#데이터 조작1
#iris 데이터
head(iris)
## 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
str(iris)
## '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 ...
iris3
## , , Setosa
##
## Sepal L. Sepal W. Petal L. Petal W.
## [1,] 5.1 3.5 1.4 0.2
## [2,] 4.9 3.0 1.4 0.2
## [3,] 4.7 3.2 1.3 0.2
## [4,] 4.6 3.1 1.5 0.2
## [5,] 5.0 3.6 1.4 0.2
## [6,] 5.4 3.9 1.7 0.4
## [7,] 4.6 3.4 1.4 0.3
## [8,] 5.0 3.4 1.5 0.2
## [9,] 4.4 2.9 1.4 0.2
## [10,] 4.9 3.1 1.5 0.1
## [11,] 5.4 3.7 1.5 0.2
## [12,] 4.8 3.4 1.6 0.2
## [13,] 4.8 3.0 1.4 0.1
## [14,] 4.3 3.0 1.1 0.1
## [15,] 5.8 4.0 1.2 0.2
## [16,] 5.7 4.4 1.5 0.4
## [17,] 5.4 3.9 1.3 0.4
## [18,] 5.1 3.5 1.4 0.3
## [19,] 5.7 3.8 1.7 0.3
## [20,] 5.1 3.8 1.5 0.3
## [21,] 5.4 3.4 1.7 0.2
## [22,] 5.1 3.7 1.5 0.4
## [23,] 4.6 3.6 1.0 0.2
## [24,] 5.1 3.3 1.7 0.5
## [25,] 4.8 3.4 1.9 0.2
## [26,] 5.0 3.0 1.6 0.2
## [27,] 5.0 3.4 1.6 0.4
## [28,] 5.2 3.5 1.5 0.2
## [29,] 5.2 3.4 1.4 0.2
## [30,] 4.7 3.2 1.6 0.2
## [31,] 4.8 3.1 1.6 0.2
## [32,] 5.4 3.4 1.5 0.4
## [33,] 5.2 4.1 1.5 0.1
## [34,] 5.5 4.2 1.4 0.2
## [35,] 4.9 3.1 1.5 0.2
## [36,] 5.0 3.2 1.2 0.2
## [37,] 5.5 3.5 1.3 0.2
## [38,] 4.9 3.6 1.4 0.1
## [39,] 4.4 3.0 1.3 0.2
## [40,] 5.1 3.4 1.5 0.2
## [41,] 5.0 3.5 1.3 0.3
## [42,] 4.5 2.3 1.3 0.3
## [43,] 4.4 3.2 1.3 0.2
## [44,] 5.0 3.5 1.6 0.6
## [45,] 5.1 3.8 1.9 0.4
## [46,] 4.8 3.0 1.4 0.3
## [47,] 5.1 3.8 1.6 0.2
## [48,] 4.6 3.2 1.4 0.2
## [49,] 5.3 3.7 1.5 0.2
## [50,] 5.0 3.3 1.4 0.2
##
## , , Versicolor
##
## Sepal L. Sepal W. Petal L. Petal W.
## [1,] 7.0 3.2 4.7 1.4
## [2,] 6.4 3.2 4.5 1.5
## [3,] 6.9 3.1 4.9 1.5
## [4,] 5.5 2.3 4.0 1.3
## [5,] 6.5 2.8 4.6 1.5
## [6,] 5.7 2.8 4.5 1.3
## [7,] 6.3 3.3 4.7 1.6
## [8,] 4.9 2.4 3.3 1.0
## [9,] 6.6 2.9 4.6 1.3
## [10,] 5.2 2.7 3.9 1.4
## [11,] 5.0 2.0 3.5 1.0
## [12,] 5.9 3.0 4.2 1.5
## [13,] 6.0 2.2 4.0 1.0
## [14,] 6.1 2.9 4.7 1.4
## [15,] 5.6 2.9 3.6 1.3
## [16,] 6.7 3.1 4.4 1.4
## [17,] 5.6 3.0 4.5 1.5
## [18,] 5.8 2.7 4.1 1.0
## [19,] 6.2 2.2 4.5 1.5
## [20,] 5.6 2.5 3.9 1.1
## [21,] 5.9 3.2 4.8 1.8
## [22,] 6.1 2.8 4.0 1.3
## [23,] 6.3 2.5 4.9 1.5
## [24,] 6.1 2.8 4.7 1.2
## [25,] 6.4 2.9 4.3 1.3
## [26,] 6.6 3.0 4.4 1.4
## [27,] 6.8 2.8 4.8 1.4
## [28,] 6.7 3.0 5.0 1.7
## [29,] 6.0 2.9 4.5 1.5
## [30,] 5.7 2.6 3.5 1.0
## [31,] 5.5 2.4 3.8 1.1
## [32,] 5.5 2.4 3.7 1.0
## [33,] 5.8 2.7 3.9 1.2
## [34,] 6.0 2.7 5.1 1.6
## [35,] 5.4 3.0 4.5 1.5
## [36,] 6.0 3.4 4.5 1.6
## [37,] 6.7 3.1 4.7 1.5
## [38,] 6.3 2.3 4.4 1.3
## [39,] 5.6 3.0 4.1 1.3
## [40,] 5.5 2.5 4.0 1.3
## [41,] 5.5 2.6 4.4 1.2
## [42,] 6.1 3.0 4.6 1.4
## [43,] 5.8 2.6 4.0 1.2
## [44,] 5.0 2.3 3.3 1.0
## [45,] 5.6 2.7 4.2 1.3
## [46,] 5.7 3.0 4.2 1.2
## [47,] 5.7 2.9 4.2 1.3
## [48,] 6.2 2.9 4.3 1.3
## [49,] 5.1 2.5 3.0 1.1
## [50,] 5.7 2.8 4.1 1.3
##
## , , Virginica
##
## Sepal L. Sepal W. Petal L. Petal W.
