Design and Analysis of Clinical Study 5. Introduction to R and Statistics

1
Design and Analysis of Clinical Study 5.
Introduction to R and Statistics

• Dr. Tuan V. Nguyen
• Garvan Institute of Medical Research
• Sydney, Australia

2
Outline

• Introduction
• Historical development
• S, Splus
• Capability
• Statistical Analysis
• References
• Calculator
• Data Type
• Resources
• Simulation and Statistical Tables
• Probability distributions

3
History

• S an interactive environment for data analysis
  developed at Bell Laboratories since 1976
• 1988 - S2 RA Becker, JM Chambers, A Wilks
• 1992 - S3 JM Chambers, TJ Hastie
• 1998 - S4 JM Chambers
• Exclusively licensed by ATT/Lucent to Insightful
  Corporation, Seattle WA. Product name "S-plus".
• R initially written by Ross Ihaka and Robert
  Gentleman at Dep. of Statistics of U of Auckland,
  New Zealand during 1990s.
• Since 1997 international "R-core" team of ca. 15
  people with access to common CVS archive.

4
Introduction

• R is "GNU S" A language and environment for
  data manipula-tion, calculation and graphical
  display.
• a suite of operators for calculations on arrays
  matrices.
• a large, coherent, integrated collection of data
  analysis tools
• graphical facilities for data analysis
• a well developed programming language

5
What R does and does not

• is not a database, but connects to DBMSs
• has no graphical user interfaces, but connects to
  Java, TclTk
• language interpreter can be very slow, but allows
  to call own C/C code
• no spreadsheet view of data, but connects to
  Excel/MsOffice
• no professional / commercial support

• data handling and storage numeric, textual
• matrix algebra
• hash tables and regular expressions
• high-level data analytic and statistical
  functions
• graphics
• programming language loops, branching,
  subroutines

6
R and Statistics

• Packaging a crucial infrastructure to
  efficiently produce, load and keep consistent
  software libraries from (many) different sources
  / authors
• Statistics most packages deal with statistics
  and data analysis
• State of the art many statistical researchers
  provide their methods as R packages

7
Data Analysis and Presentation

• The R distribution contains functionality for
  large number of statistical procedures.
• linear and generalized linear models
• nonlinear regression models
• time series analysis
• classical parametric and nonparametric tests
• clustering
• smoothing
• R also has a large set of functions which provide
  a flexible graphical environment for creating
  various kinds of data presentations.

8
R as a calculator

• gt log2(32)
• 1 5
• gt sqrt(2)
• 1 1.414214
• gt seq(0, 5, length6)
• 1 0 1 2 3 4 5
• gt plot(sin(seq(0, 2pi, length100)))

9
Objects

• Primitive (or atomic) data types in R are
• numeric (integer, double, complex)
• character
• logical
• function
• out of these, vectors, arrays, lists can be
  built.

10
R "grammar"

• object lt- function(argument1, argument2, ...,
  argumentn)
• Example
• gt reg lt- lm(y x)
• x 5 x b?ng 5
• x ! 5 x không b?ng 5
• y lt x y nh? hon x
• x gt y x l?n hon y
• z lt 7 z nh? hon ho?c b?ng 7
• p gt 1 p l?n hon ho?c b?ng 1
• is.na(x) Có ph?i x là bi?n s? missing
• A B A và B (AND)
• A B A ho?c B (OR)
• ! Không là (NOT)

11
Reading Data 1 Direct Method

• age insulin
• 50 16.5
• 62 10.8
• 60 32.3
• 40 19.3
• 48 14.2
• 47 11.3
• 57 15.5
• 70 15.8
• 48 16.2
• 67 11.2

gt age lt- c(50,62, 60,40,48,47,57,70,48,67) gt
insulin lt-c(16.5,10.8,32.3,19.3,14.2,11.3,15.5,15.
8,16.2,11.2) gt ins lt- data.frame(age, insulin)
12
Reading Data 2 read.table

