Bioinformatyka VI. Programowanie Języku R

Transkrypt

1 Bioinformatyka VI Programowanie Języku R

2 WEKTORY R jest językiem wektorowym - wszystkie elementarne struktury danych są wektorami Nawet zmienne zawierające jeden obiekt są wektorami o długości 1

3 WEKTORY Podstawowe typy wektorów numeryczne (liczby podwójnej precyzji) liczby całkowite (integer) napisy (char) logiczne (TRUE/FALSE) data/czas nominalne (kategoryczne)

4 WEKTORY Podstawowe typy wektorów numeryczne (liczby podwójnej precyzji) liczby całkowite (integer) > c(1,2,3,4,5)->x > is.numeric(x) [1] TRUE > is.integer(x) [1] FALSE > c(1l,2l,3l,4l,5l)->i > is.numeric(i) [1] TRUE > is.integer(i) [1] TRUE >

5 WEKTORY numeryczne (liczby podwójnej precyzji) liczby całkowite (integer) W praktyce używany jest niemal wyłącznie typ numeryczny - operacje na liczbach całkowitych działają tak samo. > i/7 [1] > x/7 [1] > x %% 3 [1] > i %% 3 [1] > i %/% 3 [1] > x %/% 3 [1]

6 WEKTORY Typ integer może przydać się przy przekazywaniu danych między modułami napisanymi w innych językach (C). Jest również domyślnie generowany przez operację `:` która tworzy sekwencje stosowaną w iteratorach > x15<-c(1,2,3,4,5) > i15<-1:5 > str(x15) num [1:5] > str(i15) int [1:5]

7 WEKTORY Typ integer może przydać się przy przekazywaniu danych między modułami napisanymi w innych językach (C). Jest również domyślnie generowany przez operację `:` która tworzy sekwencje stosowaną w iteratorach > x15<-c(1,2,3,4,5) > i15<-1:5 > str(x15) num [1:5] > str(i15) int [1:5] > x1e6 < e6:1.0001e6 > str(x1e6) int [1:201]

8 WEKTORY Nawet zmienne zawierające jeden obiekt są wektorami o długości 1 > str(x1e6) int [1:201] > length(x1e6) [1] 201 > x<-1 > length(x) [1] 1 > str(1) num 1 > x[1] [1] 1

9 WEKTORY Tworzenie wektorów tworzenie przez użycie funkcji c() operacja `:` zastosowanie funkcji seq() > x1<-c(22,23,24,25,26) > x2<-22:26 > x3<-seq(from=22,to=26) > x3 [1] > x2 [1] > x1 [1]

10 WEKTORY Tworzenie wektorów tworzenie przez użycie funkcji c() operacja `:` zastosowanie funkcji seq() > x7<-1:7 > x5<-1:5 > c(x7,x5)->x75 > x75 [1] > c(1:3,17:22,83:74)->x19 > length(x19) [1] 19 > x19 [1]

11 WEKTORY Funkcja seq() > seq(from=22,to=26) [1] > seq(from=22,by=1,length.out=5) [1] > seq(from=26,by=-1,length.out=5) [1] > seq(from=26,by=-1.5,length.out=5) [1] > seq(from=0.33,by=0.17,to=2.18) [1] > seq(from=0.33,by=0.17,to=2.25) [1]

12 WEKTORY LOGICZNE > x10<-runif(10) > x10 [1] > x10>0.5 [1] FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE FALSE FALSE > as.numeric(x10>0.5) [1] > x10>0.5 ->Mask > Mask [1] FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE FALSE FALSE > str(mask) logi [1:10] FALSE TRUE TRUE FALSE TRUE FALSE...

