1. Boot Camp R Fall 2013
PT651 Lab Component
Slides by Sherri Verdugo

2. • Brief definition of statistics: The subject of
statistics deals with techniques for collecting,
analyzing, and drawing conclusions from data. In
science, or any subject, even psychology, you
must know how to read the literature or how to
interpret data. Statistics is your key, to knowing
when you are given false data--or, for example, if
that new medication really works. Definition is from
Statistical Methods, Eighth Edition by George
W. Snedecor and William G. Cochran.
Statistics
Brief description.

3. R Boot Camp and Descriptive Information
Agenda
Introduction to the Lab Component
Wrap Up
R: The basics and Rstudio; The basics
Descriptive Statistics & R
Revisiting the Blog and Installation
Parameters, Statistics oh my….the magic behind the curtain
Measures of Variability & R
✓
1
2
3
4
5
6
7

4. Installing
on
your
desktop
means
that
the
environment
is
local
on
your
machine!
Installing
on
the
Cloud
means
you
are
portable
and
accessible!
• The Blog
Installation tips
- http://g2research.blogspot.com/2013_08_01_archive.html
ü PC, Mac, Unix, etc.
• Desktop or Cloud
R & R Studio
-R on the Desktop
ü R on the Cloud
The Blog and Installation of R
Revisiting the Past

5. Desktop versus the cloud
R
R is available locally on your computer and
depends on the size of your computer!
R is available on the internet! Fast!
Rstudio and R are located on the cloud.
Cloud
R is located on local machine. Drawbacks:
only in one environment.
Desktop
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6. A sample represents a population. Dependent Variables are cheaper and easier to change J
The magic behind the curtain.
Parameters, Statistics, oh my!
1
Population characteristics.
Parameters
Measures of sample data.
Statistics
3
Rank order of data values for a
variable.
f = Frequency Distribution
Caveat:
Class: grouping of scores w/ unique range
Independent Variables: costly or nearly
impossible to change. I.E.
Dependent Variables: outcome variables.
Mutually Exclusive: no overlap in classes
Mutually Exhaustive: each score fits into a
class
2
Descriptive Statistics: ways to represent data using characterization of
central tendency, shape and variability in the data.

7. Three Measures of Central Tendency
Measures of Central Tendency:
central value or a typical value for a probability distribution
M3
Which score changes the most when you change the
scores in the data?
Frequencies tell a story as well!
3
The most frequent score of the sequence.
Mode
The average of all scores. Is the sample mean and µ is the population mean.
Mean
1
The middle number in a sequence. Place the numbers in order. For N=odd number then this is the exact
mid point. For N=even number add the two middle numbers together and divide by two.
Median
2
X
X =
x1 + x2 +...+ xn
n
µx = ∑ xP(x)

8. Variability = dispersion of scores.
Adding the deviations results in zero? That’s not what I need. NEW CONCEPT: Sum of Squares (SS).
Percentiles = the grouping of the scores totaling 100%. What percentile is a score in the data set?
Quartiles: Q1=25%, Q2=50%, Q3=75%, Q4=100%
Standard Deviation = how much variation from the mean or what we expect the value to be.
Range = difference between highest and lowest scores.
Variance = how far a set of numbers are “dispersed” or spread out.
Coefficient of Variation = unitized risk expressed as a ratio.
Do you see any patterns?
Name five measures of variability
Variability
1
2
3
4
5
6
7
sx =
(xi − x)2
i=1
n
∑
n −1
sx
2
=
(xi − x)2
i=1
n
∑
n −1
cv =
σ
µ
SS = (x − x)2
∑
Range = Xhighscore − Xlowestscore
cv =
σ
µ
*100

9. Tip: Remember help() and google/google scholar < This is the command prompt
Examples in R using the Cloud or Desktop
• How can we find the descriptive statistics
in R?
• Look at R scripts for: 1) mean, 2) median,
3) mode, 4) range, 5) percentiles, 6)
variance, 7) standard deviation, and 8)
coefficient of variation
• Class Exercise: 1_descriptives_general.R
Descriptives & R Continued

