Return the First or Last Parts of an Object
head.codingsystem.RdReturns the first or last parts of a vector, matrix, table, data frame
or function. Since head() and tail() are generic
functions, they may also have been extended to other classes.
Usage
# S3 method for class 'codingsystem'
head(x, ...)Value
An object (usually) like x but generally smaller. Hence, for
arrays, the result corresponds to x[.., drop=FALSE].
For ftable objects x, a transformed format(x).
Details
For vector/array based objects, head() (tail()) returns
a subset of the same dimensionality as x, usually of
the same class. For historical reasons, by default they select the
first (last) 6 indices in the first dimension ("rows") or along the
length of a non-dimensioned vector, and the full extent (all indices)
in any remaining dimensions. head.matrix() and
tail.matrix() are exported.
The default and array(/matrix) methods for head() and
tail() are quite general. They will work as is for any class
which has a dim() method, a length() method (only
required if dim() returns NULL), and a [ method
(that accepts the drop argument and can subset in all
dimensions in the dimensioned case).
For functions, the lines of the deparsed function are returned as character strings.
When x is an array(/matrix) of dimensionality two and more,
tail() will add dimnames similar to how they would appear in a
full printing of x for all dimensions k where
n[k] is specified and non-missing and dimnames(x)[[k]]
(or dimnames(x) itself) is NULL. Specifically, the
form of the added dimnames will vary for different dimensions as follows:
k=1(rows):"[n,]"(right justified with whitespace padding)k=2(columns):"[,n]"(with no whitespace padding)k>2(higher dims):"n", i.e., the indices as character values
Setting keepnums = FALSE suppresses this behaviour.
As data.frame subsetting (‘indexing’) keeps
attributes, so do the head() and tail()
methods for data frames.
Note
For array inputs the output of tail when keepnums is TRUE,
any dimnames vectors added for dimensions >2 are the original
numeric indices in that dimension as character vectors. This
means that, e.g., for 3-dimensional array arr,
tail(arr, c(2,2,-1))[ , , 2] and
tail(arr, c(2,2,-1))[ , , "2"] may both be valid but have
completely different meanings.
Author
Patrick Burns, improved and corrected by R-Core. Negative argument added by Vincent Goulet. Multi-dimension support added by Gabriel Becker.
Examples
head(letters)
#> [1] "a" "b" "c" "d" "e" "f"
head(letters, n = -6L)
#> [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
#> [20] "t"
head(freeny.x, n = 10L)
#> lag quarterly revenue price index income level market potential
#> [1,] 8.79636 4.70997 5.82110 12.9699
#> [2,] 8.79236 4.70217 5.82558 12.9733
#> [3,] 8.79137 4.68944 5.83112 12.9774
#> [4,] 8.81486 4.68558 5.84046 12.9806
#> [5,] 8.81301 4.64019 5.85036 12.9831
#> [6,] 8.90751 4.62553 5.86464 12.9854
#> [7,] 8.93673 4.61991 5.87769 12.9900
#> [8,] 8.96161 4.61654 5.89763 12.9943
#> [9,] 8.96044 4.61407 5.92574 12.9992
#> [10,] 9.00868 4.60766 5.94232 13.0033
head(freeny.y)
#> [1] 8.79236 8.79137 8.81486 8.81301 8.90751 8.93673
head(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
#>
#> , , 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
#>
#> , , 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
#>
head(iris3, c(6L, 2L))
#> , , Setosa
#>
#> Sepal L. Sepal W.
#> [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
#>
#> , , Versicolor
#>
#> Sepal L. Sepal W.
#> [1,] 7.0 3.2
#> [2,] 6.4 3.2
#> [3,] 6.9 3.1
#> [4,] 5.5 2.3
#> [5,] 6.5 2.8
#> [6,] 5.7 2.8
#>
#> , , Virginica
#>
#> Sepal L. Sepal W.
#> [1,] 6.3 3.3
#> [2,] 5.8 2.7
#> [3,] 7.1 3.0
#> [4,] 6.3 2.9
#> [5,] 6.5 3.0
#> [6,] 7.6 3.0
#>
head(iris3, c(6L, -1L, 2L))
#> , , Setosa
#>
#> Sepal L. Sepal W. Petal L.
#> [1,] 5.1 3.5 1.4
#> [2,] 4.9 3.0 1.4
#> [3,] 4.7 3.2 1.3
#> [4,] 4.6 3.1 1.5
#> [5,] 5.0 3.6 1.4
#> [6,] 5.4 3.9 1.7
#>
#> , , Versicolor
#>
#> Sepal L. Sepal W. Petal L.
#> [1,] 7.0 3.2 4.7
#> [2,] 6.4 3.2 4.5
#> [3,] 6.9 3.1 4.9
#> [4,] 5.5 2.3 4.0
#> [5,] 6.5 2.8 4.6
#> [6,] 5.7 2.8 4.5
#>
tail(letters)
#> [1] "u" "v" "w" "x" "y" "z"
tail(letters, n = -6L)
#> [1] "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t" "u" "v" "w" "x" "y"
#> [20] "z"
tail(freeny.x)
#> lag quarterly revenue price index income level market potential
#> [34,] 9.69405 4.30909 6.17369 13.1459
#> [35,] 9.69958 4.30909 6.16135 13.1520
#> [36,] 9.68683 4.30552 6.18231 13.1593
#> [37,] 9.71774 4.29627 6.18768 13.1579
#> [38,] 9.74924 4.27839 6.19377 13.1625
#> [39,] 9.77536 4.27789 6.20030 13.1664
## the bottom-right "corner" :
tail(freeny.x, n = c(4, 2))
#> income level market potential
#> [36,] 6.18231 13.1593
#> [37,] 6.18768 13.1579
#> [38,] 6.19377 13.1625
#> [39,] 6.20030 13.1664
tail(freeny.y)
#> [1] 9.69958 9.68683 9.71774 9.74924 9.77536 9.79424
tail(iris3)
#> , , Setosa
#>
#> Sepal L. Sepal W. Petal L. Petal W.