13 WEKTORY LOGICZNE > x10<-runif(10) > x10 [1] > x10>0.5

14 GENEROWANIE ROZKŁADÓW Jednorodny Beta Histogram of rnorm(1e+05, 20, 0.3) Dwumianowy Normalny > x10<-runif(10) > xbeta <- rbeta(3000,4,8) > xbern <- rbinom(30,100000,0.3333) > xnorm <- rnorm(100000,20,0.3) Frequency rnorm(1e+05, 20, 0.3)

15 GENEROWANIE ROZKŁADÓW For the beta distribution see dbeta. For the binomial (including Bernoulli) distribution see dbinom. For the Cauchy distribution see dcauchy. For the chi-squared distribution see dchisq. For the exponential distribution see dexp. For the F distribution see df. For the gamma distribution see dgamma. For the geometric distribution see dgeom. (This is also a special case of the negative binomial.) For the hypergeometric distribution see dhyper. For the log-normal distribution see dlnorm. For the multinomial distribution see dmultinom. For the negative binomial distribution see dnbinom. For the normal distribution see dnorm. For the Poisson distribution see dpois. For the Student's t distribution see dt. For the uniform distribution see dunif. For the Weibull distribution see dweibull.

16 WYKRESY > X<-runif(100) > Y<-runif(100) > plot(x,y,col=1,cex=1,pch=1) Y X

17 > X<-runif(100) > Y<-runif(100) > plot(x,y,col=1,cex=1,pch=1) > plot(x,y,col=1,cex=2,pch=1) WYKRESY X Y Y X

18 > X<-runif(100) > Y<-runif(100) > plot(x,y,col=1,cex=1,pch=1) > plot(x,y,col=1,cex=2,pch=1) > plot(x,y,col=1,cex=2,pch=2) WYKRESY X Y Y Y X X

19 > X<-runif(100) > Y<-runif(100) > plot(x,y,col=1,cex=1,pch=1) > plot(x,y,col=1,cex=2,pch=1) > plot(x,y,col=1,cex=2,pch=1) WYKRESY > plot(x,y,col=1,cex=2,pch=1) X Y Y Y Y X X X

20 WYKRESY > X1<-runif(100) > Y1<-runif(100) > Y2<-runif(100) > X2<-runif(100) > X3<-runif(100) > Y3<-runif(100) > plot(x,y,col=1,cex=2,pch=1) Y X

21 > X1<-runif(100) > Y1<-runif(100) > Y2<-runif(100) > X2<-runif(100) > X3<-runif(100) > Y3<-runif(100) > plot(x,y,col=1,cex=2,pch=1) > points(x1,y1,col=2,cex=2,pch=2) WYKRESY Y X

22 > X1<-runif(100) > Y1<-runif(100) > Y2<-runif(100) > X2<-runif(100) > X3<-runif(100) > Y3<-runif(100) > plot(x,y,col=1,cex=2,pch=1) > points(x1,y1,col=2,cex=2,pch=2) > points(x2,y2,col=3,cex=2,pch=3) > points(x3,y3,col=4,cex=2,pch=4) WYKRESY Y X

23 WYKRESY > plot( + seq(from=-6,to=6,by=0.01), + dnorm(seq(from=-6,to=6,by=0.01)),cex=0.1)

24 ROZKŁAD NORMALNY I STUDENTA dnorm(seq(from = -6, to = 6, by = 0.01)) seq(from = -6, to = 6, by = 0.01)

25 WYKRESY > plot( + seq(from=-6,to=6,by=0.01), + dnorm(seq(from=-6,to=6,by=0.01)),cex=0.1) > points( + seq(from=-6,to=6,by=0.01), + dt(seq(from=-6,to=6,by=0.01),3),col=2,cex=0.1) > points( + seq(from=-6,to=6,by=0.01), + dt(seq(from=-6,to=6,by=0.01),6),col=3,cex=0.1) > points( + seq(from=-6,to=6,by=0.01), + dt(seq(from=-6,to=6,by=0.01),12),col=4,cex=0.1) > points( + seq(from=-6,to=6,by=0.01), + dt(seq(from=-6,to=6,by=0.01),24),col=5,cex=0.1)

33 dnorm(seq(from = -6, to = 6, by = 0.01)) 0.4 ROZKŁAD NORMALNY I STUDENTA seq(from = -6, to = 6, by = 0.01) Warszawa

38 WEKTORY LOGICZNE > x10<-runif(10) > x10 [1] > x10>0.5 [1] FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE FALSE FALSE > x10>0.5 ->Mask > Mask [1] FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE FALSE FALSE > str(mask) logi [1:10] FALSE TRUE TRUE FALSE TRUE FALSE...

