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My name is Ashish Mahabal and
we'll be looking further at R ding'g.

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So let's start with the assignments.

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We saw last time that you can use
the less than arrow and dash to do

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the assignment that is shown in the second
line here and that is what is preferred.

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So here we show that,
you are assigning 3.14 to z.

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As, we said before doing z =
3.14 is also possible but,

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you should at best avoid that.

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And there are a couple of good reasons for
that and one of the reasons that

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people feel that using two key
strokes is not a good idea but

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most editors that go with r
allow you to use the less

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than dash including the spaces on the side
with the single key stroke so you should.

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Get into that soon in emags
as well as in our studio.

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There are keystrokes available for that.

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But the main reason is that,
equal to gets used for keywords.

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Look at the, next line.

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We saw an example of rnorm before too.

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Here we are taking an rnorm,
100 numbers in that vector and

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there we say mean equal to five.

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So, here the keyword is mean and

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we are saying that we want
the mean of five for that vector.

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Here you cannot use the less than dash
equal to is reserved specifically for

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that and then people tend to get confuse
if you say a equal to rnorm 100 and so on.

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Plus, there are two other things.

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If you want to use global variables,
and we'll be seeing an example in

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the next slide, there you have to
use less than, less than, dash.

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So the next line shows how you
are assigning seven to the vector y.

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More than global, it's actually
an assignment to, the enclosing scope.

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So whatever scope encloses
that particular statement,

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that particular variable gets
that in that particular.

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And what I meant.

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And of course if you are using equal to,

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you still can not use equal to
equal to do the global assignment.

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Because equal to equal is of course for
checking something.

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So z = = 3 is where you
are trying to check for z = 3.

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So that can get quite confusing, so

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you should always try to use less
than dash, for the assignment.

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And then for global of course, you have to
use less than, less than dash and then for

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keywords equal to and for checking
you have to use the two equal tos.

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So, get that get into that habit as
soon as you can and stay that way.

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So this is how, when we use in
closing scope a variable set.

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So if at the prompt, you simply say, bar,

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where you are trying to
find out the value of bar.

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And if bar has not been set before,
then it's going to return an error,

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that, I don't know what bar is.

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But then, what you can do,
is that you can set bar in different ways.

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Here we see also how to
define a simple function.

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And, the function is defined
with the key word function, and

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then it is followed by parentheses.

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And, within the parenthesis you can give
it the arguments that you wanted to give.

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And, the return value is the object foo,
in this case.

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So, that is the name of
the function that we'll be using.

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And all that we are doing within
the function in this particular case is

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setting bar to 1, but
what we are saying is that

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set the bar equal to 1 in the enclosing
variable, in the enclosing scope.

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So if you now come out of the function
definition and if you run foo.

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Then it's going to do that setting for
you.

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And then if you ask what varies,

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the variable's not defined
outside the function.

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You'll get the appropriate value for
bar equal to 1.

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So again less than less
than dash will set the,

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variable in a higher in closing scope.

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And that is what you should do
when you need to do that well.

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Avoiding something like
that is always good,

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but that is the facility
that is available.

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Then again, we can assign entire
vectors using the combine, so

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we saw this example before,

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how you can assign several different
values using combine to variable x.

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So what is actually happening here,
is a specific function called assign is

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being called and
the function assign text to arguments.

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The first argument is the variable name
that you are giving, x in this case, and

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the second argument is a combined set
based on whatever inputs you have given,

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so that is what is into a living core.

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There's another quirk with
assignment that you can do in R and

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that is another thing that you
should avoid as best as possible.

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You can do an assignment on
the right hand side also.

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So you can say combine these five
values for me and use a dash and

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greater than and then a variable in there.

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That'll work equally well as the first,
argument, but

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again, you shouldn't use that unless
you have to for some specific reason.

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And then, you can easily work on entire
vectors as if they were variable,

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so if you have assigned five values
that combine to x, you can do 1 by x and

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get reciprocals of all those numbers.

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Similarly, you can combine
vectors with each other.

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Here in the next line we see that
we took the vector x, a zero, and

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the vector x again, combined them so
we had five values in x and

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this combination is
going to have 11 values.

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So those 11 values can be assigned to y.

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Similarly, you can use the function,
repeat.

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And now I'm trying to assign to v,
a combination of things here.

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I'm saying that repeat x and
add to that y and add to that one.

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So one of course is an atom and

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y is something that has 11 values,
whereas x has five values.

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So what it's going to do,

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is, it's going to try to repeat
x as many times as necessary.

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So that its length equals the longest
of the three arguments on

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the right hand side.

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In this case, y is 11.

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So it'll repeat x2 point 2 times.

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And then it'll repeat one 11 times.

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And so
you know have three sets of 11 values.

