## 1
source("/Users/Julia/Documents/sourcefile.R", echo = TRUE)
##
## > UCBAdmissions
## , , Dept = A
##
## Gender
## Admit Male Female
## Admitted 512 89
## Rejected 313 19
##
## , , Dept = B
##
## Gender
## Admit Male Female
## Admitted 353 17
## Rejected 207 8
##
## , , Dept = C
##
## Gender
## Admit Male Female
## Admitted 120 202
## Rejected 205 391
##
## , , Dept = D
##
## Gender
## Admit Male Female
## Admitted 138 131
## Rejected 279 244
##
## , , Dept = E
##
## Gender
## Admit Male Female
## Admitted 53 94
## Rejected 138 299
##
## , , Dept = F
##
## Gender
## Admit Male Female
## Admitted 22 24
## Rejected 351 317
##
##
## > str(UCBAdmissions)
## table [1:2, 1:2, 1:6] 512 313 89 19 353 207 17 8 120 205 ...
## - attr(*, "dimnames")=List of 3
## ..$ Admit : chr [1:2] "Admitted" "Rejected"
## ..$ Gender: chr [1:2] "Male" "Female"
## ..$ Dept : chr [1:6] "A" "B" "C" "D" ...
## 2
problem2 <- read.table("/Users/Julia/Documents/problem2datafile.dat", header = TRUE,
sep = ".", as.is = TRUE, na.strings = c(".", "Unknown"))
head(problem2)
## id county age sex syndrome date.onset date.tested death
## 1 1 San Bernardino 40 F WNF 05/19/2004 06/02/2004 No
## 2 2 San Bernardino 64 F WNF 05/22/2004 06/16/2004 No
## 3 3 San Bernardino 19 M WNF 05/22/2004 06/16/2004 No
## 4 4 San Bernardino 12 M WNF 05/16/2004 06/16/2004 No
## 5 5 San Bernardino 12 M WNF 05/14/2004 06/16/2004 No
## 6 6 San Bernardino 17 M WNF 06/07/2004 06/17/2004 No
## date.onset.international date.tested.international
## 1 2004-05-19 2004-06-02
## 2 2004-05-22 2004-06-16
## 3 2004-05-22 2004-06-16
## 4 2004-05-16 2004-06-16
## 5 2004-05-14 2004-06-16
## 6 2004-06-07 2004-06-17
## 3
data(ToothGrowth)
ToothGrowth <- transform(ToothGrowth, len.centered = len - mean(len))
ToothGrowth
## len supp dose len.centered
## 1 4.2 VC 0.5 -14.61333
## 2 11.5 VC 0.5 -7.31333
## 3 7.3 VC 0.5 -11.51333
## 4 5.8 VC 0.5 -13.01333
## 5 6.4 VC 0.5 -12.41333
## 6 10.0 VC 0.5 -8.81333
## 7 11.2 VC 0.5 -7.61333
## 8 11.2 VC 0.5 -7.61333
## 9 5.2 VC 0.5 -13.61333
## 10 7.0 VC 0.5 -11.81333
## 11 16.5 VC 1.0 -2.31333
## 12 16.5 VC 1.0 -2.31333
## 13 15.2 VC 1.0 -3.61333
## 14 17.3 VC 1.0 -1.51333
## 15 22.5 VC 1.0 3.68667
## 16 17.3 VC 1.0 -1.51333
## 17 13.6 VC 1.0 -5.21333
## 18 14.5 VC 1.0 -4.31333
## 19 18.8 VC 1.0 -0.01333
## 20 15.5 VC 1.0 -3.31333
## 21 23.6 VC 2.0 4.78667
## 22 18.5 VC 2.0 -0.31333
## 23 33.9 VC 2.0 15.08667
## 24 25.5 VC 2.0 6.68667
## 25 26.4 VC 2.0 7.58667
## 26 32.5 VC 2.0 13.68667
## 27 26.7 VC 2.0 7.88667
## 28 21.5 VC 2.0 2.68667
## 29 23.3 VC 2.0 4.48667
## 30 29.5 VC 2.0 10.68667
## 31 15.2 OJ 0.5 -3.61333
## 32 21.5 OJ 0.5 2.68667
## 33 17.6 OJ 0.5 -1.21333
## 34 9.7 OJ 0.5 -9.11333
## 35 14.5 OJ 0.5 -4.31333
## 36 10.0 OJ 0.5 -8.81333
## 37 8.2 OJ 0.5 -10.61333
## 38 9.4 OJ 0.5 -9.41333
## 39 16.5 OJ 0.5 -2.31333
## 40 9.7 OJ 0.5 -9.11333
## 41 19.7 OJ 1.0 0.88667
## 42 23.3 OJ 1.0 4.48667
## 43 23.6 OJ 1.0 4.78667
## 44 26.4 OJ 1.0 7.58667
## 45 20.0 OJ 1.0 1.18667
## 46 25.2 OJ 1.0 6.38667
## 47 25.8 OJ 1.0 6.98667
## 48 21.2 OJ 1.0 2.38667
## 49 14.5 OJ 1.0 -4.31333
## 50 27.3 OJ 1.0 8.48667
## 51 25.5 OJ 2.0 6.68667
## 52 26.4 OJ 2.0 7.58667
## 53 22.4 OJ 2.0 3.58667
## 54 24.5 OJ 2.0 5.68667
## 55 24.8 OJ 2.0 5.98667
## 56 30.9 OJ 2.0 12.08667
## 57 26.4 OJ 2.0 7.58667
## 58 27.3 OJ 2.0 8.48667
## 59 29.4 OJ 2.0 10.58667
## 60 23.0 OJ 2.0 4.18667
## 4
FamilyFriendBday <- c("1/5/1989", "2/20/1988", "3/7/1947", "3/16/1910", "4/19/1988",
