R 프로그래밍 기초
Jace
2020. 07. 31 (최근수정일)
R 프로그래밍 기초, 따라하며 배우기
R에 기본을 되짚어 보기에 찾아본 강의 중 최고는 곽기영 교수님의 유튜브 강의입니다. R 프로그래밍 / R 기초 by 곽기영 on Youtube (우클릭 새창으로 여세요) 확실한 기본을 잡고자 한다면 강의 보며, 따라하기 강력 추천! 여기는 따라하며 해본것들 한번에 훓어보기 위한 기록입니다.
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벡터, 팩터
c(), seq(), rep(), str(), length()
## [1] 1 2 3 4 5 6 7 8 9 10## [1] "we" "love" "data" "analytics"## [1] TRUE FALSE TRUE FALSE## [1] 1 3 5## [1] 2 4 6## [1] 1 3 5 2 4 6## [1] 3 4 5 6 7 8 9## [1] 9 8 7 6 5 4 3## [1] 5 4 3 2 1 0 -1 -2 -3## [1] 3 4 5 6 7 8 9## [1] 3 5 7 9## [1] 1.5## [1] 0 25 50 75 100## [1] -1.0 -0.5 0.0 0.5 1.0## [1] 1 1 1## [1] 1 2 3 1 2 3 1 2 3## [1] 1 1 1 2 2 2 3 3 3## [1] 1 2 2 3 3 3## [1] 1 2 3 1 2 3 1 2## [1] "1" "2" "3" "x" "y" "z"## num [1:3] 1 2 3## chr [1:3] "x" "y" "z"## [1] 3## [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"## [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"## [1] "January" "February" "March" "April" "May" "June"
## [7] "July" "August" "September" "October" "November" "December"## [1] "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec"## [1] 3.141593## [1] 12 9 3 5 1## [1] "December" "September" "March" "May" "January"연산자
## [1] 3## [1] 3## [1] 5 7 9## [1] 4 10 18## [1] 5 5 5## [1] 1 0 2## [1] 3 4 4## [1] 5 7 9 8 10 12## [1] 5 7 9 8 10 12## [1] 11 13 15## Warning in c(1, 2, 3) + c(4, 5, 6, 7, 8): 두 객체의 길이가 서로 배수관계에 있지
## 않습니다## [1] 5 7 9 8 10## [1] FALSE## [1] TRUE## [1] FALSE## [1] TRUE## [1] TRUE## [1] TRUE## [1] FALSE## [1] FALSE## [1] FALSE FALSE TRUE FALSE FALSE## [1] TRUE TRUE FALSE TRUE TRUE## [1] FALSE FALSE FALSE TRUE TRUE## [1] TRUE TRUE FALSE FALSE FALSE## [1] FALSE FALSE TRUE FALSE FALSE## [1] FALSE FALSE FALSE TRUE TRUE## [1] 1## [1] 0## [1] 1## [1] 0## [1] 2## [1] FALSE FALSE FALSE TRUE TRUE## [1] 2## [1] TRUE## [1] FALSE## [1] FALSE## [1] 4.440892e-16## [1] FALSE## [1] TRUE## [1] "Mean relative difference: 0.5"## [1] FALSE## [1] "Apple Pie" "Banana Juice" "Strawberry Cake"## [1] "Apple Juice" "Banana Juice" "Strawberry Juice"벡터 함수
## [1] 3 2 1 0 1 2 3## [1] 0.0000000 0.6931472 1.0986123 1.3862944 1.6094379## [1] 0.0000000 0.6931472 1.0986123 1.3862944 1.6094379## [1] 0.000000 1.000000 1.584963 2.000000 2.321928## [1] 0.0000000 0.3010300 0.4771213 0.6020600 0.6989700 0.7781513 0.8450980
## [8] 0.9030900 0.9542425 1.0000000## [1] 2.718282 7.389056 20.085537 54.598150 148.413159## [1] 2.718282 7.389056 20.085537 54.598150 148.413159## [1] 1 2 3 4 5## [1] 1 2 6 24 120## [1] 10## [1] 1.000000 1.414214 1.732051 2.000000 2.236068## $digits
## [1] 7## [1] 3.141593## [1] 314.1593## [1] 460## [1] 457## [1] 456.8## [1] 456.79## [1] 456.8## [1] 457## [1] 1.00 1.41 1.73 2.00 2.24## [1] 500## [1] 460## [1] 12## [1] 10## [1] 12## [1] 14## [1] -4## [1] -4## [1] 456## [1] -457## [1] 457## [1] -456## [1] 456## [1] -456## [1] Inf## [1] -Inf## [1] Inf## [1] -Inf## [1] FALSE FALSE FALSE FALSE TRUE TRUE## [1] NaN## [1] NaN## Warning in log(-2): NaN이 생성되었습니다## [1] NaN## [1] NaN## [1] TRUE## [1] NA## [1] NA## [1] NA## [1] TRUE## [1] TRUE## [1] TRUE## [1] 1 2 3 4 5## [1] 15## [1] 120## [1] 5## [1] 1## [1] 15## [1] 1 2 3 4 5
## attr(,"na.action")
## [1] 6
## attr(,"class")
## [1] "omit"## [1] 15## [1] NA NA NA NA NA## [1] 0## [1] 1## Warning in max(v, na.rm = TRUE): max에 전달되는 인자들 중 누락이 있어 -Inf를 반
## 환합니다## [1] -Inf## Warning in min(v, na.rm = TRUE): min에 전달되는 인자들 중 누락이 있어 Inf를 반환
## 합니다## [1] Inf## [1] 842 1571 2357 3108 3952 4803 5505## [1] 8.420000e+02 6.138180e+05 4.824609e+08 3.623282e+11 3.058050e+14
## [6] 2.602400e+17 1.826885e+20## [1] 842 842 842 842 844 851 851## [1] 842 729 729 729 729 729 702## [1] 3 8 9 NA NA## [1] 842 729 786 751 844 851 702## [1] -113 57 -35 93 7 -149## [1] 2 -4 NA NA## [1] 2 2 2## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15## [1] 6 7 8 9 10## [1] 1 2 3 4 5## [1] FALSE## [1] TRUE TRUE TRUE TRUE TRUE## [1] FALSE FALSE FALSE FALSE FALSE벡터 인덱싱
## [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
## [26] 25 26 27 28 29 30## [1] 2 3 5 7 11 13 17 19## [1] 2## [1] 3## [1] 2 3 5## [1] 7 11 13## [1] 2 2 11 11## [1] 2 5 11 17## [1] 17 11 5 2## [1] 2 5 11 17## [1] 3 5 7 11 13 17 19## [1] 7 11 13 17 19## [1] 7 11 13 17 19## [1] 8## [1] 2 3 5 7 11 13 17## [1] 2 3 5 7 11 13 17## [1] 2 4 5 7 11 14 17 18## [1] 2 3 5 7 11 14 17 18## [1] 2 3 5 7 11 13 17 19## [1] 2 3 5 7 11 13 17 19## [1] 8## [1] 2 3 5 7 11 13 17 19 23## [1] 2 3 5 7 11 13 17 19 23 29 31## [1] 2 3 5 7 11 13 17 19 23 29 31 NA NA NA 47## [1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE## [1] 2 3 5 7## [1] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE## [1] 2sweq_along(), which(), names()
## [1] 1 2 3 4 5 6 7 8## [1] FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE## [1] 3 7 13 19## [1] 5 13## [1] 3 7 13 19## [1] 5 13## [1] 21.6 23.6 45.8 77.0 102.2 133.3 327.9 348.0 137.6 49.3 53.0 24.9## [1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE## [1] 5 6 7 8 9## [1] "May" "June" "July" "August" "September"## [1] "May" "Jun" "Jul" "Aug" "Sep"## [1] 8## [1] "August"## [1] "January"## [1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE## [1] 102.2 133.3 327.9 348.0 137.6## [1] 21.6## [1] 348## [1] 842 729 786 751 844 851 702## Mon Tue Wed Thu Fri Sat Sun
## 842 729 786 751 844 851 702## Sat
## 851## Tue Thu Sun
## 729 751 702## Fri Sat Sun
## 844 851 702## Mon Tue Wed Thu Fri Sat Sun
## TRUE FALSE FALSE FALSE TRUE TRUE FALSE## Mon Fri Sat
## 842 844 851## [1] "Mon" "Fri" "Sat"팩터
## [1] "Good" "Good" "Indifferent" "Bad" "Good"
## [6] "Bad"## [1] Good Good Indifferent Bad Good Bad
## Levels: Bad Good Indifferent## [1] "Good" "Good" "Indifferent" "Bad" "Good"
## [6] "Bad"## chr [1:6] "Good" "Good" "Indifferent" "Bad" "Good" "Bad"## Factor w/ 3 levels "Bad","Good","Indifferent": 2 2 3 1 2 1## [1] 2 2 3 1 2 1eventday <- c("Mon", "Mon", "Tue", "Wed", "Mon",
"Wed", "Thu", "Fri", "Tue")
eventday.factor <- factor(eventday)
eventday.factor## [1] Mon Mon Tue Wed Mon Wed Thu Fri Tue
## Levels: Fri Mon Thu Tue Wedeventday.factor <- factor(eventday,
levels = c("Mon", "Tue", "Wed", "Thu",
"Fri", "Sat", "Sun"))
eventday.factor## [1] Mon Mon Tue Wed Mon Wed Thu Fri Tue
## Levels: Mon Tue Wed Thu Fri Sat Sun## [1] "Bad" "Good" "Indifferent"## [1] "B" "G" "I"## [1] G G I B G B
## Levels: B G I## [1] 3## [1] 3## [1] Medium Low High Medium High
## Levels: High Low Medium## [1] Medium Low High Medium High
## Levels: Low < Medium < High## eval.factor
## High Low Medium
## 2 1 2## eval.ordered
## Low Medium High
## 1 2 2sex <- c(2, 1, 2, 2, 1, 0)
sex.factor <- factor(sex, levels = c(1, 2),
labels = c("Male", "Female"))
sex.factor## [1] Female Male Female Female Male <NA>
## Levels: Male Female## sex.factor
## Male Female
## 2 3행렬, 배열
dim(), matrix()
## [1] 1 2 3 4 5 6 7 8 9 10 11 12## [,1] [,2] [,3] [,4]
## [1,] 1 4 7 10
## [2,] 2 5 8 11
## [3,] 3 6 9 12## [,1] [,2] [,3] [,4]
## [1,] 1 4 7 10
## [2,] 2 5 8 11
## [3,] 3 6 9 12## [,1] [,2] [,3] [,4]
## [1,] 1 2 3 4
## [2,] 5 6 7 8
## [3,] 9 10 11 12rnames <- c("R1", "R2", "R3")
colnames <- c("C1", "C2", "C3", "C4")
matrix(data = v, nrow = 3, ncol = 4,
dimnames = list(rnames, colnames))## C1 C2 C3 C4
## R1 1 4 7 10
## R2 2 5 8 11
## R3 3 6 9 12## [,1] [,2] [,3] [,4]
## [1,] 0 0 0 0
## [2,] 0 0 0 0
## [3,] 0 0 0 0## [,1] [,2] [,3] [,4]
## [1,] NA NA NA NA
## [2,] NA NA NA NA
## [3,] NA NA NA NA## [,1] [,2] [,3] [,4]
## [1,] 1 4 7 10
## [2,] 2 5 8 11
## [3,] 3 6 9 12## int [1:3, 1:4] 1 2 3 4 5 6 7 8 9 10 ...## [1] 3 4## [1] 3## [1] 4## [1] 3## [1] 4## [1] 12rbind(), cbind()
## [,1] [,2] [,3] [,4] [,5]
## v1 1 2 3 4 5
## v2 5 7 8 9 10## v1 v2
## [1,] 1 5
## [2,] 2 7
## [3,] 3 8
## [4,] 4 9
## [5,] 5 10## [,1] [,2] [,3] [,4]
## [1,] 1 4 7 10
## [2,] 2 5 8 11
## [3,] 3 6 9 12## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6
## [3,] 7 9 11
## [4,] 8 10 12연산
## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6## [,1] [,2] [,3]
## [1,] 2 4 6
## [2,] 3 5 7## [,1] [,2] [,3]
## [1,] 0 2 4
## [2,] 1 3 5## [,1] [,2] [,3]
## [1,] 2 6 10
## [2,] 4 8 12## [,1] [,2] [,3]
## [1,] 0.5 1.5 2.5
## [2,] 1.0 2.0 3.0## [,1] [,2] [,3]
## [1,] 7 7 7
## [2,] 7 7 7## [,1] [,2] [,3]
## [1,] -5 -1 3
## [2,] -3 1 5## [,1] [,2] [,3]
## [1,] 6 12 10
## [2,] 10 12 6## [,1] [,2] [,3]
## [1,] 0.1666667 0.750000 2.5
## [2,] 0.4000000 1.333333 6.0## [,1] [,2]
## [1,] 6 3
## [2,] 5 2
## [3,] 4 1## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6## [,1] [,2] [,3]
## [1,] 6 12 10
## [2,] 10 12 6## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6## [,1] [,2]
## [1,] 1 4
## [2,] 2 5
## [3,] 3 6## [,1] [,2]
## [1,] 22 49
## [2,] 28 64## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6## [,1]
## [1,] 22
## [2,] 28## [,1] [,2] [,3]
## [1,] 5 11 17## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6## [,1] [,2] [,3]
## [1,] 2 6 7
## [2,] 4 5 9rowSums(), colSums(), rowMeans(), colMean(), t()
## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6## [1] 9 12## [1] 3 7 11## [1] 3 4## [1] 1.5 3.5 5.5## [,1] [,2]
## [1,] 1 2
## [2,] 3 4
## [3,] 5 6## [1] 1 2 3 4 5## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 2 3 4 5## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6## [1] 2 4 6## [,1] [,2] [,3]
## [1,] 2 4 6인덱싱
## [,1] [,2] [,3] [,4]
## [1,] 1 4 7 10
## [2,] 2 5 8 11
## [3,] 3 6 9 12## int [1:3, 1:4] 1 2 3 4 5 6 7 8 9 10 ...## [1] 1 4 7 10## [1] 7 8 9## [,1] [,2] [,3] [,4]
## [1,] 1 4 7 10## [,1]
## [1,] 7
## [2,] 8
## [3,] 9## [,1] [,2] [,3] [,4]
## [1,] 2 5 8 11
## [2,] 3 6 9 12## [,1] [,2]
## [1,] 7 10
## [2,] 8 11
## [3,] 9 12## [,1] [,2]
## [1,] 4 7
## [2,] 5 8## [,1] [,2] [,3] [,4]
## [1,] 1 4 7 10
## [2,] 3 6 9 12## [,1] [,2]
## [1,] 1 10
## [2,] 2 11
## [3,] 3 12## [,1] [,2] [,3] [,4]
## [1,] 1 4 7 10
## [2,] 2 5 8 11
## [3,] 3 6 9 12## [,1] [,2] [,3] [,4]
## [1,] 1 4 77 10
## [2,] 2 5 8 11
## [3,] 3 6 9 12## [,1] [,2] [,3] [,4]
## [1,] 1 4 77 10
## [2,] 22 55 22 55
## [3,] 3 6 9 12## [,1] [,2] [,3] [,4]
## [1,] 1 4 77 10
## [2,] 22 55 1 3
## [3,] 3 6 2 4colnames(), rownames()
city.distance <- c(0, 331, 238, 269, 195,
331, 0, 95, 194, 189,
238, 95, 0, 171, 130,
269, 194, 171, 0, 77,
195, 189, 130, 77, 0)
city.distance## [1] 0 331 238 269 195 331 0 95 194 189 238 95 0 171 130 269 194 171 0
## [20] 77 195 189 130 77 0## [,1] [,2] [,3] [,4] [,5]
## [1,] 0 331 238 269 195
## [2,] 331 0 95 194 189
## [3,] 238 95 0 171 130
## [4,] 269 194 171 0 77
## [5,] 195 189 130 77 0colnames(city.distance.mat) <- c("Seoul", "Busan", "Daegu",
"Gwangju", "Jeonju")
rownames(city.distance.mat) <- c("Seoul", "Busan", "Daegu",
"Gwangju", "Jeonju")
colnames(city.distance.mat)## [1] "Seoul" "Busan" "Daegu" "Gwangju" "Jeonju"## [1] "Seoul" "Busan" "Daegu" "Gwangju" "Jeonju"## Seoul Busan Daegu Gwangju Jeonju
## Seoul 0 331 238 269 195
## Busan 331 0 95 194 189
## Daegu 238 95 0 171 130
## Gwangju 269 194 171 0 77
## Jeonju 195 189 130 77 0## [1] 331## Seoul Busan Daegu Gwangju Jeonju
## 0 331 238 269 195## Seoul Busan Daegu Gwangju Jeonju
## Seoul 0 331 238 269 195
## Gwangju 269 194 171 0 77배열
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24## , , 1
##
## [,1] [,2] [,3] [,4]
## [1,] 1 4 7 10
## [2,] 2 5 8 11
## [3,] 3 6 9 12
##
## , , 2
##
## [,1] [,2] [,3] [,4]
## [1,] 13 16 19 22
## [2,] 14 17 20 23
## [3,] 15 18 21 24## , , 1
##
## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6
##
## , , 2
##
## [,1] [,2] [,3]
## [1,] 7 9 11
## [2,] 8 10 12## , , 1
##
## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6
##
## , , 2
##
## [,1] [,2] [,3]
## [1,] 7 9 11
## [2,] 8 10 12## [1] 11## [1] 7 8## , , 1
##
## [,1]
## [1,] 7
## [2,] 8## [,1] [,2]
## [1,] 2 8
## [2,] 4 10
## [3,] 6 12리스트
list(), names(), length(), unlist()
## [[1]]
## [1] 0.6826
##
## [[2]]
## [1] 0.9544
##
## [[3]]
## [1] 0.9974## [[1]]
## [1] 1.23
##
## [[2]]
## [1] "Apple"
##
## [[3]]
## [1] 2 3 5 7
##
## [[4]]
## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6
##
## [[5]]
## function (x, ...)
## UseMethod("mean")
## <bytecode: 0x0000000017ebee00>
## <environment: namespace:base>## list()lst[[1]] <- 1.23
lst[[2]] <- "Apple"
lst[[3]] <- c(2, 3, 5, 7)
lst[[4]] <- matrix(1:6, ncol = 3)
lst[[5]] <- mean
lst## [[1]]
