R version 2.14.1 (2011-12-22)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)
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CWD = /Users/chl/Documents/Tutors/R_biomed_fr/pub
> ## adl.r
> ## R --no-save < adl.r > adl.log
> ##
> ## Shows basic R processing wrt. SPSS syntax available in `adl.spss`.
>
> ## Importation des données, en enlevant les ID et le sexe
> library(foreign)
> adl <- read.spss("adl.sav", to.data.frame=TRUE)[,-c(1,3)]
>
> ## On examine les 6 premiers enregistrements
> head(adl)
group age los diabetic hypertns afib priorstr smoker psd
1 Treatment 67 18 No No No No No No
2 Treatment 75 17 Yes Yes No No No Yes
3 Treatment 66 17 No No No No No No
4 Treatment 67 15 No No No No No No
5 Control 75 21 No No No No Yes No
6 Treatment 74 17 No No No No No No
travel cooking
1 Gets out if someone else drives Some cooking but less than normal
2 Gets out in wheelchair Does nothing for meals
3 Gets out in wheelchair Gets food out if prepared by other
4 Home or hospital bound Does nothing for meals
5 Home or hospital bound Gets food out if prepared by other
6 Home or hospital bound Some cooking but less than normal
housekpg
1 Occasional dusting of small jobs
2 No longer keeps house
3 Occasional dusting of small jobs
4 As before
5 Never did any
6 No longer keeps house
>
> ## Répartition par groupe de traitement et diabète
> table(adl$group)
Control Treatment
46 54
> table(adl$diabetic)
No Yes
85 15
>
> ## Distribution des âges
> summary(adl$age)
Min. 1st Qu. Median Mean 3rd Qu. Max.
66.00 69.00 71.50 71.76 74.00 91.00
> library(e1071) # skewness and kurtosis
> cat("Asymétrie =", skewness(adl$age), "\t Aplatissement =",
+ kurtosis(adl$age), "\n")
Asymétrie = 1.439285 Aplatissement = 4.335339
>
> hist(adl$age, freq=TRUE)
>
> ## Distribution des âges et durées d'hospitalisation selon le traitement
> boxplot(age ~ group, data=adl)
> boxplot(los ~ group, data=adl)
> with(adl, by(age, group, summary))
group: Control
Min. 1st Qu. Median Mean 3rd Qu. Max.
67.0 69.0 73.0 72.2 74.0 81.0
------------------------------------------------------------
group: Treatment
Min. 1st Qu. Median Mean 3rd Qu. Max.
66.00 68.00 70.00 71.39 73.00 91.00
> with(adl, by(los, group, summary))
group: Control
Min. 1st Qu. Median Mean 3rd Qu. Max.
13.00 17.00 18.00 17.83 20.00 22.00
------------------------------------------------------------
group: Treatment
Min. 1st Qu. Median Mean 3rd Qu. Max.
12.00 14.00 17.00 16.76 18.00 25.00
>
> ## Tri croisé hypertension x group
> xtabs(~ group + hypertns, data=adl)
hypertns
group No Yes
Control 33 13
Treatment 34 20
> library(gmodels) # tableau à la SPSS
> with(adl, CrossTable(hypertns, group, expected=TRUE, prop.r=FALSE,
+ prop.t=FALSE, prop.chisq=FALSE))
Cell Contents
|-------------------------|
| N |
| Expected N |
| N / Col Total |
|-------------------------|
Total Observations in Table: 100
| group
hypertns | Control | Treatment | Row Total |
-------------|-----------|-----------|-----------|
No | 33 | 34 | 67 |
| 30.820 | 36.180 | |
| 0.717 | 0.630 | |
-------------|-----------|-----------|-----------|
Yes | 13 | 20 | 33 |
| 15.180 | 17.820 | |
| 0.283 | 0.370 | |
-------------|-----------|-----------|-----------|
Column Total | 46 | 54 | 100 |
| 0.460 | 0.540 | |
-------------|-----------|-----------|-----------|
Statistics for All Table Factors
Pearson's Chi-squared test
------------------------------------------------------------
Chi^2 = 0.8653119 d.f. = 1 p = 0.3522565
Pearson's Chi-squared test with Yates' continuity correction
------------------------------------------------------------
Chi^2 = 0.5138995 d.f. = 1 p = 0.4734556
>
> ## Test de Student
> t.test(los ~ group, data=adl) # unequal variance (default option)
Welch Two Sample t-test
data: los by group
t = 2.1216, df = 97.549, p-value = 0.0364
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.06890053 2.06475487
sample estimates:
mean in group Control mean in group Treatment
17.82609 16.75926
> t.test(los ~ group, data=adl, var.equal=TRUE)
Two Sample t-test
data: los by group
t = 2.0831, df = 98, p-value = 0.03985
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.05051236 2.08314303
sample estimates:
mean in group Control mean in group Treatment
17.82609 16.75926
>