"6/15/1988", "8/2/1947", "9/11/2013", "9/22/1989", "9/23/1988", "10/26/1988",
"10/30/1987", "11/13/1988", "12/10/1987")
FamilyFriendBday.julian <- as.Date(FamilyFriendBday, format = "%m/%d/%Y")
Julian <- julian(FamilyFriendBday.julian)
FamilyFriendBdaydata <- data.frame(DOB = FamilyFriendBday, `DOB Standard Format` = FamilyFriendBday.julian,
Julian = Julian)
FamilyFriendBdaydata
## DOB DOB.Standard.Format Julian
## 1 1/5/1989 1989-01-05 6944
## 2 2/20/1988 1988-02-20 6624
## 3 3/7/1947 1947-03-07 -8336
## 4 3/16/1910 1910-03-16 -21841
## 5 4/19/1988 1988-04-19 6683
## 6 6/15/1988 1988-06-15 6740
## 7 8/2/1947 1947-08-02 -8188
## 8 9/11/2013 2013-09-11 15959
## 9 9/22/1989 1989-09-22 7204
## 10 9/23/1988 1988-09-23 6840
## 11 10/26/1988 1988-10-26 6873
## 12 10/30/1987 1987-10-30 6511
## 13 11/13/1988 1988-11-13 6891
## 14 12/10/1987 1987-12-10 6552
## 5
schistodata <- matrix(c(347, 13, 467, 38), 2, 2)
dimnames(schistodata) <- list(`Swam in lake` = c("Yes", "No"), Schistosomiasis = c("Positive",
"Negative"))
schistodata
## Schistosomiasis
## Swam in lake Positive Negative
## Yes 347 467
## No 13 38
library(epitools)
oddsratio.wald(schistodata)
## $data
## Schistosomiasis
## Swam in lake Positive Negative Total
## Yes 347 467 814
## No 13 38 51
## Total 360 505 865
##
## $measure
## odds ratio with 95% C.I.
## Swam in lake estimate lower upper
## Yes 1.000 NA NA
## No 2.172 1.14 4.139
##
## $p.value
## two-sided
## Swam in lake midp.exact fisher.exact chi.square
## Yes NA NA NA
## No 0.01483 0.01854 0.01601
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "Unconditional MLE & normal approximation (Wald) CI"
## 6
schistototals <- apply(schistodata, 1, sum)
schistodata2 <- cbind(schistodata, Total = schistototals)
swamtotals <- apply(schistodata2, 2, sum)
schistodatafinal <- rbind(schistodata2, Total = swamtotals)
names(dimnames(schistodatafinal)) <- c("Swam in lake", "Schistosomiasis")
schistodatafinal
## Schistosomiasis
## Swam in lake Positive Negative Total
## Yes 347 467 814
## No 13 38 51
## Total 360 505 865
for (i in schistodatafinal) {
cat(i/865)
}
## 0.40120.015030.41620.53990.043930.58380.9410.058961
## 7
ARandCIR <- function(x) {
a = x[1, 1]
b = x[1, 2]
c = x[2, 1]
d = x[2, 2]
rr = (a/(a + b))/(c/(c + d))
ar = (a/(a + b)) - (c/(c + d))
arpercent = ((rr - 1)/rr) * 100
list(data = x, Cumulative.Incidence.Ratio = rr, Attributable.Risk = ar,
Attributable.Risk.Percent = arpercent)
}
ARandCIR(schistodata)
## $data
## Schistosomiasis
## Swam in lake Positive Negative
## Yes 347 467
## No 13 38
##
## $Cumulative.Incidence.Ratio
## [1] 1.672
##
## $Attributable.Risk
## [1] 0.1714
##
## $Attributable.Risk.Percent
## [1] 40.2
## 8
ts.plot(ldeaths, mdeaths, fdeaths, gpars = list(xlab = "year", ylab = "deaths",
main = "Monthly Deaths from Lung Diseases in the UK, 1974-1979", lty = 1,
lwd = 2, col = c("black", "skyblue", "pink")))
legend(1974, 4000, legend = c("both genders", "male", "female"), lwd = 2, lty = 1,
col = c("black", "skyblue", "pink"))
## 9
state <- data.frame(state.name, state.region)
state
