Epi in r: project1
Sergio Martinez
11/20/2019
##Project 1 #question 1-Using Source File Function
source('~/ph251d-hwk/Project1/problem1.R')## [1] "Hello World"#question 2-Reading ASCII Data Set
read.table('https://raw.githubusercontent.com/taragonmd/data/master/evans.txt')## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12
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## 610 19161 0 0 64 206 0 0 82 152 0 0 0Evans <- read.table('https://raw.githubusercontent.com/taragonmd/data/master/evans.txt', sep = '', header = TRUE, na.strings = '.')Evans[1:6, 1:9]## id chd cat age chl smk ecg dbp sbp
## 1 21 0 0 56 270 0 0 80 138
## 2 31 0 0 43 159 1 0 74 128
## 3 51 1 1 56 201 1 1 112 164
## 4 71 0 1 64 179 1 0 100 200
## 5 74 0 0 49 243 1 0 82 145
## 6 91 0 0 46 252 1 0 88 142str(Evans)## 'data.frame': 609 obs. of 12 variables:
## $ id : int 21 31 51 71 74 91 111 131 141 191 ...
## $ chd: int 0 0 1 0 0 0 1 0 0 0 ...
## $ cat: int 0 0 1 1 0 0 0 0 0 0 ...
## $ age: int 56 43 56 64 49 46 52 63 42 55 ...
## $ chl: int 270 159 201 179 243 252 179 217 176 250 ...
## $ smk: int 0 1 1 1 1 1 1 0 1 0 ...
## $ ecg: int 0 0 1 0 0 0 1 0 0 1 ...
## $ dbp: int 80 74 112 100 82 88 80 92 76 114 ...
## $ sbp: int 138 128 164 200 145 142 128 135 114 182 ...
## $ hpt: int 0 0 1 1 0 0 0 0 0 1 ...
## $ ch : int 0 0 1 1 0 0 0 0 0 0 ...
## $ cc : int 0 0 201 179 0 0 0 0 0 0 ...#question 3-Discretizing Continuous Variable into a Categorical Variable
age.cat <- function(x, lower = 0, upper, by = 10,
sep = "-", above.char = "+") {
labs <- c(paste(seq(lower, upper - by, by = by),
seq(lower + by - 1, upper - 1, by = by),
sep = sep),
paste(upper, above.char, sep = ""))
cut(floor(x), breaks = c(seq(lower, upper, by = by), Inf),
right = FALSE, labels = labs)
}table(age.cat(Evans$age, upper = 70))##
## 0-9 10-19 20-29 30-39 40-49 50-59 60-69 70+
## 0 0 0 0 247 203 115 44table(age.cat(Evans$age, lower = 30, upper = 70))##
## 30-39 40-49 50-59 60-69 70+
## 0 247 203 115 44##Dates and Times #question 4 #R object
trump<- c("11/8/2016")trump## [1] "11/8/2016"#Q.4(a)-As a Julian Date
trump.julian <- as.Date(trump, format = "%m/%d/%Y")trump.julian## [1] "2016-11-08"#Q.4(b)-As day of the week
trumpdate<- c("2019-11-08")trumpdate<- as.Date(trumpdate)weekdays(trumpdate)## [1] "Friday"#Q.4(c)
format(as.Date("2019-11-08"), "%U")## [1] "44"#Question 5-2X2 Table
table(Evans$chd, Evans$smk)##
## 0 1
## 0 205 333
## 1 17 54ct <- matrix( c(205, 333, 17, 54), 2, 2)rownames(ct) <- c('chd', 'no chd')colnames(ct) <- c('Yes smk', 'No smk')coltot <- apply(ct, 2, sum)risks <- ct['chd', ]/coltotodds<- risks/(1-risks)odds.ratio <- odds/odds[2]ct## Yes smk No smk
## chd 205 17
## no chd 333 54fisher.test(Evans$smk, Evans$chd)##
## Fisher's Exact Test for Count Data
##
## data: Evans$smk and Evans$chd
## p-value = 0.02512
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 1.079813 3.697097
## sample estimates:
## odds ratio
## 1.953491#The contingency table results are statistically signification with a p-value of 0.025, assuming the alpha significance is set at 0.05. Also, since the 95% confidence interval does not include the numeric 1, one can reject the null hypothesis that the odds ratio is 1. Hence, given the table, the odds ratio of having coronary heart disease is 1.95 times that for non-smokers.