## [1] 1.23
##
## [[2]]
## [1] "Apple"
##
## [[3]]
## [1] 2 3 5 7
##
## [[4]]
## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6
##
## [[5]]
## function (x, ...)
## UseMethod("mean")
## <bytecode: 0x0000000017ebee00>
## <environment: namespace:base>## [[1]]
## [1] 0.6826
##
## [[2]]
## [1] 0.9544
##
## [[3]]
## [1] 0.9974## $sigma1
## [1] 0.6826
##
## $sigma2
## [1] 0.9544
##
## $sigma3
## [1] 0.9974## [1] "sigma1" "sigma2" "sigma3"## [1] 3worldcup1 <- list("Brazil", "Sourth Africa", "Germany")
worldcup2 <- list("Korea-Japan", "France", "USA")
c(worldcup1, worldcup2)## [[1]]
## [1] "Brazil"
##
## [[2]]
## [1] "Sourth Africa"
##
## [[3]]
## [1] "Germany"
##
## [[4]]
## [1] "Korea-Japan"
##
## [[5]]
## [1] "France"
##
## [[6]]
## [1] "USA"## Warning in mean.default(a): argument is not numeric or logical: returning NA## [1] NA## [1] 4## [1] 1## [1] 7인덱싱
## [[1]]
## [1] "A002"
##
## [[2]]
## [1] "Mouse"
##
## [[3]]
## [1] 30000## [1] 30000## [1] "Mouse"## [[1]]
## [1] 30000## [1] "numeric"## [1] "list"## [1] 27000## [[1]]
## [1] "A002"
##
## [[2]]
## [1] "Mouse"## [[1]]
## [1] "Mouse"
##
## [[2]]
## [1] 30000## [[1]]
## [1] "Mouse"
##
## [[2]]
## [1] 30000## $id
## [1] "A002"
##
## $name
## [1] "Mouse"
##
## $price
## [1] 30000## [1] "Mouse"## [1] "Mouse"## $name
## [1] "Mouse"
##
## $price
## [1] 30000## NULL## NULL## $<NA>
## NULL
##
## $name
## [1] "Mouse"
##
## $<NA>
## NULL## $<NA>
## NULL
##
## $name
## [1] "Mouse"
##
## $<NA>
## NULL## $one
## [1] 1
##
## $two
## [1] 2
##
## $three
## $three$alpha
## [1] 3.1
##
## $three$beta
## [1] 3.3## $alpha
## [1] 3.1
##
## $beta
## [1] 3.3## [1] 3.3## [1] 3.3## $id
## [1] "A001"
##
## $name
## [1] "Mouse"
##
## $price
## [1] 30000## $id
## [1] "A001"
##
## $name
## [1] "Mouse"
##
## $price
## [1] 40000## $id
## [1] "A001"
##
## $name
## [1] "Mouse"
##
## $price
## [1] 40000## $id
## [1] "A001"
##
## $name
## [1] "Mouse"
##
## $price
## [1] 40000## $id
## [1] "A001"
##
## $name
## [1] "Mouse"
##
## $price
## [1] 40000## $id
## [1] "A001"
##
## $name
## [1] "Mouse"
##
## $price
## [1] 30000 40000## $id
## [1] "A001"
##
## $name
## [1] "Mouse"
##
## $price
## [1] 30000 40000## $id
## [1] "A002"
##
## $name
## [1] "Keyboard"
##
## $price
## [1] 90000## $id
## [1] "A002"
##
## $name
## [1] "Keyboard"
##
## $price
## [1] 90000
##
## [[4]]
## [1] "Domestic" "Export"## $id
## [1] "A002"
##
## $name
## [1] "Keyboard"
##
## $price
## [1] 90000
##
## [[4]]
## [1] "Domestic" "Export"
##
## $madein
## [1] "Korea" "China"## $id
## [1] "A002"
##
## $name
## [1] "Keyboard"
##
## $price
## [1] 90000
##
## [[4]]
## [1] "Domestic" "Export"
##
## $madein
## [1] "Korea" "China"## $id
## [1] "A002"
##
## $name
## [1] "Keyboard"
##
## $price
## [1] 90000
##
## [[4]]
## [1] "Domestic" "Export"
##
## $madein
## [1] "Korea" "China"## $id
## [1] "A002"
##
## $name
## [1] "Keyboard"
##
## $price
## [1] 90000
##
## [[4]]
## [1] "Domestic" "Export"
##
## $madein
## [1] "Korea" "China"
##
## [[6]]
## [1] 0.12
##
## [[7]]
## [1] 0.15
##
## [[8]]
## [1] 0.22
##
## [[9]]
## [1] 0.27names <- c("Mon", "Tue", "Wed", "Thur", "Fri", "Sat", "Sun")
values <- c(842, 729, 786, 751, 844, 851, 702)
traffic.death <- list()
traffic.death## list()## $Mon
## [1] 842
##
## $Tue
## [1] 729
##
## $Wed
## [1] 786
##
## $Thur
## [1] 751
##
## $Fri
## [1] 844
##
## $Sat
## [1] 851
##
## $Sun
## [1] 702## $Mon
## [1] 842
##
## $Tue
## [1] 729
##
## $Wed
## [1] 786
##
## $Thur
## [1] 751
##
## $Sat
## [1] 851
##
## $Sun
## [1] 702## $Mon
## [1] 842
##
## $Tue
## [1] 729
##
## $Wed
## [1] 786
##
## $Thur
## [1] 751## Mon Tue Wed Thur
## FALSE TRUE FALSE FALSE## $Mon
## [1] 842
##
## $Wed
## [1] 786
##
## $Thur
## [1] 751데이터프레임
data.frame()
v1 <- c("A001", "A002", "A003")
v2 <- c("Mouse", "Keyboard", "USB")
v3 <- c(30000, 90000, 50000)
data.frame(v1, v2, v3)## v1 v2 v3
## 1 A001 Mouse 30000
## 2 A002 Keyboard 90000
## 3 A003 USB 50000## v2 v3
## A001 Mouse 30000
## A002 Keyboard 90000
## A003 USB 50000## 'data.frame': 3 obs. of 3 variables:
## $ id : chr "A001" "A002" "A003"
## $ name : chr "Mouse" "Keyboard" "USB"
## $ price: num 30000 90000 50000## 'data.frame': 3 obs. of 3 variables:
## $ id : Factor w/ 3 levels "A001","A002",..: 1 2 3
## $ name : Factor w/ 3 levels "Keyboard","Mouse",..: 2 1 3
## $ price: num 30000 90000 50000mat <- matrix(c(1, 3, 5, 7, 9,
2, 4, 6, 8, 10,
2, 3, 5, 7, 11), ncol = 3)
number <- as.data.frame(mat)
colnames(number) <- c("odd", "even", "prime")
number## odd even prime
## 1 1 2 2
## 2 3 4 3
## 3 5 6 5
## 4 7 8 7
## 5 9 10 11v1 <- c("A001", "A002", "A003")
v2 <- c("Mouse", "Keyboard", "USB")
v3 <- c(30000, 90000, 50000)
lst <- list(v1, v2, v3)
product <- as.data.frame(lst)
colnames(product) <- c("odd", "even", "prime")
product## odd even prime
## 1 A001 Mouse 30000
## 2 A002 Keyboard 90000
## 3 A003 USB 50000## [1] 3## [1] 3## [1] 3rbind(), cbind()
v1 <- c("A001", "A002", "A003")
v2 <- c("Mouse", "Keyboard", "USB")
v3 <- c(30000, 90000, 50000)
product <- data.frame(id = v1, name = v2,
price = v3)
product## id name price
## 1 A001 Mouse 30000
## 2 A002 Keyboard 90000
## 3 A003 USB 50000## id name price
## 1 A001 Mouse 30000
## 2 A002 Keyboard 90000
## 3 A003 USB 50000
## 4 A004 Monitor 250000new.rows <- data.frame(id = c("A005", "A006"),
name = c("Memory", "CPU"),
price = c(35000, 320000))
product <- rbind(product, new.rows)
product## id name price
## 1 A001 Mouse 30000
## 2 A002 Keyboard 90000
## 3 A003 USB 50000
## 4 A004 Monitor 250000
## 5 A005 Memory 35000
## 6 A006 CPU 320000## id name price madein
## 1 A001 Mouse 30000 Korea
## 2 A002 Keyboard 90000 China
## 3 A003 USB 50000 China
## 4 A004 Monitor 250000 Korea
## 5 A005 Memory 35000 Korea
## 6 A006 CPU 320000 USA## id name price madein
## 1 A001 Mouse 30000 Korea
## 2 A002 Keyboard 90000 China
## 3 A003 USB 50000 China
## 4 A004 Monitor 250000 Korea
## 5 A005 Memory 35000 Korea
## 6 A006 CPU 320000 USA## id name price madein madeina
## 1 A001 Mouse 30000 Korea Korea
## 2 A002 Keyboard 90000 China China
## 3 A003 USB 50000 China China
## 4 A004 Monitor 250000 Korea Korea
## 5 A005 Memory 35000 Korea Korea
## 6 A006 CPU 320000 USA USAnew.cols <- data.frame(manufacturer = c("Logitech",
"Logitech",
"Samsung",
"Samsung",
"Samsung",
"Intel"),
quantity = c(20, 15, 50, 30, 40, 10))
new.cols## manufacturer quantity
## 1 Logitech 20
## 2 Logitech 15
## 3 Samsung 50
## 4 Samsung 30
## 5 Samsung 40
## 6 Intel 10## id name price madein madeina manufacturer quantity
## 1 A001 Mouse 30000 Korea Korea Logitech 20
## 2 A002 Keyboard 90000 China China Logitech 15
## 3 A003 USB 50000 China China Samsung 50
## 4 A004 Monitor 250000 Korea Korea Samsung 30
## 5 A005 Memory 35000 Korea Korea Samsung 40
## 6 A006 CPU 320000 USA USA Intel 10cols1 <- data.frame(x = c("a", "b", "c"),
y = c(1, 2, 3))
cols2 <- data.frame(x = c("alpha", "beta", "gamma"),
y = c(100, 200, 300))
cbind(cols1, cols2)## x y x y
## 1 a 1 alpha 100
## 2 b 2 beta 200
## 3 c 3 gamma 300do.call()
df1 <- data.frame(sex = "female", months = 1, weight = 3.5)
df2 <- data.frame(sex = "male", months = 3, weight = 4.8)
df3 <- data.frame(sex = "male", months = 4, weight = 5.3)
df4 <- data.frame(sex = "female", months = 9, weight = 9.4)
df5 <- data.frame(sex = "female", months = 7, weight = 8.3)
lst <- list(df1, df2, df3, df4, df5)
lst[[1]]## sex months weight
## 1 female 1 3.5## sex months weight
## 1 male 3 4.8## sex months weight
## 1 female 1 3.5
## 2 male 3 4.8## sex months weight
## 1 female 1 3.5
## 2 male 3 4.8
## 3 male 4 5.3
## 4 female 9 9.4
## 5 female 7 8.3lst1 <- list(sex = "female", months = 1, weight = 3.5)
lst2 <- list(sex = "male", months = 3, weight = 4.8)
lst3 <- list(sex = "male", months = 4, weight = 5.3)
lst4 <- list(sex = "female", months = 9, weight = 9.4)
lst5 <- list(sex = "female", months = 7, weight = 8.3)
lst <- list(lst1, lst2, lst3, lst4, lst5)
lst[[1]]## $sex
## [1] "female"
##
## $months
## [1] 1
##
## $weight
## [1] 3.5## sex months weight
## 1 female 1 3.5## [[1]]
## sex months weight
## 1 female 1 3.5
##
## [[2]]
## sex months weight
## 1 male 3 4.8
##
## [[3]]
## sex months weight
## 1 male 4 5.3
##
## [[4]]
## sex months weight
## 1 female 9 9.4
##
## [[5]]
## sex months weight
## 1 female 7 8.3## sex months weight
## 1 female 1 3.5
## 2 male 3 4.8
## 3 male 4 5.3
## 4 female 9 9.4
## 5 female 7 8.3인덱싱
us.state <- data.frame(state.abb,
state.name,
state.region,
state.area,
stringsAsFactors = FALSE)
us.state## state.abb state.name state.region state.area
## 1 AL Alabama South 51609
## 2 AK Alaska West 589757
## 3 AZ Arizona West 113909
## 4 AR Arkansas South 53104
## 5 CA California West 158693
## 6 CO Colorado West 104247
## 7 CT Connecticut Northeast 5009
## 8 DE Delaware South 2057
## 9 FL Florida South 58560
## 10 GA Georgia South 58876
## 11 HI Hawaii West 6450
## 12 ID Idaho West 83557
## 13 IL Illinois North Central 56400
## 14 IN Indiana North Central 36291
## 15 IA Iowa North Central 56290
## 16 KS Kansas North Central 82264
## 17 KY Kentucky South 40395
## 18 LA Louisiana South 48523
## 19 ME Maine Northeast 33215
## 20 MD Maryland South 10577
## 21 MA Massachusetts Northeast 8257
## 22 MI Michigan North Central 58216
## 23 MN Minnesota North Central 84068
## 24 MS Mississippi South 47716
## 25 MO Missouri North Central 69686
## 26 MT Montana West 147138
## 27 NE Nebraska North Central 77227
## 28 NV Nevada West 110540
## 29 NH New Hampshire Northeast 9304
## 30 NJ New Jersey Northeast 7836
## 31 NM New Mexico West 121666
## 32 NY New York Northeast 49576
## 33 NC North Carolina South 52586
## 34 ND North Dakota North Central 70665
## 35 OH Ohio North Central 41222
## 36 OK Oklahoma South 69919
## 37 OR Oregon West 96981
## 38 PA Pennsylvania Northeast 45333
## 39 RI Rhode Island Northeast 1214
## 40 SC South Carolina South 31055
## 41 SD South Dakota North Central 77047
## 42 TN Tennessee South 42244
## 43 TX Texas South 267339
## 44 UT Utah West 84916
## 45 VT Vermont Northeast 9609
## 46 VA Virginia South 40815
## 47 WA Washington West 68192
## 48 WV West Virginia South 24181
## 49 WI Wisconsin North Central 56154
## 50 WY Wyoming West 97914## 'data.frame': 50 obs. of 4 variables:
## $ state.abb : chr "AL" "AK" "AZ" "AR" ...
## $ state.name : chr "Alabama" "Alaska" "Arizona" "Arkansas" ...
## $ state.region: Factor w/ 4 levels "Northeast","South",..: 2 4 4 2 4 4 1 2 2 2 ...
## $ state.area : num 51609 589757 113909 53104 158693 ...## [1] "Alabama" "Alaska" "Arizona" "Arkansas"
## [5] "California" "Colorado" "Connecticut" "Delaware"
## [9] "Florida" "Georgia" "Hawaii" "Idaho"
## [13] "Illinois" "Indiana" "Iowa" "Kansas"
## [17] "Kentucky" "Louisiana" "Maine" "Maryland"
## [21] "Massachusetts" "Michigan" "Minnesota" "Mississippi"
## [25] "Missouri" "Montana" "Nebraska" "Nevada"
## [29] "New Hampshire" "New Jersey" "New Mexico" "New York"
## [33] "North Carolina" "North Dakota" "Ohio" "Oklahoma"
## [37] "Oregon" "Pennsylvania" "Rhode Island" "South Carolina"
## [41] "South Dakota" "Tennessee" "Texas" "Utah"
## [45] "Vermont" "Virginia" "Washington" "West Virginia"
## [49] "Wisconsin" "Wyoming"## chr [1:50] "Alabama" "Alaska" "Arizona" "Arkansas" "California" "Colorado" ...## state.name
