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[AUDIO LOGO]

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YULIA: Hi, everyone.

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My name is Yulia, And I am one
of their preceptors here at CS50.

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And today we'll be taking a tour around
R Markdown, which is one of another ways

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to represent our R programs.

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So before, in lectures, while you
were completing your problem sets,

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you might have seen
something like this, which

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is called an R script, which is just
some lines of code combined under dot R

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file.

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And what is going on here, and it
is from the visualizing data week,

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is we're reading in some
votes for the candidates.

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And then we're using a ggplot
function to actually create

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a bar chart of those votes.

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And before we jump into R Markdown,
let's just run these lines of code

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to see what they output.

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So I get an error that
I can't find ggplot,

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and that's because I haven't
imported my library yet.

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So let's do that together.

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I'm going to import library(ggplot2),
and now when I run my code,

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I see it over here on the right, a
nice bar chart of the number of votes

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that Bowser, Mario, and Peach received.

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But as you can tell, this
is not a very efficient way

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to present this data to a user.

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And this is where R Markdown steps in.

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So R Markdown is often
used for cases where

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we want to [INAUDIBLE] data
analysis or report where

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we combine our code, some text,
and maybe some visualizations

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together under one file.

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So to do that let's actually
jump into votes.Rmd file

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right here that I've already created.

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And before we actually get to
the text and the code part,

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let's create our header.

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And to do that, I'm going to create some
space for it using the three dashes.

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In between them, I'm going
to set some parameters.

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The first one being the title, and
I'm going to call it "votes analysis."

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The second one is
author, which is my name.

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And the last one is the output format.

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So there are several ways in which
you can Knit or sort of like render

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your R Markdown, one of them being HTML.

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And that's what we're
going to talk about today.

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But you can also Knit it to
a PDF or a Word document.

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It just might require installation of
some other libraries prior to that.

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So I'm going to set it to
HTML document right here.

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And now that our header
is done, we can actually

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get to the body of our R Markdown.

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So as I mentioned before, it
consists of some code and some text.

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And while the code is the
simple part, we already

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have it pre-written right
here, how do we actually

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include some text into our file?

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Well, as I said, and as
may be clear from the name,

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we use markdown language for it.

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And so, as you would do in
other files or in other setups,

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we use hashtag for our titles.

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So for example, one hashtag
will indicate the biggest title.

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And so I'm going to call it CS50R.

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And then two hashtags, you can
think about it as a smaller title

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or a subtitle, and I'm going
to call it "Data Visualizing"

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because that's the week that we're on.

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And then after that, I'm going to use
three hashtags to indicate sections

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of my code that I will be writing.

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So the first section will be
loading the necessary libraries.

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And the reason I need
to do that is, remember

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just a minute ago in the console when
we tried to run our ggplot function,

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it didn't recognize it because
we didn't load the library.

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And while we already did
in the console, and it

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might work when we
run it just here right

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now, when we actually try to Knit it,
our compiler won't recognize that.

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So we need to make sure we include
it in the R Markdown, as well.

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And for this, I'm going to
create our first code chunk.

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To do that, you can click Option
Command-I on your keyboard

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if you're using a Mac.

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And this creates this gray box
where you can put your code,

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and it will actually be recognized
as code and not just as plain text.

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So here, I'm going to
import the library ggplot2.

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And nice thing about R Markdown,
is that not only everything runs

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when you Knit it, but
you can also run it

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as you are coding in R Markdown itself
by clicking this Play button right here.

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And again, we just
import library ggplot2.

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Now that we've imported our
library, let's go back to votes.R

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and actually copy our first
line of code, which is just

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reading in the CSV of votes.csv.

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So I'm going to title my next
section "Loading the Data."

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and again, by clicking Option Command-I,
create another other chunk of code

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copying votes--

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assigning into votes,
read.csv votes.csv,

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I can run this line by
clicking Command Return,

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and now we can see in our environment,
we actually have right here,

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we actually see that
we have our data frame.

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And not only can you run things in R
Markdown, you can also output them,

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but you don't need explicitly
to call the print function.

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In fact, if you just write
the name of the variable

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that you would like to
print out or output below,

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you can just write it on the last
line of code, click the Play button,

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and it will be outputted right
under that chunk of code.

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All right, so I'm going
to delete this for now.

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And next, what we're going to do
is actually display our bar plot

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from the previous file.

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So I'm going to title this
section, "Displaying Data,"

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create my chunk of code again, and then
just copy over the ggplot function right

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here, perfect.

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And so ideally, if we run this, we
should see our plot outputted right here

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on the bottom.

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So the same plot that we've
seen before, but it kind of

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goes sequentially, which
will be nice when we actually

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Knit it and see the outputs.

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And actually, before we
proceed, let's do just that.

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So I'm going to click this
button right here that says Knit,

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and it automatically Knits to HTML,
which is my preferred output method.

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And so here, we can see all the
titles that we've created before.

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So CS50.R, Data Visualizing is our
subtitle, and all the other parts

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our headings.

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And you can see that I deleted my
last line of code that said votes.

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So I actually don't see
the data frame outputted,

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but I see this line of code
that actually loaded it in.

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And if I scroll to the very
bottom, I can actually see the plot

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that I've seen before in my app.

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All right, let's go back and actually
talk about some other things.

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So, so far, I've just been adding
some titles and some chunks of code,

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but let's actually try
to add in some text.

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And nice thing about R Markdown, is that
it recognizes all characters as text,

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unless you put it in this code chunk.

