0:00
This lecture is not so much about reproducible research, per se.
But it's more about how you can communicate your results, in a way
that people can understand them, and can check your results, if they need to.
So particularly, when you're doing a large data
analysis, or, you know, a complex kind of project.
And you do your work in a reproducible manner.
You're going to generate a lot of content and a lot of files and a lot of
output and a lot of data. And those all cannot be presented at once.
And so when you present your, your kind of findings to another person you're going to
have, there's going to, there's a kind of
hierarchy of information that you're going to want to present.
Ranging from kind of least specific to most specific.
So the bottom line is that, you know, most people are, these days are very busy.
People like managers and
organization leaders are going to be very busy.
And so they are going to have to kind of accept content that you present.
At a, at a kind of a, at a, at a, at a, in a kind of hierarchical matter.
So the idea is that, you know, many times, when you, when you present a
data analysis, it will be in an oral form like a presentation or in a meeting.
But often the kind of early results or intermediate
results are, are presented via, via a medium like email.
2:00
The next will be the abstract, which would be, usually
be a couple hundred words, about what the paper's about.
You know, what motivated the problem
what they did to solve the problem.
What the, kind of the bottom line results were.
So that's only going to be in the abstract.
Then of course you have the paper itself.
You know, the body of the paper will show you, will have the, you know, the methods.
And more details about what you really did.
There will be the more detailed results any sensitivity analyses you
know, things can, things like that will be in the paper.
And a much longer discussion of the implications of the results.
2:32
But of course,
even the written paper these days, for
a complex data analysis, doesn't specify, really, all
the details that need to be used to need, that are needed to reproduce the findings.
So then you typically might have some supplementary de, materials which
have a lot more of the details about what was done.
And, and then if you really, really want to
kind of do what was done to a level of
precision that is kind of useful, you probably need
to get the code, the data and all the
gory details of kind of what happened.
So that's kind of like the, the range of things that you
might be able to present from least specific to most specific, so
not everyone, of course, is going to be writing a research paper,
but there is an analog to kind of most presentations of data analysis.
And so, if you're going to be you know, mailing results to a person.
Either a colleague, or a manager.
then, you know, the, the first line of information is the
subject of the email, all right, so the subject of the
e-mail is kind of like the title.
And you want to have it, kind of concise, you want to
be descriptive, and at a minimum, you want to have one, all right?
So don't leave it, don't, don't send an e-mail without a
subject, so it's not specific as to what the e-mail is about.
If you can summarize kind of what you've done in one
sentence, the, then it may be useful put that in the subject.
That way people reading the subject can kind of get a sense
of what's happening and maybe even make a decision based on that alone.
The next level of information of course is the email body.
And you, you don't want to go, even though there's no technical limit
on the size of the email body, you don't want to go too crazy
with this, But you want to have, you know provide a brief description of
the problem maybe if someone doesn't, you know, if the person may not remember.
If they're working on many different things at
once, they may not remember what the problem
is, precisely what you're working on, so give
them a little context, give them a little description.
Recall, you know, if there was a meeting previously.
You know, talk about what was
proposed and, you know, and what you actually did.
Summarize some of your findings and your results and maybe, you know, for
a total for one to two paragraphs in this whole, the email body.
If you need this, the person that you're presenting the
information to to take some sort of action based on these,
the results of this presentation, then you should try to
suggest some options that and make them as concrete as possible.
Alright. So and
if there are questions that need to be addressed,
if you want them to kind of comeback with
an answer, it's usually best to try to make
them yes no questions or as simple as possible.
4:49
After the email body, of course, you might be able
to attach something that's quite a bit longer, a PDF file
or another type of document, and so this can be like
a report containing more detailed
analyses, figures, tables, things like that.
You can, this can be derived from an R markdown file.
You can use it, you know, something that you create with knitr.
But of course, even in the report like this where you may
be allowed, you know, a couple of pages to, to present things,
you still want to stay concise, you don't want to spit
out pages and pages of code and tables and results.
5:21
We know that your code is available because if you
use something like knitr then obviously, you have to kind
of put the code in with the results, but you
don't necessarily have to present it all in the report.
Your opportunity to do something like that of course
will be, if, if you, if you have someone who
really wants to look at what you put, what you
did precisely, you can give em a link to some
sort of repository, like GitHub, or a project
website that would have all of the details.
Or all your code files.
All your data sets.
All your, all that kind of, all the nitty gritty like that.
All your, your software environment, things like that.
And so.
So that's kind of the level of, different levels
of detail that you might want to present to someone.
And so people who are truly interested in everything
that you did, you know, or really want to know
kind of the details, they might go to your
GitHub repository and start pulling your code and start looking
at it and start looking at the detailed results.
But someone who wants kind of top line
or I should say, you know, top line summaries.
You know, they'll, they'll read the email, they'll
look at the subject, they'll read the brief description.
And might flip through the report.
And so different, the point is that different people are going to
be interested in different levels of details of, about what you've done.
So you want to be, and so you want to
present people with those different levels, so that they might
be able to choose the level that they are most interested in.
And so this is just kind of a generic template for kind of how you
might present a an analysis or a data, or a project that you've worked on.
Not every presentation will kind of need all
these different levels but I think that, I
find this to be a useful breakdown of
the different types of presentations that you can make.
Roger D. Peng, PhDAssociate Professor, Biostatistics
Jeff Leek, PhDAssociate Professor, Biostatistics
Brian Caffo, PhDProfessor, Biostatistics