• id sex age bmi hdl ldl tc
  tg
• 1 Nam 57 17 5.000 2.0 4.0
  1.1
• 2 Nu 64 18 4.380 3.0 3.5
  2.1
• 3 Nu 60 18 3.360 3.0 4.7
  0.8
• 4 Nam 65 18 5.920 4.0 7.7
  1.1
• 5 Nam 47 18 6.250 2.1 .
  2.1
• 6 Nu 65 18 4.150 3.0 4.2
  1.5
• 7 Nam 76 19 0.737 3.0 5.9
  2.6

gt setwd("c/works/stats") gt chol lt-
read.table("chol.txt", headerTRUE,
na.missing".")
13
Reading Data 3 read.csv

• Bu?c 1 Dùng l?nh "Save as" trong Excel và luu s?
  li?u du?i d?ng "csv"
• Bu?c 2 Dùng R (l?nh read.csv) d? nh?p d? li?u
  d?ng csv
• gt setwd("c/works/stats")
• gt gh lt- read.csv ("excel.csv", headerTRUE)

14
A Simple Session

• sex lt- c("Nam", "Nu", "Nu","Nam","Nam",
  "Nu","Nam","Nam","Nam", "Nu", "Nu","Nam",
  "Nu","Nam","Nam", "Nu", "Nu", "Nu", "Nu", "Nu",
  "Nu", "Nu", "Nu", "Nu","Nam","Nam", "Nu","Nam",
  "Nu", "Nu", "Nu","Nam","Nam", "Nu", "Nu","Nam",
  "Nu","Nam", "Nu", "Nu",
• "Nam", "Nu","Nam","Nam","Nam",
  "Nu","Nam","Nam", "Nu", "Nu")
• age lt- c(57, 64, 60, 65, 47, 65, 76, 61, 59, 57,
  63, 51, 60, 42, 64, 49, 44, 45, 80, 48,
• 61, 45, 70, 51, 63, 54, 57, 70, 47, 60,
  60, 50, 60, 55, 74, 48, 46, 49, 69, 72,
• 51, 58, 60, 45, 63, 52, 64, 45, 64, 62)
• bmi lt- c( 17, 18, 18, 18, 18, 18, 19, 19, 19, 19,
  20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21,
  21, 21, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22,
  23, 23, 23, 23, 23, 23, 23, 23, 24, 24, 24, 24,
  24, 24, 25, 25)
• hdl lt- c(5.000, 4.380, 3.360, 5.920, 6.250,
  4.150, 0.737, 7.170, 6.942, 5.000, 4.217, 4.823,
  3.750, 1.904, 6.900, 0.633, 5.530, 6.625, 5.960,
  3.800, 5.375, 3.360, 5.000, 2.608, 4.130,5.000,
  6.235, 3.600, 5.625, 5.360, 6.580, 7.545,
  6.440,6.170,5.270, 3.220, 5.400, 6.300, 9.110,
  7.750, 6.200, 7.050, 6.300, 5.450,5.000,3.360,7.17
  0,7.880,7.360,7.750)
• ldl lt- c(2.0, 3.0, 3.0, 4.0, 2.1, 3.0, 3.0,
  3.0, 3.0, 2.0, 5.0, 1.3, 1.2, 0.7, 4.0,
  4.1, 4.3, 4.0, 4.3, 4.0, 3.1, 3.0, 1.7,
  2.0, 2.1, 4.0, 4.1, 4.0, 4.2, 4.2, 4.4,
  4.3, 2.3, 6.0, 3.0, 3.0, 2.6, 4.4, 4.3,
  4.0, 3.0, 4.1, 4.4, 2.8, 3.0, 2.0, 1.0,
  4.0, 4.6, 4.0)
• tc lt-c (4.0, 3.5, 4.7, 7.7, 5.0, 4.2, 5.9, 6.1,
  5.9, 4.0, 6.2, 4.1, 3.0, 4.0, 6.9, 5.7, 5.7, 5.3,
  7.1, 3.8, 4.3, 4.8, 4.0, 3.0, 3.1, 5.3, 5.3, 5.4,
  4.5, 5.9, 5.6, 8.3, 5.8, 7.6, 5.8, 3.1, 5.4, 6.3,
  8.2, 6.2, 6.2, 6.7, 6.3, 6.0, 4.0, 3.7, 6.1, 6.7,
  8.1, 6.2)
• tg lt- c(1.1, 2.1, 0.8, 1.1, 2.1, 1.5, 2.6, 1.5,
  5.4, 1.9, 1.7, 1.0, 1.6, 1.1, 1.5, 1.0, 2.7, 3.9,
  3.0, 3.1, 2.2, 2.7, 1.1, 0.7, 1.0, 1.7, 2.9, 2.5,
  6.2, 1.3, 3.3, 3.0, 1.0, 1.4, 2.5, 0.7, 2.4, 2.4,
  1.4, 2.7, 2.4, 3.3, 2.0, 2.6, 1.8, 1.2, 1.9, 3.3,
  4.0, 2.5)
• cong lt- data.frame(sex, age, bmi, hdl, ldl, tc,
  tg)
• attach(cong)

