Biomedical Engineering Theory And Practice/R Programming

R Programming R Programming Features Programming Features For Loop > for(i in 1:4){print(i*(i+1))} [1] 2 [1] 6 [1] 12 [1] 20 If statement > fac1 <- function(x) + { + f<-1 + if(x<2) return(1) + for(i in 2:x) + { f<-f*i} + f + } > sapply(0:4,fac1) [1] 1 1 2 6 24 While Loop > facto2<-function(x) + { + f<-1 + t<-x + while(t>1){ + f<-f*t + t<-t-1 + } + return(f) + } > sapply(0:4,facto2) [1] 1 1 2 6 24 Repeat Loop > facto3<-function(x) + { + f<-1 + t<-x + repeat + { + if(t<2) break + f<-f*t + t<-t-1 + } + return(f) + } > sapply(0:6,facto3) [1] 1 1 2 6 24 120 720 Ifelse statement > data(LakeHuron) > LakeHuron Time Series: Start = 1875 End = 1972 Frequency = 1 [1] 580.38 581.86 580.97 580.80 579.79 580.39 580.42 580.82 581.40 581.32 [11] 581.44 581.68 581.17 580.53 580.01 579.91 579.14 579.16 579.55 579.67 [21] 578.44 578.24 579.10 579.09 579.35 578.82 579.32 579.01 579.00 579.80 [31] 579.83 579.72 579.89 580.01 579.37 578.69 578.19 578.67 579.55 578.92 [41] 578.09 579.37 580.13 580.14 579.51 579.24 578.66 578.86 578.05 577.79 [51] 576.75 576.75 577.82 578.64 580.58 579.48 577.38 576.90 576.94 576.24 [61] 576.84 576.85 576.90 577.79 578.18 577.51 577.23 578.42 579.61 579.05 [71] 579.26 579.22 579.38 579.10 577.95 578.12 579.75 580.85 580.41 579.96 [81] 579.61 578.76 578.18 577.21 577.13 579.10 578.25 577.91 576.89 575.96 [91] 576.80 577.68 578.38 578.52 579.74 579.31 579.89 579.96 > meanLakeHuron<-mean(LakeHuron) > ifelse(LakeHuron > meanLakeHuron,"Higher","Lower") Time Series: Start = 1875 End = 1972 Frequency = 1 [1] Higher Higher Higher Higher Higher Higher Higher Higher Higher Higher [11] Higher Higher Higher Higher Higher Higher Higher Higher Higher Higher [21] Lower Lower Higher Higher Higher Lower Higher Higher Lower Higher [31] Higher Higher Higher Higher Higher Lower Lower Lower Higher Lower [41] Lower Higher Higher Higher Higher Higher Lower Lower Lower Lower [51] Lower Lower Lower Lower Higher Higher Lower Lower Lower Lower [61] Lower Lower Lower Lower Lower Lower Lower Lower Higher Higher [71] Higher Higher Higher Higher Lower Lower Higher Higher Higher Higher [81] Higher Lower Lower Lower Lower Higher Lower Lower Lower Lower [91] Lower Lower Lower Lower Higher Higher Higher Higher Text Functions > x<-"Hand" > y<-"some" > z<-c(x,y) > z [1] "Hand" "some" > paste(x,y) [1] "Hand some" > paste(x,y,sep="") [1] "Handsome" > pets <-c("cat","dog","turtle","rabbit") > nchar(pets) [1] 3 3 6 6 > class(pets) [1] "character" > letters [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" [20] "t" "u" "v" "w" "x" "y" "z" > LETTERS [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S" [20] "T" "U" "V" "W" "X" "Y" "Z" > which(letters=="d") > DNA <-"Deoxyribonucleic acid" > nchar(DNA) [1] 21 > q<-character(nchar(DNA)) > for(i in 1:21){q[i]<-substr(DNA,1,i)} > q [1] "D" "De" "Deo" [4] "Deox" "Deoxy" "Deoxyr" [7] "Deoxyri" "Deoxyrib" "Deoxyribo" [10] "Deoxyribon" "Deoxyribonu" "Deoxyribonuc" [13] "Deoxyribonucl" "Deoxyribonucle" "Deoxyribonuclei" [16] "Deoxyribonucleic" "Deoxyribonucleic " "Deoxyribonucleic a" [19] "Deoxyribonucleic ac" "Deoxyribonucleic aci" "Deoxyribonucleic acid" > AI <-"Artificial Intelligence" > nchar(AI) [1] 23 > strsplit(AI,split=character(0)) [[1]] [1] "A" "r" "t" "i" "f" "i" "c" "i" "a" "l" " " "I" "n" "t" "e" "l" "l" "i" "g" [20] "e" "n" "c" "e" > toupper(AI) [1] "ARTIFICIAL INTELLIGENCE" > tolower(AI) [1] "artificial intelligence" Pattern Matching > x<-c("P","L","P","T","A","H","G","A") > y<-c("P","T") > match(x,y) [1] 1 NA 1 2 NA NA NA NA Regular Expression > y<-read.csv("http://data.illinois.gov/api/views/876x-r9pn/rows.csv",head=TRUE) > str(y) 'data.frame': 212 obs. of 8 variables: $ Home.Nursing.Agency : Factor w/ 202 levels "1776 Home Care, LLC",..: 1 2 3 4 5 6 7 8 9 10 ... $ Address : Factor w/ 212 levels "10001 Derby Lane, Suite 201",..: 191 179 19 83 107 170 126 82 46 31 ... $ City : Factor w/ 115 levels "Addison","Alsip",..: 42 37 38 86 106 103 3 66 31 27 ... $ Zip : int 60137 60423 61036 60068 62701 60177 62002 61265 60201 62025 ... $ County : Factor w/ 40 levels "Adams","Bond",..: 10 38 16 7 34 17 26 32 7 21 ... $ Phone : Factor w/ 212 levels "(201) 403-9302",..: 69 159 169 197 10 183 49 27 192 209 ... $ License.. : int 4000481 4000465 4000364 4000140 4000102 4000348 4000227 4000335 4000473 4000470 ... $ License.Expiration.Date: Factor w/ 19 levels "01/31/16","01/31/17",..: 4 17 12 4 19 7 19 15 7 3 ... > attach(y) > grep("R",City) [1] 4 23 58 66 69 103 127 136 138 161 165 > City[grep("R",City)] [1] Park Ridge Rockford Rock Falls Rolling Meadows [5] Robinson Rockford Rock Island Rosemont [9] Rockford Rockford Rosemont 115 Levels: Addison Alsip Alton Aurora Barrington Bloomington ... Wilmette > as.vector(City[grep("R",as.character(City))]) [1] "Park Ridge" "Rockford" "Rock Falls" "Rolling Meadows" [5] "Robinson" "Rockford" "Rock Island" "Rosemont" [9] "Rockford" "Rockford" "Rosemont" Metacharacter Description Metacharacter Description . Matches any single character * Matches zero or more of the previous items + Matches one or more of the previous items ? Matches zero or one of the previous items ^ Matches the beginnning of the string $ Matches at the end of the string\ The Or operator \ The escape character [] Matches the single character that is contained within the square bracket [^] Matches the single character that is not contained within the square bracket. () Groups character into substring > as.vector(City[grep("y$",as.character(City))]) [1] "Quincy" "Calumet City" "Harvey" > as.vector(City[grep("^[D-E]",as.character(City))]) [1] "Evanston" "Edwardsville" "Edwardsville" "Downers Grove" [5] "Des Plaines" "Elmwood Park" "Des Plaines" "Eldorado" [9] "Evanston" "Dixon" "Deerfield" "East St. Louis" [13] "Elmhurst" "Des Plaines" "East Peoria" "Dixon" [17] "Des Plaines" "Decatur" "Evergreen Park" "Des Plaines" [21] "Des Plaines" > as.vector(City[-grep("[a-t]$",as.character(City))]) [1] "Quincy" "Glenview" "Calumet City" "Peru" "Harvey" [6] "Bridgeview" "Glenview" > as.vector(City[-grep("[a-j n-z]$",as.character(City))]) [1] "Oakbrook" "Oak Park" "Oak Brook" "Oak Brook" [5] "Elmwood Park" "Bolingbrook" "Orland Park" "Highland Park" [9] "Oak Brook" "Evergreen Park" "Tinley Park" "Tinley Park" [13] "Villa Park" "Northbrook" "Carol Stream" "Tinley Park" [17] "Oak Park" "Tinley Park" "Oakbrook" "Oak Park" [21] "Tinley Park" "Burbank" "Orland Park" "Oak Park" [25] "Bolingbrook" > as.vector(City[grep("^.y",as.character(City))]) [1] "Sycamore" > as.vector(City[grep("^..y",as.character(City))]) [1] "Crystal Lake" "Olympia Fields" "Crystal Lake" "Olympia Fields" [5] "Olympia Fields" > as.vector(City[grep("^.