## 1 Alabama
## 2 Alaska
## 3 Arizona
## 4 Arkansas
## 5 California
## 6 Colorado
## 7 Connecticut
## 8 Delaware
## 9 Florida
## 10 Georgia
## 11 Hawaii
## 12 Idaho
## 13 Illinois
## 14 Indiana
## 15 Iowa
## 16 Kansas
## 17 Kentucky
## 18 Louisiana
## 19 Maine
## 20 Maryland
## 21 Massachusetts
## 22 Michigan
## 23 Minnesota
## 24 Mississippi
## 25 Missouri
## 26 Montana
## 27 Nebraska
## 28 Nevada
## 29 New Hampshire
## 30 New Jersey
## 31 New Mexico
## 32 New York
## 33 North Carolina
## 34 North Dakota
## 35 Ohio
## 36 Oklahoma
## 37 Oregon
## 38 Pennsylvania
## 39 Rhode Island
## 40 South Carolina
## 41 South Dakota
## 42 Tennessee
## 43 Texas
## 44 Utah
## 45 Vermont
## 46 Virginia
## 47 Washington
## 48 West Virginia
## 49 Wisconsin
## 50 Wyoming## 'data.frame': 50 obs. of 1 variable:
## $ state.name: chr "Alabama" "Alaska" "Arizona" "Arkansas" ...## state.name state.area
## 1 Alabama 51609
## 2 Alaska 589757
## 3 Arizona 113909
## 4 Arkansas 53104
## 5 California 158693
## 6 Colorado 104247
## 7 Connecticut 5009
## 8 Delaware 2057
## 9 Florida 58560
## 10 Georgia 58876
## 11 Hawaii 6450
## 12 Idaho 83557
## 13 Illinois 56400
## 14 Indiana 36291
## 15 Iowa 56290
## 16 Kansas 82264
## 17 Kentucky 40395
## 18 Louisiana 48523
## 19 Maine 33215
## 20 Maryland 10577
## 21 Massachusetts 8257
## 22 Michigan 58216
## 23 Minnesota 84068
## 24 Mississippi 47716
## 25 Missouri 69686
## 26 Montana 147138
## 27 Nebraska 77227
## 28 Nevada 110540
## 29 New Hampshire 9304
## 30 New Jersey 7836
## 31 New Mexico 121666
## 32 New York 49576
## 33 North Carolina 52586
## 34 North Dakota 70665
## 35 Ohio 41222
## 36 Oklahoma 69919
## 37 Oregon 96981
## 38 Pennsylvania 45333
## 39 Rhode Island 1214
## 40 South Carolina 31055
## 41 South Dakota 77047
## 42 Tennessee 42244
## 43 Texas 267339
## 44 Utah 84916
## 45 Vermont 9609
## 46 Virginia 40815
## 47 Washington 68192
## 48 West Virginia 24181
## 49 Wisconsin 56154
## 50 Wyoming 97914## [1] "Alabama" "Alaska" "Arizona" "Arkansas"
## [5] "California" "Colorado" "Connecticut" "Delaware"
## [9] "Florida" "Georgia" "Hawaii" "Idaho"
## [13] "Illinois" "Indiana" "Iowa" "Kansas"
## [17] "Kentucky" "Louisiana" "Maine" "Maryland"
## [21] "Massachusetts" "Michigan" "Minnesota" "Mississippi"
## [25] "Missouri" "Montana" "Nebraska" "Nevada"
## [29] "New Hampshire" "New Jersey" "New Mexico" "New York"
## [33] "North Carolina" "North Dakota" "Ohio" "Oklahoma"
## [37] "Oregon" "Pennsylvania" "Rhode Island" "South Carolina"
## [41] "South Dakota" "Tennessee" "Texas" "Utah"
## [45] "Vermont" "Virginia" "Washington" "West Virginia"
## [49] "Wisconsin" "Wyoming"## state.name
## 1 Alabama
## 2 Alaska
## 3 Arizona
## 4 Arkansas
## 5 California
## 6 Colorado
## 7 Connecticut
## 8 Delaware
## 9 Florida
## 10 Georgia
## 11 Hawaii
## 12 Idaho
## 13 Illinois
## 14 Indiana
## 15 Iowa
## 16 Kansas
## 17 Kentucky
## 18 Louisiana
## 19 Maine
## 20 Maryland
## 21 Massachusetts
## 22 Michigan
## 23 Minnesota
## 24 Mississippi
## 25 Missouri
## 26 Montana
## 27 Nebraska
## 28 Nevada
## 29 New Hampshire
## 30 New Jersey
## 31 New Mexico
## 32 New York
## 33 North Carolina
## 34 North Dakota
## 35 Ohio
## 36 Oklahoma
## 37 Oregon
## 38 Pennsylvania
## 39 Rhode Island
## 40 South Carolina
## 41 South Dakota
## 42 Tennessee
## 43 Texas
## 44 Utah
## 45 Vermont
## 46 Virginia
## 47 Washington
## 48 West Virginia
## 49 Wisconsin
## 50 Wyoming## state.name state.area
## 1 Alabama 51609
## 2 Alaska 589757
## 3 Arizona 113909
## 4 Arkansas 53104
## 5 California 158693
## 6 Colorado 104247
## 7 Connecticut 5009
## 8 Delaware 2057
## 9 Florida 58560
## 10 Georgia 58876
## 11 Hawaii 6450
## 12 Idaho 83557
## 13 Illinois 56400
## 14 Indiana 36291
## 15 Iowa 56290
## 16 Kansas 82264
## 17 Kentucky 40395
## 18 Louisiana 48523
## 19 Maine 33215
## 20 Maryland 10577
## 21 Massachusetts 8257
## 22 Michigan 58216
## 23 Minnesota 84068
## 24 Mississippi 47716
## 25 Missouri 69686
## 26 Montana 147138
## 27 Nebraska 77227
## 28 Nevada 110540
## 29 New Hampshire 9304
## 30 New Jersey 7836
## 31 New Mexico 121666
## 32 New York 49576
## 33 North Carolina 52586
## 34 North Dakota 70665
## 35 Ohio 41222
## 36 Oklahoma 69919
## 37 Oregon 96981
## 38 Pennsylvania 45333
## 39 Rhode Island 1214
## 40 South Carolina 31055
## 41 South Dakota 77047
## 42 Tennessee 42244
## 43 Texas 267339
## 44 Utah 84916
## 45 Vermont 9609
## 46 Virginia 40815
## 47 Washington 68192
## 48 West Virginia 24181
## 49 Wisconsin 56154
## 50 Wyoming 97914## [1] "Alabama" "Alaska" "Arizona" "Arkansas"
## [5] "California" "Colorado" "Connecticut" "Delaware"
## [9] "Florida" "Georgia" "Hawaii" "Idaho"
## [13] "Illinois" "Indiana" "Iowa" "Kansas"
## [17] "Kentucky" "Louisiana" "Maine" "Maryland"
## [21] "Massachusetts" "Michigan" "Minnesota" "Mississippi"
## [25] "Missouri" "Montana" "Nebraska" "Nevada"
## [29] "New Hampshire" "New Jersey" "New Mexico" "New York"
## [33] "North Carolina" "North Dakota" "Ohio" "Oklahoma"
## [37] "Oregon" "Pennsylvania" "Rhode Island" "South Carolina"
## [41] "South Dakota" "Tennessee" "Texas" "Utah"
## [45] "Vermont" "Virginia" "Washington" "West Virginia"
## [49] "Wisconsin" "Wyoming"## [1] "Alabama" "Alaska" "Arizona" "Arkansas"
## [5] "California" "Colorado" "Connecticut" "Delaware"
## [9] "Florida" "Georgia" "Hawaii" "Idaho"
## [13] "Illinois" "Indiana" "Iowa" "Kansas"
## [17] "Kentucky" "Louisiana" "Maine" "Maryland"
## [21] "Massachusetts" "Michigan" "Minnesota" "Mississippi"
## [25] "Missouri" "Montana" "Nebraska" "Nevada"
## [29] "New Hampshire" "New Jersey" "New Mexico" "New York"
## [33] "North Carolina" "North Dakota" "Ohio" "Oklahoma"
## [37] "Oregon" "Pennsylvania" "Rhode Island" "South Carolina"
## [41] "South Dakota" "Tennessee" "Texas" "Utah"
## [45] "Vermont" "Virginia" "Washington" "West Virginia"
## [49] "Wisconsin" "Wyoming"## [1] "Alabama" "Alaska" "Arizona" "Arkansas"
## [5] "California" "Colorado" "Connecticut" "Delaware"
## [9] "Florida" "Georgia" "Hawaii" "Idaho"
## [13] "Illinois" "Indiana" "Iowa" "Kansas"
## [17] "Kentucky" "Louisiana" "Maine" "Maryland"
## [21] "Massachusetts" "Michigan" "Minnesota" "Mississippi"
## [25] "Missouri" "Montana" "Nebraska" "Nevada"
## [29] "New Hampshire" "New Jersey" "New Mexico" "New York"
## [33] "North Carolina" "North Dakota" "Ohio" "Oklahoma"
## [37] "Oregon" "Pennsylvania" "Rhode Island" "South Carolina"
## [41] "South Dakota" "Tennessee" "Texas" "Utah"
## [45] "Vermont" "Virginia" "Washington" "West Virginia"
## [49] "Wisconsin" "Wyoming"## state.name state.area
## 1 Alabama 51609
## 2 Alaska 589757
## 3 Arizona 113909
## 4 Arkansas 53104
## 5 California 158693
## 6 Colorado 104247
## 7 Connecticut 5009
## 8 Delaware 2057
## 9 Florida 58560
## 10 Georgia 58876
## 11 Hawaii 6450
## 12 Idaho 83557
## 13 Illinois 56400
## 14 Indiana 36291
## 15 Iowa 56290
## 16 Kansas 82264
## 17 Kentucky 40395
## 18 Louisiana 48523
## 19 Maine 33215
## 20 Maryland 10577
## 21 Massachusetts 8257
## 22 Michigan 58216
## 23 Minnesota 84068
## 24 Mississippi 47716
## 25 Missouri 69686
## 26 Montana 147138
## 27 Nebraska 77227
## 28 Nevada 110540
## 29 New Hampshire 9304
## 30 New Jersey 7836
## 31 New Mexico 121666
## 32 New York 49576
## 33 North Carolina 52586
## 34 North Dakota 70665
## 35 Ohio 41222
## 36 Oklahoma 69919
## 37 Oregon 96981
## 38 Pennsylvania 45333
## 39 Rhode Island 1214
## 40 South Carolina 31055
## 41 South Dakota 77047
## 42 Tennessee 42244
## 43 Texas 267339
## 44 Utah 84916
## 45 Vermont 9609
## 46 Virginia 40815
## 47 Washington 68192
## 48 West Virginia 24181
## 49 Wisconsin 56154
## 50 Wyoming 97914## state.name state.area
## 1 Alabama 51609
## 2 Alaska 589757
## 3 Arizona 113909
## 4 Arkansas 53104
## 5 California 158693
## 6 Colorado 104247
## 7 Connecticut 5009
## 8 Delaware 2057
## 9 Florida 58560
## 10 Georgia 58876
## 11 Hawaii 6450
## 12 Idaho 83557
## 13 Illinois 56400
## 14 Indiana 36291
## 15 Iowa 56290
## 16 Kansas 82264
## 17 Kentucky 40395
## 18 Louisiana 48523
## 19 Maine 33215
## 20 Maryland 10577
## 21 Massachusetts 8257
## 22 Michigan 58216
## 23 Minnesota 84068
## 24 Mississippi 47716
## 25 Missouri 69686
## 26 Montana 147138
## 27 Nebraska 77227
## 28 Nevada 110540
## 29 New Hampshire 9304
## 30 New Jersey 7836
## 31 New Mexico 121666
## 32 New York 49576
## 33 North Carolina 52586
## 34 North Dakota 70665
## 35 Ohio 41222
## 36 Oklahoma 69919
## 37 Oregon 96981
## 38 Pennsylvania 45333
## 39 Rhode Island 1214
## 40 South Carolina 31055
## 41 South Dakota 77047
## 42 Tennessee 42244
## 43 Texas 267339
## 44 Utah 84916
## 45 Vermont 9609
## 46 Virginia 40815
## 47 Washington 68192
## 48 West Virginia 24181
## 49 Wisconsin 56154
## 50 Wyoming 97914## Population Income Illiteracy Life Exp Murder HS Grad Frost
## Alabama 3615 3624 2.1 69.05 15.1 41.3 20
## Alaska 365 6315 1.5 69.31 11.3 66.7 152
## Arizona 2212 4530 1.8 70.55 7.8 58.1 15
## Arkansas 2110 3378 1.9 70.66 10.1 39.9 65
## California 21198 5114 1.1 71.71 10.3 62.6 20
## Colorado 2541 4884 0.7 72.06 6.8 63.9 166
## Connecticut 3100 5348 1.1 72.48 3.1 56.0 139
## Delaware 579 4809 0.9 70.06 6.2 54.6 103
## Florida 8277 4815 1.3 70.66 10.7 52.6 11
## Georgia 4931 4091 2.0 68.54 13.9 40.6 60
## Hawaii 868 4963 1.9 73.60 6.2 61.9 0
## Idaho 813 4119 0.6 71.87 5.3 59.5 126
## Illinois 11197 5107 0.9 70.14 10.3 52.6 127
## Indiana 5313 4458 0.7 70.88 7.1 52.9 122
## Iowa 2861 4628 0.5 72.56 2.3 59.0 140
## Kansas 2280 4669 0.6 72.58 4.5 59.9 114
## Kentucky 3387 3712 1.6 70.10 10.6 38.5 95
## Louisiana 3806 3545 2.8 68.76 13.2 42.2 12
## Maine 1058 3694 0.7 70.39 2.7 54.7 161
## Maryland 4122 5299 0.9 70.22 8.5 52.3 101
## Massachusetts 5814 4755 1.1 71.83 3.3 58.5 103
## Michigan 9111 4751 0.9 70.63 11.1 52.8 125
## Minnesota 3921 4675 0.6 72.96 2.3 57.6 160
## Mississippi 2341 3098 2.4 68.09 12.5 41.0 50
## Missouri 4767 4254 0.8 70.69 9.3 48.8 108
## Montana 746 4347 0.6 70.56 5.0 59.2 155
## Nebraska 1544 4508 0.6 72.60 2.9 59.3 139
## Nevada 590 5149 0.5 69.03 11.5 65.2 188
## New Hampshire 812 4281 0.7 71.23 3.3 57.6 174
## New Jersey 7333 5237 1.1 70.93 5.2 52.5 115
## New Mexico 1144 3601 2.2 70.32 9.7 55.2 120
## New York 18076 4903 1.4 70.55 10.9 52.7 82
## North Carolina 5441 3875 1.8 69.21 11.1 38.5 80
## North Dakota 637 5087 0.8 72.78 1.4 50.3 186
## Ohio 10735 4561 0.8 70.82 7.4 53.2 124
## Oklahoma 2715 3983 1.1 71.42 6.4 51.6 82
## Oregon 2284 4660 0.6 72.13 4.2 60.0 44
## Pennsylvania 11860 4449 1.0 70.43 6.1 50.2 126
## Rhode Island 931 4558 1.3 71.90 2.4 46.4 127
## South Carolina 2816 3635 2.3 67.96 11.6 37.8 65
## South Dakota 681 4167 0.5 72.08 1.7 53.3 172
## Tennessee 4173 3821 1.7 70.11 11.0 41.8 70
## Texas 12237 4188 2.2 70.90 12.2 47.4 35
## Utah 1203 4022 0.6 72.90 4.5 67.3 137
## Vermont 472 3907 0.6 71.64 5.5 57.1 168
## Virginia 4981 4701 1.4 70.08 9.5 47.8 85
## Washington 3559 4864 0.6 71.72 4.3 63.5 32
## West Virginia 1799 3617 1.4 69.48 6.7 41.6 100
## Wisconsin 4589 4468 0.7 72.48 3.0 54.5 149
## Wyoming 376 4566 0.6 70.29 6.9 62.9 173
## Area
## Alabama 50708
## Alaska 566432
## Arizona 113417
## Arkansas 51945
## California 156361
## Colorado 103766
## Connecticut 4862
## Delaware 1982
## Florida 54090
## Georgia 58073
## Hawaii 6425
## Idaho 82677
## Illinois 55748
## Indiana 36097
## Iowa 55941
## Kansas 81787
## Kentucky 39650
## Louisiana 44930
## Maine 30920
## Maryland 9891
## Massachusetts 7826
## Michigan 56817
## Minnesota 79289
## Mississippi 47296
## Missouri 68995
## Montana 145587
## Nebraska 76483
## Nevada 109889
## New Hampshire 9027
## New Jersey 7521
## New Mexico 121412
## New York 47831
## North Carolina 48798
## North Dakota 69273
## Ohio 40975
## Oklahoma 68782
## Oregon 96184
## Pennsylvania 44966
## Rhode Island 1049
## South Carolina 30225
## South Dakota 75955
## Tennessee 41328
## Texas 262134
## Utah 82096
## Vermont 9267
## Virginia 39780
## Washington 66570
## West Virginia 24070
## Wisconsin 54464
## Wyoming 97203## num [1:50, 1:8] 3615 365 2212 2110 21198 ...
## - attr(*, "dimnames")=List of 2
## ..$ : chr [1:50] "Alabama" "Alaska" "Arizona" "Arkansas" ...
## ..$ : chr [1:8] "Population" "Income" "Illiteracy" "Life Exp" ...## 'data.frame': 50 obs. of 8 variables:
## $ Population: num 3615 365 2212 2110 21198 ...
## $ Income : num 3624 6315 4530 3378 5114 ...
## $ Illiteracy: num 2.1 1.5 1.8 1.9 1.1 0.7 1.1 0.9 1.3 2 ...
## $ Life.Exp : num 69 69.3 70.5 70.7 71.7 ...
## $ Murder : num 15.1 11.3 7.8 10.1 10.3 6.8 3.1 6.2 10.7 13.9 ...
## $ HS.Grad : num 41.3 66.7 58.1 39.9 62.6 63.9 56 54.6 52.6 40.6 ...
## $ Frost : num 20 152 15 65 20 166 139 103 11 60 ...
## $ Area : num 50708 566432 113417 51945 156361 ...## [1] "Alabama" "Alaska" "Arizona" "Arkansas"
## [5] "California" "Colorado" "Connecticut" "Delaware"
## [9] "Florida" "Georgia" "Hawaii" "Idaho"
## [13] "Illinois" "Indiana" "Iowa" "Kansas"
## [17] "Kentucky" "Louisiana" "Maine" "Maryland"
## [21] "Massachusetts" "Michigan" "Minnesota" "Mississippi"
## [25] "Missouri" "Montana" "Nebraska" "Nevada"
## [29] "New Hampshire" "New Jersey" "New Mexico" "New York"
## [33] "North Carolina" "North Dakota" "Ohio" "Oklahoma"
## [37] "Oregon" "Pennsylvania" "Rhode Island" "South Carolina"
## [41] "South Dakota" "Tennessee" "Texas" "Utah"
## [45] "Vermont" "Virginia" "Washington" "West Virginia"
## [49] "Wisconsin" "Wyoming"## Population Income Illiteracy Life.Exp Murder HS.Grad Frost Area Name
## 1 3615 3624 2.1 69.05 15.1 41.3 20 50708 Alabama
## 2 365 6315 1.5 69.31 11.3 66.7 152 566432 Alaska
## 3 2212 4530 1.8 70.55 7.8 58.1 15 113417 Arizona
## 4 2110 3378 1.9 70.66 10.1 39.9 65 51945 Arkansas
## 5 21198 5114 1.1 71.71 10.3 62.6 20 156361 California
## 6 2541 4884 0.7 72.06 6.8 63.9 166 103766 Colorado## Name Income
## 2 Alaska 6315
## 5 California 5114
## 7 Connecticut 5348
## 13 Illinois 5107
## 20 Maryland 5299
## 28 Nevada 5149
## 30 New Jersey 5237
## 34 North Dakota 5087## Name Area
## 2 Alaska 566432
## 3 Arizona 113417
## 5 California 156361
## 6 Colorado 103766
## 26 Montana 145587
## 28 Nevada 109889
## 31 New Mexico 121412
## 43 Texas 262134## Name Income Area
## 1 Alaska 6315 566432
## 2 California 5114 156361
## 3 Nevada 5149 109889## Name Income Area
## 1 Alaska 6315 566432
## 2 Arizona NA 113417
## 3 California 5114 156361
## 4 Colorado NA 103766
## 5 Connecticut 5348 NA
## 6 Illinois 5107 NA
## 7 Maryland 5299 NA
## 8 Montana NA 145587
## 9 Nevada 5149 109889
## 10 New Jersey 5237 NA
## 11 New Mexico NA 121412
## 12 North Dakota 5087 NA
## 13 Texas NA 262134with(), within()
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 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 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa## [1] 1.457143 1.633333 1.468750 1.483871 1.388889 1.384615## [1] 1.457143 1.633333 1.468750 1.483871 1.388889 1.384615 1.352941 1.470588
## [9] 1.517241 1.580645 1.459459 1.411765 1.600000 1.433333 1.450000 1.295455
## [17] 1.384615 1.457143 1.500000 1.342105 1.588235 1.378378 1.277778 1.545455
## [25] 1.411765 1.666667 1.470588 1.485714 1.529412 1.468750 1.548387 1.588235
## [33] 1.268293 1.309524 1.580645 1.562500 1.571429 1.361111 1.466667 1.500000
## [41] 1.428571 1.956522 1.375000 1.428571 1.342105 1.600000 1.342105 1.437500
## [49] 1.432432 1.515152 2.187500 2.000000 2.225806 2.391304 2.321429 2.035714
## [57] 1.909091 2.041667 2.275862 1.925926 2.500000 1.966667 2.727273 2.103448
## [65] 1.931034 2.161290 1.866667 2.148148 2.818182 2.240000 1.843750 2.178571
## [73] 2.520000 2.178571 2.206897 2.200000 2.428571 2.233333 2.068966 2.192308
## [81] 2.291667 2.291667 2.148148 2.222222 1.800000 1.764706 2.161290 2.739130
## [89] 1.866667 2.200000 2.115385 2.033333 2.230769 2.173913 2.074074 1.900000
## [97] 1.965517 2.137931 2.040000 2.035714 1.909091 2.148148 2.366667 2.172414
## [105] 2.166667 2.533333 1.960000 2.517241 2.680000 2.000000 2.031250 2.370370
## [113] 2.266667 2.280000 2.071429 2.000000 2.166667 2.026316 2.961538 2.727273
## [121] 2.156250 2.000000 2.750000 2.333333 2.030303 2.250000 2.214286 2.033333
## [129] 2.285714 2.400000 2.642857 2.078947 2.285714 2.250000 2.346154 2.566667
## [137] 1.852941 2.064516 2.000000 2.225806 2.161290 2.225806 2.148148 2.125000
## [145] 2.030303 2.233333 2.520000 2.166667 1.823529 1.966667## [1] 1.457143 1.633333 1.468750 1.483871 1.388889 1.384615with(iris, {
print(summary(Sepal.Length))
plot(Sepal.Length, Sepal.Width)
plot(Petal.Length, Petal.Width)
})## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.300 5.100 5.800 5.843 6.400 7.900
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.300 5.100 5.800 5.843 6.400 7.900# stats
with(iris, {
stats.nokeep <- summary(Sepal.Length)
stats.keep <<- summary(Sepal.Length)
})
# stats.nokeep
stats.keep## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.300 5.100 5.800 5.843 6.400 7.900## Sepal.Length Sepal.Width Petal.Length Petal.Width Species Sepal.Ratio
## 1 5.1 3.5 1.4 0.2 setosa 1.457143
## 2 4.9 3.0 1.4 0.2 setosa 1.633333
## 3 4.7 3.2 1.3 0.2 setosa 1.468750
## 4 4.6 3.1 1.5 0.2 setosa 1.483871
## 5 5.0 3.6 1.4 0.2 setosa 1.388889
## 6 5.4 3.9 1.7 0.4 setosa 1.384615## Sepal.Length Sepal.Width Petal.Length Petal.Width Species Sepal.Ratio
## 1 5.1 3.5 1.4 0.2 setosa 1.457143
## 2 4.9 3.0 1.4 0.2 setosa 1.633333
## 3 4.7 3.2 1.3 0.2 setosa 1.468750
## 4 4.6 3.1 1.5 0.2 setosa 1.483871
## 5 5.0 3.6 1.4 0.2 setosa 1.388889
## 6 5.4 3.9 1.7 0.4 setosa 1.384615attach(), detach()
## [1] ".GlobalEnv" "iris" "package:stats"
## [4] "package:graphics" "package:grDevices" "package:utils"
## [7] "package:datasets" "package:methods" "Autoloads"
## [10] "package:base"## [1] 1.457143 1.633333 1.468750 1.483871 1.388889 1.384615## [1] ".GlobalEnv" "package:stats" "package:graphics"
## [4] "package:grDevices" "package:utils" "package:datasets"
## [7] "package:methods" "Autoloads" "package:base"## [1] 0 0 0 0 0 0## [1] 5.1 4.9 4.7 4.6 5.0 5.4## [1] 35 30 32 31 36 39## [1] "a" "ary" "b"
## [4] "c" "cha" "city.distance"
## [7] "city.distance.mat" "colnames" "cols1"
## [10] "cols2" "df1" "df2"
## [13] "df3" "df4" "df5"
## [16] "eval" "eval.factor" "eval.ordered"
## [19] "even" "eventday" "eventday.factor"
## [22] "food" "fruit" "indices"
## [25] "iris" "k" "large.states"
## [28] "lst" "lst1" "lst2"
## [31] "lst3" "lst4" "lst5"
## [34] "mat" "month" "mtx"
## [37] "names" "new.cols" "new.rows"
## [40] "num" "number" "odd"
## [43] "p" "prime" "product"
## [46] "q" "r" "rainfall"
## [49] "review" "review.factor" "rich.states"
## [52] "rnames" "Sepal.Width" "sex"
## [55] "sex.factor" "states" "stats.keep"
## [58] "traffic.death" "us.state" "v"
## [61] "v1" "v2" "v3"
## [64] "values" "w" "weekend"
## [67] "worldcup1" "worldcup2" "y"
## [70] "z"## [1] 3.5 3.0 3.2 3.1 3.6 3.9## [1] 4.5 5.3 6.7## The following object is masked _by_ .GlobalEnv:
##
## Sepal.Length## [1] 4.5 5.3 6.7subset(), cor()
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1## mpg
## Fiat 128 32.4
## Honda Civic 30.4
## Toyota Corolla 33.9
## Lotus Europa 30.4## [1] mpg hp wt
## <0 rows> (or 0-length row.names)## mpg cyl wt
## Mazda RX4 21.0 6 2.620
## Mazda RX4 Wag 21.0 6 2.875
## Datsun 710 22.8 4 2.320
## Hornet 4 Drive 21.4 6 3.215
## Merc 240D 24.4 4 3.190
## Merc 230 22.8 4 3.150
## Fiat 128 32.4 4 2.200
## Honda Civic 30.4 4 1.615
## Toyota Corolla 33.9 4 1.835
## Toyota Corona 21.5 4 2.465
## Fiat X1-9 27.3 4 1.935
## Porsche 914-2 26.0 4 2.140
## Lotus Europa 30.4 4 1.513
## Volvo 142E 21.4 4 2.780## Murder Assault UrbanPop Rape
## Alabama 13.2 236 58 21.2
## Alaska 10.0 263 48 44.5
## Arizona 8.1 294 80 31.0
## Arkansas 8.8 190 50 19.5
## California 9.0 276 91 40.6
## Colorado 7.9 204 78 38.7## Murder Assault UrbanPop Rape
## Murder 1.00000000 0.8018733 0.06957262 0.5635788
## Assault 0.80187331 1.0000000 0.25887170 0.6652412
## UrbanPop 0.06957262 0.2588717 1.00000000 0.4113412
## Rape 0.56357883 0.6652412 0.41134124 1.0000000## Murder Assault Rape
## Alabama 13.2 236 21.2
## Alaska 10.0 263 44.5
## Arizona 8.1 294 31.0
## Arkansas 8.8 190 19.5
## California 9.0 276 40.6
## Colorado 7.9 204 38.7
## Connecticut 3.3 110 11.1
## Delaware 5.9 238 15.8
## Florida 15.4 335 31.9
## Georgia 17.4 211 25.8
## Hawaii 5.3 46 20.2
## Idaho 2.6 120 14.2
## Illinois 10.4 249 24.0
## Indiana 7.2 113 21.0
## Iowa 2.2 56 11.3
## Kansas 6.0 115 18.0
## Kentucky 9.7 109 16.3
## Louisiana 15.4 249 22.2
## Maine 2.1 83 7.8
## Maryland 11.3 300 27.8
## Massachusetts 4.4 149 16.3
## Michigan 12.1 255 35.1
## Minnesota 2.7 72 14.9
## Mississippi 16.1 259 17.1
## Missouri 9.0 178 28.2
## Montana 6.0 109 16.4
## Nebraska 4.3 102 16.5
## Nevada 12.2 252 46.0
## New Hampshire 2.1 57 9.5
## New Jersey 7.4 159 18.8
## New Mexico 11.4 285 32.1
## New York 11.1 254 26.1
## North Carolina 13.0 337 16.1
## North Dakota 0.8 45 7.3
## Ohio 7.3 120 21.4
## Oklahoma 6.6 151 20.0
## Oregon 4.9 159 29.3
## Pennsylvania 6.3 106 14.9
## Rhode Island 3.4 174 8.3
## South Carolina 14.4 279 22.5
## South Dakota 3.8 86 12.8
## Tennessee 13.2 188 26.9
## Texas 12.7 201 25.5
## Utah 3.2 120 22.9
## Vermont 2.2 48 11.2
## Virginia 8.5 156 20.7
## Washington 4.0 145 26.2
## West Virginia 5.7 81 9.3
## Wisconsin 2.6 53 10.8
## Wyoming 6.8 161 15.6## Murder Assault Rape
## Murder 1.0000000 0.8018733 0.5635788
## Assault 0.8018733 1.0000000 0.6652412
## Rape 0.5635788 0.6652412 1.0000000## Murder Assault
## Murder 1.0000000 0.8018733
## Assault 0.8018733 1.0000000sqldf()
## Loading required package: gsubfn## Loading required package: proto## Loading required package: RSQLite## mpg cyl disp hp drat wt qsec vs am gear carb
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2## [1] mpg cyl disp hp drat wt qsec vs am gear carb
## <0 rows> (or 0-length row.names)sqldf("select avg(mpg) as avg_mpg, avg(wt) as avg_wt, gear from mtcars where carb in (4, 6) group by gear")## avg_mpg avg_wt gear
## 1 12.62 4.68580 3
## 2 19.75 3.09375 4
## 3 17.75 2.97000 5## Species
## 1 setosa
## 2 versicolor
## 3 virginica## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa## avg([Sepal.Length])
## 1 5.006## avg("Sepal.Length")
## 1 5.006텍스트
nchar(), strsplit(), paste(), outer()