## [1,] 6.3 3.3 6.0 2.5
## [2,] 5.8 2.7 5.1 1.9
## [3,] 7.1 3.0 5.9 2.1
## [4,] 6.3 2.9 5.6 1.8
## [5,] 6.5 3.0 5.8 2.2
## [6,] 7.6 3.0 6.6 2.1
## [7,] 4.9 2.5 4.5 1.7
## [8,] 7.3 2.9 6.3 1.8
## [9,] 6.7 2.5 5.8 1.8
## [10,] 7.2 3.6 6.1 2.5
## [11,] 6.5 3.2 5.1 2.0
## [12,] 6.4 2.7 5.3 1.9
## [13,] 6.8 3.0 5.5 2.1
## [14,] 5.7 2.5 5.0 2.0
## [15,] 5.8 2.8 5.1 2.4
## [16,] 6.4 3.2 5.3 2.3
## [17,] 6.5 3.0 5.5 1.8
## [18,] 7.7 3.8 6.7 2.2
## [19,] 7.7 2.6 6.9 2.3
## [20,] 6.0 2.2 5.0 1.5
## [21,] 6.9 3.2 5.7 2.3
## [22,] 5.6 2.8 4.9 2.0
## [23,] 7.7 2.8 6.7 2.0
## [24,] 6.3 2.7 4.9 1.8
## [25,] 6.7 3.3 5.7 2.1
## [26,] 7.2 3.2 6.0 1.8
## [27,] 6.2 2.8 4.8 1.8
## [28,] 6.1 3.0 4.9 1.8
## [29,] 6.4 2.8 5.6 2.1
## [30,] 7.2 3.0 5.8 1.6
## [31,] 7.4 2.8 6.1 1.9
## [32,] 7.9 3.8 6.4 2.0
## [33,] 6.4 2.8 5.6 2.2
## [34,] 6.3 2.8 5.1 1.5
## [35,] 6.1 2.6 5.6 1.4
## [36,] 7.7 3.0 6.1 2.3
## [37,] 6.3 3.4 5.6 2.4
## [38,] 6.4 3.1 5.5 1.8
## [39,] 6.0 3.0 4.8 1.8
## [40,] 6.9 3.1 5.4 2.1
## [41,] 6.7 3.1 5.6 2.4
## [42,] 6.9 3.1 5.1 2.3
## [43,] 5.8 2.7 5.1 1.9
## [44,] 6.8 3.2 5.9 2.3
## [45,] 6.7 3.3 5.7 2.5
## [46,] 6.7 3.0 5.2 2.3
## [47,] 6.3 2.5 5.0 1.9
## [48,] 6.5 3.0 5.2 2.0
## [49,] 6.2 3.4 5.4 2.3
## [50,] 5.9 3.0 5.1 1.8
data(mtcars)
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 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
#rbind(),cbind()
rbind(c(1,2,3),c(4,5,6))
## [,1] [,2] [,3]
## [1,] 1 2 3
## [2,] 4 5 6
x <- data.frame(id=c(1,2),name=c("a","b"),stringsAsFactors = F)
x
## id name
## 1 1 a
## 2 2 b
str(x)
## 'data.frame': 2 obs. of 2 variables:
## $ id : num 1 2
## $ name: chr "a" "b"
y <- rbind(x,c(3,"c"))
y
## id name
## 1 1 a
## 2 2 b
## 3 3 c
cbind(c(1,2,3),c(4,5,6))
## [,1] [,2]
## [1,] 1 4
## [2,] 2 5
## [3,] 3 6
y <- cbind(x,greek=c('alpha','beta'))
y
## id name greek
## 1 1 a alpha
## 2 2 b beta
str(y)
## 'data.frame': 2 obs. of 3 variables:
## $ id : num 1 2
## $ name : chr "a" "b"
## $ greek: Factor w/ 2 levels "alpha","beta": 1 2
y <- cbind(x,greek=c('alpha','beta'),stringsAsFactors=F)
str(y)
## 'data.frame': 2 obs. of 3 variables:
## $ id : num 1 2
## $ name : chr "a" "b"
## $ greek: chr "alpha" "beta"
#apply()
sum(1:10)
## [1] 55
d <- matrix(1:9, ncol=3)
d
## [,1] [,2] [,3]
## [1,] 1 4 7
## [2,] 2 5 8
## [3,] 3 6 9
apply(d,1,sum)
## [1] 12 15 18
apply(d,2,sum)
## [1] 6 15 24
head(iris)
## 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
apply(iris[,1:4],2,sum)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 876.5 458.6 563.7 179.9
colSums(iris[,1:4])
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 876.5 458.6 563.7 179.9
#lapply()
result <- lapply(1:3,function(x) {x*2})
result
## [[1]]
## [1] 2
##
## [[2]]
## [1] 4
##
## [[3]]
## [1] 6
result[[1]]