{5}y",as.character(City))]) [1] "Quincy" "Harvey" "Jerseyville" "Tinley Park" "Tinley Park" [6] "Tinley Park" "Tinley Park" "Tinley Park" Substituting text & Location of a pattern > text<-c("Amino","Peptide","Protein","Hormone") > sub("o","*",text) [1] "Amin*" "Peptide" "Pr*tein" "H*rmone" > gsub("o","*",text) [1] "Amin*" "Peptide" "Pr*tein" "H*rm*ne" > gsub("^.","*",text) [1] "*mino" "*eptide" "*rotein" "*ormone" > aliphaticaminoacid<-c("Glycine","Alanine","Valine","Leucine","Isoleucine") > regexpr("i",aliphaticaminoacid) [1] 5 5 4 5 8 attr(,"match.length") [1] 1 1 1 1 1 attr(,"useBytes") [1] TRUE > grep("I",aliphaticaminoacid) [1] 5 > aliphaticaminoacid[grep("I",aliphaticaminoacid)] [1] "Isoleucine" Time and Date Functions > Sys.time() [1] "2015-09-12 06:31:28 EDT" > as.numeric(Sys.time()) [1] 1442053902 > time.list<-as.POSIXlt(Sys.time()) > time.list [1] "2015-09-12 06:32:31 EDT" > unlist(time.list) sec min hour mday mon year wday yday 31.80883 32.00000 6.00000 12.00000 8.00000 115.00000 6.00000 254.00000 isdst 1.00000 > time.list<-as.POSIXct(Sys.time()) > time.list [1] "2015-09-12 06:33:07 EDT" > unlist(time.list) [1] "2015-09-12 06:33:07 EDT" > t1<-"2007-05-04" > t2<-Sys.time() > difftime(t1,t2) Time difference of -3053.278 days > round(difftime(t1,t2)) Time difference of -3053 days > t1<-as.POSIXlt("2007-05-04") > t2<-as.POSIXlt(Sys.time()) > t1-t2 Time difference of -3053.277 days How to write R Functions Function Definition Function-name<-Function(argument1, argument2,...) { <Body> return (expression) } variable<-Function-name(argument1,argument2,... Example of Function Mean > arithmatic.mean<-function(x) + { + return(sum(x)/length(x)) + } > y<-c(5,5,9,3,22,10) > mean(y) [1] 9 Median > arithmatic.med<-function(x) + { + odd.even<-length(x)%%2 + if(odd.even==0) + { + a<-sort(x)[length(x)/2] + b<-sort(x)[1+length(x)/2] + return((a+b)/2) + } + else + { + a<-sort(x)[ceiling(length(x)/2)] + return(a) + } + } > y<-c(5,9,5,7,9) > arithmatic.med(y) [1] 7 Return Values > oddcount<-function(x) + { + k<-0 + for(n in x) + { + if(n%%2==1) { k<-k+1 } + } + return(k) + } > y<-c(2,9,11,22,21,5) > oddcount(y) [1] 4 > oddcount<-function(x) + { + k<-0 + for(n in x) + { + if(n%%2==1){ k<-k+1 } + } + k + } > y<-c(2,9,11,22,21,5) > oddcount(y) [1] 4 Binary Operations It creates a new operator using function definition. It starts with % and end with %. > "%a5b%" <-function(a,b) return(a+5*b) > 5 %a5b% 8 [1] 45 Anonymous Functions A single line function can be written without the name. > inc <- function(x) return(3*x+10) > inc(2) [1] 16 > x<-0:5 > y<-10 > z<-x^2+y > z [1] 10 11 14 19 26 35 > (function(x,y){z<-x^2+y;return(z)})(0:5,10) [1] 10 11 14 19 26 35 Functional Programming Features Scope -Functions can be defined inside the function. Variables in Functions inside the function is not the part of top-level environment. Functions are objects Functions have no side effects Closure Recursion Switch Function > total<-function(y,measure) + { + switch(measure, + a=mean(y), + b=median(y), + c=max(y), + stop("Measure is not included") + ) + } > x<-c(2,5,9,11,13,15,8) > total(x,"a") [1] 9 > total(x,"b") [1] 9 > total(x,"c") [1] 15 > total(x,"4") Error in total(x, "4") : Measure is not included Practise References