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And those would be added as the first
one and the first one and so on, and

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you'll get a vector v, which will,
again, have 11 values.

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[COUGH] And of course you can, combine
many of these things very easily so

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in the next one, we are finding
out variance is, we say that,

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that the mean of x, over five values.

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Then you subtract the mean
from each of the values of

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x squared divided by
the link of x minus 1.

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So, you can combine those easily,
write those as functions but

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all those things are available
as various functions or so.

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And remember help is
available whenever you want.

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That's simply saying help
sum is going to tell you,

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what it is that sum is actually doing.

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Another important construct is an R frame.

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And an R frame, is easy to read into, so

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you can have a simple
space separated file.

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So here I am talking about a file
that contains simply three lines.

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The first line has num space name,
one space, value two.

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And the next two actually
have some values.

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One space one point one space three and
two space four point four and space four.

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Right?
What you see in

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the table on the right hand bottom.

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So if you have an ASCII
file of that nature,

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then you can read it [INAUDIBLE] into R,
you can simply say read.table.

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And the filename in this case
that I'm using is called foo.

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So I read in the file, foo.

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And I set header equal to true
meaning that the first line there,

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is going to define the names
of the columns for me.

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And once that is done,
x now contains the entire file and

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you can do various things with that.

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Objects is a way to look
at the objects that your R

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current in a location of R knows about and

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if you give it without any arguments just
the empty parentheses then it'll tell you

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about all objects that R knows about
in your current invocation of R.

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But X is where we have
rate our table into, so

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you can also say that tell me what
are the objects available in X.

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And then, what it'll show you are the
names of the columns that you have and

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if you type simply X then the entire
frame will be printed out for you.

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Name1, is one of the variables there.

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If I simply say Name1 at the R prompt and
each of the commands here that

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you see are something that
you can give on the R prompt.

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So if you say simply Name1,

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it's not going to give you anything back
because it doesn't know about Name1.

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The Name1 happens to be
inside the object X.

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And to access objects within another
object, you have to use the dollar symbol.

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So X dollar Name1, is what will
give you the Name1 column there.

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That is where namespaces come in.

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That's another thing that we had, talked
about in the best programming practices so

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you should try not to modify
things by combining namespaces.

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But, if you have to do that or

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if you're going to use only one name
space, you can do that by saying attach X.

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When you do attach X, then the objects
within X become available to you on

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the command line or in the enclosing
environment that you're using.

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And after that, if you say Name1,
then you'll see the column called Name1.

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Redirection, is easy to use in R.

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Just like in Unix,

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you use the less than to redirect from a
file, or greater than to direct to a file.

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Similarly, in R you can use source and
sink.

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So source myfile.R will
read from that file

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already with the commands
that you have given the, and

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sink outfile will write to file
whatever output that you are creating.

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Just like the unix command T,

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you can also sink,
use sink with a keyword called split=TRUE.

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So when you do that,

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you'll see the output on the command
line as well as save it to a file.

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And you can also capture the output,
into a variable,

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instead of a file by using capture.output.

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So here we are saying that if we want to
capture the output of one example a file

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is going to give you, that will be
stored in that particular variable.

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We talked a little bit of,
about Rdata and Rhistory.

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Rhistory saves the commands
that you have been given so

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that you can easily go back to that.

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Unless, list for you radius objects.

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And you can, use this recursively or
in tandem with other objects.

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So the last line here shows
how you can say that,

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assign the output of LS,
the objects that you had.

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To list, then you can remove
everything that is in list.

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Of course, you shouldn't try to do that in
a script, because then whatever you have

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done up to that point in the script,
is going to get erased.

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So in R, everything returns something.

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In that sense, everything is a function.

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Then other things to remember is that
spaces do not matter, but capitalization

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does, so if you are using capitals in some
cases you have to continue using those.

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Parameters often have odd names.

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They can be named,
that's a very good thing,

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but because of the organic nature
of how different packages are,

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some of them can have fairly odd names,
so you should get used to those.

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You can use NA for

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missing data and is.na with empty
parenthesis is a very useful construct.

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So you should,
try to calculate that early on.

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So it does the corresponding test to
check whether a particular point is

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available or not.

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We saw X reading the table.

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There we saw how you can read
it with the header information.

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But you can also specify the column names.

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In the command line itself.

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So all these functions you
should look up the help.

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So help read.table will
give the details of

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a large number of key words
that are available with it.

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If you don't give the names through the
header R through the command line called

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read dot table, you can always give names
later on by simply by saying names of

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X equal to and give whatever names you
want by combining the, combining function.

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Then the large number of standard
functions are available, the plus,

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minus power etcetera are their various
trigonometric once are available and

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then there are the function
like sort.list and order or so.

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The pmax and pmin are interesting
in that they return vectors.