#Question 6-risk ratio
risk.ratio<- risks/risks[2]risk.ratio## Yes smk No smk
## 1.591406 1.000000#Question 7-xtabs
xtabs(~chd + smk, data = Evans)## smk
## chd 0 1
## 0 205 333
## 1 17 54xtab3way <- xtabs(~chd + hpt + smk, data = Evans)addmargins(xtab3way)## , , smk = 0
##
## hpt
## chd 0 1 Sum
## 0 122 83 205
## 1 6 11 17
## Sum 128 94 222
##
## , , smk = 1
##
## hpt
## chd 0 1 Sum
## 0 204 129 333
## 1 22 32 54
## Sum 226 161 387
##
## , , smk = Sum
##
## hpt
## chd 0 1 Sum
## 0 326 212 538
## 1 28 43 71
## Sum 354 255 609#Question 8 & 9
library(png)myplot.png<- hist(Evans$age, main = "Evans Data", xlab = "Age")
myplot.png## $breaks
## [1] 40 45 50 55 60 65 70 75 80
##
## $counts
## [1] 134 135 108 80 68 45 36 3
##
## $density
## [1] 0.0440065681 0.0443349754 0.0354679803 0.0262725780 0.0223316913
## [6] 0.0147783251 0.0118226601 0.0009852217
##
## $mids
## [1] 42.5 47.5 52.5 57.5 62.5 67.5 72.5 77.5
##
## $xname
## [1] "Evans$age"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"#Question 9-Display PNG
knitr::include_graphics("Age Hist.png")
##Question 10 Using Regular Expressions #Read in data using Scan Function
cac<- scan('https://raw.githubusercontent.com/taragonmd/data/master/calcounty.txt', what = "" )#remove “california”
cac1 = cac[1:58]cac1## [1] "Alameda" "Alpine" "Amador"
## [4] "Butte" "Calaveras" "Colusa"
## [7] "Contra Costa" "Del Norte" "El Dorado"
## [10] "Fresno" "Glenn" "Humboldt"
## [13] "Imperial" "Inyo" "Kern"
## [16] "Kings" "Lake" "Lassen"
## [19] "Los Angeles" "Madera" "Marin"
## [22] "Mariposa" "Mendocino" "Merced"
## [25] "Modoc" "Mono" "Monterey"
## [28] "Napa" "Nevada" "Orange"
## [31] "Placer" "Plumas" "Riverside"
## [34] "Sacramento" "San Benito" "San Bernardino"
## [37] "San Diego" "San Francisco" "San Joaquin"
## [40] "San Luis Obispo" "San Mateo" "Santa Barbara"
## [43] "Santa Clara" "Santa Cruz" "Shasta"
## [46] "Sierra" "Siskiyou" "Solano"
## [49] "Sonoma" "Stanislaus" "Sutter"
## [52] "Tehama" "Trinity" "Tulare"
## [55] "Tuolumne" "Ventura" "Yolo"
## [58] "Yuba"#Use regular expressions to identify and display the County names that start with "San " and end with "o".
grep("San", cac1)## [1] 35 36 37 38 39 40 41 42 43 44grep("^San|o", cac1)## [1] 3 6 7 8 9 10 12 14 19 22 23 25 26 27 34 35 36 37 38 39 40 41 42
## [24] 43 44 47 48 49 55 57t<-grep("^San|o$", cac1)cac1[t]## [1] "El Dorado" "Fresno" "Inyo"
## [4] "Mendocino" "Mono" "Sacramento"
## [7] "San Benito" "San Bernardino" "San Diego"
## [10] "San Francisco" "San Joaquin" "San Luis Obispo"
## [13] "San Mateo" "Santa Barbara" "Santa Clara"
## [16] "Santa Cruz" "Solano" "Yolo"