39 WEKTORY LOGICZNE > x10>0.5 ->Mask > Mask [1] FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE FALSE FALSE > str(mask) logi [1:10] FALSE TRUE TRUE FALSE TRUE FALSE... > as.numeric(mask) [1] > sum(mask) [1] 4 >

40 WEKTORY LOGICZNE > x10[mask] [1] > x10[x10>0.5] [1] > which(mask) [1] > which(x10>0.5) [1]

41 WEKTORY ZNAKOWE > napis<-"bioinformatyka" > napis [1] "Bioinformatyka" > napis2<c("b","i","o","i","n","f","o","r","m","a","t","y","k","a") > napis2 [1] "B" "i" "o" "i" "n" "f" "o" "r" "m" "a" "t" "y" "k" "a" > napis3<-c("bio","in","for","ma","ty","ka") > napis3 [1] "Bio" "in" "for" "ma" "ty" "ka"

42 WEKTORY ZNAKOWE > str(napis) chr "Bioinformatyka" > str(napis2) chr [1:14] "B" "i" "o" "i" "n" "f" "o" "r" "m" "a" "t" "y" "k" "a" > str(napis3) chr [1:6] "Bio" "in" "for" "ma" "ty" "ka" > napis2[3] [1] "o" > napis3[3] [1] "for" > napis[3] [1] NA

43 WEKTORY ZNAKOWE > napis4<-c("b","io","inf","orma","tyka ") > napis4 [1] "B" "io" "inf" "orma" "tyka " > str(napis4) chr [1:5] "B" "io" "inf" "orma" "tyka > napis2 [1] "B" "i" "o" "i" "n" "f" "o" "r" "m" "a" "t" "y" "k" "a" > paste(napis2,collapse="") [1] "Bioinformatyka" > paste(napis2,collapse=" ") [1] "B i o i n f o r m a t y k a" > paste(napis2,collapse="-") [1] "B-i-o-i-n-f-o-r-m-a-t-y-k-a"

44 WEKTORY ZNAKOWE > AA<-c("a","aa","aaa","aaaa","aaaaa","aaaaaa") > BB<-c("b","bb") > paste(aa,bb) [1] "a b" "aa bb" "aaa b" "aaaa bb" [5] "aaaaa b" "aaaaaa bb" > paste(aa,bb,sep="-") [1] "a-b" "aa-bb" "aaa-b" [5] "aaaa-bb" "aaaaa-b" "aaaaaa-bb" > paste(aa,nb,sep="-",collapse=" ") [1] "a-b aa-bb aaa-b aaaa-bb aaaaa-b aaaaaa-bb" > paste(aa,bb,sep="-",collapse="") [1] "a-baa-bbaaa-baaaa-bbaaaaa-baaaaaa-bb"

45 CZAS >> Sys.time() [1] " :23:11 CEST" > options(digits.secs=2) > Sys.time() [1] " :23:42.87 CEST"

46 CZAS > start.time<-sys.time() > end.time<-sys.time() > start.time [1] " :24:44.56 CEST" > end.time [1] " :24:53.26 CEST" > end.time-start.time Time difference of secs > as.numeric(end.time-start.time) [1]

47 > z <- as.posixlt(sys.time()) > zz <- Sys.time() > z [1] " :12:30.37 CEST" > zz [1] " :12:39.48 CEST" > unclass(zz) [1] > unclass(z) $sec [1] $min [1] 12 $hour [1] 13 $mday [1] 17 $mon [1] 4 $year [1] 115 $wday [1] 0 $yday [1] 136 $isdst [1] 1 attr(,"tzone") [1] "" "CET" "CEST" CZAS

