Allison Gonzales — Sep 21, 2013, 10:32 AM
## Allison Gonzales
## PH 251D
## Homework 1
## 23 September 2013
##Prob 1.1.
getwd()
[1] "C:/Users/Allie/Documents/MPH Fall 2013/PH 251D/Assignments"
##Prob 1.2.
searchpaths()
[1] ".GlobalEnv"
[2] "C:/Users/Allie/Documents/R/win-library/3.0/knitr"
[3] "C:/Program Files/R/R-3.0.1/library/stats"
[4] "C:/Program Files/R/R-3.0.1/library/graphics"
[5] "C:/Program Files/R/R-3.0.1/library/grDevices"
[6] "C:/Program Files/R/R-3.0.1/library/utils"
[7] "C:/Program Files/R/R-3.0.1/library/datasets"
[8] "C:/Program Files/R/R-3.0.1/library/methods"
[9] "Autoloads"
[10] "C:/PROGRA~1/R/R-30~1.1/library/base"
##Prob 1.3.
x <- 1
y <- 2*x
ls()
[1] "x" "y"
rm(list = ls())
##Prob 1.4.
inches <- 1:12
centimeters <- inches*2.54
cbind(inches, centimeters)
inches centimeters
[1,] 1 2.54
[2,] 2 5.08
[3,] 3 7.62
[4,] 4 10.16
[5,] 5 12.70
[6,] 6 15.24
[7,] 7 17.78
[8,] 8 20.32
[9,] 9 22.86
[10,] 10 25.40
[11,] 11 27.94
[12,] 12 30.48
##Prob 1.5.
degC <- 0:100
degF <- (9/5)*degC+32
cbind(degC, degF)
degC degF
[1,] 0 32.0
[2,] 1 33.8
[3,] 2 35.6
[4,] 3 37.4
[5,] 4 39.2
[6,] 5 41.0
[7,] 6 42.8
[8,] 7 44.6
[9,] 8 46.4
[10,] 9 48.2
[11,] 10 50.0
[12,] 11 51.8
[13,] 12 53.6
[14,] 13 55.4
[15,] 14 57.2
[16,] 15 59.0
[17,] 16 60.8
[18,] 17 62.6
[19,] 18 64.4
[20,] 19 66.2
[21,] 20 68.0
[22,] 21 69.8
[23,] 22 71.6
[24,] 23 73.4
[25,] 24 75.2
[26,] 25 77.0
[27,] 26 78.8
[28,] 27 80.6
[29,] 28 82.4
[30,] 29 84.2
[31,] 30 86.0
[32,] 31 87.8
[33,] 32 89.6
[34,] 33 91.4
[35,] 34 93.2
[36,] 35 95.0
[37,] 36 96.8
[38,] 37 98.6
[39,] 38 100.4
[40,] 39 102.2
[41,] 40 104.0
[42,] 41 105.8
[43,] 42 107.6
[44,] 43 109.4
[45,] 44 111.2
[46,] 45 113.0
[47,] 46 114.8
[48,] 47 116.6
[49,] 48 118.4
[50,] 49 120.2
[51,] 50 122.0
[52,] 51 123.8
[53,] 52 125.6
[54,] 53 127.4
[55,] 54 129.2
[56,] 55 131.0
[57,] 56 132.8
[58,] 57 134.6
[59,] 58 136.4
[60,] 59 138.2
[61,] 60 140.0
[62,] 61 141.8
[63,] 62 143.6
[64,] 63 145.4
[65,] 64 147.2
[66,] 65 149.0
[67,] 66 150.8
[68,] 67 152.6
[69,] 68 154.4
[70,] 69 156.2
[71,] 70 158.0
[72,] 71 159.8
[73,] 72 161.6
[74,] 73 163.4
[75,] 74 165.2
[76,] 75 167.0
[77,] 76 168.8
[78,] 77 170.6
[79,] 78 172.4
[80,] 79 174.2
[81,] 80 176.0
[82,] 81 177.8
[83,] 82 179.6
[84,] 83 181.4
[85,] 84 183.2
[86,] 85 185.0
[87,] 86 186.8
[88,] 87 188.6
[89,] 88 190.4
[90,] 89 192.2
[91,] 90 194.0
[92,] 91 195.8
[93,] 92 197.6
[94,] 93 199.4
[95,] 94 201.2
[96,] 95 203.0
[97,] 96 204.8
[98,] 97 206.6
[99,] 98 208.4
[100,] 99 210.2
[101,] 100 212.0
##Freezing point is 32degF and boiling point is 212degF.