## [1] 2
unlist(result)
## [1] 2 4 6
x <- list(a=1:3, b=4:6)
x
## $a
## [1] 1 2 3
##
## $b
## [1] 4 5 6
lapply(x,mean)
## $a
## [1] 2
##
## $b
## [1] 5
lapply(iris[,1:4],mean)
## $Sepal.Length
## [1] 5.843333
##
## $Sepal.Width
## [1] 3.057333
##
## $Petal.Length
## [1] 3.758
##
## $Petal.Width
## [1] 1.199333
colMeans(iris[,1:4])
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 5.843333 3.057333 3.758000 1.199333
d <- as.data.frame(matrix(unlist(lapply(iris[,1:4],mean)),
ncol=4,byrow=TRUE))
names(d) <- names(iris[,1:4])
d
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 5.843333 3.057333 3.758 1.199333
#sapply()
lapply(iris[,1:4],mean)
## $Sepal.Length
## [1] 5.843333
##
## $Sepal.Width
## [1] 3.057333
##
## $Petal.Length
## [1] 3.758
##
## $Petal.Width
## [1] 1.199333
sapply(iris[,1:4],mean)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 5.843333 3.057333 3.758000 1.199333
class(sapply(iris[,1:4],mean))
## [1] "numeric"
x <- sapply(iris[, 1:4],mean)
as.data.frame(x)
## x
## Sepal.Length 5.843333
## Sepal.Width 3.057333
## Petal.Length 3.758000
## Petal.Width 1.199333
as.data.frame(t(x))
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 5.843333 3.057333 3.758 1.199333
sapply(iris,class)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## "numeric" "numeric" "numeric" "numeric" "factor"
y <- sapply(iris[,1:4],function(x){x>3})
y
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## [1,] TRUE TRUE FALSE FALSE
## [2,] TRUE FALSE FALSE FALSE
## [3,] TRUE TRUE FALSE FALSE
## [4,] TRUE TRUE FALSE FALSE
## [5,] TRUE TRUE FALSE FALSE
## [6,] TRUE TRUE FALSE FALSE
## [7,] TRUE TRUE FALSE FALSE
## [8,] TRUE TRUE FALSE FALSE
## [9,] TRUE FALSE FALSE FALSE
## [10,] TRUE TRUE FALSE FALSE
## [11,] TRUE TRUE FALSE FALSE
## [12,] TRUE TRUE FALSE FALSE
## [13,] TRUE FALSE FALSE FALSE
## [14,] TRUE FALSE FALSE FALSE
## [15,] TRUE TRUE FALSE FALSE
## [16,] TRUE TRUE FALSE FALSE
## [17,] TRUE TRUE FALSE FALSE
## [18,] TRUE TRUE FALSE FALSE
## [19,] TRUE TRUE FALSE FALSE
## [20,] TRUE TRUE FALSE FALSE
## [21,] TRUE TRUE FALSE FALSE
## [22,] TRUE TRUE FALSE FALSE
## [23,] TRUE TRUE FALSE FALSE
## [24,] TRUE TRUE FALSE FALSE
## [25,] TRUE TRUE FALSE FALSE
## [26,] TRUE FALSE FALSE FALSE
## [27,] TRUE TRUE FALSE FALSE
## [28,] TRUE TRUE FALSE FALSE
## [29,] TRUE TRUE FALSE FALSE
## [30,] TRUE TRUE FALSE FALSE
## [31,] TRUE TRUE FALSE FALSE
## [32,] TRUE TRUE FALSE FALSE
## [33,] TRUE TRUE FALSE FALSE
## [34,] TRUE TRUE FALSE FALSE
## [35,] TRUE TRUE FALSE FALSE
## [36,] TRUE TRUE FALSE FALSE
## [37,] TRUE TRUE FALSE FALSE
## [38,] TRUE TRUE FALSE FALSE
## [39,] TRUE FALSE FALSE FALSE
## [40,] TRUE TRUE FALSE FALSE
## [41,] TRUE TRUE FALSE FALSE
## [42,] TRUE FALSE FALSE FALSE
## [43,] TRUE TRUE