## [1] 15## [1] 1## [1] 2 4 1 5## [1] 4## [1] 5## [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"## [1] "z" "y" "x" "w" "v" "u" "t" "s" "r" "q" "p" "o" "n" "m" "l" "k" "j" "i" "h"
## [20] "g" "f" "e" "d" "c" "b" "a"## [1] "it is only with the heart"## [1] "IT IS ONLY WITH THE HEART"## [[1]]
## [1] "It" "is" "only" "with" "the" "HEART"## [[1]]
## [1] "I" "t" " " "i" "s" " " "o" "n" "l" "y" " " "w" "i" "t" "h" " " "t" "h" "e"
## [20] " " "H" "E" "A" "R" "T"## [1] "It" "is" "only" "with" "the" "HEART"## [1] "only"## [1] "only"## [[1]]
## [1] "We" "have" "a" "dream"
##
## [[2]]
## [1] "It" "is" "only" "with" "the" "HEART"## [[1]]
## [1] "We" "have" "a" "dream"
##
## [[2]]
## [1] "It" "is" "only" "with" "the" "HEART"## [1] "It" "is" "only" "with" "the" "HEART"## [1] "the"fox.says <- "It is only with the HEART it"
fox.says.word <- strsplit(fox.says, " ")[[1]]
unique(fox.says.word)## [1] "It" "is" "only" "with" "the" "HEART" "it"## [1] "it" "is" "only" "with" "the" "heart"## [1] "Everybody wants to fly"## [1] "Everybody" "wants" "to" "fly"## [1] "Everybody-wants-to-fly"## [1] "Everybodywantstofly"## [1] "Everybodywantstofly"## [1] "3.14159265358979 1.77245385090552"## [1] "25 dgrees celsius is 77 degree Fahrenheit"heroes <- c("Batman", "Captain America", "Hulk")
colors <- c("Black", "Blue", "Green")
paste(heroes, colors)## [1] "Batman Black" "Captain America Blue" "Hulk Green"## [1] "Type 1" "Type 2" "Type 3" "Type 4" "Type 5"## [1] "Batman wants to fly" "Captain America wants to fly"
## [3] "Hulk wants to fly"## [1] "Everybody" "wants" "to" "fly"## [1] "Everybody wants to fly"## [1] "Batman wants to fly, and Captain America wants to fly, and Hulk wants to fly"## [1] "Jan 1" "Feb 2" "Mar 3" "Apr 4" "May 5" "Jun 6" "Jul 7" "Aug 8"
## [9] "Sep 9" "Oct 10" "Nov 11" "Dec 12"## [1] "Jan_1" "Feb_2" "Mar_3" "Apr_4" "May_5" "Jun_6" "Jul_7" "Aug_8"
## [9] "Sep_9" "Oct_10" "Nov_11" "Dec_12"## [1] "Jan_1-Feb_2-Mar_3-Apr_4-May_5-Jun_6-Jul_7-Aug_8-Sep_9-Oct_10-Nov_11-Dec_12"## [,1] [,2] [,3]
## [1,] 1 2 3
## [2,] 2 4 6
## [3,] 3 6 9asian.countries <- c("Korea", "Japan","China")
info <- c("GDP", "Population", "Area")
outer(asian.countries, info, FUN = paste, sep = "-")## [,1] [,2] [,3]
## [1,] "Korea-GDP" "Korea-Population" "Korea-Area"
## [2,] "Japan-GDP" "Japan-Population" "Japan-Area"
## [3,] "China-GDP" "China-Population" "China-Area"## [1] "Korea-Korea" "Korea-Japan" "Japan-Japan" "Korea-China" "Japan-China"
## [6] "China-China"sprintf()
customer <- "Jobs"
buysize <- 10
deliveryday <- 3
paste("Hello ", customer, ", your order of ", buysize,
" product(s) will be dilivered within ", deliveryday,
"day(s)", sep = "")## [1] "Hello Jobs, your order of 10 product(s) will be dilivered within 3day(s)"sprintf("Hello %s your order of %s product(s) will be
dilivered within %s day(s)", customer, buysize, deliveryday)## [1] "Hello Jobs your order of 10 product(s) will be \n dilivered within 3 day(s)"customer <- c("Jobs", "Gates", "Bezos")
buysize <- c(10, 7, 12)
deliveryday <- c(3, 2, 7.5)
sprintf("Hello %s your order of %s product(s) will be
dilivered within %s day(s)",
customer, buysize, deliveryday)## [1] "Hello Jobs your order of 10 product(s) will be \n dilivered within 3 day(s)"
## [2] "Hello Gates your order of 7 product(s) will be \n dilivered within 2 day(s)"
## [3] "Hello Bezos your order of 12 product(s) will be \n dilivered within 7.5 day(s)"substr(), substring(), grep(), sub()
## [1] "Data"## [1] "Analytics"## [1] "Analytics"## [1] "Data" "Data" "Data"countries <- c("Korea, KR", "Unites States, US", "China, CH")
substr(countries, nchar(countries) - 1, nchar(countries))## [1] "KR" "US" "CH"## Africa Antarctica Asia Australia Axel Heiberg Baffin
## 11506 5500 16988 2968 16 184## [1] "New Britain" "New Guinea" "New Zealand (N)" "New Zealand (S)"
## [5] "Newfoundland"## [1] "New Britain" "New Guinea" "New Zealand (N)" "New Zealand (S)"
## [5] "Newfoundland"## [1] "Axel Heiberg" "New Britain" "New Guinea" "New Zealand (N)"
## [5] "New Zealand (S)" "North America" "Novaya Zemlya" "Prince of Wales"
## [9] "South America" "Tierra del Fuego"## [1] "Axel Heiberg" "New Britain" "New Guinea" "New Zealand (N)"
## [5] "New Zealand (S)" "North America" "Novaya Zemlya" "Prince of Wales"
## [9] "South America" "Tierra del Fuego"fox.says <- "It is only with the HEART that is"
sub(pattern = "is", replacement = "was", x = fox.says)## [1] "It was only with the HEART that is"## [1] "It was only with the HEART that was"## [1] "product" "customer" "supplier"정규표현식
[:digit:] : [0-9]
[:lower:] : [a-z]
[:upper:] : [A-Z]
[:alpha:] : [A-z]
[:alnum:] : [A-z0-9]
[:punct:] : 문장부호
[:blank:] : space, tab
[:space:] : space, tab, newline, form feed, carrage return
[:print:] : [[:alnum:][:punct:][:space:]]
[:graph:] : 그래프 문자(읽을 수 있는 문자)
? : 0~1회
* : 0회 이상
+ : 1회 이상
{n} : n회 반복
{n,} : n회 이상 반복
{n, m} : n회~m회 반복
\w : [[:alnum:]_] 단어 문자
\W : [^[:alnum:]_] 단어 문자를 제외한 문자
\d : [[:digit:]] 숫자
\D : [^[:digit:]] 숫자를 제외한 문자
\s : [[:space:]] 스페이스 문자
\S : [^[:space:]] 스페이스 문자를 제외한 문자
\b : 단어 경계의 빈 문자열
\B : 단어 경계의 빈 문자열을 제외한 문자
\< : 단어 시작
\> : 단어 끝words <- c("at", "bat", "cat", "chaenomelss", "chase", "chasse",
"cheap", "check", "cheese", "chick", "hat")
grep("che", words, value = TRUE)## [1] "cheap" "check" "cheese"## [1] "at" "bat" "cat" "hat"## [1] "cat" "chaenomelss" "chase" "chasse" "cheap"
## [6] "check" "cheese" "chick" "hat"## [1] "at" "bat" "cat" "chaenomelss" "chase"
## [6] "chasse" "cheap" "hat"## [1] "at" "bat" "cat" "chaenomelss" "chase"
## [6] "chasse" "cheap" "check" "cheese" "chick"
## [11] "hat"## [1] "check" "chick"## [1] "chaenomelss" "chase"## [1] "chaenomelss" "chase" "chasse"## [1] "chase" "chasse"## [1] "chase" "chasse" "cheese"## [1] "cat" "chaenomelss" "chase" "chasse" "cheap"
## [6] "check" "cheese" "chick"## [1] "at" "bat" "cat" "hat"## [1] "cat"## [1] "at" "cat" "hat"words2 <- c("12 Dec", "OK", "http://", "<TITLE>Time?</TITLE>",
"12345", "Hi there")
grep("[[:alnum:]]", words2, value = TRUE)## [1] "12 Dec" "OK" "http://"
## [4] "<TITLE>Time?</TITLE>" "12345" "Hi there"## [1] "12 Dec" "OK" "http://"
## [4] "<TITLE>Time?</TITLE>" "Hi there"## [1] "12 Dec" "12345"## [1] "http://" "<TITLE>Time?</TITLE>"## [1] "12 Dec" "Hi there"## [1] "12 Dec" "OK" "http://"
## [4] "<TITLE>Time?</TITLE>" "12345" "Hi there"## [1] "12 Dec" "12345"## [1] "12 Dec" "Hi there"library(base)
grep(), grepl()
string <- c("data analytics in useful",
"business analytics is helpful",
"visualization of data is interesting for data scientists")
grep(pattern = "data", x = string)## [1] 1 3## [1] "data analytics in useful"
## [2] "visualization of data is interesting for data scientists"## [1] "data analytics in useful"
## [2] "visualization of data is interesting for data scientists"## [1] "data analytics in useful" "business analytics is helpful"## [1] "visualization of data is interesting for data scientists"## [1] TRUE FALSE TRUE## [1] "Alabama" "Alaska" "Arizona" "Arkansas"
## [5] "California" "Colorado" "Connecticut" "Delaware"
## [9] "Florida" "Georgia" "Hawaii" "Idaho"
## [13] "Illinois" "Indiana" "Iowa" "Kansas"
## [17] "Kentucky" "Louisiana" "Maine" "Maryland"
## [21] "Massachusetts" "Michigan" "Minnesota" "Mississippi"
## [25] "Missouri" "Montana" "Nebraska" "Nevada"
## [29] "New Hampshire" "New Jersey" "New Mexico" "New York"
## [33] "North Carolina" "North Dakota" "Ohio" "Oklahoma"
## [37] "Oregon" "Pennsylvania" "Rhode Island" "South Carolina"
## [41] "South Dakota" "Tennessee" "Texas" "Utah"
## [45] "Vermont" "Virginia" "Washington" "West Virginia"
## [49] "Wisconsin" "Wyoming"## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [49] FALSE FALSE## [1] "New Hampshire" "New Jersey" "New Mexico" "New York"## [1] 4regexpr(), gregexpr(), rematches()
## [1] 1 -1 18
## attr(,"match.length")
## [1] 4 -1 4
## attr(,"index.type")
## [1] "chars"
## attr(,"useBytes")
## [1] TRUE## [[1]]
## [1] 1
## attr(,"match.length")
## [1] 4
## attr(,"index.type")
## [1] "chars"
## attr(,"useBytes")
## [1] TRUE
##
## [[2]]
## [1] -1
## attr(,"match.length")
## [1] -1
## attr(,"index.type")
## [1] "chars"
## attr(,"useBytes")
## [1] TRUE
##
## [[3]]
## [1] 18 42
## attr(,"match.length")
## [1] 4 4
## attr(,"index.type")
## [1] "chars"
## attr(,"useBytes")
## [1] TRUE## [1] "data" "data"## [[1]]
## [1] "data"
##
## [[2]]
## character(0)
##
## [[3]]
## [1] "data" "data"## [[1]]
## [1] "" " analytics in useful"
##
## [[2]]
## [1] "business analytics is helpful"
##
## [[3]]
## [1] "visualization of " " is interesting for " " scientists"sub(), gsub(), strsplit()
## [1] "text analytics in useful"
## [2] "business analytics is helpful"
## [3] "visualization of text is interesting for data scientists"## [1] "text analytics in useful"
## [2] "business analytics is helpful"
## [3] "visualization of text is interesting for text scientists"## [[1]]
## [1] "data" "analytics" "in" "useful"
##
## [[2]]
## [1] "business" "analytics" "is" "helpful"
##
## [[3]]
## [1] "visualization" "of" "data" "is"
## [5] "interesting" "for" "data" "scientists"## [1] "data" "analytics" "in" "useful"
## [5] "business" "analytics" "is" "helpful"
## [9] "visualization" "of" "data" "is"
## [13] "interesting" "for" "data" "scientists"## [1] "data" "analytics" "in" "useful"
## [5] "business" "is" "helpful" "visualization"
## [9] "of" "interesting" "for" "scientists"library(stringr)
ste_detect()
string <- c("data analytics in useful",
"business analytics is helpful",
"visualization of data is interesting for data scientists")
library(stringr)##
## Attaching package: 'stringr'## The following objects are masked _by_ '.GlobalEnv':
##
## fruit, words## [1] TRUE FALSE TRUE## [1] FALSE FALSE FALSE## [1] TRUE FALSE TRUE## [1] TRUE TRUE TRUE## [1] FALSE FALSE TRUE## [1] FALSE FALSE TRUEstr_locate(), str_locate_all(), str_extract(), str_extract_all()
## start end
## [1,] 1 4
## [2,] NA NA
## [3,] 18 21## [[1]]
## start end
## [1,] 1 4
##
## [[2]]
## start end
##
## [[3]]
## start end
## [1,] 18 21
## [2,] 42 45## [1] "data" NA "data"## [[1]]
## [1] "data"
##
## [[2]]
## character(0)
##
## [[3]]
## [1] "data" "data"## [,1] [,2]
## [1,] "data" ""
## [2,] "" ""
## [3,] "data" "data"## [1] "data" "data" "data"str_match(), str_match_all(), str_replace(), str_replace_all()
## [1] "The birch canoe slid on the smooth planks."
## [2] "Glue the sheet to the dark blue background."
## [3] "It's easy to tell the depth of a well."
## [4] "These days a chicken leg is a rare dish."
## [5] "Rice is often served in round bowls."## [1] "the smooth" "the sheet" "the depth" "a chicken" NA## [,1] [,2] [,3]
## [1,] "the smooth" "the" "smooth"
## [2,] "the sheet" "the" "sheet"
## [3,] "the depth" "the" "depth"
## [4,] "a chicken" "a" "chicken"
## [5,] NA NA NA## [[1]]
## [,1] [,2] [,3]
## [1,] "the smooth" "the" "smooth"
##
## [[2]]
## [,1] [,2] [,3]
## [1,] "the sheet" "the" "sheet"
## [2,] "the dark" "the" "dark"
##
## [[3]]
## [,1] [,2] [,3]
## [1,] "the depth" "the" "depth"
## [2,] "a well" "a" "well"
##
## [[4]]
## [,1] [,2] [,3]
## [1,] "a chicken" "a" "chicken"
## [2,] "a rare" "a" "rare"
##
## [[5]]
## [,1] [,2] [,3]## [1] "text analytics in useful"
## [2] "business analytics is helpful"
## [3] "visualization of text is interesting for data scientists"## [1] "text analytics in useful"
## [2] "business analytics is helpful"
## [3] "visualization of text is interesting for text scientists"str_split(), str_length(), str_count()
## [[1]]
## [1] "data" "analytics" "in" "useful"
##
## [[2]]
## [1] "business" "analytics" "is" "helpful"
##
## [[3]]
## [1] "visualization" "of" "data" "is"
## [5] "interesting" "for" "data" "scientists"## [1] "data" "analytics" "in" "useful"
## [5] "business" "analytics" "is" "helpful"
## [9] "visualization" "of" "data" "is"
## [13] "interesting" "for" "data" "scientists"## [1] "data" "analytics" "in" "useful"
## [5] "business" "is" "helpful" "visualization"
## [9] "of" "interesting" "for" "scientists"## [[1]]
## [1] "data" "analytics" "in useful"
##
## [[2]]
## [1] "business" "analytics" "is helpful"
##
## [[3]]
## [1] "visualization"
## [2] "of"
## [3] "data is interesting for data scientists"## [,1] [,2] [,3]
## [1,] "data" "analytics" "in useful"
## [2,] "business" "analytics" "is helpful"
## [3,] "visualization" "of" "data is interesting for data scientists"## [1] 24 29 56## [1] 1 0 2## [1] 4 4 8str_pad(), str_trim()
## [1] " a" " abc" " abcde"## [1] "01" "02" "03" "04" "05" "06" "07" "08" "09" "10" "11" "12"string <- c("data analytics in useful",
"business analytics is helpful",
"visualization of data is interesting for data scientists")
str.pad <- str_pad(string,
width = max(str_length(string)),
side = "both",
pad = " ")
str.pad## [1] " data analytics in useful "
## [2] " business analytics is helpful "
## [3] "visualization of data is interesting for data scientists"## [1] "data analytics in useful"
## [2] "business analytics is helpful"
## [3] "visualization of data is interesting for data scientists"str_c()
## [1] "data mining"## [1] "data mining is useful" "text mining is useful"## [1] "data mining is useful; text mining is useful"## [1] "data mining is useful\ntext mining is useful"## data mining is useful
## text mining is useful## [1] "data" "text"## [1] " " " "## [1] "data-mining is useful" "text-mining is useful"## [1] "fg"## [1] "abcde"파일 읽기
Sample Files : https://github.com/kykwahk/YouTube
read.csv(), read.table(), read.fwf()
# list.files("rBasicLec")
# library(pander)
# openFileInOS("C:/Users/jacea/workspaceR/RPubs/rBasicLec/product.csv")
read.csv("rBasicLec/product.csv")## id name price
## 1 A001 Mouse 30000
## 2 A002 Keyboard 90000
## 3 A003 USB 50000## V1 V2 V3
## 1 A001 Mouse 30000
## 2 A002 Keyboard 90000
## 3 A003 USB 50000## 'data.frame': 3 obs. of 3 variables:
## $ id : chr "A001" "A002" "A003"
## $ name : chr "Mouse" "Keyboard" "USB"
## $ price: int 30000 90000 50000## V1 V2 V3
## 1 id name price
## 2 A001 Mouse 30000
## 3 A002 Keyboard 90000
## 4 A003 USB 50000## 'data.frame': 3 obs. of 3 variables:
## $ id : chr "A001" "A002" "A003"
## $ name : chr "Mouse" "Keyboard" "USB"
## $ price: int 30000 90000 50000## 'data.frame': 3 obs. of 3 variables:
## $ id : chr "A001" "A002" "A003"
## $ name : chr "Mouse" "Keyboard" "USB"
## $ price: int 30000 90000 50000p <- read.table("rBasicLec/product-colon.txt",
sep = ":",
header = TRUE,
stringsAsFactors = FALSE)
str(p)## 'data.frame': 3 obs. of 3 variables:
## $ id : chr "A001" "A002" "A003"
## $ name : chr " Mouse" " Keyboard" " USB"
## $ price: int 30000 90000 50000## 'data.frame': 3 obs. of 3 variables:
## $ id : chr "A001" "A002" "A003"
## $ name : chr "Mouse" "Keyboard" "USB"
## $ price: chr "30000" "." "50000"## 'data.frame': 3 obs. of 3 variables:
## $ id : chr "A001" "A002" "A003"
## $ name : chr "Mouse" "Keyboard" "USB"
## $ price: int 30000 NA 50000p <- read.fwf("rBasicLec/product-fwf.txt",
widths = c(4, -1, 10, 8),
col.names = c("id", "name", "price"))
str(p)## 'data.frame': 3 obs. of 3 variables:
## $ id : chr "A001" "A002" "A003"
## $ name : chr "Mouse " "Keyboard " "USB "
## $ price: int 30000 90000 50000readLines(), scan()
## [1] "2014-11-27 1116.70 1078.30 2014-11-28 1127.89 1089.11"
## [2] "2014-12-01 1130.13 1091.27 2014-12-02 1130.13 1091.27 2014-12-03 1131.86 1092.94"
## [3] "2014-12-04 1134.51 1095.49"
## [4] "2014-12-05 1134.51 1095.49 2014-12-08 1139.60 1100.40"
## [5] "2014-12-09 1134.51 1095.49 2014-12-10 1121.79 1083.21"## [1] "2014-11-27 1116.70 1078.30 2014-11-28 1127.89 1089.11"
## [2] "2014-12-01 1130.13 1091.27 2014-12-02 1130.13 1091.27 2014-12-03 1131.86 1092.94"## [1] "2014-11-27" "1116.70" "1078.30" "2014-11-28" "1127.89"
## [6] "1089.11" "2014-12-01" "1130.13" "1091.27" "2014-12-02"
## [11] "1130.13" "1091.27" "2014-12-03" "1131.86" "1092.94"
## [16] "2014-12-04" "1134.51" "1095.49" "2014-12-05" "1134.51"
## [21] "1095.49" "2014-12-08" "1139.60" "1100.40" "2014-12-09"
## [26] "1134.51" "1095.49" "2014-12-10" "1121.79" "1083.21"## [[1]]
## [1] "2014-11-27" "2014-11-28" "2014-12-01" "2014-12-02" "2014-12-03"
## [6] "2014-12-04" "2014-12-05" "2014-12-08" "2014-12-09" "2014-12-10"
##
## [[2]]
## [1] 1116.70 1127.89 1130.13 1130.13 1131.86 1134.51 1134.51 1139.60 1134.51
## [10] 1121.79
##
## [[3]]
## [1] 1078.30 1089.11 1091.27 1091.27 1092.94 1095.49 1095.49 1100.40 1095.49
## [10] 1083.21scan("rBasicLec/won-dollar.txt",
what = list(date = character(),
buy = numeric(),
sell = numeric()),
nlines = 2)## $date
## [1] "2014-11-27" "2014-11-28" "2014-12-01" "2014-12-02" "2014-12-03"
##
## $buy
## [1] 1116.70 1127.89 1130.13 1130.13 1131.86
##
## $sell
## [1] 1078.30 1089.11 1091.27 1091.27 1092.94scan("rBasicLec/won-dollar.txt",
what = list(date = character(),
buy = numeric(),
sell = numeric()),
skip = 3)## $date
## [1] "2014-12-05" "2014-12-08" "2014-12-09" "2014-12-10"
##
## $buy
## [1] 1134.51 1139.60 1134.51 1121.79
##
## $sell
## [1] 1095.49 1100.40 1095.49 1083.21날짜
Sys.Date(), date(), Sys.time(), weekdays()
## [1] "2020-07-31"## [1] "Date"## [1] "Fri Jul 31 23:30:28 2020"## [1] "character"## [1] "2020-07-31 23:30:28 KST"## [1] "POSIXct" "POSIXt"## [1] "2025-12-31"## [1] "2020-11-02"## [1] "2021-12-03"## [1] "12/31/2025"## [1] "2020/07/31"## [1] "2020/07/31 금요일"## [1] "2020/07/31 금"## [1] "수요일"## [1] "2026-01-07"## [1] "2026-01-01" "2026-01-02" "2026-01-03" "2026-01-04" "2026-01-05"
## [6] "2026-01-06" "2026-01-07"## [1] "목요일" "금요일" "토요일" "일요일" "월요일" "화요일" "수요일"## [1] "2025-01-01" "2025-01-02" "2025-01-03" "2025-01-04" "2025-01-05"
## [6] "2025-01-06" "2025-01-07" "2025-01-08" "2025-01-09" "2025-01-10"
## [11] "2025-01-11" "2025-01-12" "2025-01-13" "2025-01-14" "2025-01-15"
## [16] "2025-01-16" "2025-01-17" "2025-01-18" "2025-01-19" "2025-01-20"
## [21] "2025-01-21" "2025-01-22" "2025-01-23" "2025-01-24" "2025-01-25"
## [26] "2025-01-26" "2025-01-27" "2025-01-28" "2025-01-29" "2025-01-30"
## [31] "2025-01-31"## [1] "2025-01-01" "2025-01-02" "2025-01-03" "2025-01-04" "2025-01-05"
## [6] "2025-01-06" "2025-01-07"## [1] "2025-01-01" "2025-01-08" "2025-01-15" "2025-01-22" "2025-01-29"
## [6] "2025-02-05" "2025-02-12"## [1] "2025-01-01" "2025-01-08" "2025-01-15" "2025-01-22" "2025-01-29"
## [6] "2025-02-05" "2025-02-12"## [1] "2025-01-01" "2025-02-01" "2025-03-01" "2025-04-01" "2025-05-01"
## [6] "2025-06-01" "2025-07-01" "2025-08-01" "2025-09-01" "2025-10-01"
## [11] "2025-11-01" "2025-12-01"## [1] "2025-01-01" "2025-04-01" "2025-07-01" "2025-10-01"## [1] "2025-01-01" "2026-01-01" "2027-01-01" "2028-01-01" "2029-01-01"
## [6] "2030-01-01" "2031-01-01" "2032-01-01" "2033-01-01" "2034-01-01"## [1] "2025-01-30" "2025-03-02" "2025-03-30" "2025-04-30" "2025-05-30"
## [6] "2025-06-30"months(), quarters(), Sys.setlocale()
## [1] "2025-01-01"## [1] "2025-01-01" "2025-04-01" "2025-07-01" "2025-10-01"## [1] "1월" "4월" "7월" "10월"## [1] "Q1" "Q2" "Q3" "Q4"## [1] "LC_COLLATE=Korean_Korea.949;LC_CTYPE=Korean_Korea.949;LC_MONETARY=Korean_Korea.949;LC_NUMERIC=C;LC_TIME=Korean_Korea.949"## [1] "C"## [1] "January" "April" "July" "October"## [1] "Korean_Korea.949"## [1] "1월" "4월" "7월" "10월"## [1] "LC_COLLATE=Korean_Korea.949;LC_CTYPE=Korean_Korea.949;LC_MONETARY=Korean_Korea.949;LC_NUMERIC=C;LC_TIME=Korean_Korea.949"as.POSIXct(), as.POSIXlt(), strptime()
## [1] "2025-03-15 15:03:02 KST"## [1] "POSIXct" "POSIXt"## [1] 1742018582plt <- as.POSIXlt("2025/03/15, 15:03:02", format("%Y/%m/%d, %H:%M:%S"), tz = "Asia/Seoul") # 리스트
plt## [1] "2025-03-15 15:03:02 KST"## [1] "POSIXlt" "POSIXt"## Warning: 강제형변환에 의해 생성된 NA 입니다## [1] 2 3 15 15 2 125 6 73 0 NA NA## $sec
## [1] 2
##
## $min
## [1] 3
##
## $hour
## [1] 15
##
## $mday
## [1] 15
##
## $mon
## [1] 2
##
## $year
## [1] 125
##
## $wday
## [1] 6
##
## $yday
## [1] 73
##
## $isdst
## [1] 0
##
## $zone
## [1] "KST"
##
## $gmtoff
## [1] NA
##
## attr(,"tzone")
## [1] "Asia/Seoul"## [1] 15## [1] 2## [1] 125## [1] 6## [1] 15## [1] "2025-12-31"## [1] 3## [1] 364## [1] 2025## [1] 12## [1] "2025-12-31 KST"## [1] "POSIXlt" "POSIXt"## [1] 2025format(), ISOdate(), difftime()
## [1] "1969-07-20 20:17:39 UTC"## [1] "The time of the Apollo moon landing was 1969/07/20, at 20:17:39."## [1] "2020-12-31 12:00:00 GMT"## [1] "POSIXct" "POSIXt"## [1] "2020-12-31"years <- c(2025, 2026, 2027, 2028)
months <- c(1, 4, 7, 10)
days <- c(12, 19, 25, 17)
ISOdate(years, months, days)## [1] "2025-01-12 12:00:00 GMT" "2026-04-19 12:00:00 GMT"
## [3] "2027-07-25 12:00:00 GMT" "2028-10-17 12:00:00 GMT"## [1] "2025-12-31"## [1] 20453## [1] 20453
## attr(,"origin")
## [1] "1970-01-01"## [1] 0## [1] -1## [1] 1## [1] "2025-12-31"## [1] "POSIXct" "POSIXt"## [1] "1969-07-20 22:17:39 UTC"## [1] "1969-07-27 20:17:39 UTC"## [1] "1969-07-13 20:17:39 UTC"## [1] "1969-07-27"## [1] "1988-09-17"## [1] "2018-02-09"## Time difference of 10737 days## Time difference of 13615 days## Time difference of 1945 weeks## [1] "POSIXct" "POSIXt"## [1] TRUE## [1] TRUE함수
function(), ls(), rm()
rm(list = ls())
transLength <- function(x) {
tlength <- round(x * 0.9144, digits = 1)
result <- paste(tlength, "m", sep = "")
return(result)
}
ls()## [1] "transLength"## [1] "91.4m" "137.2m" "182.9m"## function(x) {
## tlength <- round(x * 0.9144, digits = 1)
## result <- paste(tlength, "m", sep = "")
## return(result)
## }## [1] "91.4m" "137.2m" "182.9m"transLength <- function(x) {
tlength <- round(x * 0.9144, digits = 1)
result <- paste(tlength, "m", sep = "")
}
transLength(y)
print(transLength(y))## [1] "91.4m" "137.2m" "182.9m"transLength <- function(x) {
tlength <- round(x * 0.9144, digits = 1)
paste(tlength, "m", sep = "")
}
transLength(y)## [1] "91.4m" "137.2m" "182.9m"transLength <- function(x) {
if (!is.numeric(x)) return("Not a Number")
tlength <- round(x * 0.9144, digits = 1)
paste(tlength, "m", sep = "")
}
transLength("ABC")## [1] "Not a Number"## [1] 4## [1] 4## [1] "91.4m" "137.2m" "182.9m"transLength <- function(x, mult, unit) {
tlength <- round(x * mult, digits = 1)
paste(tlength, unit, sep = "")
}
transLength(y, mult = 3, unit = "ft")## [1] "300ft" "450ft" "600ft"## [1] "3600in" "5400in" "7200in"# transLength(y) # ERROR!
