Subsetting a Vector
R Programming for Data Science
Roger D. Peng
September 29, 2022
Reference
R Programming for Data Science by Roger D. Peng, May 31, 2022
Textbook
There are three operators that can be used to extract subsets of R objects.
The
[operator always returns an object of the same class as the original. It can be used to select multiple elements of an objectThe
[[operator is used to extract elements of a list or a data frame. It can only be used to extract a single element and the class of the returned object will not necessarily be a list or data frame.The
$operator is used to extract elements of a list or data frame by literal name. Its semantics are similar to that of[[.
1 Subsetting a Vector
Vectors are basic objects in R and they can be subsetted using the
[ operator.
x <- c("a", "b", "c", "c", "d", "a")
x[1] ## Extract the first element## [1] "a"x[2] ## Extract the second element## [1] "b"The [ operator can be used to extract multiple elements
of a vector by passing the operator an integer sequence. Here we extract
the first four elements of the vector.
x[1:4]## [1] "a" "b" "c" "c"The sequence does not have to be in order; you can specify any arbitrary integer vector.
x[c(1, 3, 4)]## [1] "a" "c" "c"We can also pass a logical sequence to the [ operator to
extract elements of a vector that satisfy a given condition. For
example, here we want the elements of x that come
lexicographically after the letter “a”.
x## [1] "a" "b" "c" "c" "d" "a"u <- x > "a"
u## [1] FALSE TRUE TRUE TRUE TRUE FALSEx[u]## [1] "b" "c" "c" "d""A" > "a" # a=1 < A=27## [1] TRUE"z" > "A"## [1] TRUE"ab" > "aa" # Alphanumeric comparison, if first letter are same value move on to the next value## [1] TRUEAnother, more compact, way to do this would be to skip the creation of a logical vector and just subset the vector directly with the logical expression.
x[x > "a"]## [1] "b" "c" "c" "d"2 Subsetting a Matrix
Matrices can be subsetted in the usual way with
(i,j)type indices. Here, we create simple
2 x 3 matrix with the matrix function.
x <- matrix(data = 1:6, nrow = 2) #row-wise distribution
x## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6We can access the (1,2) or the (2,1)
element of this matrix using the appropriate indices.
x[1,2]## [1] 3x[2,1]## [1] 2Indices can also be missing. This behavior is used to access entire rows or columns of a matrix.
x## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6x[1, ] ## Extract the first row## [1] 1 3 5x[, 2] ## Extract the second column## [1] 3 42.1 Dropping matrix dimensions
x <- matrix(1:6, ncol = 3)
print(x)## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6x[1, c(1,3)]## [1] 1 5x[1, c(1,3), drop = FALSE]## [,1] [,2]
## [1,] 1 5y <- matrix(1:12, nrow=4)
y## [,1] [,2] [,3]
## [1,] 1 5 9
## [2,] 2 6 10
## [3,] 3 7 11
## [4,] 4 8 12z <- y[2, 2:3, drop = FALSE]
z## [,1] [,2]
## [1,] 6 103 Subsetting a Data Frame
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 ...iris[iris$Sepal.Length > 7.0, ]## 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 virginicastr(swiss)## 'data.frame': 47 obs. of 6 variables:
## $ Fertility : num 80.2 83.1 92.5 85.8 76.9 76.1 83.8 92.4 82.4 82.9 ...
## $ Agriculture : num 17 45.1 39.7 36.5 43.5 35.3 70.2 67.8 53.3 45.2 ...
## $ Examination : int 15 6 5 12 17 9 16 14 12 16 ...
## $ Education : int 12 9 5 7 15 7 7 8 7 13 ...
## $ Catholic : num 9.96 84.84 93.4 33.77 5.16 ...
## $ Infant.Mortality: num 22.2 22.2 20.2 20.3 20.6 26.6 23.6 24.9 21 24.4 ...swiss[swiss$Catholic > 99, ]## Fertility Agriculture Examination Education Catholic
## Conthey 75.5 85.9 3 2 99.71
## Entremont 69.3 84.9 7 6 99.68
## Herens 77.3 89.7 5 2 100.00
## St Maurice 65.0 75.9 9 9 99.06
## Sierre 92.2 84.6 3 3 99.46
## Infant.Mortality
## Conthey 15.1
## Entremont 19.8
## Herens 18.3
## St Maurice 17.8
## Sierre 16.3swiss[swiss$Catholic > 99, 4, drop = FALSE]## Education
## Conthey 2
## Entremont 6
## Herens 2
## St Maurice 9
## Sierre 3# Subsetting the 1st 6 rows of a data frame
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# Subsetting the last 6 rows of a data frame
tail(iris, 8)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 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# Subsetting rows and columns of a data frame
iris[ 10:15 , c(5, 1, 3)]## Species Sepal.Length Petal.Length
## 10 setosa 4.9 1.5
## 11 setosa 5.4 1.5
## 12 setosa 4.8 1.6
## 13 setosa 4.8 1.4
## 14 setosa 4.3 1.1
## 15 setosa 5.8 1.2# Subsetting rows and columns
iris[c(1:3, 147:150) , c(5, 1, 3)]## Species Sepal.Length Petal.Length
## 1 setosa 5.1 1.4
## 2 setosa 4.9 1.4
## 3 setosa 4.7 1.3
## 147 virginica 6.3 5.0
## 148 virginica 6.5 5.2
## 149 virginica 6.2 5.4
## 150 virginica 5.9 5.1# Subsetting rows and columns
iris[c(1:3, 147:150) , c("Species", "Sepal.Length", "Sepal.Width")]## Species Sepal.Length Sepal.Width
## 1 setosa 5.1 3.5
## 2 setosa 4.9 3.0
## 3 setosa 4.7 3.2
## 147 virginica 6.3 2.5
## 148 virginica 6.5 3.0
## 149 virginica 6.2 3.4
## 150 virginica 5.9 3.04 Subsetting Lists
x <- list(foo = 1:4, bar = 0.6)
x## $foo
## [1] 1 2 3 4
##
## $bar
## [1] 0.6The [[ operator can be used to extract single elements
from a list. Here we extract the first element of the list.
x[[1]]## [1] 1 2 3 4Notice you don’t need the quotes when you use the $
operator.
One thing that differentiates the [[ operator from the
$ is that the [[ operator can be used with
computed indices. The $ operator can only be used with
literal names.
x <- list(foo = 1:4, bar = 0.6, baz = "hello")
x## $foo
## [1] 1 2 3 4
##
## $bar
## [1] 0.6
##
## $baz
## [1] "hello"x$foo## [1] 1 2 3 4x[["foo"]]## [1] 1 2 3 45 Subsetting Nested Elements of a List
The [[ operator can take an integer sequence if you want
to extract a nested element of a list.
x <- list(a = list(10, 12, 14), b = c(3.14, 2.81))
## Get the 3rd element of the 1st element
x[[c(1, 3)]] ## [1] 14## Same as above
x[[1]][[3]] ## [1] 14# 1st element of the 2nd element
x[[c(2, 1)]] ## [1] 3.14x[[2]][1] ## [1] 3.146 Extracting Multiple Elements of a List
The [ operator can be used to extract multiple elements
from a list. For example, if you wanted to extract the first and third
elements of a list, you would do the following
x <- list(foo = 1:4, bar = 0.6, baz = "hello")
x[c(1, 3)]## $foo
## [1] 1 2 3 4
##
## $baz
## [1] "hello"Note that x[c(1, 3)] is NOT the same as
x[[c(1, 3)]].
# x <- list(foo = 1:4, bar = 0.6, baz = "hello")
x[[c(1, 3)]]## [1] 3Remember that the [ operator always returns an object of
the same class as the original. Since the original object was a list,
the [ operator returns a list. In the above code, we
returned a list with two elements (the first and the third).
7 Partial Matching
Partial matching of names is allowed with [[ and
$. This is often very useful during interactive work if the
object you’re working with has very long element names. You can just
abbreviate those names and R will figure out what element you’re
referring to.
x <- list(aardvark = 1:5)
x## $aardvark
## [1] 1 2 3 4 5x$a## [1] 1 2 3 4 5x[["a"]]## NULLx[["a", exact = TRUE]]## NULLx[["aar", exact = FALSE]]## [1] 1 2 3 4 5In general, this is fine for interactive work, but you shouldn’t resort to partial matching if you are writing longer scripts, functions, or programs. In those cases, you should refer to the full element name if possible. That way there’s no ambiguity in your code.