FALSE FALSE
## [44,] TRUE TRUE FALSE FALSE
## [45,] TRUE TRUE FALSE FALSE
## [46,] TRUE FALSE FALSE FALSE
## [47,] TRUE TRUE FALSE FALSE
## [48,] TRUE TRUE FALSE FALSE
## [49,] TRUE TRUE FALSE FALSE
## [50,] TRUE TRUE FALSE FALSE
## [51,] TRUE TRUE TRUE FALSE
## [52,] TRUE TRUE TRUE FALSE
## [53,] TRUE TRUE TRUE FALSE
## [54,] TRUE FALSE TRUE FALSE
## [55,] TRUE FALSE TRUE FALSE
## [56,] TRUE FALSE TRUE FALSE
## [57,] TRUE TRUE TRUE FALSE
## [58,] TRUE FALSE TRUE FALSE
## [59,] TRUE FALSE TRUE FALSE
## [60,] TRUE FALSE TRUE FALSE
## [61,] TRUE FALSE TRUE FALSE
## [62,] TRUE FALSE TRUE FALSE
## [63,] TRUE FALSE TRUE FALSE
## [64,] TRUE FALSE TRUE FALSE
## [65,] TRUE FALSE TRUE FALSE
## [66,] TRUE TRUE TRUE FALSE
## [67,] TRUE FALSE TRUE FALSE
## [68,] TRUE FALSE TRUE FALSE
## [69,] TRUE FALSE TRUE FALSE
## [70,] TRUE FALSE TRUE FALSE
## [71,] TRUE TRUE TRUE FALSE
## [72,] TRUE FALSE TRUE FALSE
## [73,] TRUE FALSE TRUE FALSE
## [74,] TRUE FALSE TRUE FALSE
## [75,] TRUE FALSE TRUE FALSE
## [76,] TRUE FALSE TRUE FALSE
## [77,] TRUE FALSE TRUE FALSE
## [78,] TRUE FALSE TRUE FALSE
## [79,] TRUE FALSE TRUE FALSE
## [80,] TRUE FALSE TRUE FALSE
## [81,] TRUE FALSE TRUE FALSE
## [82,] TRUE FALSE TRUE FALSE
## [83,] TRUE FALSE TRUE FALSE
## [84,] TRUE FALSE TRUE FALSE
## [85,] TRUE FALSE TRUE FALSE
## [86,] TRUE TRUE TRUE FALSE
## [87,] TRUE TRUE TRUE FALSE
## [88,] TRUE FALSE TRUE FALSE
## [89,] TRUE FALSE TRUE FALSE
## [90,] TRUE FALSE TRUE FALSE
## [91,] TRUE FALSE TRUE FALSE
## [92,] TRUE FALSE TRUE FALSE
## [93,] TRUE FALSE TRUE FALSE
## [94,] TRUE FALSE TRUE FALSE
## [95,] TRUE FALSE TRUE FALSE
## [96,] TRUE FALSE TRUE FALSE
## [97,] TRUE FALSE TRUE FALSE
## [98,] TRUE FALSE TRUE FALSE
## [99,] TRUE FALSE FALSE FALSE
## [100,] TRUE FALSE TRUE FALSE
## [101,] TRUE TRUE TRUE FALSE
## [102,] TRUE FALSE TRUE FALSE
## [103,] TRUE FALSE TRUE FALSE
## [104,] TRUE FALSE TRUE FALSE
## [105,] TRUE FALSE TRUE FALSE
## [106,] TRUE FALSE TRUE FALSE
## [107,] TRUE FALSE TRUE FALSE
## [108,] TRUE FALSE TRUE FALSE
## [109,] TRUE FALSE TRUE FALSE
## [110,] TRUE TRUE TRUE FALSE
## [111,] TRUE TRUE TRUE FALSE
## [112,] TRUE FALSE TRUE FALSE
## [113,] TRUE FALSE TRUE FALSE
## [114,] TRUE FALSE TRUE FALSE
## [115,] TRUE FALSE TRUE FALSE
## [116,] TRUE TRUE TRUE FALSE
## [117,] TRUE FALSE TRUE FALSE
## [118,] TRUE TRUE TRUE FALSE
## [119,] TRUE FALSE TRUE FALSE
## [120,] TRUE FALSE TRUE FALSE
## [121,] TRUE TRUE TRUE FALSE
## [122,] TRUE FALSE TRUE FALSE
## [123,] TRUE FALSE TRUE FALSE
## [124,] TRUE FALSE TRUE FALSE
## [125,] TRUE TRUE TRUE FALSE
## [126,] TRUE TRUE TRUE FALSE
## [127,] TRUE FALSE TRUE FALSE
## [128,] TRUE FALSE TRUE FALSE
## [129,] TRUE FALSE TRUE FALSE
## [130,] TRUE FALSE TRUE FALSE
## [131,] TRUE FALSE TRUE FALSE
## [132,] TRUE