpful for code re-use, avoiding typos and ensuring that if you have to change or fix something, you change it in one place and one place only.. If you find yourself doing a sequence of steps many times in an R script, try writing a function that does what you want to an argument, like:

dataset_2010 = read.csv('work/data/2010.csv')
missing_score = is.na(dataset_2010)
dataset_2010[missing_attainment, 'score'] = mean(dataset_2010$score)
clean_model_2010 = lm(score ~ class + race + background, data=dataset_2010)

# copy and paste, hope that you change all the 2010s into 2011s
dataset_2011 = read.csv('work/data/2011.csv')
missing_score = is.na(dataset_2011)
dataset_2011[missing_attainment, 'score'] = mean(dataset_2011$score)
clean_model_2011 = lm(score ~ class + race + background, data=dataset_2011)

# copy and paste, hope that you change all the 2011s into 2012s
dataset_2012 = read.csv('work/data/2012.csv')
missing_score = is.na(dataset_2012)
dataset_2012[missing_attainment, 'score'] = mean(dataset_2012$score)
clean_model_2012 = lm(score ~ class + race + background, data=dataset_2012)

might become:

clean_and_model_year = function(year){
                         filename = paste('work/data/', year, '.csv', sep='')
                         dataset = read.csv(filename)
                         missing_score = is.na(dataset$score)
                         dataset[missing_score, 'score'] = mean(dataset$score)
                         clean_model = lm(score ~ class + race + background, 
                                          data = dataset)
                         return(clean_model)
                        }
clean_model_2010 = clean_and_model_year(2010)
clean_model_2011 = clean_and_model_year(2011)
clean_model_2012 = clean_and_model_year(2012)