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0:00
Hello. I'm Tom MacFarland
and this video lecture
is on the topic
Graphics in Biostatistics.
I will be talking about
the use of graphics
and how they are used
in biostatistics,
but I should mention
at this point
all of the graphics that you
will see in this lecture
were prepared using
the R language.
This is my contact information.
I'm Tom Mac tommac@nova.edu.
0:33
The point that I want
to make in this lecture
is that graphics are
used in biostatistics,
and they are used to
tell a story about data.
I'm going to highlight a few
of the many graphics
that are used in biostatistics.
Certainly not all but many.
The most common ones,
and a few of the graphics
that are up and coming,
we'll see how much they
show in the literature.
Once again I use the R language
to prepare all of
these graphics.
Very briefly, I'll mention
that R is open-source freeware.
It grew out of a prior
language called S.
S was developed in the mid 70s.
Then after evolution
of the software,
it eventually reconfigured
into what is now
called the R language.
I'll make a few minor comments
now and then about R,
but the use of R itself
is the purpose of
another lecture,
not this lecture.
1:37
Before you look at the use of R
or any other software program
to prepare graphics
and biostatistics,
let me step back very briefly
and mention we're
dealing with data,
and data can take
many different types.
There's character data.
Here I list the three presidents
of the United States:
Washington, Adams,
and Jefferson.
Notice how those names
are enclosed in double quotes
to show that they're characters.
There's also integers.
The numbers 1, 3, and 5.
As integers,
these numbers are
being used many times
with R and other
languages too as codes.
You might have the
code for female 1
and code for male 2 integers.
We also deal with numbers,
numeric data.
The number 12.3,
the number 45.6,
the number 78.9.
These are numbers.
You might have what
looks to be an integer,
the number 12,
when really it's a number 12.0.
The .0 is not expressed,
but it is still being used
in a numeric fashion.
We also have logical or
what's called Boolean data.
The typical ones would be
did the subject die
or did the subject live?
One of two conditions.
Was the outcome false
or was the outcome true?
One of two conditions.
Then you also have dates.
There are many other
data types beyond this,
but these are the ones
that you will most
frequently encounter.
If you do start preparing
graphics in biostatistics,
I will mention dates
can be very tricky
to work with because
they are presented
in so many different ways.
July 4, 1776 could
also be expressed
as 1776-07-04.
There are many other
ways that same date
could be expressed in
R and other languages.