TRUE TRUE FALSE
## [133,] TRUE FALSE TRUE FALSE
## [134,] TRUE FALSE TRUE FALSE
## [135,] TRUE FALSE TRUE FALSE
## [136,] TRUE FALSE TRUE FALSE
## [137,] TRUE TRUE TRUE FALSE
## [138,] TRUE TRUE TRUE FALSE
## [139,] TRUE FALSE TRUE FALSE
## [140,] TRUE TRUE TRUE FALSE
## [141,] TRUE TRUE TRUE FALSE
## [142,] TRUE TRUE TRUE FALSE
## [143,] TRUE FALSE TRUE FALSE
## [144,] TRUE TRUE TRUE FALSE
## [145,] TRUE TRUE TRUE FALSE
## [146,] TRUE FALSE TRUE FALSE
## [147,] TRUE FALSE TRUE FALSE
## [148,] TRUE FALSE TRUE FALSE
## [149,] TRUE TRUE TRUE FALSE
## [150,] TRUE FALSE TRUE FALSE
class(y)
## [1] "matrix"
head(y)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## [1,] TRUE TRUE FALSE FALSE
## [2,] TRUE FALSE FALSE FALSE
## [3,] TRUE TRUE FALSE FALSE
## [4,] TRUE TRUE FALSE FALSE
## [5,] TRUE TRUE FALSE FALSE
## [6,] TRUE TRUE FALSE FALSE
#tapply
tapply(1:10,rep(1,10),sum)
## 1
## 55
tapply(1:10, 1:10 %% 2==1, sum)
## FALSE TRUE
## 30 25
tapply(iris$Sepal.Length, iris$Species, mean)
## setosa versicolor virginica
## 5.006 5.936 6.588
m <- matrix(1:8,
ncol=2,
dimnames=list(c("spring","summer","fall","winter"),
c("male","female")))
m
## male female
## spring 1 5
## summer 2 6
## fall 3 7
## winter 4 8
tapply(m, list(c(1,1,2,2,1,1,2,2),
c(1,1,1,1,2,2,2,2)),sum)
## 1 2
## 1 3 11
## 2 7 15
#mapply()
#doBy 패키지
summary(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
##
quantile(iris$Sepal.Length)
## 0% 25% 50% 75% 100%
## 4.3 5.1 5.8 6.4 7.9
quantile(iris$Sepal.Length,seq(0,1,by=0.1))
## 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
## 4.30 4.80 5.00 5.27 5.60 5.80 6.10 6.30 6.52 6.90 7.90
#summaryBy(Sepal.Width+Sepal.Length~Species,iris)
#split()
split(iris, iris$Species)
## $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 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
##
## $versicolor
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
##
## $virginica
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
lapply(split(iris$Sepal.Length,iris$Species),mean)
## $setosa
## [1] 5.006
##
## $versicolor
## [1] 5.936
##
## $virginica
## [1] 6.588
#subset()
subset(iris, Species=='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 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
subset(iris, Species=="setosa"&Sepal.Length>5.0)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
subset(iris, select=c(Sepal.Length, Species))
