Posted by: Gary Ernest Davis on: June 11, 2015

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For programming purposes, one should think of a function as a type of object that takes certain types of INPUTS, and – once those inputs are given – produces a definite OUTPUT.

A simple example is the AVERAGE function, for which the input is a list of numbers

and the output is the average

We can think of the AVERAGE function schematically as follows:

The “object” that acts as input to the function is a finite list of numeric data. The “object” that is output by the function is a single numeric value. The function itself is also an “object”.

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You can define functions yourself in R using the **function** command. When you create a function it is an **object**, that can be used in R like any other object.

A function in R has 3 components:

- The function body. This is the entire code inside the function.
- The function arguments. These are the types of input needed for the function to work. They are specified in the body of the function.
- The function environment. This is a specification of the location of the inputs to the function.

The structure of a function will typically look like this:

functionname<-function(*argument1, argument2, … *){computations using the arguments}

In this example we use a function to compute the fraction of data points, from a given data set, that lie within standard deviations from the mean.

We INPUT a vector of numerical data and a positive number .

Note that the input vector is a single object in R: it has numerical components, yet so far as R is concerned it is a column vector (= an ordered list).

We OUTPUT a single number: the fraction of data points, from, that lie within standard deviations from the mean of .

In specifying this function we will do something that is very common – we will make use of other, existing R functions, such as **mean** and **standard deviation** (among others).

Let’s name the function** zsf** (for “z-score function”).

So our definition will begin:

zsf<-function(*argument1, argument2, … *){computations using the arguments}

and we have to fill in the arguments and the computations to get the output.

The function arguments will be a column vector and a numeric value. Let’s call the data vector argument **data**, and the numeric argument * k*:

zsf<-function(data*, *k){computations using the arguments}

To do the computation in the body of the function we have to compute the **mean** and **standard deviation** of the input data set:

mean(data), sd(data)

Note that the functions **mean** and **sd** are part of the R stats package, which loads automatically when you open R.

Now we have to carry out the remaining computations that will give us the output:

- First, we extract from the data those data points that are within
*k*standard deviations of the mean*m*. We do this using the built-in**subset**and**abs**function:

subset(data, abs(data-mean(data))<=k*sd(data))

- Then we want the ratio of the size of this data subset to the full data set:

length(subset(data, abs(data-mean(data))<=k*sd(data)))/length(data)

Stringing this together, the final form for the function is:

zsf<-function(data*, *k){length(subset(data, abs(data-mean(data))<=k*sd(data)))/length(data)}

To realize this function in R we first need to import a data set. Here is a data set consisting of birth weights, in ounces, of babies of mothers who did not smoke during pregnancy, taken from the Stat Labs Data Page, and formatted as a .txt file: **nosmoke**

By right-clicking on the “nosmoke” file link you can get the URL of this file, which is *http://www.blog.republicofmath.com/wp-content/uploads/2015/06/nosmoke.txt*

We import this data into R, name it “nosmoke” and define the z-score function as above, and use it to compute what fraction of the data is within 1 standard deviation of the mean:

- First, the
**read.table**command reads in the nosmoke data, which has no header row, and for which we have blank separators:

> nosmoke<-read.table(“http://www.blog.republicofmath.com/wp-content/uploads/2015/06/nosmoke.txt”, header=FALSE, sep=””)

- However, when R reads the data into memory in this format, it is a
**data frame**and not a simple column vector. You can see this by entering:

> str(nosmoke)

to get the structure of the data frame “nosmoke”. You will see in the structure of the data frame a header for the birth weights. In our case the header, given by R, is ‘V1’:

‘data.frame’: 742 obs. of 1 variable:

$ V1: int 120 113 123 136 138 132 120 140 114 115 …

We can extract the column headed ‘V1’ as follows:

> nosmokedata=nosmoke[[‘V1’]]

The “nosmokedata” is now a column vector, and we can, for example, plot a histogram of this data:

> hist(nosmokedata)

- Now we define the z-score function:

> zsf<-function(data,k){length(subset(data,abs(data-mean(data))<=k*sd(data)))/length(data)}

- And then we use the function to calculate what fraction of the data is within 1 standard deviation of the mean:

> zsf(nosmokedata,1)

[1] 0.7277628

So, approximately 72.8% of the data is within 1 standard deviation of the mean.

Given a data set, such as the “nosmokedata” above, we can vary *k* and plot the fraction of data within *k* standard deviations of the mean, as simply a function of *k* (with the data as a given: in other words, with the data argument fixed).

- First we need to calculate a potential range of values for the variable
*k*.

We calculate:

> (mean(nosmokedata)-min(nosmokedata))/sd(nosmokedata)

[1] 3.911052

and

> (max(nosmokedata)-mean(nosmokedata))/sd(nosmokedata)

[1] 3.043495

and so see that all the data lies within 4 standard deviations of the mean.

- We then create a vector of possible values for
*k*using the**seq**function:

> values<-seq(0, 4, by=4/20)

Here we have chosen the range from 0 through 4, and divided that into 20 equally spaced intervals. A readout shows us the computed range of k values:

> values

[1] 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 4.0

We check that “values” is a vector:

> is.vector(values)

[1] TRUE

- Now we apply a function of 1 variable,
*k*, to this vector of values.

First we define the function of the variable *k*:

> func<-function(k){zsf(nosmokedata,k)}

Then we use the function **lapply** to create a list as a result of applying this function of 1 variable to the vector “values”, and we **unlist** the result to get a data frame:

> y=unlist(lapply(values, func))

- We combine these two data frames with the
**cbind**function, to get a data frame of*k*values paired with*zsf(nosmokedata, k)*values, and then plot that data frame:

> pairs<-cbind(values,z)

> plot(pairs,type=”b”,main=”zsf(k) as a function of k”, xlab=”k”, ylab=”zsf(k)”)

The plot allows us to see visually how the proportion of data within *k* standard deviations of the mean varies with *k.*

Note: the option type=“b” in the plot function draws both points and lines.

A skilled R programmer could have done this quicker and slicker: I hope I have laid out enough information so that you can see how functions are defined, can be applied to vectors, and plotted to give useful visual information.

Having defined a function such as the **zsf** function above, we would like to save it for future use.

Customizing R at* Quick R* byRob Kabacoff has a useful discussion on loading user-defined functions into R on startup.

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- User-written Functions from
*Quick R*by Rob Kabacoff - Functions from
*Advanced R*by Hadley Wickham

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