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My name is Ashish Mahabal, and
we'll continue to look at R today.

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Today we'll be looking at classes in R.

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And as we know, R has come from S, and

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the nomenclature that was used in
S has continued with R classes.

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So, S3 and S4 are the two main classes,
that are used in R.

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Google has a style guide on
how to use our classes, and

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they recommend that S3
is much better than S4,

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though S4 ones are more rigorous, and
we'll be seeing more details about that.

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But one important thing that is
said by everybody respected for

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that they say S3 are better than S4 or S4
are better than S3, is that you should try

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not to mix the two classes, because
S4 are definitely more rigorous and

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S3 are more burdening, but again if you
are not expecting some kind of behavior.

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That comes about in S4 and
S3 can lead you to grieve.

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So, there are two more blood types of
classes, the base class where typically

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C is used, and referenced classes where
in-place modification is possible.

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We may not be seeing more details of that,
but the URL that is given here,

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will provide you more details if you
are interested in something like that.

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So let's start with S3 classes,
they have existed for

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long time the coalition [INAUDIBLE]
came about, essentially what

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an S3 class has is a list, and some method
and inheritance that can be done at that.

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And then it's a bit more generic and

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casual very unlike what
a class would be in C++.

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In particular there's no way of checking,
so if you have certain variables

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that exist in your class and
you make a typo and

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during some assignment provide
a different variable name.

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It will happily take that and

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will not even tell you that, that is not
what you had first given, et cetera.

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So, you had to be sure that,
that is what you'll be wanting.

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Let's try to define an anastry class.

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Let's say that we have a few galaxies and
we want to define the class called Galaxy.

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So here,
you see that we have a variable g and

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we make it a vector by
assigning a list to it.

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We provide some named variables.

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Name equal to say NGC 4261, RA such and
such Declination such and such.

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RA and Declination are the two variables
that tell you the position of the object.

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Galaxy in this case on the sky
just like latitude and longitude.

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And then there is a certain magnitude.

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And then once you have defined
this variable called g,

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then you can say that
class of g is Galaxy.

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And just doing these two things
together defines for you that class.

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Now you can go ahead and
check the attributes of the classes.

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G is an example,
you can say attributes of g, and

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then it'll immediately tell you what
are the names of a label with g.

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And those are in this case,
Name, RA, Declination and mag.

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And then it'll go ahead and

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tell you that the class is Galaxy,
that it belongs to the class Galaxy.

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And then you can print info
about the particular object.

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So, if you were to simply say, g,
which is equivalent to, say print g,

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then it will tell you that the name
that is available is NGC 4261.

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The RA value is given there,
the declination value is given there, and

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the magnitude is given there.

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And that the attribute
of the class is Galaxy.

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So, one of the most
interesting thing what we

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alluded to in an earlier talk is how we
can change some of the default methods.

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So, in this case,

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if we wanted to change the print method,
then we can do that fairly easily.

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Here we saw that if you simply say print
GRG, you get the default print values.

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But that's not what we want to print, say.

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Suppose we wanted to take a look at where
the Galaxy is, try to get a sky map of it.

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One can easily do that in R,
using how the matrix can be overloaded or

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modified for this specific case.

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In particular, let's look at one
particular, one cla, one method called

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browseURL, which takes as an argument,
URL and simply goes to that particularly

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URL software, like in other cases,
takes many different arguments, but,

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in this case, we'll just worry about
the single argument called the URL.

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So, let's define a new print class for
our Galaxy.

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To display an image.

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So, the definition of the function
is given on the slide.

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So, if you say print.galaxy,
that is what our method is going to be.

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Print is the default name, and
.galaxy tells us that it is for

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the class galaxy that
we are defining that.

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And the function takes as
an argument just the object for

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which we are going to be talking about,
in this case let's say g.

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And then we define within
the function a base URL,

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which can be the URL
that we want to go to.

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And the URL is going to depend
on two variables in this case,

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the RA and declaration for the galaxy.

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So then we got the name of the Galaxy
which will get printed on the command,

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on the output.

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And then we paste the baseurl, the ra
with its value, the dec with its value.

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And the last command is also
the last argument that's also very

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important separator without any spaces,
because the default is a space.

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So when you paste those together and

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assign that to g_url, then you can simply
call browseURL with that argument g_url.

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And then your define, your print function
has been defined for that class.

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Now, if you look at meters of
this particular class Galaxy,

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then you'll find that you are told that it
does have one method called plain.galaxy.

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And now if you go ahead and
say g, or print of g,

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you will see that the gives
you NGC4261 on the output.

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But at the same time, it opens a browser.

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And in the browser, you in fact
see the position in sky at that

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particular that you have said
that the object g belongs to.

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And that is what you see
on the right hand side.

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This is a slow image of NGC 4261.

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So again, this is an S3 class
that we have defined and

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a specific print method with it.

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If we had misspelled any of the attribute
names, for instance, in declination.

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If we had spelled it with.

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The lowercase of our RA, lowercase ra,
then the program would have accepted that.

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Would not have given us any error.

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But just that there wouldn't
have been any useful output.

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So that is one thing
that you are to remember.

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Now S4 objects, are more rigorous.

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Here, the way they are defined
is with a slightly,

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writing a slightly different syntax.

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Use a set class.

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Here, we are using the class
name to be GalaxyS4.

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And then you have a representation for it.

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Again, we'll use the same variable names.

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But here,
we tell what types those variables are.

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Remember, earlier, when we used with S3.

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When we defined the class, we didn't
have to say anything of that kind.

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We just went ahead and defined the list.

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Whatever that list contained.

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But here, we are to say that
the name is of type character and

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the other three variables
are of type numeric.