## Sepal.Length Species
## 1 5.1 setosa
## 2 4.9 setosa
## 3 4.7 setosa
## 4 4.6 setosa
## 5 5.0 setosa
## 6 5.4 setosa
## 7 4.6 setosa
## 8 5.0 setosa
## 9 4.4 setosa
## 10 4.9 setosa
## 11 5.4 setosa
## 12 4.8 setosa
## 13 4.8 setosa
## 14 4.3 setosa
## 15 5.8 setosa
## 16 5.7 setosa
## 17 5.4 setosa
## 18 5.1 setosa
## 19 5.7 setosa
## 20 5.1 setosa
## 21 5.4 setosa
## 22 5.1 setosa
## 23 4.6 setosa
## 24 5.1 setosa
## 25 4.8 setosa
## 26 5.0 setosa
## 27 5.0 setosa
## 28 5.2 setosa
## 29 5.2 setosa
## 30 4.7 setosa
## 31 4.8 setosa
## 32 5.4 setosa
## 33 5.2 setosa
## 34 5.5 setosa
## 35 4.9 setosa
## 36 5.0 setosa
## 37 5.5 setosa
## 38 4.9 setosa
## 39 4.4 setosa
## 40 5.1 setosa
## 41 5.0 setosa
## 42 4.5 setosa
## 43 4.4 setosa
## 44 5.0 setosa
## 45 5.1 setosa
## 46 4.8 setosa
## 47 5.1 setosa
## 48 4.6 setosa
## 49 5.3 setosa
## 50 5.0 setosa
## 51 7.0 versicolor
## 52 6.4 versicolor
## 53 6.9 versicolor
## 54 5.5 versicolor
## 55 6.5 versicolor
## 56 5.7 versicolor
## 57 6.3 versicolor
## 58 4.9 versicolor
## 59 6.6 versicolor
## 60 5.2 versicolor
## 61 5.0 versicolor
## 62 5.9 versicolor
## 63 6.0 versicolor
## 64 6.1 versicolor
## 65 5.6 versicolor
## 66 6.7 versicolor
## 67 5.6 versicolor
## 68 5.8 versicolor
## 69 6.2 versicolor
## 70 5.6 versicolor
## 71 5.9 versicolor
## 72 6.1 versicolor
## 73 6.3 versicolor
## 74 6.1 versicolor
## 75 6.4 versicolor
## 76 6.6 versicolor
## 77 6.8 versicolor
## 78 6.7 versicolor
## 79 6.0 versicolor
## 80 5.7 versicolor
## 81 5.5 versicolor
## 82 5.5 versicolor
## 83 5.8 versicolor
## 84 6.0 versicolor
## 85 5.4 versicolor
## 86 6.0 versicolor
## 87 6.7 versicolor
## 88 6.3 versicolor
## 89 5.6 versicolor
## 90 5.5 versicolor
## 91 5.5 versicolor
## 92 6.1 versicolor
## 93 5.8 versicolor
## 94 5.0 versicolor
## 95 5.6 versicolor
## 96 5.7 versicolor
## 97 5.7 versicolor
## 98 6.2 versicolor
## 99 5.1 versicolor
## 100 5.7 versicolor
## 101 6.3 virginica
## 102 5.8 virginica
## 103 7.1 virginica
## 104 6.3 virginica
## 105 6.5 virginica
## 106 7.6 virginica
## 107 4.9 virginica
## 108 7.3 virginica
## 109 6.7 virginica
## 110 7.2 virginica
## 111 6.5 virginica
## 112 6.4 virginica
## 113 6.8 virginica
## 114 5.7 virginica
## 115 5.8 virginica
## 116 6.4 virginica
## 117 6.5 virginica
## 118 7.7 virginica
## 119 7.7 virginica
## 120 6.0 virginica
## 121 6.9 virginica
## 122 5.6 virginica
## 123 7.7 virginica
## 124 6.3 virginica
## 125 6.7 virginica
## 126 7.2 virginica
## 127 6.2 virginica
## 128 6.1 virginica
## 129 6.4 virginica
## 130 7.2 virginica
## 131 7.4 virginica
## 132 7.9 virginica
## 133 6.4 virginica
## 134 6.3 virginica
## 135 6.1 virginica
## 136 7.7 virginica
## 137 6.3 virginica
## 138 6.4 virginica
## 139 6.0 virginica
## 140 6.9 virginica
## 141 6.7 virginica
## 142 6.9 virginica
## 143 5.8 virginica
## 144 6.8 virginica
## 145 6.7 virginica
## 146 6.7 virginica
## 147 6.3 virginica
## 148 6.5 virginica
## 149 6.2 virginica
## 150 5.9 virginica
subset(iris, select=-c(Sepal.Length, Species))
