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R Programming Coursera Assignment 17

Functions represent some of the most powerful aspects of the R language.

And they really represent the transition of the user

of R into the kind of programmer of R.

And the basic idea is that you can type the command

line and kind of explore some data, and run some code.

But eventually you'll probably get to the point where

you need to do something a little bit more complex.

A little bit more than, than can be expressed

in a single line or maybe in two lines.

And if you have to do this over and over again, then you're

usually going to want to encode this kind of functionality in a function.

I'm going to talk about functions in three parts here.

First I'll talk just about the basics of how

to write functions and how they are written, in R.

Then I'm going to talk a little bit about lexical

scoping and the scoping rules, in, for the R language.

And then last, I'm going to end with a little example.

So, functions in R are created using the function directive

and functions are stored as R objects just like anything else.

So you might have a vector of integers a list of

different things, a data frame, and then you have a function.

So, in particular, R objects, R functions are

R objects that are of the class function, okay?

So, the basic instruction here is that you assign

to some object, here I call it F, the,

the function directive, which will take some

arguments, and then inside the curly braces

there is R, there is R code, which does something that the function does.

So one nice thing about R is that functions

are con, considered what are called first class objects.

So you can treat a function just like you can treat pretty much any other R object.

So importantly, this means that you can

pass functions as arguments to other functions.

This is actually

ver, a very useful feature in statistics. And also functions can be nested.

So you can define a function inside of another function, and we'll

see what the implications of this are we talk about lexical scoping.

So the return value of a function is simply the

very last R expression in the function value to be evaluated.

so, there's no special expression for returning something for a function.

Although, there is a function called Return.

Which we'll talk about in a second.

So functions have what are called named arguments.

And the named arguments can potentially have default values.

So, a lot of these features are useful for when

you're designing functions that, that may be used by other people.

For example, you may have a function that had a lot

of different arguments so you can tweak a lot of different things.

But most of the time, you don't have to change all those different arguments.

You may only care about one or two.

So it's useful for some of the arguments to have default values.

So first of all, there's the formal arguments, which

are the arguments that are included in the function definition.

So if you go back to the previous slide the formal

arguments are the ones that are included inside this function definition here.

The formal's function actually will, takes a function as an input

and returns a list of all the formal arguments of a function.

So not every function call in R makes use of all the formal arguments.

So for example, if a, if a function has ten different arguments you may

not, you may not have to specify a value for all ten of those arguments.

So function arguments can be missing or they

may have default values that are used when they are not specified by the users.

So R function arguments can be matched positionally or by name.

So when, this is very, this is key when

you're writing a function and also when you're calling it.

So for example, take a look at the function sd, which calculates the standard

deviation of, of, of a set of numbers. So sd takes a input x, which is the name

of the argument and which is going to be a vector of data.

And there's a second argument called na.rm and this controls whether

the missing values in the data should be removed or not.

And the default value is for na.rm to be equal to false.

So by default if you have missing data in your, in the, in the set of

numbers for which you want to calculate the

standard deviation the missing values will not be included.

So, here I'm

simulating some data and I'm just simulating a hundred

normal random variables, and there's no missing data here.

So, if I just calculate sd on the vector

it'll give me an estimate of the standard deviation.

If I say X equals my data that's the same thing.

So here I've named the argument but I haven't but otherwise

the data are the same so it'll calculate the standard deviation.

In the first example I didn't

name the argument.

So it defaulted to passing mydata to be the first argument of the function.

So in the next example here, I'm going to name both arguments.

I'm going to say X equals mydata, and na.rm equals false.

That calculates the same thing as before.

Now when I name the arguments, I don't have to put them in any special order.

So for example, I could reverse the order of the argument here.

Say na.rm is equals false first, and then say x

equals mydata second, and that will produce exactly the same

results because I've named the arguments.

Now, what happens if I name one argument and don't name the other?

Well what happens is that the named argument is set, and

you can figure it as being removed from the argument list, and

then any other, any other things that are past will be matched

to the function arguments in the order in which they, they come.

So for example, SD after you remove the na.rm

argument only has one more argument left and so mydata

would be assigned to that argument.

So all these expressions return the same exact value.

So although it's generally, all these expressions are

equivalent, I don't say recommend all of them equally.

So for example, I don't necessarily recommend reversing the order of the

arguments just because you can even though if you name them, it's appropriate.

so, just, just because that can lead to some confusion.

So positional matching and matching by name can be mixed and this

is quite useful often for functions that have very long argument lists.

And so for example the lm function here which

fits linear models to data has this argument list here.

So the first is the formula, the second is

the data And then subset, the weights et cetera.

And you see that the first five arguments here don't have any default value.

So, the user has to specify them.

So the but then the method, the model, the X argument, they all have

default values so if you don't specify

them they will use those values by default.

And so the following two function calls are equivalent.

I could have specified the data first and then the formula and then the model.

And then, and then, and then the subset arguments

or I could specify the formula first, the data second,

the subset and then say model is equal to false.