48 CZAS > z <- as.posixlt(sys.time()) > zz <- Sys.time() > z [1] " :12:30.37 CEST" > zz [1] " :12:39.48 CEST" > unclass(zz) [1] > unlist(unclass(z)) sec min hour mday mon year wday yday isdst

49 CZAS > z [1] " :12:30.37 CEST" > zz [1] " :12:39.48 CEST" > unclass(zz) [1] > unlist(unclass(z)) sec min hour mday mon year wday yday isdst > unclass(zz) [1] > unclass(zz) [1]

50 Class "POSIXct" represents the (signed) number of seconds since the beginning of 1970 as a numeric vector. CZAS Class "POSIXlt" is a named list of vectors represenlng sec 0 61: seconds min 0 59: minutes hour 0 23: hours mday 1 31: day of the month mon 0 11: months aoer the first of the year. year Years since wday 0 6 day of the week, starlng on Sunday. yday 0 365: day of the year. isdst Daylight savings lme flag. Posilve if in force, zero if not, negalve if unknown.

51 CZAS > MyTime_1 [1] " :00:00 Europe/Warsaw" > MyTime_2 [1] " :00:00" > MyTime_3 [1] " :00:00 UTC > as.posixct(mytime_1) [1] " :00:00 CEST" > as.posixct(mytime_2) [1] " :00:00 CEST" > as.posixct(mytime_3) [1] " :00:00 CEST"

52 CZAS > MyTime_1 [1] " :00:00 Europe/Warsaw" > MyTime_2 [1] " :00:00" > MyTime_3 [1] " :00:00 UTC" > > as.posixlt(mytime_3) [1] " :00:00" > as.posixlt(mytime_2) [1] " :00:00" > as.posixlt(mytime_1) [1] " :00:00"

53 CZAS > as.posixct(mytime_1,tz="utc") [1] " :00:00 UTC" > as.posixct(mytime_1,tz="europe/london") [1] " :00:00 BST" > as.posixct(mytime_1,tz="europe/warsaw") [1] " :00:00 CEST" > as.posixct(mytime_1,tz="europe/kiev") [1] " :00:00 EEST"

54 CZAS > as.numeric(as.posixct(mytime_1,tz="utc")) [1] > as.numeric(as.posixct(mytime_1,tz="utc")) [1] 0 > as.numeric(as.posixct(mytime_1,tz="europe/london")) [1] > as.numeric(as.posixct(mytime_1,tz="europe/warsaw")) [1] > as.numeric(as.posixct(mytime_1,tz="europe/kiev")) [1]

55 WEKTORY NOMINALNE > str(iris) 'data.frame': 150 obs. of 5 variables: $ Sepal.Length: num $ Sepal.Width : num $ Petal.Length: num $ Petal.Width : num $ Species : Factor w/ 3 levels "setosa","versicolor",..: > str(esoph) 'data.frame': 88 obs. of 5 variables: $ agegp : Ord.factor w/ 6 levels "25-34"<"35-44"<..: $ alcgp : Ord.factor w/ 4 levels "0-39g/day"<"40-79"<..: $ tobgp : Ord.factor w/ 4 levels "0-9g/day"<"10-19"<..: $ ncases : num $ ncontrols: num

56 WEKTORY NOMINALNE > levels(iris$species) [1] "setosa" "versicolor" "virginica" > levels(esoph$agegp) [1] "25-34" "35-44" "45-54" "55-64" "65-74" "75+" > levels(esoph$alcgp) [1] "0-39g/day" "40-79" "80-119" "120+" > levels(esoph$tobgp) [1] "0-9g/day" "10-19" "20-29" "30+"

57 WEKTORY NOMINALNE > as.numeric(esoph$agegp) [1] [34] [67] > as.numeric(esoph$alcgp) [1] [34] [67] > as.numeric(esoph$tobgp) [1] [34] [67] >

58 WEKTORY NOMINALNE > oceny<-c("3","3.5","3","5","5","5","4","4","2","4.5") > as.factor(oceny)->oceny > oceny [1] Levels: > as.numeric(oceny) [1] >