transLength <- function(x, mult = 0.9144, unit = "m") {
tlength <- round(x * mult, digits = 1)
paste(tlength, unit, sep = "")
}
transLength(y)## [1] "91.4m" "137.2m" "182.9m"## [1] "300ft" "450ft" "600ft"## [1] "300ft" "450ft" "600ft"transLength <- function(x, mult = 0.9144, unit = "m", ...) {
tlength <- round(x * mult, ...)
paste(tlength, unit, sep = "")
}
transLength(y, digits = 2)## [1] "91.44m" "137.16m" "182.88m"## [1] "91m" "137m" "183m"transLength <- function(x, mult = 0.9144, unit = "m", digits = 1) {
tlength <- round(x * mult, digits = digits)
paste(tlength, unit, sep = "")
}
transLength(y, digits = 2)## [1] "91.44m" "137.16m" "182.88m"## [1] "91.4m" "137.2m" "182.9m"transLength <- function(x, mult = 0.9144, unit = "m", FUN = round, ...) {
tlength <- FUN(x * mult, ...)
paste(tlength, unit, sep = "")
}
transLength(y, FUN = signif, digits = 3)## [1] "91.4m" "137m" "183m"## [1] "91m" "137m" "182m"## [1] "91m" "137m" "183m"x <- 11:15
scopetest <- function(x) {
cat("This is x: ", x, "\n")
rm(x)
cat("This is x after removing x", x, "\n")
}
scopetest(x = 15:11)## This is x: 15 14 13 12 11
## This is x after removing x 11 12 13 14 15논리흐름 제어
if(), ifelse()
## [1] 3.141593## [1] 3## Warning in if (x < y) x else y: length > 1 이라는 조건이 있고, 첫번째 요소만이
## 사용될 것입니다## [1] 1 2 3 4 5## Warning in if (x > y) x else y: length > 1 이라는 조건이 있고, 첫번째 요소만이
## 사용될 것입니다## [1] 3.141593## [1] 1 0 3 4 0## [1] 3.141593 3.141593 3.141593 4.000000 5.000000switch()
center <- function(x, type) {
switch(type,
mean = mean(x),
median = median(x),
trimmed = mean(x, trim = 0.1),
"Choose one of mean, median, and trimmed"
)
}
x <- c(2, 3, 5, 7, 11, 13, 17, 19, 23, 29)
center(x, "mean")## [1] 12.9## [1] 12## [1] 12.25## [1] "Choose one of mean, median, and trimmed"서브셋
subset()
## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...## [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
## [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
## [31] 15.0 21.4## [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
## [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
## [31] 15.0 21.4## [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
## [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
## [31] 15.0 21.4## mpg hp
## Mazda RX4 21.0 110
## Mazda RX4 Wag 21.0 110
## Datsun 710 22.8 93
## Hornet 4 Drive 21.4 110
## Hornet Sportabout 18.7 175
## Valiant 18.1 105
## Duster 360 14.3 245
## Merc 240D 24.4 62
## Merc 230 22.8 95
## Merc 280 19.2 123
## Merc 280C 17.8 123
## Merc 450SE 16.4 180
## Merc 450SL 17.3 180
## Merc 450SLC 15.2 180
## Cadillac Fleetwood 10.4 205
## Lincoln Continental 10.4 215
## Chrysler Imperial 14.7 230
## Fiat 128 32.4 66
## Honda Civic 30.4 52
## Toyota Corolla 33.9 65
## Toyota Corona 21.5 97
## Dodge Challenger 15.5 150
## AMC Javelin 15.2 150
## Camaro Z28 13.3 245
## Pontiac Firebird 19.2 175
## Fiat X1-9 27.3 66
## Porsche 914-2 26.0 91
## Lotus Europa 30.4 113
## Ford Pantera L 15.8 264
## Ferrari Dino 19.7 175
## Maserati Bora 15.0 335
## Volvo 142E 21.4 109## mpg hp
## Mazda RX4 21.0 110
## Mazda RX4 Wag 21.0 110
## Datsun 710 22.8 93
## Hornet 4 Drive 21.4 110
## Hornet Sportabout 18.7 175
## Valiant 18.1 105
## Duster 360 14.3 245
## Merc 240D 24.4 62
## Merc 230 22.8 95
## Merc 280 19.2 123
## Merc 280C 17.8 123
## Merc 450SE 16.4 180
## Merc 450SL 17.3 180
## Merc 450SLC 15.2 180
## Cadillac Fleetwood 10.4 205
## Lincoln Continental 10.4 215
## Chrysler Imperial 14.7 230
## Fiat 128 32.4 66
## Honda Civic 30.4 52
## Toyota Corolla 33.9 65
## Toyota Corona 21.5 97
## Dodge Challenger 15.5 150
## AMC Javelin 15.2 150
## Camaro Z28 13.3 245
## Pontiac Firebird 19.2 175
## Fiat X1-9 27.3 66
## Porsche 914-2 26.0 91
## Lotus Europa 30.4 113
## Ford Pantera L 15.8 264
## Ferrari Dino 19.7 175
## Maserati Bora 15.0 335
## Volvo 142E 21.4 109## mpg hp wt
## Mazda RX4 21.0 110 2.620
## Mazda RX4 Wag 21.0 110 2.875
## Datsun 710 22.8 93 2.320
## Hornet 4 Drive 21.4 110 3.215
## Hornet Sportabout 18.7 175 3.440
## Valiant 18.1 105 3.460
## Duster 360 14.3 245 3.570
## Merc 240D 24.4 62 3.190
## Merc 230 22.8 95 3.150
## Merc 280 19.2 123 3.440
## Merc 280C 17.8 123 3.440
## Merc 450SE 16.4 180 4.070
## Merc 450SL 17.3 180 3.730
## Merc 450SLC 15.2 180 3.780
## Cadillac Fleetwood 10.4 205 5.250
## Lincoln Continental 10.4 215 5.424
## Chrysler Imperial 14.7 230 5.345
## Fiat 128 32.4 66 2.200
## Honda Civic 30.4 52 1.615
## Toyota Corolla 33.9 65 1.835
## Toyota Corona 21.5 97 2.465
## Dodge Challenger 15.5 150 3.520
## AMC Javelin 15.2 150 3.435
## Camaro Z28 13.3 245 3.840
## Pontiac Firebird 19.2 175 3.845
## Fiat X1-9 27.3 66 1.935
## Porsche 914-2 26.0 91 2.140
## Lotus Europa 30.4 113 1.513
## Ford Pantera L 15.8 264 3.170
## Ferrari Dino 19.7 175 2.770
## Maserati Bora 15.0 335 3.570
## Volvo 142E 21.4 109 2.780## cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 4 121.0 109 4.11 2.780 18.60 1 1 4 2## cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 4 121.0 109 4.11 2.780 18.60 1 1 4 2## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 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 5.0 3.6 1.4 0.2 setosa## Sepal.Length Sepal.Width
## 1 5.1 3.5
## 2 4.9 3.0
## 3 4.7 3.2
## 4 4.6 3.1
## 5 5.0 3.6
## 6 5.4 3.9
## 7 4.6 3.4
## 8 5.0 3.4
## 9 4.4 2.9
## 10 4.9 3.1
## 11 5.4 3.7
## 12 4.8 3.4
## 13 4.8 3.0
## 14 4.3 3.0
## 15 5.8 4.0
## 16 5.7 4.4
## 17 5.4 3.9
## 18 5.1 3.5
## 19 5.7 3.8
## 20 5.1 3.8
## 21 5.4 3.4
## 22 5.1 3.7
## 23 4.6 3.6
## 24 5.1 3.3
## 25 4.8 3.4
## 26 5.0 3.0
## 27 5.0 3.4
## 28 5.2 3.5
## 29 5.2 3.4
## 30 4.7 3.2
## 31 4.8 3.1
## 32 5.4 3.4
## 33 5.2 4.1
## 34 5.5 4.2
## 35 4.9 3.1
## 36 5.0 3.2
## 37 5.5 3.5
## 38 4.9 3.6
## 39 4.4 3.0
## 40 5.1 3.4
## 41 5.0 3.5
## 42 4.5 2.3
## 43 4.4 3.2
## 44 5.0 3.5
## 45 5.1 3.8
## 46 4.8 3.0
## 47 5.1 3.8
## 48 4.6 3.2
## 49 5.3 3.7
## 50 5.0 3.3
## 51 7.0 3.2
## 52 6.4 3.2
## 53 6.9 3.1
## 54 5.5 2.3
## 55 6.5 2.8
## 56 5.7 2.8
## 57 6.3 3.3
## 58 4.9 2.4
## 59 6.6 2.9
## 60 5.2 2.7
## 61 5.0 2.0
## 62 5.9 3.0
## 63 6.0 2.2
## 64 6.1 2.9
## 65 5.6 2.9
## 66 6.7 3.1
## 67 5.6 3.0
## 68 5.8 2.7
## 69 6.2 2.2
## 70 5.6 2.5
## 71 5.9 3.2
## 72 6.1 2.8
## 73 6.3 2.5
## 74 6.1 2.8
## 75 6.4 2.9
## 76 6.6 3.0
## 77 6.8 2.8
## 78 6.7 3.0
## 79 6.0 2.9
## 80 5.7 2.6
## 81 5.5 2.4
## 82 5.5 2.4
## 83 5.8 2.7
## 84 6.0 2.7
## 85 5.4 3.0
## 86 6.0 3.4
## 87 6.7 3.1
## 88 6.3 2.3
## 89 5.6 3.0
## 90 5.5 2.5
## 91 5.5 2.6
## 92 6.1 3.0
## 93 5.8 2.6
## 94 5.0 2.3
## 95 5.6 2.7
## 96 5.7 3.0
## 97 5.7 2.9
## 98 6.2 2.9
## 99 5.1 2.5
## 100 5.7 2.8
## 101 6.3 3.3
## 102 5.8 2.7
## 103 7.1 3.0
## 104 6.3 2.9
## 105 6.5 3.0
## 106 7.6 3.0
## 107 4.9 2.5
## 108 7.3 2.9
## 109 6.7 2.5
## 110 7.2 3.6
## 111 6.5 3.2
## 112 6.4 2.7
## 113 6.8 3.0
## 114 5.7 2.5
## 115 5.8 2.8
## 116 6.4 3.2
## 117 6.5 3.0
## 118 7.7 3.8
## 119 7.7 2.6
## 120 6.0 2.2
## 121 6.9 3.2
## 122 5.6 2.8
## 123 7.7 2.8
## 124 6.3 2.7
## 125 6.7 3.3
## 126 7.2 3.2
## 127 6.2 2.8
## 128 6.1 3.0
## 129 6.4 2.8
## 130 7.2 3.0
## 131 7.4 2.8
## 132 7.9 3.8
## 133 6.4 2.8
## 134 6.3 2.8
## 135 6.1 2.6
## 136 7.7 3.0
## 137 6.3 3.4
## 138 6.4 3.1
## 139 6.0 3.0
## 140 6.9 3.1
## 141 6.7 3.1
## 142 6.9 3.1
## 143 5.8 2.7
## 144 6.8 3.2
## 145 6.7 3.3
## 146 6.7 3.0
## 147 6.3 2.5
## 148 6.5 3.0
## 149 6.2 3.4
## 150 5.9 3.0## [1] 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4 5.1
## [19] 5.7 5.1 5.4 5.1 4.6 5.1 4.8 5.0 5.0 5.2 5.2 4.7 4.8 5.4 5.2 5.5 4.9 5.0
## [37] 5.5 4.9 4.4 5.1 5.0 4.5 4.4 5.0 5.1 4.8 5.1 4.6 5.3 5.0 7.0 6.4 6.9 5.5
## [55] 6.5 5.7 6.3 4.9 6.6 5.2 5.0 5.9 6.0 6.1 5.6 6.7 5.6 5.8 6.2 5.6 5.9 6.1
## [73] 6.3 6.1 6.4 6.6 6.8 6.7 6.0 5.7 5.5 5.5 5.8 6.0 5.4 6.0 6.7 6.3 5.6 5.5
## [91] 5.5 6.1 5.8 5.0 5.6 5.7 5.7 6.2 5.1 5.7 6.3 5.8 7.1 6.3 6.5 7.6 4.9 7.3
## [109] 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 7.7 6.0 6.9 5.6 7.7 6.3 6.7 7.2
## [127] 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7 6.3 6.4 6.0 6.9 6.7 6.9 5.8 6.8
## [145] 6.7 6.7 6.3 6.5 6.2 5.9## Sepal.Length
## 1 5.1
## 2 4.9
## 3 4.7
## 4 4.6
## 5 5.0
## 6 5.4
## 7 4.6
## 8 5.0
## 9 4.4
## 10 4.9
## 11 5.4
## 12 4.8
## 13 4.8
## 14 4.3
## 15 5.8
## 16 5.7
## 17 5.4
## 18 5.1
## 19 5.7
## 20 5.1
## 21 5.4
## 22 5.1
## 23 4.6
## 24 5.1
## 25 4.8
## 26 5.0
## 27 5.0
## 28 5.2
## 29 5.2
## 30 4.7
## 31 4.8
## 32 5.4
## 33 5.2
## 34 5.5
## 35 4.9
## 36 5.0
## 37 5.5
## 38 4.9
## 39 4.4
## 40 5.1
## 41 5.0
## 42 4.5
## 43 4.4
## 44 5.0
## 45 5.1
## 46 4.8
## 47 5.1
## 48 4.6
## 49 5.3
## 50 5.0
## 51 7.0
## 52 6.4
## 53 6.9
## 54 5.5
## 55 6.5
## 56 5.7
## 57 6.3
## 58 4.9
## 59 6.6
## 60 5.2
## 61 5.0
## 62 5.9
## 63 6.0
## 64 6.1
## 65 5.6
## 66 6.7
## 67 5.6
## 68 5.8
## 69 6.2
## 70 5.6
## 71 5.9
## 72 6.1
## 73 6.3
## 74 6.1
## 75 6.4
## 76 6.6
## 77 6.8
## 78 6.7
## 79 6.0
## 80 5.7
## 81 5.5
## 82 5.5
## 83 5.8
## 84 6.0
## 85 5.4
## 86 6.0
## 87 6.7
## 88 6.3
## 89 5.6
## 90 5.5
## 91 5.5
## 92 6.1
## 93 5.8
## 94 5.0
## 95 5.6
## 96 5.7
## 97 5.7
## 98 6.2
## 99 5.1
## 100 5.7
## 101 6.3
## 102 5.8
## 103 7.1
## 104 6.3
## 105 6.5
## 106 7.6
## 107 4.9
## 108 7.3
## 109 6.7
## 110 7.2
## 111 6.5
## 112 6.4
## 113 6.8
## 114 5.7
## 115 5.8
## 116 6.4
## 117 6.5
## 118 7.7
## 119 7.7
## 120 6.0
## 121 6.9
## 122 5.6
## 123 7.7
## 124 6.3
## 125 6.7
## 126 7.2
## 127 6.2
## 128 6.1
## 129 6.4
## 130 7.2
## 131 7.4
## 132 7.9
## 133 6.4
## 134 6.3
## 135 6.1
## 136 7.7
## 137 6.3
## 138 6.4
## 139 6.0
## 140 6.9
## 141 6.7
## 142 6.9
## 143 5.8
## 144 6.8
## 145 6.7
## 146 6.7
## 147 6.3
## 148 6.5
## 149 6.2
## 150 5.9## Sepal.Length
## 1 5.1
## 2 4.9
## 3 4.7
## 4 4.6
## 5 5.0
## 6 5.4
## 7 4.6
## 8 5.0
## 9 4.4
## 10 4.9
## 11 5.4
## 12 4.8
## 13 4.8
## 14 4.3
## 15 5.8
## 16 5.7
## 17 5.4
## 18 5.1
## 19 5.7
## 20 5.1
## 21 5.4
## 22 5.1
## 23 4.6
## 24 5.1
## 25 4.8
## 26 5.0
## 27 5.0
## 28 5.2
## 29 5.2
## 30 4.7
## 31 4.8
## 32 5.4
## 33 5.2
## 34 5.5
## 35 4.9
## 36 5.0
## 37 5.5
## 38 4.9
## 39 4.4
## 40 5.1
## 41 5.0
## 42 4.5
## 43 4.4
## 44 5.0
## 45 5.1
## 46 4.8
## 47 5.1
## 48 4.6
## 49 5.3
## 50 5.0
## 51 7.0
## 52 6.4
## 53 6.9
## 54 5.5
## 55 6.5
## 56 5.7
## 57 6.3
## 58 4.9
## 59 6.6
## 60 5.2
## 61 5.0
## 62 5.9
## 63 6.0
## 64 6.1
## 65 5.6
## 66 6.7
## 67 5.6
## 68 5.8
## 69 6.2
## 70 5.6
## 71 5.9
## 72 6.1
## 73 6.3
## 74 6.1
## 75 6.4
## 76 6.6
## 77 6.8
## 78 6.7
## 79 6.0
## 80 5.7
## 81 5.5
## 82 5.5
## 83 5.8
## 84 6.0
## 85 5.4
## 86 6.0
## 87 6.7
## 88 6.3
## 89 5.6
## 90 5.5
## 91 5.5
## 92 6.1
## 93 5.8
## 94 5.0
## 95 5.6
## 96 5.7
## 97 5.7
## 98 6.2
## 99 5.1
## 100 5.7
## 101 6.3
## 102 5.8
## 103 7.1
## 104 6.3
## 105 6.5
## 106 7.6
## 107 4.9
## 108 7.3
## 109 6.7
## 110 7.2
## 111 6.5
## 112 6.4
## 113 6.8
## 114 5.7
## 115 5.8
## 116 6.4
## 117 6.5
## 118 7.7
## 119 7.7
## 120 6.0
## 121 6.9
## 122 5.6
## 123 7.7
## 124 6.3
## 125 6.7
## 126 7.2
## 127 6.2
## 128 6.1
## 129 6.4
## 130 7.2
## 131 7.4
## 132 7.9
## 133 6.4
## 134 6.3
## 135 6.1
## 136 7.7
## 137 6.3
## 138 6.4
## 139 6.0
## 140 6.9
## 141 6.7
## 142 6.9
## 143 5.8
## 144 6.8
## 145 6.7
## 146 6.7
## 147 6.3
## 148 6.5
## 149 6.2
## 150 5.9## Sepal.Length Sepal.Width
## 1 5.1 3.5
## 2 4.9 3.0
## 3 4.7 3.2
## 4 4.6 3.1
## 5 5.0 3.6## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 103 7.1 3.0 5.9 2.1 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 136 7.7 3.0 6.1 2.3 virginica## Sepal.Length Sepal.Width Species
## 103 7.1 3.0 virginica
## 106 7.6 3.0 virginica
## 108 7.3 2.9 virginica
## 110 7.2 3.6 virginica
## 118 7.7 3.8 virginica
## 119 7.7 2.6 virginica
## 123 7.7 2.8 virginica
## 126 7.2 3.2 virginica
## 130 7.2 3.0 virginica
## 131 7.4 2.8 virginica
## 132 7.9 3.8 virginica
## 136 7.7 3.0 virginica## Sepal.Length Sepal.Width Species
## 103 7.1 3.0 virginica
## 106 7.6 3.0 virginica
## 108 7.3 2.9 virginica
## 110 7.2 3.6 virginica
## 118 7.7 3.8 virginica
## 119 7.7 2.6 virginica
## 123 7.7 2.8 virginica
## 126 7.2 3.2 virginica
## 130 7.2 3.0 virginica
## 131 7.4 2.8 virginica
## 132 7.9 3.8 virginica
## 136 7.7 3.0 virginicasample(), set.seed()
## [1] 9 4 3 1 6## [1] 9 1 7 10 6## [1] 1 4 5 9 7## [1] 4 5 8 10 1 2 3 9 6 7## [1] 9 4 7 1 2## [1] 7 2 3 1 5## [1] 9 4 7 1 2## Species Petal.Length Petal.Width
## 1 setosa 1.4 0.2
## 2 setosa 1.4 0.2
## 3 setosa 1.3 0.2
## 4 setosa 1.5 0.2
## 5 setosa 1.4 0.2
## 6 setosa 1.7 0.4
## 7 setosa 1.4 0.3
## 8 setosa 1.5 0.2
## 9 setosa 1.4 0.2
## 10 setosa 1.5 0.1
## 11 setosa 1.5 0.2
## 12 setosa 1.6 0.2
## 13 setosa 1.4 0.1
## 14 setosa 1.1 0.1
## 15 setosa 1.2 0.2
## 16 setosa 1.5 0.4
## 17 setosa 1.3 0.4
## 18 setosa 1.4 0.3
## 19 setosa 1.7 0.3
## 20 setosa 1.5 0.3
## 21 setosa 1.7 0.2
## 22 setosa 1.5 0.4
## 23 setosa 1.0 0.2
## 24 setosa 1.7 0.5
## 25 setosa 1.9 0.2
## 26 setosa 1.6 0.2
## 27 setosa 1.6 0.4
## 28 setosa 1.5 0.2
## 29 setosa 1.4 0.2
## 30 setosa 1.6 0.2
## 31 setosa 1.6 0.2
## 32 setosa 1.5 0.4
## 33 setosa 1.5 0.1
## 34 setosa 1.4 0.2
## 35 setosa 1.5 0.2
## 36 setosa 1.2 0.2
## 37 setosa 1.3 0.2
## 38 setosa 1.4 0.1
## 39 setosa 1.3 0.2
## 40 setosa 1.5 0.2
## 41 setosa 1.3 0.3
## 42 setosa 1.3 0.3
## 43 setosa 1.3 0.2
## 44 setosa 1.6 0.6
## 45 setosa 1.9 0.4
## 46 setosa 1.4 0.3
## 47 setosa 1.6 0.2
## 48 setosa 1.4 0.2
## 49 setosa 1.5 0.2
## 50 setosa 1.4 0.2
## 51 versicolor 4.7 1.4
## 52 versicolor 4.5 1.5
## 53 versicolor 4.9 1.5
## 54 versicolor 4.0 1.3
## 55 versicolor 4.6 1.5
## 56 versicolor 4.5 1.3
## 57 versicolor 4.7 1.6
## 58 versicolor 3.3 1.0
## 59 versicolor 4.6 1.3
## 60 versicolor 3.9 1.4
## 61 versicolor 3.5 1.0
## 62 versicolor 4.2 1.5
## 63 versicolor 4.0 1.0
## 64 versicolor 4.7 1.4
## 65 versicolor 3.6 1.3
## 66 versicolor 4.4 1.4
## 67 versicolor 4.5 1.5
## 68 versicolor 4.1 1.0
## 69 versicolor 4.5 1.5
## 70 versicolor 3.9 1.1
## 71 versicolor 4.8 1.8
## 72 versicolor 4.0 1.3
## 73 versicolor 4.9 1.5
## 74 versicolor 4.7 1.2
## 75 versicolor 4.3 1.3
## 76 versicolor 4.4 1.4
## 77 versicolor 4.8 1.4
## 78 versicolor 5.0 1.7
## 79 versicolor 4.5 1.5
## 80 versicolor 3.5 1.0
## 81 versicolor 3.8 1.1
## 82 versicolor 3.7 1.0
## 83 versicolor 3.9 1.2
## 84 versicolor 5.1 1.6
## 85 versicolor 4.5 1.5
## 86 versicolor 4.5 1.6
## 87 versicolor 4.7 1.5
## 88 versicolor 4.4 1.3
## 89 versicolor 4.1 1.3
## 90 versicolor 4.0 1.3
## 91 versicolor 4.4 1.2
## 92 versicolor 4.6 1.4
## 93 versicolor 4.0 1.2
## 94 versicolor 3.3 1.0
## 95 versicolor 4.2 1.3
## 96 versicolor 4.2 1.2
## 97 versicolor 4.2 1.3
## 98 versicolor 4.3 1.3
## 99 versicolor 3.0 1.1
## 100 versicolor 4.1 1.3
## 101 virginica 6.0 2.5
## 102 virginica 5.1 1.9
## 103 virginica 5.9 2.1
## 104 virginica 5.6 1.8
## 105 virginica 5.8 2.2
## 106 virginica 6.6 2.1
## 107 virginica 4.5 1.7
## 108 virginica 6.3 1.8
## 109 virginica 5.8 1.8
## 110 virginica 6.1 2.5
## 111 virginica 5.1 2.0
## 112 virginica 5.3 1.9
## 113 virginica 5.5 2.1
## 114 virginica 5.0 2.0
## 115 virginica 5.1 2.4
## 116 virginica 5.3 2.3
## 117 virginica 5.5 1.8
## 118 virginica 6.7 2.2
## 119 virginica 6.9 2.3
## 120 virginica 5.0 1.5
## 121 virginica 5.7 2.3
## 122 virginica 4.9 2.0
## 123 virginica 6.7 2.0
## 124 virginica 4.9 1.8
## 125 virginica 5.7 2.1
## 126 virginica 6.0 1.8
## 127 virginica 4.8 1.8
## 128 virginica 4.9 1.8
## 129 virginica 5.6 2.1
## 130 virginica 5.8 1.6
## 131 virginica 6.1 1.9
## 132 virginica 6.4 2.0
## 133 virginica 5.6 2.2
## 134 virginica 5.1 1.5
## 135 virginica 5.6 1.4
## 136 virginica 6.1 2.3
## 137 virginica 5.6 2.4
## 138 virginica 5.5 1.8
## 139 virginica 4.8 1.8
## 140 virginica 5.4 2.1
## 141 virginica 5.6 2.4
## 142 virginica 5.1 2.3
## 143 virginica 5.1 1.9
## 144 virginica 5.9 2.3
## 145 virginica 5.7 2.5
## 146 virginica 5.2 2.3
## 147 virginica 5.0 1.9
## 148 virginica 5.2 2.0
## 149 virginica 5.4 2.3
## 150 virginica 5.1 1.8## [1] 68 129 43## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 68 5.8 2.7 4.1 1.0 versicolor
## 129 6.4 2.8 5.6 2.1 virginica
## 43 4.4 3.2 1.3 0.2 setosaduplicated(), which(), unique()
## [1] FALSE FALSE FALSE TRUE TRUE FALSE TRUEid <- c("A001", "A002", "A003")
name <- c("Mouse", "Keyboard", "USB")
price <- c(30000, 90000, 50000)
product <- data.frame(id = id, name = name, price = price)