Now the reason why the first one is okay is

because I, so I matched the data argument by name.

You can imagine that that's kind of taken out of the argument

list now, then Y till the X doesn't, isn't specified by name.

So it's given to the first argument that hasn't already been matched.

And I, in which case that's the formula.

Model equal to false, so that's been matched by name so

I can kind of get rid of that from the argument list.

And then 1 through 100 has to be assigned

to the argument that has not yet already been matched.

So in this case formula was already matched, data was already matched.

And so the next one is subset.

So 1 to 100 get's assigned to the subset argument.

So this is somewhat a confusing way to call lm,

and I don't recommend that you do it this way.

But, I, I wrote it this way just to demonstrate

how positional matching, and matching by name can work together.

A common usage for lm though is the second

version here. Which say lm Y til the X.

So there is a formula there.

And then the next one is mydata, which the

data set which you're going to grab the data from.

The subset argument and then, so the first three arguments,

you know, are commonly specified, every time you call lm.

But then, the rest you may or may not specify and so

you may, if you just want to specify one of the following arguments.

It's easier just to call it out by name.

so, most of the time, the named arguments are useful in the command line.

When you have a long argument list and you want to use the defaults for everything

except for one of the arguments, which may be in the middle or near the end

of the list, and you can't usually, you

know, you can't remember exactly which argument it

is, whether it's the fourth, or the sixth,

or the tenth argument on the argument list.

And so you just call it by name, and that way

you don't have to remember the order of the arguments on

the argument list.

Another example where this comes in handy is for plotting, because

mo, many of the plot functions have very long argument lists.

All of which have default values and you

may only want to tweak one specific argument.

And so it's useful not to have to remember, you know, what

the order of that argument is on the arg, on the argument list.

So function arguments can, can also be partially matched

which is used, mostly useful primarily for interactive work,

not so much for programming.

But when you call a function, if the argument has a very long name

you can match it partially so you can type part of the argument name

and as long as there's a unique match there then it will, the R

system will match the argument and assign the value to, to, to the correct one.

So the, the, the order of the operations that

R uses, first it'll check for an exact match.

So if you name an argument

it'll check, check to see if there's

an argument that, that exactly matches that name.

If there's no exact match it'll look for a partial match.

And then if that doesn't work, it'll look for a positional match.

Coding standards in R are really important becasue they help you, make your code

readable and allow you and other people to understand what's going on in your code.

Now, of course, just like it is with any

other, style whether it comes, when you, you know, whether

it's your clothing or whatever it is, it's difficult

to get everyone to agree on one set of ideas.

But I think there are a couple of very basic, kind

of minimal standards that are important when you're coding in R.

Alright, so I'm just going to talk a little bit about some of

the coding standards, that I think are important to, when you're writing

R code, and I think will help make your code more readable

and more usable by others if that's what you're trying to, to achieve.

So, the first principle that I think is very

important in pretty much any programming language, not just

R, is that you should always write your code

using a text editor and save as a text file.

Okay, so, a text

file is a kind of basic standard.

It usually doesn't have any sort of formatting or any

sort of, kind of special, appearance, it's just text, right?

And usually, typically, typically it's going to be

ASCII text, but if you're, on, in places

outside the US or the UK using non-English

languages there may be other standard text formats.

But the basic idea is that a text format, can be read by pretty much any

basic editing program.

These days, you know, when you're writing something there's a

lot different of tools that you can use to write.

If you're writing a book, or or a webpage or something like that, there's

all kinds of different tools that you can use to write, to write those things.

But you're, when you're writing code, you should always try to

use a text editor, because that's like kind of like the, the

kind of least common denominator, and it makes it so that

everyone will be able to access your code and improve upon it.

The second principle is, which is very

important for readability, is to indent your code.

So indenting is something that's often hotly debated in lots of mailing lists

and other types of discussion groups in

terms of how much indenting is appropriate.

Now I'm not going to talk about that although I do have some recommendations.

But I think the most important thing

is that you understand why indenting is important.

So indenting is the idea that different blocks of code

should be spaced over to the right a little bit more

than other blocks of code so you can see kind of how the

control flow how the flow of the program goes based on the indenting alone.

So coupled with indenting, is the third principle which I think

is very simple which is, limit the width of your code.

So you have indenting it's possible to kind of

indent off to the right forever so you need

to limit on the right hand side how wide

your code is going to be and usually this is

kind of determined by the number of columns of text.

And so one possibility is you limit your text to about 80 columns of

text and then and so that your, the width of your code never exceeds that.

So, let's take a look for, at a quick example here.

So here you can see I've got R Studio open, here

with a simple code file with some R code in it.

And, first of all, let me just mention that

the editor in R Studio is a text editor.

So it

will always save the R files that you write as text format files.

So, so we've already got that kind of handled.

But you can see the indenting scheme here is equal to one space.

So every indent is one space.

And you can see that all the code is

kind of mashed together here on the left hand side.