10. • #Descriptive Statistics: General Entry Level Exercise
• 2+2;f=2+2;f #R is a calculator and you can store an answer:
• a=4;a; c = a*2; c#Solve an equation and store variables
• fun1=((a+c+2)/2);fun1#Result is 7 (4+8+2)=14 and this is 14/2 = 7
• #Step 1: generate a few random numbers to look at
• rnorm(10, mean=1.2, sd=3.4);s=rnorm(10, mean=1.2, sd=3.4);s
• me=mean(s); me #Mean
• med=median(s);med #Median
• table(s);names(sort(-table(s)))[1]#Mode. Not interesting because we don't have any duplicates
• ra=range(s);ra#range low and high scores
• mran=max(s);mran;miran=min(s);miran
• ran=mran-miran;ran#we are running the range from the extremes of the data (hi and lo)
• qua=quantile(s);qua#quantiles
• per=quantile(s, c(.32, .57, .98));per#percentiles
• varia=var(s);varia#variance
• stds=sd(s);stds#standard deviation
• stdsc=sqrt(varia);stdsc#double check the std. deviation :)
• coefs=((stds/me)*100);coefs# sanity check
• library(raster);cv(s, na.rm=TRUE); #double check with a package.
Descriptive Statistics & R
An Example Script.

11. Can we normalize scores? Of course we can.
Descriptive Statistics and R
Normal…we want normal for now J Work through the tutorial J
tYes!
1) Samples from a normal
distribution = distribution of
sample means are normal.
2)The mean of the distribution
of sample means = the mean
of the "parent population," the
population from which the
samples are drawn.
3)The higher the sample size
that is drawn, the "narrower"
will be the spread of the
distribution of sample means.
Can we do this in R?
1
Central Limit Theorem
This is the point where you
should see the pattern
between the previous slides
and the new concept of the
CLT. If you feel like it
doesn’t make sense, that is
normal (Yes, I like that word
a lot!)…. However, you can
use this to work through
statistical problems….and
solve complex problems
with relative ease.
Parts of the puzzle have
been given to you.
Sanity Check!
Distribution of many many
trials is normal….even if the
distribution of each trial is
not normal!
http://www.learner.org/
courses/mathilluminated/
units/7/textbook/06.php
Why is this important?
You can work with the law
of large numbers…
Approximations.
32

12. It might not be normal from the population. It
might however be normal using the Central Limit
Theorem and that means that 68.26% will fall ± 1
s.d. from the mean.
Standard scores (i.e. z-scores) represent scores
distances from the mean. When the mean and
standard deviation are known….you can have a
z-score attached to it. Think IQ tests, SAT, GRE,
MCAT…etc.
To solve any mystery…you are looking for
patterns. Statistics is a tool that gives you the
ability to look for a pattern in a sample that
represents a portion of the population.
Dispersions of the scores
Measures of Variability & R
2
Patterns.
Can we
Standardize?
Is the Population
Normal???

13. Looking at Real Data that is “Clean”
Measures of Variability & R
Nothing is impossible with R. If you can’t figure it out…think logically. What is it that you want to do and then readdress
the steps that you have taken so far.
• Clean Data
Has no missing values
-Homework Examples
ü Extremely rare in the real world….
• Real Data
Has missing values
-Can be handled by R
ü Big steps involved in cleaning the data set….

14. Statistics…your guide to the universe
Every Number tells a story
Clean Data
Collect Data
Design a Study.
Analyze Data
Present Data
Conclusions J

15. Your own sub headline
Wrap Up
3
These have to be loaded before you use them. If you have to use them….make sure you comment on
them in the code so that you can use them again later.
Library or Packages
R Packages have templates ready for you to learn with…some packages are better than others.
Templates
1
Present the descriptive information for the data set. This is key when you are presenting inferential
statistics in a journal, report, homework, or even understanding the data you are reading about.
Always…Always…Always
2

16. Have productivity anywhere you have internet.
Taking it to the Cloud
When you are in a clinical setting…time is of the
essence. You are dealing with patients and the
information you present must be accurate.
Population
Sample
Information
Patient
Information

17. THANK YOU!
That wasn’t so hard J Remember you are now
programming! Who needs Excel or SPSS now?
Free is sometimes better in more ways than one!