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And that's again, all for the definition.

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Then you can say that gS4.

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And assign to it a new GalaxyS4,

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and then you give it the data through
the names and variables there.

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So we're going to see 4260,
same as before, right?

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And then that defines for
you an S4 type of class.

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In this case GS4 is an example.

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So, now if you go in and say gS4,
then it'll tell you that it's an object of

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class GalaxyS4, and now what it
uses are what are called slots.

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So it says that it has four slots.

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One is the name, and RA,
declination and magnitude.

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And these slots, in S4 are accessible
using the at character.

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Remember in S3,
we had the dollar character.

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So, you could have said g$Name and
got back NGC 4261.

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To do that with S4 classes,
what you are to do is gS4@Name.

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And you'll get the same information.

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There are these minor differences, but
the main difference, of course, is error

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checking involvement, the rigorousness
with which the class itself is defined.

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So, what is print after all which
we have managed to in this fashion.

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So, it completely depends on
the context in which it is being used.

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So, if you are simply to
say print at the R prompt,

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you'll be told that it is
a function which takes x's and

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argument and dot, dot, dot indicating
that there are many more arguments.

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And all that is happening is the use
method print is being called.

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And so, when you says print galaxy
without any arguments despite self,

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it does so
how that particular function is defined.

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That is another very useful thing in R.

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If there is a function and if you simply
give the function name, it provides for

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you the party of the function, so
you can take a look at any function that R

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is using at any time,
just by typing the name of that function.

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So here we see the definition R for
[COUGH] function print Galaxy.

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So there are many different prints
available and all you are to do is say

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methods print on the R prompt and you will
get to know that there are over 100 or

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200 prints that are accessible to you.

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The ones with stars are typically
in other named spaces.

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So you may have to do something
specific to get access to those.

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But so take a look at the let's see,

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print.by, which is the 18th one,
the last one in this row.

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Our print.aspell which is thirteenth here.

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So the aspell comes with an asterisks,
which tells you that if you have to

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simply say print out aspell you
won't get imager access to it,

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but you have to do something different.

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When it's print.by,
it's without an asterisk, so

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you can simply say printed by,
and you'll get access to that.

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But aspell, the printer aspell you
don't get access to that directly,

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you can get access to
aspell itself directly.

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What it does here is that R even
has things like spellchecks.

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Just under prompt in the command line
you can simply take take a file and

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check for its spelling very easily.

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The [INAUDIBLE] using R through python,
and

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you can simply, you,
you get a package called rpi2 [INAUDIBLE].

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And you can load it within Python,

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so the way to do it in Python of
course is using the import command.

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So if you say import rpy2.robjects
as robjects, then it

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becomes accessible to you, and here
a simple example of that has been shown.

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So x equal to robjects.IntVector
range ten gives you

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first the numbers there and r.rnorm.

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Rnorm is the family r function
that we have seen many times, we

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are simply taking ten numbers out of that
and calling x element to plot it later on.

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So, this functionality of using r
through python is also possible,

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and with normpi you can use it
in a very effective manner.

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I don't know if some of you noticed, but
when we printed information about the way

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these variables in our Galaxy, the input
to RA was given to five decimal places.

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But when we printed it out,
then it was only to four decimal places.

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So was the fifth decimal place lost?

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Is R not precise?

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That's certainly not so because, if you
see slot of RA here you get only four

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places, but so
where did the last digit vanish?

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So, this is something
about the precision in R.

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The default precision
is at a certain level.

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But you can also insert R to use
a slightly different precision.

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So you could say that I
want to use ten digits, and

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you can say that by saying options,
digit equal, digits equal to ten.

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And that's again, a suggestion,
you can go up to 22.

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So, when does it get used and
when does it not get used?

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So, look at the next example.

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Here, we are saying that
there is a sprint of pi, and

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we want it to 100 decimal places, and

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output is shown there it becomes
inaccurate after about 15 decimal places.

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But does that mean again that R is not
precise after about 15 decimal places?

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No.
Again there it depends on whether you

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are using large numbers or
you are sing very small numbers.

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So the precision can go arbitrary small.

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Well, not arbitrary small but up to 308
into the R of 308, which is fairly small.

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And then you can see by seeing
what the limit of double is,

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the Machine$double that is using.

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And then you can use additional tools
within R to go to quite high precision.

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But then again, when it is that
you made into problems, and

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that is what is shown in the next example.

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So if you are trying to
compare very small numbers.

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Versus significant digits, then some
things that you have to keep in mind

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are that if the differences
are very small, so that the numbers

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start looking constant and they're not
close to zero, then you can be in trouble.

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So the first example here shows that
we are assigning to x1 50 numbers with.

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A mean of one and
a standard deviation that is very small.

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One minus 15.

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Right.

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And why when is where we use something

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where we say that again we want
50 numbers and in this case,

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the mean is slightly different
with the same standard deviation.

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So, if you notice, then one and,
one and, just a small delta.

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Now these two numbers are very close
to each other and not close to zero.

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So, there comparison is going to look
as if it is a series of constants.

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And that is going to throw an error for
you,

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because all the numbers seem constant.

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But in the second example, what we do is
that we assign to x2 numbers that are,

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that have a mean of 0, and again,
a very small sign of deviation.

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And to the second vector,

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we assign numbers that are very
close to zero, mean, but not zero.

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And the same standard deviation.

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In this case if you try to
compare them you'll be fine.

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So again, it depends very much on what
kind of numbers you are trying to

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compare and what kind of
precision that you want with them.

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R will allow you to,
use them in a proper fashion.

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Next time we'll be seeing ggplot and
some advanced plotting.