15
Bar Graph

• gt sex.freq lt- table(sex)
• gt sex.freq
• sex
• Nam Nu
• 22 28
• gt barplot(sex.freq, main"Frequency of males and
  females")
• gt barplot(table(sex), main"Frequency of males
  and females")
• gt stripchart(tg, main"Strip chart for
  triglycerides", xlab"mg/L")

16
Histogram, Boxplot

• gt hist(age)
• gt hist(age, main"Frequency distribution by age
  group", xlab"Age group", ylab"No of patients")
• gt plot(density(age),addTRUE)
• gt boxplot(tc, main"Box plot of total
  cholesterol", ylab"mg/L")
• gt boxplot(tcsex, horizontalTRUE, main"Box plot
  of total cholesterol", ylab"mg/L", col "pink")

17
Multiple Graphs

• gt op lt- par(mfrowc(2,3))
• gt hist(tc)
• gt hist(hdl)
• gt hist(ldl)
• gt hist(tg)
• gt hist(bmi)
• gt hist(age)

18
Scatter Plots

• gt plot(tc, hdl)
• gt plot(hdl, tc, pchifelse(sex"Nam", 16, 22))
• gt plot(hdl, tc, pchifelse(sex"Nam", "M", "F"))
• gt plot(hdl tc, pch16, main"Total cholesterol
  and HDL cholesterol with LOEWSS smooth function",
  xlab"Total cholesterol", ylab"HDL cholesterol",
  bty"l")
• gt lines(lowess(hdl, tc, f2/3, iter3),
  col"red")
• gt lipid lt- data.frame(age,bmi,hdl,ldl,tc)
• gt pairs(lipid, pch16)

19
Descriptive Statistics

• gt mean(tc)
• 1 5.414
• gt var(tc)
• 1 1.962045
• gt sd(tc)
• 1 1.40073
• gt summary(cong)
• sex age bmi
  hdl ldl
• Nam22 Min. 42.00 Min. 17.00 Min.
  0.633 Min. 0.700
• Nu 28 1st Qu.49.25 1st Qu.20.00 1st
  Qu.4.167 1st Qu.2.650
• Median 59.50 Median 22.00 Median
  5.425 Median 3.050
• Mean 57.64 Mean 21.38 Mean
  5.333 Mean 3.292
• 3rd Qu.63.75 3rd Qu.23.00 3rd
  Qu.6.545 3rd Qu.4.100
• Max. 80.00 Max. 25.00 Max.
  9.110 Max. 6.000
• tc tg
• Min. 3.000 Min. 0.700

20
Descriptive Statistics by Group, t-test

• gt tapply(tc, list(sex), mean)
• Nam Nu
• 5.554545 5.303571
• gt t.test(tc sex, datacong)
• Welch Two Sample t-test
• data tc by sex
• t 0.6283, df 46.09, p-value 0.5329
• alternative hypothesis true difference in means
  is not equal to 0
• 95 percent confidence interval
• -0.553024 1.054972
• sample estimates
• mean in group Nam mean in group Nu
• 5.554545 5.303571

21
Wilcoxon test

• gt wilcox.test(tc sex, datacong)
• Wilcoxon rank sum test with continuity
  correction
• data tc by sex
• W 355, p-value 0.3629
• alternative hypothesis true mu is not equal to 0
  