59 WEKTORY NOMINALNE > oceny<c("dobry","dobry","dostateczny","niedostateczny","dostateczny +","bardzo dobry","bardzo dobry","bardzo dobry","dobry +","dostateczny +") > as.factor(oceny)->oceny > oceny [1] dobry dobry dostateczny niedostateczny [5] dostateczny + bardzo dobry bardzo dobry bardzo dobry [9] dobry + dostateczny + 6 Levels: bardzo dobry dobry dobry + dostateczny... niedostateczny > as.numeric(oceny) [1] > levels(oceny) [1] "bardzo dobry" "dobry" "dobry +" [4] "dostateczny" "dostateczny +" "niedostateczny"

60 MACIERZE > 1:60->M2d > dim(m2d)<-c(6,10) > M2d [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] [2,] [3,] [4,] [5,] [6,]

61 MACIERZE > dim(m2d)<-c(10,6) > M2d [,1] [,2] [,3] [,4] [,5] [,6] [1,] [2,] [3,] [4,] [5,] [6,] [7,] [8,] [9,] [10,]

62 MACIERZE > M2d[3] [1] 3 > M2d[,3] [1] > M2d[3,] [1] > colnames(m2d)<-c("x1","x2","x3","x4","x5","x6") > M2d[,"x3"] [1]

63 MACIERZE > M3d<-1:60 > dim(m3d)<-c(4,5,3) > M3d,, 1 [,1] [,2] [,3] [,4] [,5] [1,] [2,] [3,] [4,] ,, 2 [,1] [,2] [,3] [,4] [,5] [1,] [2,] [3,] [4,] ,, 3 [,1] [,2] [,3] [,4] [,5] [1,] [2,] [3,] [4,]

64 MACIERZE > M3d[3] [1] 3 > M3d[3,,] [,1] [,2] [,3] [1,] [2,] [3,] [4,] [5,] ,, 1 [,1] [,2] [,3] [,4] [,5] [1,] [2,] [3,] [4,] ,, 2 [,1] [,2] [,3] [,4] [,5] [1,] [2,] [3,] [4,] ,, 3 [,1] [,2] [,3] [,4] [,5] [1,] [2,] [3,] [4,]

65 MACIERZE > M3d[3] [1] 3 > M3d[,3,] [,1] [,2] [,3] [1,] [2,] [3,] [4,] ,, 1 [,1] [,2] [,3] [,4] [,5] [1,] [2,] [3,] [4,] ,, 2 [,1] [,2] [,3] [,4] [,5] [1,] [2,] [3,] [4,] ,, 3 [,1] [,2] [,3] [,4] [,5] [1,] [2,] [3,] [4,]

66 MACIERZE > M3d[3] [1] 3 > M3d[,,3] [,1] [,2] [,3] [,4] [,5] [1,] [2,] [3,] [4,] ,, 1 [,1] [,2] [,3] [,4] [,5] [1,] [2,] [3,] [4,] ,, 2 [,1] [,2] [,3] [,4] [,5] [1,] [2,] [3,] [4,] ,, 3 [,1] [,2] [,3] [,4] [,5] [1,] [2,] [3,] [4,]

67 NAZWY W WEKTORACH > str(islands) Named num [1:48] attr(*, "names")= chr [1:48] "Africa" "Antarctica" "Asia" "Australia"... > islands[1:6] Africa Antarctica Asia Australia Axel Heiberg Baffin

68 NAZWY W WEKTORACH > names(sort(islands,decreasing=true))[1:10] [1] "Asia" "Africa" "North America" [4] "South America" "Antarctica" "Europe" [7] "Australia" "Greenland" "New Guinea" [10] "Borneo"

69 NAZWY W WEKTORACH > names(sort(islands,decreasing=true))[1:10] [1] "Asia" "Africa" "North America" [4] "South America" "Antarctica" "Europe" [7] "Australia" "Greenland" "New Guinea" [10] "Borneo" > sort(islands,decreasing=true)[1:10] Asia Africa North America South America Antarctica Europe Australia Greenland New Guinea Borneo