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So if I start typing
something like, Here I

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am going to include electoral data--

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let's be fancy --for
Mario and his friends.

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Nice thing about it, it's going
to be recognized as friends.

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It's not going to try in and look for
a variable called Mario or friends.

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This is just plain text, and that's it.

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And let's add some features to it.

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So for example, what if I wanted
to make electoral data in italics?

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I can do that by putting some
underscores in front and after it.

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And maybe I wanted to bold
"Mario," and for that,

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I can use these two asterisks before
and after to make it look bolder.

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So let's Save it and Knit it
just to see what it outputs.

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And right here, I can see that my text
is included, in fact, as plain text,

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and my "Mario and his
friends" are bolded.

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"Electoral data" is in fact in italic.

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That's nice.

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And let's go back to our code
and add in some extra things.

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So in the beginning, I mentioned that R
Markdown is helpful for data analysis,

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right?

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Well, we loaded our data, we created
a bar chart, we wrote in some tags,

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but let's actually add in
a conclusion in the end.

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For that, I'm again going to create a
section using this three hashtag called

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"Conclusion."

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And here, I'm going to write
something like, In the end,

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Mario received X votes.

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Beach received X votes and
Bowser received X votes.

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And I'm leaving it as X
because I'm not really

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sure how many votes it got, right?

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I'm looking at the bar chart,
and I can kind of tell,

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but I don't know the precise numbers.

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So here comes another nifty
thing about R Markdown,

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is that you don't actually need a whole
code chunk to call for some variable.

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You can actually do it inline.

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And to do that, can use two backticks,
and write "r" inside of them.

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So now this chunk will
be recognized as R code.

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And inside of here, I'm going to
call votes, which is my data frame.

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I'm going to grab column votes and
then grab the first value of it.

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And I'm just going to do the
same for Peach and Bowser.

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So votes, dollar sign,
votes, second index.

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And for Bowser, it will be votes,
dollar sign, votes, third index.

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Let's make sure that we
have all the syntax correct,

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and let's actually try to
Knit it, fingers crossed.

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OK, and right here on
the very, very bottom,

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you can see that Mario, in
fact, received 100 votes.

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Peach received 200 votes and
Bowser received 150 votes.

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Even though I did not type
in those explicit numbers,

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R markdown recognizes that I'm
actually looking inside my data frame

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and trying to grab
those values from there.

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Let's go back and add
in some other things.

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So not only you can just grab a specific
value, you can use some functions

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and compute values inside these
tiny R inline code chunks.

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So maybe I want to know how many total
votes were casted in this election.

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So I can say something,
The total number of votes

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was, again, using my
inline R code chunk.

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And I'm going to call a
sum function, and I'm just

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going to sum up all the votes
that appear in my votes column.

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And I'm going to make them
bolded, just so that the numbers

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stand out a little bit more.

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And I'm going to do that by using two
asterisks before and after, just like we

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did on the top and right here, as well.

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Save it, and if I Knit it, I should see
all of these numbers on the very bottom.

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So you can see that my
numbers are now bolded.

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And I also summed up all
the votes in the very end.

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Let's go back and see just one more
thing that we can do with R Markdown.

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So before, when we Knitted, we would
see, for example, both the plot

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and the code.

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But what if I just want
to see the plot, right?

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I'm not really interested in
how I got to the plot itself

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but rather just seeing
the visualization.

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So to do that, you can actually
change the specific settings

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of that code chunk.

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And so right here, what I'm going
to say is, echo equals false.

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Essentially, what it will do, is it will
say, OK, I want to see the output of it.

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I want to see the plot,
but I don't really

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want to see the code in
my final Knitted file.

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And so if I Knit it
again, and I go here,

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I see this blank data,
which is a good sign.

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This is where my section is.

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I see the plot, and
I don't see the code.

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That is what I wanted.

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I didn't want to see those few
lines of code with all the details.

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I just want to see the final
result. OK, that's cool.

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And maybe let's add in one more thing.

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So you notice how I have
different sections, my conclusion,

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displaying the data, loading
the data, loading the libraries.

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And it can get really tedious
having so many different headings,

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and maybe you have like a
pages and pages of things.

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So cool thing about R
Markdown, you can automatically

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create a table of contents,
that will be generated up

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top, that are actually hyperlinks.

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And you can click on
each of those sections,

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and it will automatically
take you to that part.

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So to do that, I'm actually going
to make some changes to our header.

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So I'm going to say that
my output is HTML document,

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but inside that HTML document, I also
someone to include Table Of Contents,

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or TOC for short.

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And I'm going to set it to true.

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So now I actually want to
have my table of contents.

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And if I Knit it, I see, on
the very top, my bullet points.

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And it cascades in the same way in which
you assign hash tags to those titles.

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So you can see that says CS50R
had only one hashtag, so it

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means it's the biggest one.

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It's on top.

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Data, visualizing is kind of
like my big theme of this week,

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so it comes as the second subtitle.

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And after that, I just
have my sections where

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I actually broke down my program into
parts and actually wrote some code.

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And so if we click on conclusion,
it takes us to the very, very end.

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So to summarize, we've
seen some cool things

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we can do with R Markdown,
including text, headers,

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generating table of contents, as well
as figuring out what lines of code

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you want to show on your final
Knitted file, what lines of code,

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you don't want to show.

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And there are lots of
other things that you

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can do with R Markdown,
including different features

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and other different markdown
styles that you can include,

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which we definitely don't
have time to cover today,

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but I encourage you to
look into it yourself.

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I hope this was helpful, and I hope
you learned something new today.

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