product## id name price
## 1 A001 Mouse 30000
## 2 A002 Keyboard 90000
## 3 A003 USB 50000## id name price
## 1 A001 Mouse 30000
## 2 A002 Keyboard 90000
## 3 A003 USB 50000
## 4 A001 Mouse 30000## [1] FALSE FALSE FALSE TRUE## id name price
## 1 A001 Mouse 30000
## 2 A002 Keyboard 90000
## 3 A003 USB 50000## [1] 4## id name price
## 1 A001 Mouse 30000
## 2 A002 Keyboard 90000
## 3 A003 USB 50000## id name price
## 1 A001 Mouse 30000
## 2 A002 Keyboard 90000
## 3 A003 USB 50000complete.cases(), na.omit(), cut()
## 'data.frame': 153 obs. of 6 variables:
## $ Ozone : int 41 36 12 18 NA 28 23 19 8 NA ...
## $ Solar.R: int 190 118 149 313 NA NA 299 99 19 194 ...
## $ Wind : num 7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...
## $ Temp : int 67 72 74 62 56 66 65 59 61 69 ...
## $ Month : int 5 5 5 5 5 5 5 5 5 5 ...
## $ Day : int 1 2 3 4 5 6 7 8 9 10 ...## [1] TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE TRUE
## [13] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [25] FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
## [37] FALSE TRUE FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE TRUE TRUE
## [49] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [61] FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [73] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
## [85] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [97] FALSE FALSE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE FALSE TRUE
## [109] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE
## [121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [133] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [145] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE## 'data.frame': 111 obs. of 6 variables:
## $ Ozone : int 41 36 12 18 23 19 8 16 11 14 ...
## $ Solar.R: int 190 118 149 313 299 99 19 256 290 274 ...
## $ Wind : num 7.4 8 12.6 11.5 8.6 13.8 20.1 9.7 9.2 10.9 ...
## $ Temp : int 67 72 74 62 65 59 61 69 66 68 ...
## $ Month : int 5 5 5 5 5 5 5 5 5 5 ...
## $ Day : int 1 2 3 4 7 8 9 12 13 14 ...## 'data.frame': 111 obs. of 6 variables:
## $ Ozone : int 41 36 12 18 23 19 8 16 11 14 ...
## $ Solar.R: int 190 118 149 313 299 99 19 256 290 274 ...
## $ Wind : num 7.4 8 12.6 11.5 8.6 13.8 20.1 9.7 9.2 10.9 ...
## $ Temp : int 67 72 74 62 65 59 61 69 66 68 ...
## $ Month : int 5 5 5 5 5 5 5 5 5 5 ...
## $ Day : int 1 2 3 4 7 8 9 12 13 14 ...
## - attr(*, "na.action")= 'omit' Named int [1:42] 5 6 10 11 25 26 27 32 33 34 ...
## ..- attr(*, "names")= chr [1:42] "5" "6" "10" "11" ...## [1] (3,4] (2,3] (3,4] (3,4] (3,4] (3,4] (3,4] (3,4] (2,3] (3,4] (3,4] (3,4]
## [13] (2,3] (2,3] (3,4] (4,5] (3,4] (3,4] (3,4] (3,4] (3,4] (3,4] (3,4] (3,4]
## [25] (3,4] (2,3] (3,4] (3,4] (3,4] (3,4] (3,4] (3,4] (4,5] (4,5] (3,4] (3,4]
## [37] (3,4] (3,4] (2,3] (3,4] (3,4] (2,3] (3,4] (3,4] (3,4] (2,3] (3,4] (3,4]
## [49] (3,4] (3,4] (3,4] (3,4] (3,4] (2,3] (2,3] (2,3] (3,4] (2,3] (2,3] (2,3]
## [61] (1,2] (2,3] (2,3] (2,3] (2,3] (3,4] (2,3] (2,3] (2,3] (2,3] (3,4] (2,3]
## [73] (2,3] (2,3] (2,3] (2,3] (2,3] (2,3] (2,3] (2,3] (2,3] (2,3] (2,3] (2,3]
## [85] (2,3] (3,4] (3,4] (2,3] (2,3] (2,3] (2,3] (2,3] (2,3] (2,3] (2,3] (2,3]
## [97] (2,3] (2,3] (2,3] (2,3] (3,4] (2,3] (2,3] (2,3] (2,3] (2,3] (2,3] (2,3]
## [109] (2,3] (3,4] (3,4] (2,3] (2,3] (2,3] (2,3] (3,4] (2,3] (3,4] (2,3] (2,3]
## [121] (3,4] (2,3] (2,3] (2,3] (3,4] (3,4] (2,3] (2,3] (2,3] (2,3] (2,3] (3,4]
## [133] (2,3] (2,3] (2,3] (2,3] (3,4] (3,4] (2,3] (3,4] (3,4] (3,4] (2,3] (3,4]
## [145] (3,4] (2,3] (2,3] (2,3] (3,4] (2,3]
## Levels: (0,1] (1,2] (2,3] (3,4] (4,5]## [1] (3.44,3.92] (2.96,3.44] (2.96,3.44] (2.96,3.44] (3.44,3.92] (3.44,3.92]
## [7] (2.96,3.44] (2.96,3.44] (2.48,2.96] (2.96,3.44] (3.44,3.92] (2.96,3.44]
## [13] (2.96,3.44] (2.96,3.44] (3.92,4.4] (3.92,4.4] (3.44,3.92] (3.44,3.92]
## [19] (3.44,3.92] (3.44,3.92] (2.96,3.44] (3.44,3.92] (3.44,3.92] (2.96,3.44]
## [25] (2.96,3.44] (2.96,3.44] (2.96,3.44] (3.44,3.92] (2.96,3.44] (2.96,3.44]
## [31] (2.96,3.44] (2.96,3.44] (3.92,4.4] (3.92,4.4] (2.96,3.44] (2.96,3.44]
## [37] (3.44,3.92] (3.44,3.92] (2.96,3.44] (2.96,3.44] (3.44,3.92] (2,2.48]
## [43] (2.96,3.44] (3.44,3.92] (3.44,3.92] (2.96,3.44] (3.44,3.92] (2.96,3.44]
## [49] (3.44,3.92] (2.96,3.44] (2.96,3.44] (2.96,3.44] (2.96,3.44] (2,2.48]
## [55] (2.48,2.96] (2.48,2.96] (2.96,3.44] (2,2.48] (2.48,2.96] (2.48,2.96]
## [61] (2,2.48] (2.96,3.44] (2,2.48] (2.48,2.96] (2.48,2.96] (2.96,3.44]
## [67] (2.96,3.44] (2.48,2.96] (2,2.48] (2.48,2.96] (2.96,3.44] (2.48,2.96]
## [73] (2.48,2.96] (2.48,2.96] (2.48,2.96] (2.96,3.44] (2.48,2.96] (2.96,3.44]
## [79] (2.48,2.96] (2.48,2.96] (2,2.48] (2,2.48] (2.48,2.96] (2.48,2.96]
## [85] (2.96,3.44] (2.96,3.44] (2.96,3.44] (2,2.48] (2.96,3.44] (2.48,2.96]
## [91] (2.48,2.96] (2.96,3.44] (2.48,2.96] (2,2.48] (2.48,2.96] (2.96,3.44]
## [97] (2.48,2.96] (2.48,2.96] (2.48,2.96] (2.48,2.96] (2.96,3.44] (2.48,2.96]
## [103] (2.96,3.44] (2.48,2.96] (2.96,3.44] (2.96,3.44] (2.48,2.96] (2.48,2.96]
## [109] (2.48,2.96] (3.44,3.92] (2.96,3.44] (2.48,2.96] (2.96,3.44] (2.48,2.96]
## [115] (2.48,2.96] (2.96,3.44] (2.96,3.44] (3.44,3.92] (2.48,2.96] (2,2.48]
## [121] (2.96,3.44] (2.48,2.96] (2.48,2.96] (2.48,2.96] (2.96,3.44] (2.96,3.44]
## [127] (2.48,2.96] (2.96,3.44] (2.48,2.96] (2.96,3.44] (2.48,2.96] (3.44,3.92]
## [133] (2.48,2.96] (2.48,2.96] (2.48,2.96] (2.96,3.44] (2.96,3.44] (2.96,3.44]
## [139] (2.96,3.44] (2.96,3.44] (2.96,3.44] (2.96,3.44] (2.48,2.96] (2.96,3.44]
## [145] (2.96,3.44] (2.96,3.44] (2.48,2.96] (2.96,3.44] (2.96,3.44] (2.96,3.44]
## Levels: (2,2.48] (2.48,2.96] (2.96,3.44] (3.44,3.92] (3.92,4.4]## iris.cut
## (0,1] (1,2] (2,3] (3,4] (4,5]
## 0 1 82 64 3## (0,1] (1,2] (2,3] (3,4] (4,5]
## 0 1 82 64 3iris.cut <- cut(x = iris$Sepal.Width,
breaks = c(0, 1, 2, 3 ,4, 5),
labels = c("Smaller", "Small", "Medium", "Big", "Bigger"))
iris.cut## [1] Big Medium Big Big Big Big Big Big Medium Big
## [11] Big Big Medium Medium Big Bigger Big Big Big Big
## [21] Big Big Big Big Big Medium Big Big Big Big
## [31] Big Big Bigger Bigger Big Big Big Big Medium Big
## [41] Big Medium Big Big Big Medium Big Big Big Big
## [51] Big Big Big Medium Medium Medium Big Medium Medium Medium
## [61] Small Medium Medium Medium Medium Big Medium Medium Medium Medium
## [71] Big Medium Medium Medium Medium Medium Medium Medium Medium Medium
## [81] Medium Medium Medium Medium Medium Big Big Medium Medium Medium
## [91] Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium
## [101] Big Medium Medium Medium Medium Medium Medium Medium Medium Big
## [111] Big Medium Medium Medium Medium Big Medium Big Medium Medium
## [121] Big Medium Medium Medium Big Big Medium Medium Medium Medium
## [131] Medium Big Medium Medium Medium Medium Big Big Medium Big
## [141] Big Big Medium Big Big Medium Medium Medium Big Medium
## Levels: Smaller Small Medium Big Bigger## iris.cut
## Smaller Small Medium Big Bigger
## 0 1 82 64 3반복 적용
apply()
2D 행렬에서
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 5 9 13 17
## [2,] 2 6 10 14 18
## [3,] 3 7 11 15 19
## [4,] 4 8 12 16 20## [1] 17 18 19 20## [1] 4 8 12 16 203D 배열에서
## , , 1
##
## [,1] [,2] [,3]
## [1,] 1 5 9
## [2,] 2 6 10
## [3,] 3 7 11
## [4,] 4 8 12
##
## , , 2
##
## [,1] [,2] [,3]
## [1,] 13 17 21
## [2,] 14 18 22
## [3,] 15 19 23
## [4,] 16 20 24## [1] "1,5,9,13,17,21" "2,6,10,14,18,22" "3,7,11,15,19,23" "4,8,12,16,20,24"## [1] "1,2,3,4,13,14,15,16" "5,6,7,8,17,18,19,20" "9,10,11,12,21,22,23,24"## [1] "1,2,3,4,5,6,7,8,9,10,11,12" "13,14,15,16,17,18,19,20,21,22,23,24"## [,1] [,2] [,3]
## [1,] "1,13" "5,17" "9,21"
## [2,] "2,14" "6,18" "10,22"
## [3,] "3,15" "7,19" "11,23"
## [4,] "4,16" "8,20" "12,24"4D 배열에서
## , , Age = Child, Survived = No
##
## Sex
## Class Male Female
## 1st 0 0
## 2nd 0 0
## 3rd 35 17
## Crew 0 0
##
## , , Age = Adult, Survived = No
##
## Sex
## Class Male Female
## 1st 118 4
## 2nd 154 13
## 3rd 387 89
## Crew 670 3
##
## , , Age = Child, Survived = Yes
##
## Sex
## Class Male Female
## 1st 5 1
## 2nd 11 13
## 3rd 13 14
## Crew 0 0
##
## , , Age = Adult, Survived = Yes
##
## Sex
## Class Male Female
## 1st 57 140
## 2nd 14 80
## 3rd 75 76
## Crew 192 20## 'table' num [1:4, 1:2, 1:2, 1:2] 0 0 35 0 0 0 17 0 118 154 ...
## - attr(*, "dimnames")=List of 4
## ..$ Class : chr [1:4] "1st" "2nd" "3rd" "Crew"
## ..$ Sex : chr [1:2] "Male" "Female"
## ..$ Age : chr [1:2] "Child" "Adult"
## ..$ Survived: chr [1:2] "No" "Yes"## 1st 2nd 3rd Crew
## 325 285 706 885## No Yes
## 1490 711## 1st 2nd 3rd Crew
## 325 285 706 885## Survived
## Class No Yes
## 1st 122 203
## 2nd 167 118
## 3rd 528 178
## Crew 673 212lapply(), sapply()
행렬, 배열에서
exams <- list(s20 = c(78, 89, 91, 85, 85, 87),
s21 = c(85, 86, 97, 99, 90),
s22 = c(98, 96, 89, 90, 93, 85, 92),
s23 = c(98, 96, 91, 88, 93, 99)
)
exams## $s20
## [1] 78 89 91 85 85 87
##
## $s21
## [1] 85 86 97 99 90
##
## $s22
## [1] 98 96 89 90 93 85 92
##
## $s23
## [1] 98 96 91 88 93 99## $s20
## [1] 6
##
## $s21
## [1] 5
##
## $s22
## [1] 7
##
## $s23
## [1] 6## s20 s21 s22 s23
## 6 5 7 6## s20 s21 s22 s23
## 85.83333 91.40000 91.85714 94.16667## s20 s21 s22 s23
## 4.490731 6.348228 4.375255 4.262237## s20 s21 s22 s23
## [1,] 78 85 85 88
## [2,] 91 99 98 99데이터프레임에서
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 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 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa## $Sepal.Length
## [1] "numeric"
##
## $Sepal.Width
## [1] "numeric"
##
## $Petal.Length
## [1] "numeric"
##
## $Petal.Width
## [1] "numeric"
##
## $Species
## [1] "factor"## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## "numeric" "numeric" "numeric" "numeric" "factor"## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 5.843333 3.057333 3.758000 1.199333 NA## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 5.843333 3.057333 3.758000 1.199333 NA## [[1]]
## [1] 1 1 1 1
##
## [[2]]
## [1] 2 2 2
##
## [[3]]
## [1] 3 3
##
## [[4]]
## [1] 4집단 요약
sapply()
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1mtcars <- within(mtcars,
am <- factor(am,
levels = c(0, 1),
labels = c("Automatic",
"Manual")
)
)
head(mtcars)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 Manual 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 Manual 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 Manual 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 Automatic 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 Automatic 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 Automatic 3 1## $Automatic
## [1] 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4 10.4 14.7 21.5
## [16] 15.5 15.2 13.3 19.2
##
## $Manual
## [1] 21.0 21.0 22.8 32.4 30.4 33.9 27.3 26.0 30.4 15.8 19.7 15.0 21.4## [1] 17.14737## [1] 24.39231## Automatic Manual
## 17.14737 24.39231## $Automatic
## [1] 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4 10.4 14.7 21.5
## [16] 15.5 15.2 13.3 19.2
##
## $Manual
## [1] 21.0 21.0 22.8 32.4 30.4 33.9 27.3 26.0 30.4 15.8 19.7 15.0 21.4unstack()
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 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 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa## setosa versicolor virginica
## 1 5.1 7.0 6.3
## 2 4.9 6.4 5.8
## 3 4.7 6.9 7.1
## 4 4.6 5.5 6.3
## 5 5.0 6.5 6.5
## 6 5.4 5.7 7.6## 'data.frame': 50 obs. of 3 variables:
## $ setosa : num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ versicolor: num 7 6.4 6.9 5.5 6.5 5.7 6.3 4.9 6.6 5.2 ...
