Lecture1.Intro - Medical University of South Carolina

Report
Computing for Research I
Spring 2014
Lecture 1: January 8
Primary Instructor:
Elizabeth Garrett-Mayer
Introduction
•
Description: Students learn to use the primary statistical software packages for
data manipulation and analysis, including (but not limited to): R, R Bioconductor,
SAS, and Stata. Additionally, students will learn: how to use the division's high
speed cluster-computing environment, how to practice the principles of
reproducible research using Sweave in R, how to use LaTeX and BibTeX for
manuscript and presentation development, and how to create and maintain a
website. This is a three credit course.
•
Course Organization: This course is given by faculty and students in the
Department of Public Health Science. Instructors will take turns giving lectures in
their areas of expertise. Dr. Garrett-Mayer is the primary instructor and the course
director.
•
Textbooks: No textbook. Reading material (primarily found on the web) will be
provided as necessary.
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Prerequisites: Biometry 700
Evaluation
• Grading: Instructors will give short exercises to be completed and turned
into the primary instructor by the Wednesday of the week following when
it was assigned (e.g., assignments given on Mon Feb 3 and Wed Feb 5 are
both due on Wed Feb 12). Each assignment will count equally towards
75% of the course grade. There will be a final project which will account
for the remaining 20% of the course grade. The remaining 5% of the
course grade will reflect class participation.
• Homeworks Policy: Homeworks are due by 5pm on the due date. All
homeworks should be emailed to the primary instructor
([email protected]) or turned in at lecture time. Asking for extensions
on homeworks is discouraged. However, it is expected that, on occasion,
extenuating circumstances may arise. Therefore, the policy is that each
student may request an extension on homework twice and the extension
is to be no more than 2 days. After using two extensions, no more
extensions will be granted except with a medical note.
Classroom Etiquette
• Attention to material: Laptops are permitted in class, but it is expected
that if they are used, it is to follow along with the lecture. Email and web
browsers should not be visited during class time. Checking phones during
lecture is not acceptable. The instructors are giving their time and
expertise. Be respectful and give them your attention.
• Classroom disruptions: Many of us have small children and others who
we need to be able to be in contact with during lectures. It is acceptable
to bring pagers or cell phones to class. Please be sure they are on silent
mode. If you need to leave during lecture to take a phone call, or make a
phone call, please do so. However, this should be a relatively rare
occurrence. Texting and emailing during lecture time is not acceptable.
• Violations of classroom etiquette policies will result in a 0 for
class participation.
Contact
Primary Elizabeth Garrett-Mayer
Instructor:
Website: http://people.musc.edu/~elg26/teaching/statcomputing.2014/statcomputingI.2014.htm
Contact Info: Hollings Cancer Center, Rm. 118G
[email protected] (preferred mode of contact is email)
792-7764
Time: Mondays and Wednesdays, 9:00am-10:30am
Location: Cannon 301
Office Hours: By appointment
Teaching
Assistant: Liqiong Fan
Office Hours: The primary instructor will be available by appointment. Liqiong
Fan will also have office hours. However, given the nature of the course, the
primary instructor (or TA) may not be knowledgeable regarding all of the topics
covered. As a result, additional help may be needed to complete assignments
from the lecturers. Be considerate and responsible in scheduling time with
course instructors and recognize that they all have busy schedules.
Course Objectives
Upon successful completion of the course, the student will be
able to
• Import, perform simple analyses and produce graphical
displays in Stata, SAS and R
• Create new functions or commands in each of R, Stata and
SAS
• Generate professional quality scientific manuscripts and
presentations using Latex along with statistical software
• Perform standard power and sample size calculations using
available software and simulations.