## state.name state.region
## 1 Alabama South
## 2 Alaska West
## 3 Arizona West
## 4 Arkansas South
## 5 California West
## 6 Colorado West
## 7 Connecticut Northeast
## 8 Delaware South
## 9 Florida South
## 10 Georgia South
## 11 Hawaii West
## 12 Idaho West
## 13 Illinois North Central
## 14 Indiana North Central
## 15 Iowa North Central
## 16 Kansas North Central
## 17 Kentucky South
## 18 Louisiana South
## 19 Maine Northeast
## 20 Maryland South
## 21 Massachusetts Northeast
## 22 Michigan North Central
## 23 Minnesota North Central
## 24 Mississippi South
## 25 Missouri North Central
## 26 Montana West
## 27 Nebraska North Central
## 28 Nevada West
## 29 New Hampshire Northeast
## 30 New Jersey Northeast
## 31 New Mexico West
## 32 New York Northeast
## 33 North Carolina South
## 34 North Dakota North Central
## 35 Ohio North Central
## 36 Oklahoma South
## 37 Oregon West
## 38 Pennsylvania Northeast
## 39 Rhode Island Northeast
## 40 South Carolina South
## 41 South Dakota North Central
## 42 Tennessee South
## 43 Texas South
## 44 Utah West
## 45 Vermont Northeast
## 46 Virginia South
## 47 Washington West
## 48 West Virginia South
## 49 Wisconsin North Central
## 50 Wyoming West
western.states <- state[grep("West", state$state.region), ]
western.states
## state.name state.region
## 2 Alaska West
## 3 Arizona West
## 5 California West
## 6 Colorado West
## 11 Hawaii West
## 12 Idaho West
## 26 Montana West
## 28 Nevada West
## 31 New Mexico West
## 37 Oregon West
## 44 Utah West
## 47 Washington West
## 50 Wyoming West
newstate <- gsub("West", "W", state$state.region)
state2 <- data.frame(state.name, newstate)
state2
## state.name newstate
## 1 Alabama South
## 2 Alaska W
## 3 Arizona W
## 4 Arkansas South
## 5 California W
## 6 Colorado W
## 7 Connecticut Northeast
## 8 Delaware South
## 9 Florida South
## 10 Georgia South
## 11 Hawaii W
## 12 Idaho W
## 13 Illinois North Central
## 14 Indiana North Central
## 15 Iowa North Central
## 16 Kansas North Central
## 17 Kentucky South
## 18 Louisiana South
## 19 Maine Northeast
## 20 Maryland South
## 21 Massachusetts Northeast
## 22 Michigan North Central
## 23 Minnesota North Central
## 24 Mississippi South
## 25 Missouri North Central
## 26 Montana W
## 27 Nebraska North Central
## 28 Nevada W
## 29 New Hampshire Northeast
## 30 New Jersey Northeast
## 31 New Mexico W
## 32 New York Northeast
## 33 North Carolina South
## 34 North Dakota North Central
## 35 Ohio North Central
## 36 Oklahoma South
## 37 Oregon W
## 38 Pennsylvania Northeast
## 39 Rhode Island Northeast
## 40 South Carolina South
## 41 South Dakota North Central
## 42 Tennessee South
## 43 Texas South
## 44 Utah W
## 45 Vermont Northeast
## 46 Virginia South
## 47 Washington W
## 48 West Virginia South
## 49 Wisconsin North Central
## 50 Wyoming W
## 10
sink("/Users/Julia/Documents/sinkfile.log")
source("/Users/Julia/Documents/sourcefile.R", echo = TRUE)
##
## > UCBAdmissions
## , , Dept = A
##
## Gender
## Admit Male Female
## Admitted 512 89
## Rejected 313 19
##
## , , Dept = B
##
## Gender
## Admit Male Female
## Admitted 353 17
## Rejected 207 8
##
## , , Dept = C
##
## Gender
## Admit Male Female
## Admitted 120 202
## Rejected 205 391
##
## , , Dept = D
##
## Gender
## Admit Male Female
## Admitted 138 131
## Rejected 279 244
##
## , , Dept = E
##
## Gender
## Admit Male Female
## Admitted 53 94
## Rejected 138 299
##
## , , Dept = F
##
## Gender
## Admit Male Female
## Admitted 22 24
## Rejected 351 317
##
##
## > str(UCBAdmissions)
## table [1:2, 1:2, 1:6] 512 313 89 19 353 207 17 8 120 205 ...
## - attr(*, "dimnames")=List of 3
## ..$ Admit : chr [1:2] "Admitted" "Rejected"
## ..$ Gender: chr [1:2] "Male" "Female"
## ..$ Dept : chr [1:6] "A" "B" "C" "D" ...
sink()
## I used the sink function to open a connection and create an output file
## (named sinkfile.log) and then closed the connection. The output file
## prints all that is contained in sourcefile.R