## Sepal.Width Petal.Length Petal.Width
## 1 3.5 1.4 0.2
## 2 3.0 1.4 0.2
## 3 3.2 1.3 0.2
## 4 3.1 1.5 0.2
## 5 3.6 1.4 0.2
## 6 3.9 1.7 0.4
## 7 3.4 1.4 0.3
## 8 3.4 1.5 0.2
## 9 2.9 1.4 0.2
## 10 3.1 1.5 0.1
## 11 3.7 1.5 0.2
## 12 3.4 1.6 0.2
## 13 3.0 1.4 0.1
## 14 3.0 1.1 0.1
## 15 4.0 1.2 0.2
## 16 4.4 1.5 0.4
## 17 3.9 1.3 0.4
## 18 3.5 1.4 0.3
## 19 3.8 1.7 0.3
## 20 3.8 1.5 0.3
## 21 3.4 1.7 0.2
## 22 3.7 1.5 0.4
## 23 3.6 1.0 0.2
## 24 3.3 1.7 0.5
## 25 3.4 1.9 0.2
## 26 3.0 1.6 0.2
## 27 3.4 1.6 0.4
## 28 3.5 1.5 0.2
## 29 3.4 1.4 0.2
## 30 3.2 1.6 0.2
## 31 3.1 1.6 0.2
## 32 3.4 1.5 0.4
## 33 4.1 1.5 0.1
## 34 4.2 1.4 0.2
## 35 3.1 1.5 0.2
## 36 3.2 1.2 0.2
## 37 3.5 1.3 0.2
## 38 3.6 1.4 0.1
## 39 3.0 1.3 0.2
## 40 3.4 1.5 0.2
## 41 3.5 1.3 0.3
## 42 2.3 1.3 0.3
## 43 3.2 1.3 0.2
## 44 3.5 1.6 0.6
## 45 3.8 1.9 0.4
## 46 3.0 1.4 0.3
## 47 3.8 1.6 0.2
## 48 3.2 1.4 0.2
## 49 3.7 1.5 0.2
## 50 3.3 1.4 0.2
## 51 3.2 4.7 1.4
## 52 3.2 4.5 1.5
## 53 3.1 4.9 1.5
## 54 2.3 4.0 1.3
## 55 2.8 4.6 1.5
## 56 2.8 4.5 1.3
## 57 3.3 4.7 1.6
## 58 2.4 3.3 1.0
## 59 2.9 4.6 1.3
## 60 2.7 3.9 1.4
## 61 2.0 3.5 1.0
## 62 3.0 4.2 1.5
## 63 2.2 4.0 1.0
## 64 2.9 4.7 1.4
## 65 2.9 3.6 1.3
## 66 3.1 4.4 1.4
## 67 3.0 4.5 1.5
## 68 2.7 4.1 1.0
## 69 2.2 4.5 1.5
## 70 2.5 3.9 1.1
## 71 3.2 4.8 1.8
## 72 2.8 4.0 1.3
## 73 2.5 4.9 1.5
## 74 2.8 4.7 1.2
## 75 2.9 4.3 1.3
## 76 3.0 4.4 1.4
## 77 2.8 4.8 1.4
## 78 3.0 5.0 1.7
## 79 2.9 4.5 1.5
## 80 2.6 3.5 1.0
## 81 2.4 3.8 1.1
## 82 2.4 3.7 1.0
## 83 2.7 3.9 1.2
## 84 2.7 5.1 1.6
## 85 3.0 4.5 1.5
## 86 3.4 4.5 1.6
## 87 3.1 4.7 1.5
## 88 2.3 4.4 1.3
## 89 3.0 4.1 1.3
## 90 2.5 4.0 1.3
## 91 2.6 4.4 1.2
## 92 3.0 4.6 1.4
## 93 2.6 4.0 1.2
## 94 2.3 3.3 1.0
## 95 2.7 4.2 1.3
## 96 3.0 4.2 1.2
## 97 2.9 4.2 1.3
## 98 2.9 4.3 1.3
## 99 2.5 3.0 1.1
## 100 2.8 4.1 1.3
## 101 3.3 6.0 2.5
## 102 2.7 5.1 1.9
## 103 3.0 5.9 2.1
## 104 2.9 5.6 1.8
## 105 3.0 5.8 2.2
## 106 3.0 6.6 2.1
## 107 2.5 4.5 1.7
## 108 2.9 6.3 1.8
## 109 2.5 5.8 1.8
## 110 3.6 6.1 2.5
## 111 3.2 5.1 2.0
## 112 2.7 5.3 1.9
## 113 3.0 5.5 2.1
## 114 2.5 5.0 2.0
## 115 2.8 5.1 2.4
## 116 3.2 5.3 2.3
## 117 3.0 5.5 1.8
## 118 3.8 6.7 2.2
## 119 2.6 6.9 2.3
## 120 2.2 5.0 1.5
## 121 3.2 5.7 2.3
## 122 2.8 4.9 2.0
## 123 2.8 6.7 2.0
## 124 2.7 4.9 1.8
## 125 3.3 5.7 2.1
## 126 3.2 6.0 1.8
## 127 2.8 4.8 1.8
## 128 3.0 4.9 1.8
## 129 2.8 5.6 2.1
## 130 3.0 5.8 1.6
## 131 2.8 6.1 1.9
## 132 3.8 6.4 2.0
## 133 2.8 5.6 2.2
## 134 2.8 5.1 1.5
## 135 2.6 5.6 1.4
## 136 3.0 6.1 2.3
## 137 3.4 5.6 2.4
## 138 3.1 5.5 1.8
## 139 3.0 4.8 1.8
## 140 3.1 5.4 2.1
## 141 3.1 5.6 2.4
## 142 3.1 5.1 2.3
## 143 2.7 5.1 1.9
## 144 3.2 5.9 2.3
## 145 3.3 5.7 2.5
## 146 3.0 5.2 2.3
## 147 2.5 5.0 1.9
## 148 3.0 5.2 2.0
## 149 3.4 5.4 2.3
## 150 3.0 5.1 1.8
#merge()
x <- data.frame(name=c("a","b","c"),math=c(1,2,3))
y <- data.frame(name=c("a","b","c"),english=c(4,5,6))
merge(x,y)