• Warning message
• cannot compute exact p-value with ties in
  wilcox.test.default(x c(4, 7.7, 5, 5.9, 6.1,
  5.9, 4.1, 4, 6.9,

22
Test for Two Proportions

• gt fracture lt- c(7, 20)
• gt total lt- c(100, 110)
• gt prop.test(fracture, total)
• 2-sample test for equality of proportions
  with continuity correction
• data fracture out of total
• X-squared 4.8901, df 1, p-value 0.02701
• alternative hypothesis two.sided
• 95 percent confidence interval
• -0.20908963 -0.01454673
• sample estimates
• prop 1 prop 2
• 0.0700000 0.1818182

23
Comparison of Multiple Proportions

• gt female lt- c( 4, 43, 22, 0)
• gt total lt- c(8, 60, 30, 2)
• gt prop.test(female, total)
• 4-sample test for equality of proportions
  without continuity
• correction
• data female out of total
• X-squared 6.2646, df 3, p-value 0.09942
• alternative hypothesis two.sided
• sample estimates
• prop 1 prop 2 prop 3 prop 4
• 0.5000000 0.7166667 0.7333333 0.0000000
• Warning message
• Chi-squared approximation may be incorrect in
  prop.test(female, total)

24
Linear Regression Analysis

• gt age lt- c(46,20,52,30,57,25,28,36,22,43,57,33,22,
  63,40,48,28,49)
• gt bmi lt-c(25.4,20.6,26.2,22.6,25.4,23.1,22.7,24.9,
  19.8,25.3,23.2, 21.8,20.9,26.7,26.4,21.2,21.2,22.8
  )
• gt chol lt- c(3.5,1.9,4.0,2.6,4.5,3.0,2.9,3.8,2.1,3.
  8,4.1,3.0,
• 2.5,4.6,3.2, 4.2,2.3,4.0)
• gt data lt- data.frame(age, bmi, chol)
• gt plot(chol age, pch16)

25
Coefficient of Correlation

• gt cor.test(age, chol)
• Pearson's product-moment correlation
• data age and chol
• t 10.7035, df 16, p-value 1.058e-08
• alternative hypothesis true correlation is not
  equal to 0
• 95 percent confidence interval
• 0.8350463 0.9765306
• sample estimates
• cor
• 0.936726

26
Simple Linear Regression Analysis

• gt reg lt- lm(chol age)
• gt summary(reg)
• Call
• lm(formula chol age)
• Residuals
• Min 1Q Median 3Q Max
• -0.40729 -0.24133 -0.04522 0.17939 0.63040
• Coefficients
• Estimate Std. Error t value Pr(gtt)
  
• (Intercept) 1.089218 0.221466 4.918 0.000154
  
• age 0.057788 0.005399 10.704 1.06e-08
  
• ---
• Signif. codes 0 '' 0.001 '' 0.01 '' 0.05
  '.' 0.1 ' ' 1
• Residual standard error 0.3027 on 16 degrees of
  freedom
• Multiple R-Squared 0.8775, Adjusted
  R-squared 0.8698
• F-statistic 114.6 on 1 and 16 DF, p-value
  1.058e-08

27
Multiple Linear Regression Analysis

• gt mreg lt- lm(chol age bmi)
• gt summary(mreg)
• Call
• lm(formula chol age bmi)
• Residuals
• Min 1Q Median 3Q Max
• -0.3762 -0.2259 -0.0534 0.1698 0.5679
• Coefficients
• Estimate Std. Error t value Pr(gtt)
  
• (Intercept) 0.455458 0.918230 0.496 0.627
  
• age 0.054052 0.007591 7.120 3.50e-06
  
• bmi 0.033364 0.046866 0.712 0.487
  
• ---
• Signif. codes 0 '' 0.001 '' 0.01 '' 0.05
  '.' 0.1 ' ' 1
• Residual standard error 0.3074 on 15 degrees of
  freedom
• Multiple R-Squared 0.8815, Adjusted
  R-squared 0.8657
• F-statistic 55.77 on 2 and 15 DF, p-value
  1.132e-07