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So, if you had a data frame which
had numbers and you say the max of

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that data frame, it won't worry
about the different columns and

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rows, it'll combine all of them as if it
was a vector and give you a single number.

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On the other hand,

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if you use pmax, then its going to
give you a column, y is maximum.

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And there are various other
such functions available also.

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Square root as you can see here we are
getting a square root of a complex number.

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So it's clearly overloaded.

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Because you can use the same square
root for just ordinary numbers and,

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get it equally well.

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Sequences are another important construct.

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Simply doing 1:30,
gives you the numbers one through 30.

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Again, R starts with one, not with zero so
you should remember that.

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In this case, colon is also a function.

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So, when you say 1:30,
that function is being invoked.

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And, colon binds in a strong way,
so if you do something like assign

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ten to n and then say that I want segments
one going from one, two and minus one,

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using the colon function, then the colon
is going to bind, so you're going to

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get one colon and first and then one bind
will be subtracted from each of them.

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So, if you want to do something slightly
different you may want to enclose n minus

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in two bracket, similarly 2*1:15
is going to give you 2,4 and

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all multiples of two, up to 30.

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So, sequences can also of course take name
parameters you can use from and to and

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by and length, so here you can have
sequence of length 50 when starting from

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minus five with jumps of point two,
that's a trivial thing to do.

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You can also use a repeat with that so
sequence of five we are assigning.

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Add to that a repetition of x times
sequence of five so it's going to take,

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it's going to give you x, and then another
copy of x, then another copy of x.

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Instead of that, if you wanted
repeats of individual elements first,

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then you can do each equal to five
rather than times equal to five.

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So again,
doing just help repeat is going to

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give you all the good
details of what is available.

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Another important construct
is the logical vectors.

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So you can assign to n x, but
only where x is greater than 13.

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So, what this is going to do,

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is it's going to find out which all values
are greater than 13, assign them to TRUE,

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assign TRUE to them, assign FALSE to
the others, and then write the vector n.

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So the vector n is going to have
the same length as the vector x.

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You can similarly do intersection
by using and, union using R, and

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negation using bank.

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Remember also that FALSE becomes zero and
TRUE becomes one when you coerce them and

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you say that, okay, you want numbers
rather than the logical values.

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And then similarly, missing values,
you can use them wherever you want to.

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And that's another very
important aspect of R.

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Indexing is also a very
trivial thing to use.

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Just square brackets and 1:10 is going
to give you the first ten values of it.

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Similarly, if you want to leave
out something, that's easy also.

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Just minus and
then in round brackets 1:5 will do that.

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Replacing missing values.

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That's something that Is needed very
frequently when you deal with data sets.

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So you use the function
is.na(x) that finds out for

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you where that particular thing is TRUE.

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And then, only for those values.

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You assign zero.

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So immediately all the, missing values
will be separate, replaced by zero.

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And then next line shows how absolute y
can be defined in a different way also.

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Now, z assigning to z
something like 6,7,8,

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that vector, and then trying to
replace only one value out of that.

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What it's going to do, is that it'll
make the 6, 7, 8 into 6, 7, 5.

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But when that is done,
a copy of z is first made.

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And in the copy,
you replace the third one with 5.

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And when this is being done,

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actually, the square bracket less than
dash is being used as a function.

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And, so again many many functions are used
in very interesting ways internally.

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Similarly you can go to arrays and
matrices.

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If you simply say c(3,5,100) you are going
to get a vector with those three values.

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But instead of that if you assign
that to dim(z) then you are going to

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get a 3D array with those sizes.

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So you will get an array of size (3,
5, 100) Similarly, you can combine

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radius sections all of 3D arrays and
combine them in a preview version.

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00:16:44,899 --> 00:16:47,570
If you want the entire array,
you can use commas.

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So, square brackets and

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00:16:49,490 --> 00:16:53,350
then separated by empty commas is
going to give the entire array.

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00:16:53,350 --> 00:16:57,129
Next couple of lines indicate
something quite interesting.

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00:16:58,370 --> 00:17:03,610
Here, what we are doing is that we take
the numbers one to 20, we make that

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into an array, and the key word dim tells
us that it should be a four by five array,

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other than, say, a two by ten array.

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And we assign that to x.

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Then we make another three by two array.

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In this case we call it i, and
there we are denoting the values that you

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00:17:21,870 --> 00:17:26,270
want within the array by 1:3, 3:1 and

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00:17:26,270 --> 00:17:31,980
those six values now, we say that
arrange them in a three by two damages.

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And then if you say something like x of
i and assign zeros to those, then what's

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going to happen is that the three by two
array that is in i is like three pairs.

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00:17:45,990 --> 00:17:49,170
And those three pairs
represent three positions, and

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those three positions are looked
up in the array called x and

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00:17:52,710 --> 00:17:55,280
it is those three positions
that are set to zero.