## $ virginica : num 6.3 5.8 7.1 6.3 6.5 7.6 4.9 7.3 6.7 7.2 ...## setosa versicolor virginica
## Min. :4.300 Min. :4.900 Min. :4.900
## 1st Qu.:4.800 1st Qu.:5.600 1st Qu.:6.225
## Median :5.000 Median :5.900 Median :6.500
## Mean :5.006 Mean :5.936 Mean :6.588
## 3rd Qu.:5.200 3rd Qu.:6.300 3rd Qu.:6.900
## Max. :5.800 Max. :7.000 Max. :7.900tapply()
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 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 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa## setosa versicolor virginica
## 5.006 5.936 6.588## setosa versicolor virginica
## 50 50 50## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 Manual 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 Manual 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 Manual 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 Automatic 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 Automatic 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 Automatic 3 1## Automatic Manual
## 4 22.900 28.07500
## 6 19.125 20.56667
## 8 15.050 15.40000## Transmission
## Cyliner Automatic Manual
## 4 22.900 28.07500
## 6 19.125 20.56667
## 8 15.050 15.40000aggregate()
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 Manual 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 Manual 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 Manual 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 Automatic 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 Automatic 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 Automatic 3 1## Group.1 Group.2 x
## 1 4 Automatic 22.90000
## 2 6 Automatic 19.12500
## 3 8 Automatic 15.05000
## 4 4 Manual 28.07500
## 5 6 Manual 20.56667
## 6 8 Manual 15.40000## Group.cyl Group.am mpg cyl disp hp drat wt
## 1 4 Automatic 22.90000 4 135.8667 84.66667 3.770000 2.935000
## 2 6 Automatic 19.12500 6 204.5500 115.25000 3.420000 3.388750
## 3 8 Automatic 15.05000 8 357.6167 194.16667 3.120833 4.104083
## 4 4 Manual 28.07500 4 93.6125 81.87500 4.183750 2.042250
## 5 6 Manual 20.56667 6 155.0000 131.66667 3.806667 2.755000
## 6 8 Manual 15.40000 8 326.0000 299.50000 3.880000 3.370000## Species Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 setosa 5.006 3.428 1.462 0.246
## 2 versicolor 5.936 2.770 4.260 1.326
## 3 virginica 6.588 2.974 5.552 2.026by()
## iris$Species: setosa
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.300 Min. :1.000 Min. :0.100
## 1st Qu.:4.800 1st Qu.:3.200 1st Qu.:1.400 1st Qu.:0.200
## Median :5.000 Median :3.400 Median :1.500 Median :0.200
## Mean :5.006 Mean :3.428 Mean :1.462 Mean :0.246
## 3rd Qu.:5.200 3rd Qu.:3.675 3rd Qu.:1.575 3rd Qu.:0.300
## Max. :5.800 Max. :4.400 Max. :1.900 Max. :0.600
## Species
## setosa :50
## versicolor: 0
## virginica : 0
##
##
##
## ------------------------------------------------------------
## iris$Species: versicolor
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## Min. :4.900 Min. :2.000 Min. :3.00 Min. :1.000 setosa : 0
## 1st Qu.:5.600 1st Qu.:2.525 1st Qu.:4.00 1st Qu.:1.200 versicolor:50
## Median :5.900 Median :2.800 Median :4.35 Median :1.300 virginica : 0
## Mean :5.936 Mean :2.770 Mean :4.26 Mean :1.326
## 3rd Qu.:6.300 3rd Qu.:3.000 3rd Qu.:4.60 3rd Qu.:1.500
## Max. :7.000 Max. :3.400 Max. :5.10 Max. :1.800
## ------------------------------------------------------------
## iris$Species: virginica
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.900 Min. :2.200 Min. :4.500 Min. :1.400
## 1st Qu.:6.225 1st Qu.:2.800 1st Qu.:5.100 1st Qu.:1.800
## Median :6.500 Median :3.000 Median :5.550 Median :2.000
## Mean :6.588 Mean :2.974 Mean :5.552 Mean :2.026
## 3rd Qu.:6.900 3rd Qu.:3.175 3rd Qu.:5.875 3rd Qu.:2.300
## Max. :7.900 Max. :3.800 Max. :6.900 Max. :2.500
## Species
## setosa : 0
## versicolor: 0
## virginica :50
##
##
## table()
##
## 3 4 5
## 15 12 5##
## Automatic Manual
## 19 13##
## 3 4 5
## Automatic 15 4 0
## Manual 0 8 5분할, 적용, 결합
##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union## Ozone Solar.R Wind Temp Month Day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 NA NA 14.3 56 5 5
## 6 28 NA 14.9 66 5 6## 'data.frame': 153 obs. of 6 variables:
## $ Ozone : int 41 36 12 18 NA 28 23 19 8 NA ...
## $ Solar.R: int 190 118 149 313 NA NA 299 99 19 194 ...
## $ Wind : num 7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...
## $ Temp : int 67 72 74 62 56 66 65 59 61 69 ...
## $ Month : int 5 5 5 5 5 5 5 5 5 5 ...
## $ Day : int 1 2 3 4 5 6 7 8 9 10 ...filter()
## Ozone Solar.R Wind Temp Month Day
## 1 NA 286 8.6 78 6 1
## 2 NA 287 9.7 74 6 2
## 3 NA 242 16.1 67 6 3
## 4 NA 186 9.2 84 6 4
## 5 NA 220 8.6 85 6 5
## 6 NA 264 14.3 79 6 6
## 7 29 127 9.7 82 6 7
## 8 NA 273 6.9 87 6 8
## 9 71 291 13.8 90 6 9
## 10 39 323 11.5 87 6 10
## 11 NA 259 10.9 93 6 11
## 12 NA 250 9.2 92 6 12
## 13 23 148 8.0 82 6 13
## 14 NA 332 13.8 80 6 14
## 15 NA 322 11.5 79 6 15
## 16 21 191 14.9 77 6 16
## 17 37 284 20.7 72 6 17
## 18 20 37 9.2 65 6 18
## 19 12 120 11.5 73 6 19
## 20 13 137 10.3 76 6 20
## 21 NA 150 6.3 77 6 21
## 22 NA 59 1.7 76 6 22
## 23 NA 91 4.6 76 6 23
## 24 NA 250 6.3 76 6 24
## 25 NA 135 8.0 75 6 25
## 26 NA 127 8.0 78 6 26
## 27 NA 47 10.3 73 6 27
## 28 NA 98 11.5 80 6 28
## 29 NA 31 14.9 77 6 29
## 30 NA 138 8.0 83 6 30## Ozone Solar.R Wind Temp Month Day
## 32 NA 286 8.6 78 6 1
## 33 NA 287 9.7 74 6 2
## 34 NA 242 16.1 67 6 3
## 35 NA 186 9.2 84 6 4
## 36 NA 220 8.6 85 6 5
## 37 NA 264 14.3 79 6 6
## 38 29 127 9.7 82 6 7
## 39 NA 273 6.9 87 6 8
## 40 71 291 13.8 90 6 9
## 41 39 323 11.5 87 6 10
## 42 NA 259 10.9 93 6 11
## 43 NA 250 9.2 92 6 12
## 44 23 148 8.0 82 6 13
## 45 NA 332 13.8 80 6 14
## 46 NA 322 11.5 79 6 15
## 47 21 191 14.9 77 6 16
## 48 37 284 20.7 72 6 17
## 49 20 37 9.2 65 6 18
## 50 12 120 11.5 73 6 19
## 51 13 137 10.3 76 6 20
## 52 NA 150 6.3 77 6 21
## 53 NA 59 1.7 76 6 22
## 54 NA 91 4.6 76 6 23
## 55 NA 250 6.3 76 6 24
## 56 NA 135 8.0 75 6 25
## 57 NA 127 8.0 78 6 26
## 58 NA 47 10.3 73 6 27
## 59 NA 98 11.5 80 6 28
## 60 NA 31 14.9 77 6 29
## 61 NA 138 8.0 83 6 30## Ozone Solar.R Wind Temp Month Day
## 32 NA 286 8.6 78 6 1
## 33 NA 287 9.7 74 6 2
## 34 NA 242 16.1 67 6 3
## 35 NA 186 9.2 84 6 4
## 36 NA 220 8.6 85 6 5
## 37 NA 264 14.3 79 6 6
## 38 29 127 9.7 82 6 7
## 39 NA 273 6.9 87 6 8
## 40 71 291 13.8 90 6 9
## 41 39 323 11.5 87 6 10
## 42 NA 259 10.9 93 6 11
## 43 NA 250 9.2 92 6 12
## 44 23 148 8.0 82 6 13
## 45 NA 332 13.8 80 6 14
## 46 NA 322 11.5 79 6 15
## 47 21 191 14.9 77 6 16
## 48 37 284 20.7 72 6 17
## 49 20 37 9.2 65 6 18
## 50 12 120 11.5 73 6 19
## 51 13 137 10.3 76 6 20
## 52 NA 150 6.3 77 6 21
## 53 NA 59 1.7 76 6 22
## 54 NA 91 4.6 76 6 23
## 55 NA 250 6.3 76 6 24
## 56 NA 135 8.0 75 6 25
## 57 NA 127 8.0 78 6 26
## 58 NA 47 10.3 73 6 27
## 59 NA 98 11.5 80 6 28
## 60 NA 31 14.9 77 6 29
## 61 NA 138 8.0 83 6 30## Ozone Solar.R Wind Temp Month Day
## 1 NA 259 10.9 93 6 11
## 2 NA 250 9.2 92 6 12## Ozone Solar.R Wind Temp Month Day
## 1 NA 259 10.9 93 6 11
## 2 NA 250 9.2 92 6 12## Ozone Solar.R Wind Temp Month Day
## 1 115 223 5.7 79 5 30
## 2 NA 259 10.9 93 6 11
## 3 NA 250 9.2 92 6 12
## 4 135 269 4.1 84 7 1
## 5 97 267 6.3 92 7 8
## 6 97 272 5.7 92 7 9
## 7 85 175 7.4 89 7 10
## 8 NA 291 14.9 91 7 14
## 9 108 223 8.0 85 7 25
## 10 82 213 7.4 88 7 28
## 11 122 255 4.0 89 8 7
## 12 89 229 10.3 90 8 8
## 13 110 207 8.0 90 8 9
## 14 NA 222 8.6 92 8 10
## 15 168 238 3.4 81 8 25
## 16 76 203 9.7 97 8 28
## 17 118 225 2.3 94 8 29
## 18 84 237 6.3 96 8 30
## 19 85 188 6.3 94 8 31
## 20 96 167 6.9 91 9 1
## 21 78 197 5.1 92 9 2
## 22 73 183 2.8 93 9 3
## 23 91 189 4.6 93 9 4slice()
## Ozone Solar.R Wind Temp Month Day
## 1 28 NA 14.9 66 5 6
## 2 23 299 8.6 65 5 7
## 3 19 99 13.8 59 5 8
## 4 8 19 20.1 61 5 9
## 5 NA 194 8.6 69 5 10## Ozone Solar.R Wind Temp Month Day
## 1 20 223 11.5 68 9 30## Ozone Solar.R Wind Temp Month Day
## 1 30 193 6.9 70 9 26
## 2 NA 145 13.2 77 9 27
## 3 14 191 14.3 75 9 28
## 4 18 131 8.0 76 9 29
## 5 20 223 11.5 68 9 30arrange()
## Ozone Solar.R Wind Temp Month Day
## 1 NA NA 14.3 56 5 5
## 2 6 78 18.4 57 5 18
## 3 NA 66 16.6 57 5 25
## 4 NA NA 8.0 57 5 27
## 5 18 65 13.2 58 5 15## Ozone Solar.R Wind Temp Month Day
## 1 76 203 9.7 97 8 28
## 2 84 237 6.3 96 8 30
## 3 118 225 2.3 94 8 29
## 4 85 188 6.3 94 8 31
## 5 NA 259 10.9 93 6 11select(), rename(), distinct()
## Month Day Temp
## 1 5 1 67
## 2 5 2 72
## 3 5 3 74
## 4 5 4 62
## 5 5 5 56## Temp Month Day
## 1 67 5 1
## 2 72 5 2
## 3 74 5 3
## 4 62 5 4
## 5 56 5 5## Ozone Solar.R Wind
## 1 41 190 7.4
## 2 36 118 8.0
## 3 12 149 12.6
## 4 18 313 11.5
## 5 NA NA 14.3## Solar
## 1 190
## 2 118
## 3 149
## 4 313
## 5 NA## Ozone Solar Wind Temp Month Day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 NA NA 14.3 56 5 5## Month
## 1 5
## 2 6
## 3 7
## 4 8
## 5 9mutate()
## Ozone Solar.R Wind Temp Month Day Temp.C Diff
## 1 41 190 7.4 67 5 1 19.44444 -6.045752
## 2 36 118 8.0 72 5 2 22.22222 -3.267974
## 3 12 149 12.6 74 5 3 23.33333 -2.156863
## 4 18 313 11.5 62 5 4 16.66667 -8.823529
## 5 NA NA 14.3 56 5 5 13.33333 -12.156863
## 6 28 NA 14.9 66 5 6 18.88889 -6.601307## Ozone Solar.R Wind Temp Month Day Temp.C
## 1 41 190 7.4 67 5 1 19.44444
## 2 36 118 8.0 72 5 2 22.22222
## 3 12 149 12.6 74 5 3 23.33333
## 4 18 313 11.5 62 5 4 16.66667
## 5 NA NA 14.3 56 5 5 13.33333
## 6 28 NA 14.9 66 5 6 18.88889summarise()
summarise(airquality,
mean(Temp),
median(Temp, na.rm = TRUE),
sd(Temp, na.rm = TRUE),
max(Temp, na.rm = TRUE),
min(Temp, na.rm = TRUE))## mean(Temp) median(Temp, na.rm = TRUE) sd(Temp, na.rm = TRUE)
## 1 77.88235 79 9.46527
## max(Temp, na.rm = TRUE) min(Temp, na.rm = TRUE)
## 1 97 56summarise(airquality,
Mean = mean(Temp),
Median = median(Temp, na.rm = TRUE),
SD = sd(Temp, na.rm = TRUE),
Max = max(Temp, na.rm = TRUE),
Min = min(Temp, na.rm = TRUE),
N = n(),
Distinct.Month = n_distinct(Month),
Distinct.First = first(Month),
Distinct.Last = last(Month))## Mean Median SD Max Min N Distinct.Month Distinct.First
## 1 77.88235 79 9.46527 97 56 153 5 5
## Distinct.Last
## 1 9## Ozone Solar.R Wind Temp Month Day
## 1 14 274 10.9 68 5 14
## 2 13 137 10.3 76 6 20
## 3 80 294 8.6 86 7 24
## 4 1 8 9.7 59 5 21
## 5 65 157 9.7 80 8 14## Ozone Solar.R Wind Temp Month Day
## 1 27 175 14.9 81 7 13
## 2 23 299 8.6 65 5 7
## 3 10 264 14.3 73 7 12
## 4 61 285 6.3 84 7 18
## 5 NA 264 14.3 79 6 6
## 6 28 273 11.5 82 8 13
## 7 23 115 7.4 76 8 18
## 8 NA 242 16.1 67 6 3group_by()
## [1] "grouped_df" "tbl_df" "tbl" "data.frame"## # A tibble: 153 x 6
## # Groups: Month [5]
## Ozone Solar.R Wind Temp Month Day
## <int> <int> <dbl> <int> <int> <int>
## 1 41 190 7.4 67 5 1
## 2 36 118 8 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 NA NA 14.3 56 5 5
## 6 28 NA 14.9 66 5 6
## 7 23 299 8.6 65 5 7
## 8 19 99 13.8 59 5 8
## 9 8 19 20.1 61 5 9
## 10 NA 194 8.6 69 5 10
## # ... with 143 more rows## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 5 x 2
## Month Mean.Temp
## <int> <dbl>
## 1 5 65.5
## 2 6 79.1
## 3 7 83.9
## 4 8 84.0
## 5 9 76.9summarise(air.group,
Mean.Temp = mean(Temp, na.rm = TRUE),
SD.Temp = sd(Temp, na.rm = TRUE),
Days = n())## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 5 x 4
## Month Mean.Temp SD.Temp Days
## <int> <dbl> <dbl> <int>
## 1 5 65.5 6.85 31
## 2 6 79.1 6.60 30
## 3 7 83.9 4.32 31
## 4 8 84.0 6.59 31
## 5 9 76.9 8.36 30%>%
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 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 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 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 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosagroup_by(), summarise()
a1 <- select(airquality, Ozone, Temp, Month)
a2 <- group_by(a1, Month)
a3 <- summarise(a2,
Mean.Ozone = mean(Ozone, na.rm = TRUE),
Mean.Temp = mean(Temp, na.rm = TRUE))## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 3
## Month Mean.Ozone Mean.Temp
## <int> <dbl> <dbl>
## 1 7 59.1 83.9
## 2 8 60.0 84.0air <- airquality %>%
select(Ozone, Temp, Month) %>%
group_by(Month) %>%
summarise(Mean.Ozone = mean(Ozone, na.rm = TRUE),
Mean.Temp = mean(Temp, na.rm = TRUE)) %>%
filter(Mean.Ozone > 40 | Mean.Temp > 80)## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 3
## Month Mean.Ozone Mean.Temp
## <int> <dbl> <dbl>
## 1 7 59.1 83.9
## 2 8 60.0 84.0형태 변환
library(reshape2)
## subject time age weight height
## 1 John Smith 1 33 90 1.87
## 2 Mary Smith 1 NA NA 1.54melt()
## Using subject as id variables## subject variable value
## 1 John Smith time 1.00
## 2 Mary Smith time 1.00
## 3 John Smith age 33.00
## 4 Mary Smith age NA
## 5 John Smith weight 90.00
## 6 Mary Smith weight NA
## 7 John Smith height 1.87
## 8 Mary Smith height 1.54## subject variable value
## 1 John Smith time 1.00
## 2 Mary Smith time 1.00
## 3 John Smith age 33.00
## 4 Mary Smith age NA
## 5 John Smith weight 90.00
## 6 Mary Smith weight NA
## 7 John Smith height 1.87
## 8 Mary Smith height 1.54## subject variable value
## 1 John Smith time 1.00
## 2 Mary Smith time 1.00
## 3 John Smith age 33.00
## 4 Mary Smith age NA
## 5 John Smith weight 90.00
## 6 Mary Smith weight NA
## 7 John Smith height 1.87
## 8 Mary Smith height 1.54## subject variable value
## 1 John Smith time 1.00
## 2 Mary Smith time 1.00
## 3 John Smith age 33.00
## 4 Mary Smith age NA
## 5 John Smith weight 90.00
## 6 Mary Smith weight NA
## 7 John Smith height 1.87
## 8 Mary Smith height 1.54dcast()
smiths.long <- melt(data = smiths,
id.vars = "subject",
measure.vars = c("time", "age", "weight", "height"),
variable.name = "var",
value.name = "val")
dcast(data = smiths.long, formula = subject ~ var,
value.var = "val")## subject time age weight height
## 1 John Smith 1 33 90 1.87
## 2 Mary Smith 1 NA NA 1.54## Ozone Solar.R Wind Temp Month Day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 NA NA 14.3 56 5 5
## 6 28 NA 14.9 66 5 6## Month Day variable value
## 1 5 1 Ozone 41
## 2 5 2 Ozone 36
## 3 5 3 Ozone 12
## 4 5 4 Ozone 18
## 5 5 5 Ozone NA
## 6 5 6 Ozone 28## Month Day Ozone Solar.R Wind Temp
## 1 5 1 41 190 7.4 67
## 2 5 2 36 118 8.0 72
## 3 5 3 12 149 12.6 74
## 4 5 4 18 313 11.5 62
## 5 5 5 NA NA 14.3 56
## 6 5 6 28 NA 14.9 66## Aggregation function missing: defaulting to length## Month Ozone Solar.R Wind Temp
## 1 5 31 31 31 31
## 2 6 30 30 30 30
## 3 7 31 31 31 31
## 4 8 31 31 31 31
## 5 9 30 30 30 30## Month Ozone Solar.R Wind Temp
## 1 5 23.61538 181.2963 11.622581 65.54839
## 2 6 29.44444 190.1667 10.266667 79.10000
## 3 7 59.11538 216.4839 8.941935 83.90323
## 4 8 59.96154 171.8571 8.793548 83.96774
## 5 9 31.44828 167.4333 10.180000 76.90000library(tidyr)
gather()
##
## Attaching package: 'tidyr'## The following object is masked from 'package:reshape2':
##
## smithsaq.long <- gather(airquality,
key = Factor,
value = Measurement,
Ozone:Temp)
aq.long <- gather(airquality,
key = Factor,
value = Measurement,
-Month, -Day)
aq.long <- gather(airquality,
key = Factor,
value = Measurement,
1:4)
aq.long <- gather(airquality,
key = Factor,
value = Measurement,
Ozone, Solar.R, Wind, Temp)spread()