8 Removing
NA Values
A common task in data analysis is removing missing values
(NAs).
x <- c(1, 2, NA, 4, NA, 5)
x## [1] 1 2 NA 4 NA 5bad <- is.na(x)
print(bad)## [1] FALSE FALSE TRUE FALSE TRUE FALSEx[!bad]## [1] 1 2 4 5x[!is.na(x)]## [1] 1 2 4 5What if there are multiple R objects and you want to take the subset with no missing values in any of those objects?
x <- c(1, 2, NA, 4, NA, 5)
y <- c("a", "b", NA, "d", NA, "f")
good <- complete.cases(x, y)good## [1] TRUE TRUE FALSE TRUE FALSE TRUEx[good]## [1] 1 2 4 5y[good]## [1] "a" "b" "d" "f"x[complete.cases(x)]## [1] 1 2 4 5You can use complete.cases on data frames too.
str(airquality)## '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 ...head(airquality)## 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 6summary(airquality)## Ozone Solar.R Wind Temp
## Min. : 1.00 Min. : 7.0 Min. : 1.700 Min. :56.00
## 1st Qu.: 18.00 1st Qu.:115.8 1st Qu.: 7.400 1st Qu.:72.00
## Median : 31.50 Median :205.0 Median : 9.700 Median :79.00
## Mean : 42.13 Mean :185.9 Mean : 9.958 Mean :77.88
## 3rd Qu.: 63.25 3rd Qu.:258.8 3rd Qu.:11.500 3rd Qu.:85.00
## Max. :168.00 Max. :334.0 Max. :20.700 Max. :97.00
## NA's :37 NA's :7
## Month Day
## Min. :5.000 Min. : 1.0
## 1st Qu.:6.000 1st Qu.: 8.0
## Median :7.000 Median :16.0
## Mean :6.993 Mean :15.8
## 3rd Qu.:8.000 3rd Qu.:23.0
## Max. :9.000 Max. :31.0
## good <- complete.cases(airquality)
airquality[good, ]## 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
## 7 23 299 8.6 65 5 7
## 8 19 99 13.8 59 5 8
## 9 8 19 20.1 61 5 9
## 12 16 256 9.7 69 5 12
## 13 11 290 9.2 66 5 13
## 14 14 274 10.9 68 5 14
## 15 18 65 13.2 58 5 15
## 16 14 334 11.5 64 5 16
## 17 34 307 12.0 66 5 17
## 18 6 78 18.4 57 5 18
## 19 30 322 11.5 68 5 19
## 20 11 44 9.7 62 5 20
## 21 1 8 9.7 59 5 21
## 22 11 320 16.6 73 5 22
## 23 4 25 9.7 61 5 23
## 24 32 92 12.0 61 5 24
## 28 23 13 12.0 67 5 28
## 29 45 252 14.9 81 5 29
## 30 115 223 5.7 79 5 30
## 31 37 279 7.4 76 5 31
## 38 29 127 9.7 82 6 7
## 40 71 291 13.8 90 6 9
## 41 39 323 11.5 87 6 10
## 44 23 148 8.0 82 6 13
## 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
## 62 135 269 4.1 84 7 1
## 63 49 248 9.2 85 7 2
## 64 32 236 9.2 81 7 3
## 66 64 175 4.6 83 7 5
## 67 40 314 10.9 83 7 6
## 68 77 276 5.1 88 7 7
## 69 97 267 6.3 92 7 8
## 70 97 272 5.7 92 7 9
## 71 85 175 7.4 89 7 10
## 73 10 264 14.3 73 7 12
## 74 27 175 14.9 81 7 13
## 76 7 48 14.3 80 7 15
## 77 48 260 6.9 81 7 16
## 78 35 274 10.3 82 7 17
## 79 61 285 6.3 84 7 18
## 80 79 187 5.1 87 7 19
## 81 63 220 11.5 85 7 20
## 82 16 7 6.9 74 7 21
## 85 