It's difficult to tell kind of where the if blocks are.

Where the else blocks are.

Where does the function kind of end and begin?

And so the indenting scheme kind of makes the code not

very readable in this case.

So we can change the indenting in R Studio.

If we just go up to the Preferences menu here.

And go to Code Editing.

And let me just change it to four.

And you can see that the column, the margin column is set to

80 characters, so it will show you the margin when you've reached 80 characters.

And so I'm going to select all here with Cmd+A, and then Cmd+I to indent it.

So now you can see that the

indenting is a little bit nicer now.

You can see, kind of, where the function begins and ends, you can see where the

if blocks start and end, and the, kind

of, structure of the program is much more obvious.

So, I'm going to change this one more time though and my, because my personal

preference for indenting is to use eight spaces,

so I'm going to change this to eight.

Hit OK, and select all. Cmd+I.

And now you can see,

I prefer the eight spaces just because it

really makes the structure of the code very obvious.

And the spacing is nice and clear.

And it makes the code very readable in general.

So you can see that indenting is very important.

And the biggest problem you might have is, with the, with, with too little indenting.

If you don't indent at all or if you only use

a very small amount the code becomes kind of very mashed together.

So I recommend at least four

spaces for an indent and I'm pref, I

prefer, you know, eight spaces for an indent, just

because it makes the code much more readable

and spaces it out much nice, much more nicely.

One of the advantages of having something like an

eight space indent, is coupled with an 80 character margin

on the right hand side, is that it forces you

to think about your code in a slightly different way.

So for example, if you have eight space

indents, if you're going to have a for-loop, nested within

another for-loop within another for-loop, every time you nest another

for-loop, for example, you have to indent over eight spaces.

And by the time you get to maybe your fourth nested for-loop you're

pretty much hitting the right hand column at the 80 column margin, right?

And so the nice thing about the eight space

indent, coupled with the 80 column margin, is that it

prevents you from kind of writing very basic, making very

kind of fundamental, kind of mistakes with, with code readability.

So, for example, with an eight space indent and 80 column

margin, you might not be able to do feasibly more than

two nested for loops, and, but I think that's really the,

kind of, the boundary of what is readable in terms of code.

Typically except for some special cases, a three, you

know, a three nested or four nested four loop is

difficult to read, and it's probably better off, you

know, splitting off into separate functions or something like that.

So a good indenting policy not only

makes the code more readable, but it actually can force you

to think about writing your code in a slightly different way.

And so that's a really nice advantage of, of having a logical

indenting policy with, coupled with a, you know, a right-hand side restriction.

Alright.

So the last thing I want to talk about is to limit the length of your functions.

Alright so, functions in R can, can theoretically go on for quite

a long time and of course just like in any other language but

just like in any other language I think that the, the logical thing

to do with a function is limit it to kind of one basic activity.

So for example, if you're function's named read the data.

Then your function should simply read the data, it should not read

the data, process it, fit a model, and then print some output, alright?

So you should, the logical kind of steps like

that, should, should probably be spit, split, into separate functions.

There are a couple of advantages to doing this.

First of all, it's nice to be able to

have a function written on a single page of code,

so you don't have to scroll endlessly to see,

you know, where all the code for this function goes.

If you could put all the function, the entire function on like one screen of the

editor, then you can look at the whole function and see what it does all at once.

Another advantage of splitting up your code into logical sections,

to logical functions, is that if you use functions like traceback,

or the profiler, or the debugger, these often tell you, you know,

where in the function call stack you are when a problem occurs.

And if you have multiple functions that are all logically divided

in to separate pieces then when a bug occurs and you know

that it occurs in a certain type of function or a certain

function then you know kind of where to go fix things, right?

So if you have, but if just have a single function that just goes

on forever and a bug occurs then the only thing that the debugger or

the traceback or the profiler can tell you

is that there's a problem in this one function.

But it, it doesn't, it, it's difficult to tell you where exactly the problem occurs.

So splitting up your functions has a secondary benefit, which

is that it can help you in debugging and profiling.

So limiting the size of your functions is

very useful for readability and for, kind of, debugging.

Of course, it's easy to go overboard and

having, you know, a hundred different three-line functions.

So that's not really what

you want to do.

So you just want to make it so that the, the separation of different functions

into, is logical, and that each function

kind of does, does one thing in particular.

So those are my basic guidelines for writing code in R.

There are, of course, many other things that you might be able to think about.

But then we start bordering into areas that

we might, we might kind of disagree on.

And so I'm not going to talk about too much more

in terms of coding standards, but the basic ideas are always

use a text editor, always indent your code, I'd say at least four spaces.

Limit on the right hand side how, how wide your code can be.

And and always limit the size of your functions, so that you

can, so that they're, kind of grouped into logical pieces of your program.

So with those four things, I think you'll,

your, your code will be much more readable.

It'll be readable to you, it'll be readable to others, and it'll make kind

of writing R code much more useful to everyone.

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