#> [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.
#> [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.
#> [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
#>
tail(iris3, c(6L, 2L))
#> , , Setosa
#>
#> Petal L. Petal W.
#> [45,] 1.9 0.4
#> [46,] 1.4 0.3
#> [47,] 1.6 0.2
#> [48,] 1.4 0.2
#> [49,] 1.5 0.2
#> [50,] 1.4 0.2
#>
#> , , Versicolor
#>
#> Petal L. Petal W.
#> [45,] 4.2 1.3
#> [46,] 4.2 1.2
#> [47,] 4.2 1.3
#> [48,] 4.3 1.3
#> [49,] 3.0 1.1
#> [50,] 4.1 1.3
#>
#> , , Virginica
#>
#> Petal L. Petal W.
#> [45,] 5.7 2.5
#> [46,] 5.2 2.3
#> [47,] 5.0 1.9
#> [48,] 5.2 2.0
#> [49,] 5.4 2.3
#> [50,] 5.1 1.8
#>
tail(iris3, c(6L, -1L, 2L))
#> , , Versicolor
#>
#> Sepal W. Petal L. Petal W.
#> [45,] 2.7 4.2 1.3
#> [46,] 3.0 4.2 1.2
#> [47,] 2.9 4.2 1.3
#> [48,] 2.9 4.3 1.3
#> [49,] 2.5 3.0 1.1
#> [50,] 2.8 4.1 1.3
#>
#> , , Virginica
#>
#> Sepal W. Petal L. Petal W.
#> [45,] 3.3 5.7 2.5
#> [46,] 3.0 5.2 2.3
#> [47,] 2.5 5.0 1.9
#> [48,] 3.0 5.2 2.0
#> [49,] 3.4 5.4 2.3
#> [50,] 3.0 5.1 1.8
#>
## iris with dimnames stripped
a3d <- iris3 ; dimnames(a3d) <- NULL
tail(a3d, c(6, -1, 2)) # keepnums = TRUE is default here!
#> , , 2
#>
#> [,2] [,3] [,4]
#> [45,] 2.7 4.2 1.3
#> [46,] 3.0 4.2 1.2
#> [47,] 2.9 4.2 1.3
#> [48,] 2.9 4.3 1.3
#> [49,] 2.5 3.0 1.1
#> [50,] 2.8 4.1 1.3
#>
#> , , 3
#>
#> [,2] [,3] [,4]
#> [45,] 3.3 5.7 2.5
#> [46,] 3.0 5.2 2.3
#> [47,] 2.5 5.0 1.9
#> [48,] 3.0 5.2 2.0
#> [49,] 3.4 5.4 2.3
#> [50,] 3.0 5.1 1.8
#>
tail(a3d, c(6, -1, 2), keepnums = FALSE)
#> , , 1
#>
#> [,1] [,2] [,3]
#> [1,] 2.7 4.2 1.3
#> [2,] 3.0 4.2 1.2
#> [3,] 2.9 4.2 1.3
#> [4,] 2.9 4.3 1.3
#> [5,] 2.5 3.0 1.1
#> [6,] 2.8 4.1 1.3
#>
#> , , 2
#>
#> [,1] [,2] [,3]
#> [1,] 3.3 5.7 2.5
#> [2,] 3.0 5.2 2.3
#> [3,] 2.5 5.0 1.9
#> [4,] 3.0 5.2 2.0
#> [5,] 3.4 5.4 2.3
#> [6,] 3.0 5.1 1.8
#>
## data frame w/ a (non-standard) attribute:
treeS <- structure(trees, foo = "bar")
(n <- nrow(treeS))
#> [1] 31
stopifnot(exprs = { # attribute is kept
identical(htS <- head(treeS), treeS[1:6, ])
identical(attr(htS, "foo") , "bar")
identical(tlS <- tail(treeS), treeS[(n-5):n, ])
## BUT if I use "useAttrib(.)", this is *not* ok, when n is of length 2:
## --- because [i,j]-indexing of data frames *also* drops "other" attributes ..
identical(tail(treeS, 3:2), treeS[(n-2):n, 2:3] )
})
tail(library) # last lines of function
#>
#> 374 return(y)
#> 375 }
#> 376 if (logical.return)
#> 377 TRUE
#> 378 else invisible(.packages())
#> 379 }
head(stats::ftable(Titanic))
#>
#> "Survived" "No" "Yes"
#> "Class" "Sex" "Age"
#> "1st" "Male" "Child" 0 5
#> "Adult" 118 57
#> "Female" "Child" 0 1
#> "Adult" 4 140
#> "2nd" "Male" "Child" 0 11
#> "Adult" 154 14
## 1d-array (with named dim) :
a1 <- array(1:7, 7); names(dim(a1)) <- "O2"
stopifnot(exprs = {
identical( tail(a1, 10), a1)
identical( head(a1, 10), a1)
identical( head(a1, 1), a1 [1 , drop=FALSE] ) # was a1[1] in R <= 3.6.x
identical( tail(a1, 2), a1[6:7])
identical( tail(a1, 1), a1 [7 , drop=FALSE] ) # was a1[7] in R <= 3.6.x
})