80 294 8.6 86 7 24
## 86 108 223 8.0 85 7 25
## 87 20 81 8.6 82 7 26
## 88 52 82 12.0 86 7 27
## 89 82 213 7.4 88 7 28
## 90 50 275 7.4 86 7 29
## 91 64 253 7.4 83 7 30
## 92 59 254 9.2 81 7 31
## 93 39 83 6.9 81 8 1
## 94 9 24 13.8 81 8 2
## 95 16 77 7.4 82 8 3
## 99 122 255 4.0 89 8 7
## 100 89 229 10.3 90 8 8
## 101 110 207 8.0 90 8 9
## 104 44 192 11.5 86 8 12
## 105 28 273 11.5 82 8 13
## 106 65 157 9.7 80 8 14
## 108 22 71 10.3 77 8 16
## 109 59 51 6.3 79 8 17
## 110 23 115 7.4 76 8 18
## 111 31 244 10.9 78 8 19
## 112 44 190 10.3 78 8 20
## 113 21 259 15.5 77 8 21
## 114 9 36 14.3 72 8 22
## 116 45 212 9.7 79 8 24
## 117 168 238 3.4 81 8 25
## 118 73 215 8.0 86 8 26
## 120 76 203 9.7 97 8 28
## 121 118 225 2.3 94 8 29
## 122 84 237 6.3 96 8 30
## 123 85 188 6.3 94 8 31
## 124 96 167 6.9 91 9 1
## 125 78 197 5.1 92 9 2
## 126 73 183 2.8 93 9 3
## 127 91 189 4.6 93 9 4
## 128 47 95 7.4 87 9 5
## 129 32 92 15.5 84 9 6
## 130 20 252 10.9 80 9 7
## 131 23 220 10.3 78 9 8
## 132 21 230 10.9 75 9 9
## 133 24 259 9.7 73 9 10
## 134 44 236 14.9 81 9 11
## 135 21 259 15.5 76 9 12
## 136 28 238 6.3 77 9 13
## 137 9 24 10.9 71 9 14
## 138 13 112 11.5 71 9 15
## 139 46 237 6.9 78 9 16
## 140 18 224 13.8 67 9 17
## 141 13 27 10.3 76 9 18
## 142 24 238 10.3 68 9 19
## 143 16 201 8.0 82 9 20
## 144 13 238 12.6 64 9 21
## 145 23 14 9.2 71 9 22
## 146 36 139 10.3 81 9 23
## 147 7 49 10.3 69 9 24
## 148 14 20 16.6 63 9 25
## 149 30 193 6.9 70 9 26
## 151 14 191 14.3 75 9 28
## 152 18 131 8.0 76 9 29
## 153 20 223 11.5 68 9 30airquality## 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
## 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
## 11 7 NA 6.9 74 5 11
## 12 16 256 9.7 69 5 12
## 13 11 290 9.2 66 5 13
## 14 14 274 10.9 68 5 14
## 15 18 65 13.2 58 5 15
## 16 14 334 11.5 64 5 16
## 17 34 307 12.0 66 5 17
## 18 6 78 18.4 57 5 18
## 19 30 322 11.5 68 5 19
## 20 11 44 9.7 62 5 20
## 21 1 8 9.7 59 5 21
## 22 11 320 16.6 73 5 22
## 23 4 25 9.7 61 5 23
## 24 32 92 12.0 61 5 24
## 25 NA 66 16.6 57 5 25
## 26 NA 266 14.9 58 5 26
## 27 NA NA 8.0 57 5 27
## 28 23 13 12.0 67 5 28
## 29 45 252 14.9 81 5 29
## 30 115 223 5.7 79 5 30
## 31 37 279 7.4 76 5 31
## 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
## 62 135 269 4.1 84 7 1
## 63 49 248 9.2 85 7 2
## 64 32 236 9.2 81 7 3
## 65 NA 101 10.9 84 7 4
## 66 64 175 4.6 83 7 5
## 67 40 314 10.9 83 7 6
## 68 77 276 5.1 88 7 7
## 69 97 267 6.3 92 7 8
## 70 97 272 5.7 92 7 9
## 71 85 175 7.4 89 7 10
## 72 NA 139 8.6 82 7 11
## 73 10 264 14.3 73 7 12
## 74 27 175 14.9 81 7 13
## 75 NA 291 