## name math english
## 1 a 1 4
## 2 b 2 5
## 3 c 3 6
x <- data.frame(name=c("a","b","c"), math=c(1,2,3))
y <- data.frame(name=c("c","b","a"),english=c(4,5,6))
cbind(x,y)
## name math name english
## 1 a 1 c 4
## 2 b 2 b 5
## 3 c 3 a 6
merge(x,y,all=TRUE)
## name math english
## 1 a 1 6
## 2 b 2 5
## 3 c 3 4
#order()
x <- c(20,11,33,50,47)
x
## [1] 20 11 33 50 47
order(x)
## [1] 2 1 3 5 4
order(-x)
## [1] 4 5 3 1 2
head(iris[order(iris$Sepal.Length),])
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 14 4.3 3.0 1.1 0.1 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 4 4.6 3.1 1.5 0.2 setosa
head(iris[order(iris$Sepal.Length, iris$Petal.Length),])
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 14 4.3 3.0 1.1 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 23 4.6 3.6 1.0 0.2 setosa
#with(),within()
print(mean(iris$Sepal.Length))
## [1] 5.843333
print(mean(iris$Sepal.Width))
## [1] 3.057333
with(iris,{
print(mean(Sepal.Length))
print(mean(Sepal.Width))
})
## [1] 5.843333
## [1] 3.057333
x <- data.frame(val=c(1,2,3,4,NA,5,NA))
x
## val
## 1 1
## 2 2
## 3 3
## 4 4
## 5 NA
## 6 5
## 7 NA
x <- within(x,{
val <- ifelse(is.na(val),median(val,na.rm=TRUE),val)
})
x
## val
## 1 1
## 2 2
## 3 3
## 4 4
## 5 3
## 6 5
## 7 3
x$val[is.na(x$val)] <- median(x$val, na.rm=TRUE)
data(iris)
iris[1,1] =NA
head(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 NA 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
median_per_species <- sapply(split(iris$Sepal.Length, iris$Species),median,na.rm=TRUE)
iris <- within(iris,{
Sepal.Length <- ifelse(is.na(Sepal.Length),median_per_species[Species],Sepal.Length)
})
head(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.0 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
split(iris$Sepal.Length, iris$Species)
## $setosa
## [1] 5.0 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
## [18] 5.1 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
## [35] 4.9 5.0 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
##
## $versicolor
## [1] 7.0 6.4 6.9 5.5 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
## [18] 5.8 6.2 5.6 5.9 6.1 6.3 6.1 6.4 6.6 6.8 6.7 6.0 5.7 5.5 5.5 5.8 6.0
## [35] 5.4 6.0 6.7 6.3 5.6 5.5 5.5 6.1 5.8 5.0 5.6 5.7 5.7 6.2 5.1 5.7
##
## $virginica
## [1] 6.3 5.8 7.1 6.3 6.5 7.6 4.9 7.3 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5
## [18] 7.7 7.7 6.0 6.9 5.6 7.7 6.3 6.7 7.2 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3
## [35] 6.1 7.7 6.3 6.4 6.0 6.9 6.7 6.9 5.8 6.8 6.7 6.7 6.3 6.5 6.2 5.9
sapply(split(iris$Sepal.Length, iris$Species),median, na.rm=TRUE)
## setosa versicolor virginica
## 5.0 5.9 6.5
#aggregate()
aggregate(Sepal.Width~Species, iris, mean)
## Species Sepal.Width
## 1 setosa 3.428
## 2 versicolor 2.770
## 3 virginica 2.974