## Month Day Ozone Solar.R Temp Wind
## 1 5 1 41 190 67 7.4
## 2 5 2 36 118 72 8.0
## 3 5 3 12 149 74 12.6
## 4 5 4 18 313 62 11.5
## 5 5 5 NA NA 56 14.3
## 6 5 6 28 NA 66 14.9
## 7 5 7 23 299 65 8.6
## 8 5 8 19 99 59 13.8
## 9 5 9 8 19 61 20.1
## 10 5 10 NA 194 69 8.6
## 11 5 11 7 NA 74 6.9
## 12 5 12 16 256 69 9.7
## 13 5 13 11 290 66 9.2
## 14 5 14 14 274 68 10.9
## 15 5 15 18 65 58 13.2
## 16 5 16 14 334 64 11.5
## 17 5 17 34 307 66 12.0
## 18 5 18 6 78 57 18.4
## 19 5 19 30 322 68 11.5
## 20 5 20 11 44 62 9.7
## 21 5 21 1 8 59 9.7
## 22 5 22 11 320 73 16.6
## 23 5 23 4 25 61 9.7
## 24 5 24 32 92 61 12.0
## 25 5 25 NA 66 57 16.6
## 26 5 26 NA 266 58 14.9
## 27 5 27 NA NA 57 8.0
## 28 5 28 23 13 67 12.0
## 29 5 29 45 252 81 14.9
## 30 5 30 115 223 79 5.7
## 31 5 31 37 279 76 7.4
## 32 6 1 NA 286 78 8.6
## 33 6 2 NA 287 74 9.7
## 34 6 3 NA 242 67 16.1
## 35 6 4 NA 186 84 9.2
## 36 6 5 NA 220 85 8.6
## 37 6 6 NA 264 79 14.3
## 38 6 7 29 127 82 9.7
## 39 6 8 NA 273 87 6.9
## 40 6 9 71 291 90 13.8
## 41 6 10 39 323 87 11.5
## 42 6 11 NA 259 93 10.9
## 43 6 12 NA 250 92 9.2
## 44 6 13 23 148 82 8.0
## 45 6 14 NA 332 80 13.8
## 46 6 15 NA 322 79 11.5
## 47 6 16 21 191 77 14.9
## 48 6 17 37 284 72 20.7
## 49 6 18 20 37 65 9.2
## 50 6 19 12 120 73 11.5
## 51 6 20 13 137 76 10.3
## 52 6 21 NA 150 77 6.3
## 53 6 22 NA 59 76 1.7
## 54 6 23 NA 91 76 4.6
## 55 6 24 NA 250 76 6.3
## 56 6 25 NA 135 75 8.0
## 57 6 26 NA 127 78 8.0
## 58 6 27 NA 47 73 10.3
## 59 6 28 NA 98 80 11.5
## 60 6 29 NA 31 77 14.9
## 61 6 30 NA 138 83 8.0
## 62 7 1 135 269 84 4.1
## 63 7 2 49 248 85 9.2
## 64 7 3 32 236 81 9.2
## 65 7 4 NA 101 84 10.9
## 66 7 5 64 175 83 4.6
## 67 7 6 40 314 83 10.9
## 68 7 7 77 276 88 5.1
## 69 7 8 97 267 92 6.3
## 70 7 9 97 272 92 5.7
## 71 7 10 85 175 89 7.4
## 72 7 11 NA 139 82 8.6
## 73 7 12 10 264 73 14.3
## 74 7 13 27 175 81 14.9
## 75 7 14 NA 291 91 14.9
## 76 7 15 7 48 80 14.3
## 77 7 16 48 260 81 6.9
## 78 7 17 35 274 82 10.3
## 79 7 18 61 285 84 6.3
## 80 7 19 79 187 87 5.1
## 81 7 20 63 220 85 11.5
## 82 7 21 16 7 74 6.9
## 83 7 22 NA 258 81 9.7
## 84 7 23 NA 295 82 11.5
## 85 7 24 80 294 86 8.6
## 86 7 25 108 223 85 8.0
## 87 7 26 20 81 82 8.6
## 88 7 27 52 82 86 12.0
## 89 7 28 82 213 88 7.4
## 90 7 29 50 275 86 7.4
## 91 7 30 64 253 83 7.4
## 92 7 31 59 254 81 9.2
## 93 8 1 39 83 81 6.9
## 94 8 2 9 24 81 13.8
## 95 8 3 16 77 82 7.4
## 96 8 4 78 NA 86 6.9
## 97 8 5 35 NA 85 7.4
## 98 8 6 66 NA 87 4.6
## 99 8 7 122 255 89 4.0
## 100 8 8 89 229 90 10.3
## 101 8 9 110 207 90 8.0
## 102 8 10 NA 222 92 8.6
## 103 8 11 NA 137 86 11.5
## 104 8 12 44 192 86 11.5
## 105 8 13 28 273 82 11.5
## 106 8 14 65 157 80 9.7
## 107 8 15 NA 64 79 11.5
## 108 8 16 22 71 77 10.3
## 109 8 17 59 51 79 6.3
## 110 8 18 23 115 76 7.4
## 111 8 19 31 244 78 10.9
## 112 8 20 44 190 78 10.3
## 113 8 21 21 259 77 15.5
## 114 8 22 9 36 72 14.3
## 115 8 23 NA 255 75 12.6
## 116 8 24 45 212 79 9.7
## 117 8 25 168 238 81 3.4
## 118 8 26 73 215 86 8.0
## 119 8 27 NA 153 88 5.7
## 120 8 28 76 203 97 9.7
## 121 8 29 118 225 94 2.3
## 122 8 30 84 237 96 6.3
## 123 8 31 85 188 94 6.3
## 124 9 1 96 167 91 6.9
## 125 9 2 78 197 92 5.1
## 126 9 3 73 183 93 2.8
## 127 9 4 91 189 93 4.6
## 128 9 5 47 95 87 7.4
## 129 9 6 32 92 84 15.5
## 130 9 7 20 252 80 10.9
## 131 9 8 23 220 78 10.3
## 132 9 9 21 230 75 10.9
## 133 9 10 24 259 73 9.7
## 134 9 11 44 236 81 14.9
## 135 9 12 21 259 76 15.5
## 136 9 13 28 238 77 6.3
## 137 9 14 9 24 71 10.9
## 138 9 15 13 112 71 11.5
## 139 9 16 46 237 78 6.9
## 140 9 17 18 224 67 13.8
## 141 9 18 13 27 76 10.3
## 142 9 19 24 238 68 10.3
## 143 9 20 16 201 82 8.0
## 144 9 21 13 238 64 12.6
## 145 9 22 23 14 71 9.2
## 146 9 23 36 139 81 10.3
## 147 9 24 7 49 69 10.3
## 148 9 25 14 20 63 16.6
## 149 9 26 30 193 70 6.9
## 150 9 27 NA 145 77 13.2
## 151 9 28 14 191 75 14.3
## 152 9 29 18 131 76 8.0
## 153 9 30 20 223 68 11.5## Month Day Ozone Solar.R Temp Wind
## 1 5 1 41 190 67 7.4
## 2 5 2 36 118 72 8.0
## 3 5 3 12 149 74 12.6
## 4 5 4 18 313 62 11.5
## 5 5 5 NA NA 56 14.3
## 6 5 6 28 NA 66 14.9
## 7 5 7 23 299 65 8.6
## 8 5 8 19 99 59 13.8
## 9 5 9 8 19 61 20.1
## 10 5 10 NA 194 69 8.6
## 11 5 11 7 NA 74 6.9
## 12 5 12 16 256 69 9.7
## 13 5 13 11 290 66 9.2
## 14 5 14 14 274 68 10.9
## 15 5 15 18 65 58 13.2
## 16 5 16 14 334 64 11.5
## 17 5 17 34 307 66 12.0
## 18 5 18 6 78 57 18.4
## 19 5 19 30 322 68 11.5
## 20 5 20 11 44 62 9.7
## 21 5 21 1 8 59 9.7
## 22 5 22 11 320 73 16.6
## 23 5 23 4 25 61 9.7
## 24 5 24 32 92 61 12.0
## 25 5 25 NA 66 57 16.6
## 26 5 26 NA 266 58 14.9
## 27 5 27 NA NA 57 8.0
## 28 5 28 23 13 67 12.0
## 29 5 29 45 252 81 14.9
## 30 5 30 115 223 79 5.7
## 31 5 31 37 279 76 7.4
## 32 6 1 NA 286 78 8.6
## 33 6 2 NA 287 74 9.7
## 34 6 3 NA 242 67 16.1
## 35 6 4 NA 186 84 9.2
## 36 6 5 NA 220 85 8.6
## 37 6 6 NA 264 79 14.3
## 38 6 7 29 127 82 9.7
## 39 6 8 NA 273 87 6.9
## 40 6 9 71 291 90 13.8
## 41 6 10 39 323 87 11.5
## 42 6 11 NA 259 93 10.9
## 43 6 12 NA 250 92 9.2
## 44 6 13 23 148 82 8.0
## 45 6 14 NA 332 80 13.8
## 46 6 15 NA 322 79 11.5
## 47 6 16 21 191 77 14.9
## 48 6 17 37 284 72 20.7
## 49 6 18 20 37 65 9.2
## 50 6 19 12 120 73 11.5
## 51 6 20 13 137 76 10.3
## 52 6 21 NA 150 77 6.3
## 53 6 22 NA 59 76 1.7
## 54 6 23 NA 91 76 4.6
## 55 6 24 NA 250 76 6.3
## 56 6 25 NA 135 75 8.0
## 57 6 26 NA 127 78 8.0
## 58 6 27 NA 47 73 10.3
## 59 6 28 NA 98 80 11.5
## 60 6 29 NA 31 77 14.9
## 61 6 30 NA 138 83 8.0
## 62 7 1 135 269 84 4.1
## 63 7 2 49 248 85 9.2
## 64 7 3 32 236 81 9.2
## 65 7 4 NA 101 84 10.9
## 66 7 5 64 175 83 4.6
## 67 7 6 40 314 83 10.9
## 68 7 7 77 276 88 5.1
## 69 7 8 97 267 92 6.3
## 70 7 9 97 272 92 5.7
## 71 7 10 85 175 89 7.4
## 72 7 11 NA 139 82 8.6
## 73 7 12 10 264 73 14.3
## 74 7 13 27 175 81 14.9
## 75 7 14 NA 291 91 14.9
## 76 7 15 7 48 80 14.3
## 77 7 16 48 260 81 6.9
## 78 7 17 35 274 82 10.3
## 79 7 18 61 285 84 6.3
## 80 7 19 79 187 87 5.1
## 81 7 20 63 220 85 11.5
## 82 7 21 16 7 74 6.9
## 83 7 22 NA 258 81 9.7
## 84 7 23 NA 295 82 11.5
## 85 7 24 80 294 86 8.6
## 86 7 25 108 223 85 8.0
## 87 7 26 20 81 82 8.6
## 88 7 27 52 82 86 12.0
## 89 7 28 82 213 88 7.4
## 90 7 29 50 275 86 7.4
## 91 7 30 64 253 83 7.4
## 92 7 31 59 254 81 9.2
## 93 8 1 39 83 81 6.9
## 94 8 2 9 24 81 13.8
## 95 8 3 16 77 82 7.4
## 96 8 4 78 NA 86 6.9
## 97 8 5 35 NA 85 7.4
## 98 8 6 66 NA 87 4.6
## 99 8 7 122 255 89 4.0
## 100 8 8 89 229 90 10.3
## 101 8 9 110 207 90 8.0
## 102 8 10 NA 222 92 8.6
## 103 8 11 NA 137 86 11.5
## 104 8 12 44 192 86 11.5
## 105 8 13 28 273 82 11.5
## 106 8 14 65 157 80 9.7
## 107 8 15 NA 64 79 11.5
## 108 8 16 22 71 77 10.3
## 109 8 17 59 51 79 6.3
## 110 8 18 23 115 76 7.4
## 111 8 19 31 244 78 10.9
## 112 8 20 44 190 78 10.3
## 113 8 21 21 259 77 15.5
## 114 8 22 9 36 72 14.3
## 115 8 23 NA 255 75 12.6
## 116 8 24 45 212 79 9.7
## 117 8 25 168 238 81 3.4
## 118 8 26 73 215 86 8.0
## 119 8 27 NA 153 88 5.7
## 120 8 28 76 203 97 9.7
## 121 8 29 118 225 94 2.3
## 122 8 30 84 237 96 6.3
## 123 8 31 85 188 94 6.3
## 124 9 1 96 167 91 6.9
## 125 9 2 78 197 92 5.1
## 126 9 3 73 183 93 2.8
## 127 9 4 91 189 93 4.6
## 128 9 5 47 95 87 7.4
## 129 9 6 32 92 84 15.5
## 130 9 7 20 252 80 10.9
## 131 9 8 23 220 78 10.3
## 132 9 9 21 230 75 10.9
## 133 9 10 24 259 73 9.7
## 134 9 11 44 236 81 14.9
## 135 9 12 21 259 76 15.5
## 136 9 13 28 238 77 6.3
## 137 9 14 9 24 71 10.9
## 138 9 15 13 112 71 11.5
## 139 9 16 46 237 78 6.9
## 140 9 17 18 224 67 13.8
## 141 9 18 13 27 76 10.3
## 142 9 19 24 238 68 10.3
## 143 9 20 16 201 82 8.0
## 144 9 21 13 238 64 12.6
## 145 9 22 23 14 71 9.2
## 146 9 23 36 139 81 10.3
## 147 9 24 7 49 69 10.3
## 148 9 25 14 20 63 16.6
## 149 9 26 30 193 70 6.9
## 150 9 27 NA 145 77 13.2
## 151 9 28 14 191 75 14.3
## 152 9 29 18 131 76 8.0
## 153 9 30 20 223 68 11.5separate()
## Species Element Measurement
## 595 virginica Petal.Width 2.5
## 596 virginica Petal.Width 2.3
## 597 virginica Petal.Width 1.9
## 598 virginica Petal.Width 2.0
## 599 virginica Petal.Width 2.3
## 600 virginica Petal.Width 1.8## Species Part Measures Measurement
## 595 virginica Petal Width 2.5
## 596 virginica Petal Width 2.3
## 597 virginica Petal Width 1.9
## 598 virginica Petal Width 2.0
## 599 virginica Petal Width 2.3
## 600 virginica Petal Width 1.8unite()
## Species Factor Measurement
## 595 virginica Petal_Width 2.5
## 596 virginica Petal_Width 2.3
## 597 virginica Petal_Width 1.9
## 598 virginica Petal_Width 2.0
## 599 virginica Petal_Width 2.3
## 600 virginica Petal_Width 1.8ggplot2
Grammar of Graphics
library(ggplot2)
ggplot(data = mtcars, aes(x = wt, y = mpg)) +
geom_point() +
labs(x = "weight (1,000 lbs)",
y = "Fuel Consumption (miles per gallon)",
title = "Fuel Consumption vs. Weight",
subtitle = "Negative relationship betweeen fuel efficiency and car weight",
caption = "Source: mpg dataset")
ggplot(data = mtcars, aes(x = mpg)) +
geom_histogram() +
facet_grid(cyl ~ .) +
labs(title = "geom_histogram()",
x = "Miles per Gallon")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
mtcars$cyl <- factor(mtcars$cyl,
levels = c(4, 6, 8),
labels = c("4 cylinders", "6 cylinders", "8 cylinders"))
ggplot(data = mtcars, aes(x = cyl, y = mpg)) +
geom_boxplot() +
labs(title = "geom_boxplot()",
x = "Number of Cylinders",
y = "Miles per Gallon")
ggplot(data = mtcars, aes(x = mpg, fill = cyl)) +
geom_density() +
labs(title = "geom_density()",
x = "Miles per Gallon")
ggplot(data = mtcars, aes(x = wt, y = mpg, col = cyl)) +
geom_point() +
labs(title = "geom_point()",
x = "Weight (1,000 lbs)",
y = "Miles per Gallon")
ggplot(data = mtcars, aes(x = wt, y = mpg)) +
geom_smooth() +
labs(title = "geom_smooth()",
x = "Weight (1,000lbs)",
y = "Miles per Gallon")## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggplot(data = economics, aes(x = date, y = unemploy)) +
geom_line() +
labs(title = "geom_line()",
x = "Year", y = "Number of Unemployed (thousands)")
geom()
ggplot(data = mtcars, aes(x = wt, y = mpg)) +
geom_point(shape = 21,
color = "blue",
bg = "skyblue", # 내부 색
size = 2,
stroke = 1) + # 외부 라인 두께
geom_smooth(method = "lm", # 회귀 방법
color = "red",
linetype = 2,
size = 1) +
geom_text(label = rownames(mtcars),
hjust = 0,
vjust = 0,
nudge_y = 0.7,
size =2) +
labs(x = "weight (1,000 lbs)",
y = "Fuel Consumption (miles per gallon)",
title = "Fuel Consumption vs. Weight",
subtitle = "Negative relationship betweeen fuel efficiency and car weight",
caption = "Source: mpg dataset")## `geom_smooth()` using formula 'y ~ x'
geom_boxplot()
## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recode## 'data.frame': 397 obs. of 6 variables:
## $ rank : Factor w/ 3 levels "AsstProf","AssocProf",..: 3 3 1 3 3 2 3 3 3 3 ...
## $ discipline : Factor w/ 2 levels "A","B": 2 2 2 2 2 2 2 2 2 2 ...
## $ yrs.since.phd: int 19 20 4 45 40 6 30 45 21 18 ...
## $ yrs.service : int 18 16 3 39 41 6 23 45 20 18 ...
## $ sex : Factor w/ 2 levels "Female","Male": 2 2 2 2 2 2 2 2 2 1 ...
## $ salary : int 139750 173200 79750 115000 141500 97000 175000 147765 119250 129000 ...ggplot(Salaries, aes(x = rank, y = salary)) +
geom_boxplot(fill = "salmon",
color = "dimgray",
notch = TRUE) +
geom_point(position = "jitter", # 퍼트리기
color = "royalblue",
alpha = 0.5) + # 투명도
geom_rug(sides = "l",
color = "dimgray")
geom_violin()
## height voice.part
## 1 64 Soprano 1
## 2 62 Soprano 1
## 3 66 Soprano 1
## 4 65 Soprano 1
## 5 60 Soprano 1
## 6 61 Soprano 1ggplot(singer, aes(x = voice.part, y = height)) +
geom_violin(fill = "honeydew2") +
geom_boxplot(fill = "lightgreen", width = 0.2)
geom_density()
## 'data.frame': 397 obs. of 6 variables:
## $ rank : Factor w/ 3 levels "AsstProf","AssocProf",..: 3 3 1 3 3 2 3 3 3 3 ...
## $ discipline : Factor w/ 2 levels "A","B": 2 2 2 2 2 2 2 2 2 2 ...
## $ yrs.since.phd: int 19 20 4 45 40 6 30 45 21 18 ...
## $ yrs.service : int 18 16 3 39 41 6 23 45 20 18 ...
## $ sex : Factor w/ 2 levels "Female","Male": 2 2 2 2 2 2 2 2 2 1 ...
## $ salary : int 139750 173200 79750 115000 141500 97000 175000 147765 119250 129000 ...
ggplot(Salaries, aes(x = rank, fill = sex)) +
geom_bar(position = "fill") + # stack, dodge, fill
labs(y = "Proportion")
geom_bar()
presummed <- data.frame(Grade = c("A", "B", "C", "D", "F"),
Frequency = c(20, 40, 20, 10, 5))
presummed## Grade Frequency
## 1 A 20
## 2 B 40
## 3 C 20
## 4 D 10
## 5 F 5
facet_wrap(), facet_grid()
## height voice.part
## 1 64 Soprano 1
## 2 62 Soprano 1
## 3 66 Soprano 1
## 4 65 Soprano 1
## 5 60 Soprano 1
## 6 61 Soprano 1## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(Salaries, aes(x = yrs.since.phd,
y = salary,
color = rank,
shape = rank)) +
geom_point() +
facet_grid(. ~ sex)
scale_<aesthetic>_<name/data type>
## 'data.frame': 397 obs. of 6 variables:
## $ rank : Factor w/ 3 levels "AsstProf","AssocProf",..: 3 3 1 3 3 2 3 3 3 3 ...
## $ discipline : Factor w/ 2 levels "A","B": 2 2 2 2 2 2 2 2 2 2 ...
## $ yrs.since.phd: int 19 20 4 45 40 6 30 45 21 18 ...
## $ yrs.service : int 18 16 3 39 41 6 23 45 20 18 ...
## $ sex : Factor w/ 2 levels "Female","Male": 2 2 2 2 2 2 2 2 2 1 ...
## $ salary : int 139750 173200 79750 115000 141500 97000 175000 147765 119250 129000 ...ggplot(Salaries, aes(x = rank, y = salary, fill = sex)) +
geom_boxplot() +
scale_x_discrete(breaks = c("AsstProf", "AssocProf", "Prof"),
labels = c("Assistant\nProfessor",
"Associate\nProfessor",
"Professor")) +
scale_y_continuous(breaks = c(50000, 100000, 150000, 200000),
labels = c("$50k", "$100k", "$150k", "$200k")) +
labs(fill = "Gender")
ggplot(Salaries, aes(x = rank, y = salary, fill = sex)) +
geom_boxplot() +
scale_x_discrete(breaks = c("AsstProf", "AssocProf", "Prof"),
labels = c("Assistant\nProfessor",
"Associate\nProfessor",
"Professor")) +
scale_y_continuous(breaks = c(50000, 100000, 150000, 200000),
labels = c("$50k", "$100k", "$150k", "$200k")) +
scale_fill_discrete(name = "Gender") + # 범례명
theme(legend.position = c(0.15, 0.75)) # 범례위치
data(mtcars)
ggplot(mtcars, aes(x = wt, y = mpg,
shape = factor(cyl),
color = factor(cyl))) +
geom_point() +
labs(shape = "Cylinder",
color = "Cylinder")
ggplot(mtcars, aes(x = wt, y = mpg,
shape = factor(cyl),
color = factor(cyl))) +
geom_point() +
scale_shape_discrete(name = "Cylinder") +
scale_color_discrete(name = "Cylinder")
ggplot(mtcars, aes(x = wt, y = mpg, size = disp)) +
geom_point(shape = 21,
color = "black",
fill = "wheat") +
labs(size = "Engine\nDisplacement")
ggplot(mtcars, aes(x = wt, y = mpg, size = disp)) +
geom_point(shape = 21,
color = "black",
fill = "wheat") +
scale_size_continuous(name = "Engine\nDisplacement")
ggplot(Salaries, aes(x = rank, fill = sex)) +
geom_bar() +
scale_fill_manual(values = c("tomato", "cornflowerblue"))
ggplot(Salaries, aes(x = yrs.since.phd, y = salary, color = rank)) +
geom_point(size = 2) +
scale_color_manual(values = c("orange", "violetred", "steelblue"))
ggplot(Salaries, aes(x = yrs.since.phd, y = salary, color = rank)) +
geom_point(size = 2) +
scale_color_brewer(palette = "Accent")
ggplot(Salaries, aes(x = yrs.since.phd, y = salary,
color = rank,
shape = rank)) +
geom_point(size = 2) +
scale_shape_manual(values = c(15, 17, 19))
theme_<theme>
## 'data.frame': 397 obs. of 6 variables:
## $ rank : Factor w/ 3 levels "AsstProf","AssocProf",..: 3 3 1 3 3 2 3 3 3 3 ...
## $ discipline : Factor w/ 2 levels "A","B": 2 2 2 2 2 2 2 2 2 2 ...
## $ yrs.since.phd: int 19 20 4 45 40 6 30 45 21 18 ...
## $ yrs.service : int 18 16 3 39 41 6 23 45 20 18 ...
## $ sex : Factor w/ 2 levels "Female","Male": 2 2 2 2 2 2 2 2 2 1 ...
## $ salary : int 139750 173200 79750 115000 141500 97000 175000 147765 119250 129000 ...ggplot(Salaries, aes(x = yrs.since.phd, y = salary,
color = rank,
shape = rank)) +
geom_point() +
facet_grid(. ~ sex) +
theme_light() # default: theme_gray()
ggplot(Salaries, aes(x = rank, y = salary, fill = sex)) +
geom_boxplot() +
labs(title = "Salary by Rank and Sex",
x = "Rank",
y = "Salary") +
theme(plot.title = element_text(face = "bold.italic",
size = 14,
color = "brown"),
axis.title = element_text(face = "bold.italic",
size = 10,
color = "tomato"),
axis.text = element_text(face = "bold",
size = 9,
color = "royalblue"),
panel.background = element_rect(fill = "snow",
color = "darkblue"),
panel.grid.major.y = element_line(color = "gray",
linetype = "solid"),
panel.grid.minor.y = element_line(color = "gray",
linetype = "dashed"),
legend.position = "top")
mytheme <- theme(plot.title = element_text(face = "bold.italic",
size = 14,
color = "brown"),
axis.title = element_text(face = "bold.italic",
size = 10,
color = "tomato"),
axis.text = element_text(face = "bold",
size = 9,
color = "royalblue"),
panel.background = element_rect(fill = "snow",
color = "darkblue"),
panel.grid.major.y = element_line(color = "gray",
linetype = "solid"),
panel.grid.minor.y = element_line(color = "gray",
linetype = "dashed"),
legend.position = "top")
library(lattice)
ggplot(singer, aes(x = voice.part, y = height)) +
geom_boxplot() +
labs(title = "Height by voice part",
x = "Voice Part",
y = "Height") +
mytheme
library(gridExtra)
library(ggplot2)
library(car)
p1 <- ggplot(Salaries, aes(x = rank)) +
geom_bar(fill = "steelblue")
p2 <- ggplot(Salaries, aes(x = salary)) +
geom_histogram(fill = "maroon")
p3 <- ggplot(Salaries, aes(x = yrs.since.phd, y = salary)) +
geom_point(color = "orange")
p4 <- ggplot(Salaries, aes(x = rank, y = salary)) +
geom_boxplot(fill = "mistyrose")
# install.packages("gridExtra")
library(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combine## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggsave(file = "myplot.png",
plot = myggplot,
width = 7.0, # inch
height = 5.5)
ggplot(Salaries, aes(x = rank, y = salary)) +
geom_boxplot()
## Saving 7 x 5 in image질문은 여기
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