14.9 91 7 14
## 76 7 48 14.3 80 7 15
## 77 48 260 6.9 81 7 16
## 78 35 274 10.3 82 7 17
## 79 61 285 6.3 84 7 18
## 80 79 187 5.1 87 7 19
## 81 63 220 11.5 85 7 20
## 82 16 7 6.9 74 7 21
## 83 NA 258 9.7 81 7 22
## 84 NA 295 11.5 82 7 23
## 85 80 294 8.6 86 7 24
## 86 108 223 8.0 85 7 25
## 87 20 81 8.6 82 7 26
## 88 52 82 12.0 86 7 27
## 89 82 213 7.4 88 7 28
## 90 50 275 7.4 86 7 29
## 91 64 253 7.4 83 7 30
## 92 59 254 9.2 81 7 31
## 93 39 83 6.9 81 8 1
## 94 9 24 13.8 81 8 2
## 95 16 77 7.4 82 8 3
## 96 78 NA 6.9 86 8 4
## 97 35 NA 7.4 85 8 5
## 98 66 NA 4.6 87 8 6
## 99 122 255 4.0 89 8 7
## 100 89 229 10.3 90 8 8
## 101 110 207 8.0 90 8 9
## 102 NA 222 8.6 92 8 10
## 103 NA 137 11.5 86 8 11
## 104 44 192 11.5 86 8 12
## 105 28 273 11.5 82 8 13
## 106 65 157 9.7 80 8 14
## 107 NA 64 11.5 79 8 15
## 108 22 71 10.3 77 8 16
## 109 59 51 6.3 79 8 17
## 110 23 115 7.4 76 8 18
## 111 31 244 10.9 78 8 19
## 112 44 190 10.3 78 8 20
## 113 21 259 15.5 77 8 21
## 114 9 36 14.3 72 8 22
## 115 NA 255 12.6 75 8 23
## 116 45 212 9.7 79 8 24
## 117 168 238 3.4 81 8 25
## 118 73 215 8.0 86 8 26
## 119 NA 153 5.7 88 8 27
## 120 76 203 9.7 97 8 28
## 121 118 225 2.3 94 8 29
## 122 84 237 6.3 96 8 30
## 123 85 188 6.3 94 8 31
## 124 96 167 6.9 91 9 1
## 125 78 197 5.1 92 9 2
## 126 73 183 2.8 93 9 3
## 127 91 189 4.6 93 9 4
## 128 47 95 7.4 87 9 5
## 129 32 92 15.5 84 9 6
## 130 20 252 10.9 80 9 7
## 131 23 220 10.3 78 9 8
## 132 21 230 10.9 75 9 9
## 133 24 259 9.7 73 9 10
## 134 44 236 14.9 81 9 11
## 135 21 259 15.5 76 9 12
## 136 28 238 6.3 77 9 13
## 137 9 24 10.9 71 9 14
## 138 13 112 11.5 71 9 15
## 139 46 237 6.9 78 9 16
## 140 18 224 13.8 67 9 17
## 141 13 27 10.3 76 9 18
## 142 24 238 10.3 68 9 19
## 143 16 201 8.0 82 9 20
## 144 13 238 12.6 64 9 21
## 145 23 14 9.2 71 9 22
## 146 36 139 10.3 81 9 23
## 147 7 49 10.3 69 9 24
## 148 14 20 16.6 63 9 25
## 149 30 193 6.9 70 9 26
## 150 NA 145 13.2 77 9 27
## 151 14 191 14.3 75 9 28
## 152 18 131 8.0 76 9 29
## 153 20 223 11.5 68 9 30summary(airquality[good, ])## Ozone Solar.R Wind Temp
## Min. : 1.0 Min. : 7.0 Min. : 2.30 Min. :57.00
## 1st Qu.: 18.0 1st Qu.:113.5 1st Qu.: 7.40 1st Qu.:71.00
## Median : 31.0 Median :207.0 Median : 9.70 Median :79.00
## Mean : 42.1 Mean :184.8 Mean : 9.94 Mean :77.79
## 3rd Qu.: 62.0 3rd Qu.:255.5 3rd Qu.:11.50 3rd Qu.:84.50
## Max. :168.0 Max. :334.0 Max. :20.70 Max. :97.00
## Month Day
## Min. :5.000 Min. : 1.00
## 1st Qu.:6.000 1st Qu.: 9.00
## Median :7.000 Median :16.00
## Mean :7.216 Mean :15.95
## 3rd Qu.:9.000 3rd Qu.:22.50
## Max. :9.000 Max. :31.00