70 NAZWY W WEKTORACH > names(sort(islands,decreasing=true))[1:10] [1] "Asia" "Africa" "North America" [4] "South America" "Antarctica" "Europe" [7] "Australia" "Greenland" "New Guinea" [10] "Borneo" >sort(islands,decreasing=true)[8:17] Greenland New Guinea Borneo Madagascar Baffin Sumatra Honshu Britain Ellesmere Victoria

71 FUNKCJE AddTwoNumbers<-function(x,y){ print(x) print(y) z<-x+y print(z) return(z) } > AddTwoNumbers(2,44) [1] 2 [1] 44 [1] 46 [1] 46 >

72 FUNKCJE MyAvg<-function(x,y){ a1<-(x+y)/2 a2<-sqrt(x*y) a3<-2/(1/x+1/y) return(list(arithmetic=a1,geometric=a2,harmonic=a3)) } > MyAvg(3,1) $arithmetic [1] 2 $geometric [1] $harmonic [1] 1.5

73 FUNKCJE MyAvg<-function(x=5,y=11){ a1<-(x+y)/2 a2<-sqrt(x*y) a3<-2/(1/x+1/y) return(list(arithmetic=a1,geometric=a2,harmonic=a3)) } > MyAvg() $arithmetic [1] 8 $geometric [1] $harmonic [1] 6.875

74 FUNKCJE Nie modyfikują argumentów Tworzą wewnętrzne kopie danych Nie mają efektów ubocznych Mogą przyjmować argumenty domyślne Mogą przyjmować argumenty niezdefiniowane dla danej funkcji > MyAvg(25,1,col=3) Błąd w MyAvg(25, 1, col = 3) : nieużywane argument(y) (col = 3)

75 FUNKCJE Mogą przyjmować argumenty niezdefiniowane dla danej funkcji MyAvg<-function(x,y,...){ a1<-(x+y)/2 a2<-sqrt(x*y) a3<-2/(1/x+1/y) return(list(arithmetic=a1,geometric=a2,harmonic=a3)) } > MyAvg(25,1) $arithmetic [1] 13 $geometric [1] 5 $harmonic [1]

76 FUNKCJE Mogą przyjmować argumenty niezdefiniowane dla danej funkcji MyAvg<-function(x,y,...){ a1<-(x+y)/2 a2<-sqrt(x*y) a3<-2/(1/x+1/y) return(list(arithmetic=a1,geometric=a2,harmonic=a3)) } > MyAvg(25,1,col=3) $arithmetic [1] 13 $geometric [1] 5 $harmonic [1]

77 WEJSCIE / WYJŚCIE "Sepal.Length";"Sepal.Width";"Petal.Length";"Petal.Width";"Species" > head(iris) Sepal.Length Sepal.Width Petal.Length Petal.Width Species setosa setosa setosa setosa setosa setosa > write.table(iris,file="iris.csv",sep=";") > system("head -5 iris.csv ) "1";5.1;3.5;1.4;0.2;"setosa" "2";4.9;3;1.4;0.2;"setosa" "3";4.7;3.2;1.3;0.2;"setosa" "4";4.6;3.1;1.5;0.2;"setosa" "5";5;3.6;1.4;0.2;"setosa"

78 WEJSCIE / WYJŚCIE > read.table(file="iris.csv",sep=";",header=true)->newiris > head(newiris) Sepal.Length Sepal.Width Petal.Length Petal.Width Species setosa setosa setosa setosa setosa setosa >

79 WEJSCIE / WYJŚCIE > save(newiris,file="iris.rd") > rm(newiris) > ls() [1] "Iris" "iris.lm" "iris10" "MyAvg" "MySample" "rownames" "student1" "student2" [9] "team1" "Team2" "test" > load("iris.rd") > ls() [1] "Iris" "iris.lm" "iris10" "MyAvg" "MySample" "newiris" "rownames" "student1" [9] "student2" "team1" "Team2" "test"