• Operate the division’s cluster computer with batch
computing
Schedule, briefly
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SAS
data mangement
STATA
website design
R
sample size/power calculations
Batch processing
Latex + Sweave
Detailed Schedule
Date
W Jan 8
M Jan 13
W Jan 15
W Jan 22
M Jan 27
W Jan 29
M Feb 3
W Feb 5
M Feb 10
W Feb 12
M Feb 17
W Feb 19
M Feb 24
W Feb 26
M Mar 3
W Mar 5
Lecturer
EGM
Fan
Ellerbe
Battenhouse
Foster
Baker
Nicholas
Elm
Wahlquist
EGM
EGM
EGM
EGM
EGM
EGM
EGM
Topic
Introduction; Overview and Principles
SAS: introduction
SAS: IML
SAS: macros
SAS: proc tabulate and proc report
SAS: Gplot
SAS: ODS
SAS: array processing
Data management: RedCap
Data management principles & Excel
STATA: introduction, “immediate” commands
STATA: graphical displays
STATA: exploratory data analysis;
STATA regression commands
STATA: programming and do files
Designing your own website
Detailed Schedule (continued)
Date
Lecturer
Topic
M Mar 17
EGM
R: introduction to object-oriented programming
W Mar 19
Moss
R: downloading packages/libraries; data input & output
M Mar 24
EGM
R: graphics
W Mar 26
Onicescu
R: basic language structure (ifelse, where, looping)
M Mar 31
EGM
R: exploratory data analysis; writing commands
W Apr 2
Wei
M Apr 7
Fan
R: regression commands
R: simulations; random number generation; sampling from
distributions
W Apr 9
Wolf
R: bioconductor
M Apr 14
EGM
Sample size calculation software packages
W Apr 16
Batch processing (using R) and cluster computing
M Apr 21
Ellerbe
Latex and Bibtex: manuscript production
W Apr 23
Hill
KistnerGriffin
Reproducible Research
M Apr 28
Latex and Bibtex: presentations
Alternate lecture: Mendeley
Housekeeping
• We are meeting in a regular classroom
• Bringing laptops is allowed
• Data, code, etc. needed for class will be on the
website prior to class
• For optimal interface, install packages ASAP
– R (http://cran.r-project.org/)
– Stata (DPHS helpdesk request)
– SAS (DPHS helpdesk request)
• Create a bookmark to the course website:
http://people.musc.edu/~elg26/teaching/statcomputing.2014/statcomputingI.2014.htm
Lecture Notes
• Every lecturer will have his/her own style
• Notes may be
– prepared ahead of time and posted
– Prepared and posted after the lecture
– Nonexistent
• Lecture notes will NOT be printed by the
instructors prior to lecture.
• If they are available and you would like a paper
copy, it is your responsibility to print them out.
Introduction
• 2014: to be a successful
biostatistician/epidemiologist, you MUST be
competent on the computer.
• Historically: students learned in labs from (older)
students
• Moving forward:
– many options for analysis and generation of results
– Efficiency in computing is essential.
– Your computer IS your lab!
Data analysis software
• In this course:
–R
– Stata
– SAS
• Many other options:
SPSS
S, Splus
Epi Info
GraphPad
JMP
Matlab
JAGS
Systat
Minitab
EGRET
BMDP
MedCalc
Mathematica
WinBugs
GLIM
….
SAS: History
• SAS was conceived by Anthony J. Barr in 1966. As a North Carolina
State University graduate student from 1962 to 1964, Barr had
created an analysis of variance modeling language. From 1966 to
1968, Barr developed the fundamental structure and language of
SAS.
• In January 1968, Barr and James Goodnight collaborated,
integrating new multiple regression and analysis of variance
routines developed by Goodnight into Barr's framework.
• By 1971, SAS was gaining popularity within the academic
community. One strength of the system was analyzing experiments
with missing data, which was useful to the pharmaceutical and
agricultural industries, among others.
• In 1976, SAS Institute, Inc. was incorporated.
• The latest version, SAS version 9.4, was released in July 2013
SAS: functioning
• SAS consists of a number of components,
which organizations separately license and
install as required.
• Licenses expire! Software cannot be used
after expiration (unless renewed)
Why (or why not) SAS?
• Most commonly used in pharma (although that may be changing!)
• “FDA likes SAS”: truth or myth?
• Many jobs for MS statisticians and/or epidemiologists require SAS
expertise
• The most common language
• Becoming less the choice of academia
– Updates are less frequent than freeware
– ‘pros’ of competitors are starting to outweigh the ‘pros of SAS
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Licensing costs
Slow to add new functionality
Lack of consistency with syntax
Learning curve is slower than other programs that now have similar capability
Stata
• Stata is a general-purpose statistical software
package created in 1985 by StataCorp.
• Most of its users work in research, especially
in the fields of economics, sociology, political
science, biomedicine and epidemiology.