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00:17:55,280 --> 00:17:57,912
So in this particular case,
you can work that out as an exercise.

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00:17:57,912 --> 00:18:03,050
The positions 9, 6, 3 are what
corresponds to what we have set in i,

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00:18:03,050 --> 00:18:06,231
and those values within
x will get set to 0.

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00:18:06,231 --> 00:18:08,630
So, you can do fairly complex
things in that version.

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00:18:08,630 --> 00:18:11,440
And again, for data massaging and

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00:18:11,440 --> 00:18:14,770
data munching these
functionalities are very useful.

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00:18:16,530 --> 00:18:19,400
Now, looking that variant,
what you can do is that you

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00:18:19,400 --> 00:18:23,780
can combine different structures
into a single structure, also.

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00:18:23,780 --> 00:18:27,540
So, here,
we have something called name, Fred.

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00:18:27,540 --> 00:18:28,589
Wife, Mary.

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00:18:28,589 --> 00:18:30,377
Number of children, three.

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00:18:30,377 --> 00:18:33,290
And child ages, a vector.

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00:18:33,290 --> 00:18:36,920
So, we have a vector here,
a number here, and some.

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Strings, those can all be combined into a
list and assigned to something called Lst.

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00:18:41,720 --> 00:18:45,230
You should remember that
these are always numbered.

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00:18:45,230 --> 00:18:51,520
So if you want, the value of, in the
fourth parameter of this particular list,

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00:18:51,520 --> 00:18:54,950
you can say Lst with two
square brackets of four.

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00:18:54,950 --> 00:18:57,300
And then it's going to return you.

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00:18:57,300 --> 00:19:00,790
The vector that is there for child.ages.

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00:19:00,790 --> 00:19:03,640
And if you want the age
of a particular child,

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then you will suffix that list with
two square brackets of four with two.

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00:19:08,840 --> 00:19:11,410
So that'll give you
the second value of seven.

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00:19:11,410 --> 00:19:14,200
If you were to leave out that
second pair of square brackets,

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00:19:14,200 --> 00:19:18,140
that is the enclosing second pair
of square brackets from the four.

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00:19:18,140 --> 00:19:20,120
It's going to return null to you.

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00:19:20,120 --> 00:19:22,400
And then, if you were to ask only for

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00:19:22,400 --> 00:19:24,550
a list of three is going
to give you a number back.

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00:19:24,550 --> 00:19:28,240
And so,
remember that if you want to get to one of

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00:19:28,240 --> 00:19:31,310
the specific elements you can use
the square brackets combined with

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00:19:31,310 --> 00:19:34,880
individual elements within
that particular one.

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00:19:36,500 --> 00:19:38,300
Then there are matrix operations.

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00:19:38,300 --> 00:19:41,610
You can simply do A * B,
element by element matrix product or,

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00:19:41,610 --> 00:19:43,280
you can do matrix multiplication.

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00:19:43,280 --> 00:19:48,800
Again, look at the slightly quirky
base this is used,% * % for

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00:19:48,800 --> 00:19:49,870
matrix multiplication.

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00:19:49,870 --> 00:19:53,950
And there are a large number of
matrix operations that you can do.

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00:19:53,950 --> 00:19:55,410
I mentioned earlier that re,

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00:19:55,410 --> 00:20:00,370
reading a file read the table that we did,
you can look at help of that and

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00:20:00,370 --> 00:20:04,640
you can see lots of of various
keywords that you can use with that.

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00:20:04,640 --> 00:20:08,900
There are some built in ones to
specifically read.csv, read.csv2 and

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00:20:08,900 --> 00:20:11,498
so on, so you should also look at that.

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00:20:11,498 --> 00:20:14,910
Then we'll be, look,
looking at more details about

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00:20:14,910 --> 00:20:19,440
various other commands and
objects within that.

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00:20:19,440 --> 00:20:21,190
So, attach, we have already seen here.

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00:20:21,190 --> 00:20:23,610
I am simply giving
an example of plotting that.

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00:20:23,610 --> 00:20:25,300
You can read a data set.

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00:20:25,300 --> 00:20:28,490
And then you can attach those
variables within that and

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00:20:28,490 --> 00:20:32,390
you can simply plot two
different variables in there.

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00:20:32,390 --> 00:20:36,650
Pairs is one such example we'll be seeing
more details about plotting later.

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00:20:36,650 --> 00:20:40,890
So I just wanted to give you a hint of
how to use that within a single command.

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00:20:40,890 --> 00:20:44,240
So let's not go into
details of that right now.

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00:20:44,240 --> 00:20:48,190
And, next time we'll be talking
about built in datasets.

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00:20:48,190 --> 00:20:51,800
I could just be bugging and get to
the basic plotting that I hinted at here.