##Prob 1.6.
degCby5 <- seq(0, 100, 5)
degF <- (9/5)*degCby5 + 32
cbind(degCby5, degF)
degCby5 degF
[1,] 0 32
[2,] 5 41
[3,] 10 50
[4,] 15 59
[5,] 20 68
[6,] 25 77
[7,] 30 86
[8,] 35 95
[9,] 40 104
[10,] 45 113
[11,] 50 122
[12,] 55 131
[13,] 60 140
[14,] 65 149
[15,] 70 158
[16,] 75 167
[17,] 80 176
[18,] 85 185
[19,] 90 194
[20,] 95 203
[21,] 100 212
##Prob 1.7.
kg <- 45:90
m <- 1:2
BMI <- kg/m^2
##Prob 1.8.
##Modulus divides the first by the second number and the gives the remainder only.
10%%3
[1] 1
##Integer divide divides the first number by the second number and gives the largest
## integer the first number can be divided into as the answer.
10%/%3
[1] 3
##Prob 1.9.
curve(log(x), 0, 6)
abline(v = c(1, exp(1)), h = c(0,1), lty = 2)
##Ln and e are inverses of each other.
##Prob 1.10.
curve(x/(1-x), 0, 1)
curve(log(x/(1-x)), 0, 1)
##Log(odds) allows the risk to be interpreted
##as a negative or positive association on either
##side of zero.
##Prob 1.11.
##IDU
1-(1-(67/10000))^365
[1] 0.914
##RAI
1-(1-(50/10000))^365
[1] 0.8395
##PNS
1-(1-(30/10000))^365
[1] 0.666
##RPVI
1-(1-(10/10000))^365
[1] 0.3059
##IAI
1-(1-(6.5/10000))^365
[1] 0.2113
##IPVI
1-(1-(5/10000))^365
[1] 0.1669
##ROI
1-(1-(1/10000))^365
[1] 0.03584
##IOI
1-(1-(0.5/10000))^365
[1] 0.01808
##These probabilities do make sense because the most risky acts have a higher probability
## of becoming infected.
##Prob 1.12.
source("C:/Users/Allie/My Documents/MPH Fall 2013/PH 251D/Jobs/job01.R")
##The variables were created in my workspace, but there is no output in the console.
source("C:/Users/Allie/My Documents/MPH Fall 2013/PH 251D/Jobs/job01.R", echo = TRUE)
> inches <- 1:12
> centimeters <- inches * 2.54
> cbind(inches, centimeters)
inches centimeters
[1,] 1 2.54
[2,] 2 5.08
[3,] 3 7.62
[4,] 4 10.16
[5,] 5 12.70
[6,] 6 15.24
[7,] 7 17.78
[8,] 8 20.32
[9,] 9 22.86
[10,] 10 25.40
[11,] 11 27.94
[12,] 12 30.48
##The code appeared in the console and created output.
##Prob 1.13.
sink("C:/Users/Allie/My Documents/MPH Fall 2013/PH 251D/Jobs/job01.log1a")
source("C:/Users/Allie/My Documents/MPH Fall 2013/PH 251D/Jobs/job01.R")
sink()
##Log file is empty.
sink("C:/Users/Allie/My Documents/MPH Fall 2013/PH 251D/Jobs/job01.log1b")
source("C:/Users/Allie/My Documents/MPH Fall 2013/PH 251D/Jobs/job01.R", echo = TRUE)
> inches <- 1:12
> centimeters <- inches * 2.54
> cbind(inches, centimeters)
inches centimeters
[1,] 1 2.54
[2,] 2 5.08
[3,] 3 7.62
[4,] 4 10.16
[5,] 5 12.70
[6,] 6 15.24
[7,] 7 17.78
[8,] 8 20.32
[9,] 9 22.86
[10,] 10 25.40
[11,] 11 27.94
[12,] 12 30.48
sink()
##Log file has the code from text file and output from R.
##Prob 1.14.
source("C:/Users/Allie/My Documents/MPH Fall 2013/PH 251D/Jobs/job02.R", echo = TRUE)
> n <- 365
> per.act.risk <- c(0.5, 1, 5, 6.5, 10, 30, 50, 67)/10000
> risks <- 1 - (1 - per.act.risk)^n
> show(risks)
[1] 0.01808 0.03584 0.16685 0.21127 0.30593 0.66601 0.83952 0.91403
##This code gives the cumulative risks for the exposures from Table 1.6