• Relatively simple to learn yet powerful
• Latest version is Stata 13 (released June 2013).
• Lots of add-ons for epi users
Why (or why not) Stata?
• Relatively inexpensive (especially as student or singleuser)
• Biomedical focus: output and functions are tailored to
medical research
• Fast and big: can handle and manipulate large datasets
• Sophisticated with wide range of tools
• Easy to learn language with consistent syntax
• Graphics are not as good as other packages (although
that has improved)
• Programming (simulations, loops, etc.) is more
challenging
R: History
• R is a programming language and software environment for statistical
computing and graphics.
• The R language has become a de facto standard among statisticians for the
development of statistical software, and is widely used for statistical
software development and data analysis.
• R is an implementation of the S programming language. S was created by
John Chambers while at Bell Labs. R was created by Ross Ihaka and Robert
Gentleman, and is now developed by the R Development Core Team. R is
named partly after the first names of the first two R authors, and partly as
a play on the name of S.
• R source code is freely available under the GNU General Public License.
• The capabilities of R are extended through user-submitted packages,
which allow specialized statistical techniques, graphical devices, as well
as import/export capabilities to many external data formats.
• A core set of packages are included with the installation of R, with more
than 5000 (as of January 2013) available at the Comprehensive R Archive
Network (CRAN).
• The most recent version is R 3.0.2 released September 2013.
R: functionality
• Freeware: latest version can be installed
anywhere at anytime
• Packages (a.k.a. libraries) that are usercontributed allow additional
features/commands
• Relatively simple interface
• Rstudio provides a nicer interface and is
gaining in popularity.
Why (or why not) R?
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Great for programming and simulations
Handles looping well
Flexible language
FREE!
User-contributed packages included in real-time (i.e., no delay in
their availability)
Most PhD Biostatistics programs teach their students R and
many/most academic statisticians in top programs use R.
Interfaces nicely with other programs such as Latex (Sweave),
WinBugs, C, Emacs.
Can be clunky for data management.
Memory is not as good as SAS and Stata
Quality-control on user-contributed packages not evident
Overview
• Not a question of which one.
• Question is “for my current problem, which
package makes the most sense to use?”
• Each has strengths and weaknesses
Data management
• Analysis of clean data is easy!
• The real world: you will get messy data most of
the time from your colleagues
• Data management tools will help you;
– Deal with messy data
– Set up data capture approaches for your colleagues to
minimize messiness
• Excel, RedCap and general principles of data
management for statistical analysis will be
covered
Example
Patient #
cycle #
0
3
2
0
3
5
534.8
461.6
527.3
148.4
182.8
151.4
9
10.8
11.5
16.4888889
16.9259259
13.1652174
3
0
760.5
214.5
12
17.875
4
0
3
5
359
375.9
475.6
167.3
125.3
116.2
4.3
4.6
4.4
38.9069767
27.2391304
26.4090909
5
0
394.1
163.1
5.7
28.6140351
6
0
3
848.7
1083.6
132.5
203.9
10.8
13.5
12.2685185
15.1037037
7
0
684.6
191.4
8.1
23.6296296
8
0
822.7
219.5
8.9
24.6629213
9
0
486.3
581.3
699.6
561.7
754
198
186.8
42.3
130.4
320.6
5.7
9.6
11.4
6.7
14.4
34.7368421
19.4583333
3.71052632
19.4626866
22.2638889
CR
CR
total ceramide levels S1P levels C18 ceramide S1P/C18
743.6
197.2
9.8
20.122449
625.6
177.9
9.9
17.969697
1
Latex and Sweave
• LaTeX is a document markup language and document preparation
system for the TeX typesetting program.
• The term LaTeX refers only to the language in which documents are
written, not to the editor used to write those documents. In order
to create a document in LaTeX, a .tex file must be created using
some form of text editor. (e.g. WinEdt)
• LaTeX is most widely used by mathematicians, scientists, engineers,
philosophers, lawyers, linguists, economists, researchers, and other
scholars in academia.
• LaTeX is used because of the high quality of typesetting achievable
by TeX. The typesetting system offers extensive facilities for
automating most aspects of typesetting and desktop publishing,
including numbering and cross-referencing, tables and figures, page
layout and bibliographies.
Latex and Sweave
• Sweave is a function in R that enables integration of R code into LaTeX
documents. The purpose is "to create dynamic reports, which can be
updated automatically if data or analysis change".
• The data analysis is performed at the moment of writing the report, or
more exactly, at the moment of compiling the Sweave code with Sweave
(i.e., essentially with R) and subsequently with LaTeX. This can facilitate
the creation of up-to-date reports for the author.
• Because the Sweave files together with any external R files that might be
sourced from them and the data files contain all the information
necessary to trace back all steps of the data analyses,
• Sweave also has the potential to make research more transparent and
reproducible to others. However, this is only the case to the extent that
the author makes the data and the R and Sweave code available.
• New this year: reproducible research lecture (by Dr. Hill) will be
modified. Sweave may or may not be covered.
Sample size and power
• We don’t really use textbook formulas anymore
to do simple power calculations (just like we don’t
really invert matrices by hand when we analyze data).
• There are a number of packages that quickly and
easily perform simple power calculations
• R, SAS and Stata can do some.
• But, packages like Nquery, EAST and PASS do a lot
more.
• In some non-standard settings, simulations are
required to determine power.
Website development
• It is important in this day and age to “market”
yourself.
– allows you to show your best attributes
– makes you multidimensional (e.g., hobbies,
background, etc.)
• It will be important for gaining recognition and
opportunities in your field and for making your
own work available.
• It isn’t hard, but you do need to learn some skills
to set up and maintain your own site.
Before getting started…
• Types of files involved in statistical computing
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Data files
Results files
Command/batch files
Function files
Graphics files
+ more(?)
• TIPS:
– develop a common nomenclature for naming files and
folders
– Organize projects within folders
Organization is key!
• DO NOT overwrite old files (especially data files)
• Save with a new name
– Mousedata.xls (file sent from colleague)
– Mousedata.clean.xls (your clean version of the data)
• Use a consistent approach, but think ahead
– Naming files *.new.* is not a good idea. You may have
a new ‘new’ next week
– Numerics are good, but if you think you may need
more than 9 versions, consider how data2 and data10
would be alphabetized.
Examples
• For each Principal Investigator I work with, I have
a folder
• Within the PI folder, for each project, I have a
folder
• For each time I get a new dataset (or work on a
new grant) for that project, I have a folder named
with month and year
• Example:
I:\\MUSC Oncology\\Kraft, Andrew\\VelcadeTrial\\May2008
I:\\MUSC Oncology\\Kraft, Andrew\\R01 June 2007
Examples
• Within each folder of data analysis or grant
development calculations, I use the same naming
conventions for files:
– Rbatch.R: a set of R commands that implement all of
the computation or analyses
– Rfunctions.R: a set of R functions that are used by
the batch file
– I always save the original data file from the investigator
before making any changes
– I add ‘clean’ to the datafile name and save it as a .csv
before use (e.g. mousedata.clean.csv)
– My Rbatch.R files always include a line sourcing in the
data, including the folder where the data resides.
Friends in Statistical Computing
1. Google is your friend
2. ‘Help’ functions and ‘see also’ links are
your friends
3. ‘examples’ are your friends
4. Your fellow students are your friends
Friends help friends figure out statistical
computing!
Using your noggin
• Example 1:
– SPSS is not included in this curriculum.
– Can you ever use it? YES!
– Will you be able to learn it better and faster after having taken this
course? YES!
• Example 2:
– We will probably not cover the R package nnc (Neareset Neighbor
Autocovariates)
– Does that mean you need to find someone to teach it to you? NO!
– Will you be able to teach it to yourself? YES!
• Example 3:
– None of your instructors are computer scientists.
– Does this mean that they are not qualified to teach you? NO!
– Most of them are self-taught with regards to these techniques
Final Thoughts for Today
• PhD PROGRAMS: THE TRAINING OF INDEPENDENT
RESEARCHERS!
• THIS COURSE WILL POINT YOU IN THE RIGHT
DIRECTION AND PROVIDE A SET OF TOOLS
• IT IS YOUR JOB TO MAKE THEM FIT TOGETHER AND
USE THEM AS A LAUNCHING PAD TO SOLVE PROBLEMS
• Next up: Intro to SAS on Monday!
References
• Some background info on R, SAS, Stata, Latex
and Sweave was all pilfered from Wikipedia.

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