DataFrame-class: DataFrame objects
In S4Vectors: Foundation of vector-like and list-like containers in Bioconductor
Description
Details
Constructor
Accessors
Coercion
Subsetting
Combining
Author(s)
See Also
Examples
Description
The DataFrame class extends the RectangularData virtual
class supports the storage of any type of object (with length
and [ methods) as columns.
Details
On the whole, the DataFrame behaves very similarly to
data.frame, in terms of construction, subsetting, splitting,
combining, etc. The most notable exception is that the row names are
optional. This means calling rownames(x) will return
NULL if there are no row names. Of course, it could return
seq_len(nrow(x)), but returning NULL informs, for
example, combination functions that no row names are desired (they are
often a luxury when dealing with large data).
As DataFrame derives from Vector, it is
possible to set an annotation string. Also, another
DataFrame can hold metadata on the columns.
For a class to be supported as a column, it must have length
and [ methods, where [ supports subsetting only by
i and respects drop=FALSE. Optionally, a method may be
defined for the showAsCell generic, which should return a
vector of the same length as the subset of the column passed to
it. This vector is then placed into a data.frame and converted
to text with format. Thus, each element of the vector should be
some simple, usually character, representation of the corresponding
element in the column.
Constructor
-
DataFrame(..., row.names = NULL, check.names = TRUE,
stringsAsFactors)
Constructs a DataFrame in similar fashion to
data.frame. Each argument in ... is coerced to
a DataFrame and combined column-wise. No special effort is
expended to automatically determine the row names from the
arguments. The row names should be given in
row.names; otherwise, there are no row names. This is by
design, as row names are normally undesirable when data is
large. If check.names is TRUE, the column names will
be checked for syntactic validity and made unique, if necessary.
To store an object of a class that does not support coercion to
DataFrame, wrap it in I(). The class must still have
methods for length and [.
The stringsAsFactors argument is ignored. The coercion of
column arguments to DataFrame determines whether strings
become factors.
-
make_zero_col_DFrame(nrow)
Constructs a zero-column DFrame object with nrow rows.
Intended for developers to use in other packages and typically
not needed by the end user.
Accessors
In the following code snippets, x is a DataFrame.
dim(x):
Get the length two integer vector indicating in the first and
second element the number of rows and columns, respectively.
dimnames(x), dimnames(x) <- value:
Get and set the two element list containing the row names
(character vector of length nrow(x) or NULL)
and the column names (character vector of length ncol(x)).
Coercion
as(from, "DataFrame"):
By default, constructs a new DataFrame with from as
its only column. If from is a matrix or
data.frame, all of its columns become columns in the new
DataFrame. If from is a list, each element becomes a
column, recycling as necessary. Note that for the DataFrame
to behave correctly, each column object must support element-wise
subsetting via the [ method and return the number of elements with
length. It is recommended to use the DataFrame
constructor, rather than this interface.
as.list(x): Coerces x, a DataFrame,
to a list.
as.data.frame(x, row.names=NULL, optional=FALSE):
Coerces x, a DataFrame, to a data.frame.
Each column is coerced to a data.frame and then column
bound together. If row.names is NULL, they are
retrieved from x, if it has any. Otherwise, they are
inferred by the data.frame constructor.
NOTE: conversion of x to a data.frame is not
supported if x contains any list, SimpleList,
or CompressedList columns.
as(from, "data.frame"): Coerces a DataFrame
to a data.frame by calling as.data.frame(from).
as.matrix(x): Coerces the DataFrame to a
matrix, if possible.
as.env(x, enclos = parent.frame()):
Creates an environment from x with a symbol for each
colnames(x). The values are not actually copied into the
environment. Rather, they are dynamically bound using
makeActiveBinding. This prevents unnecessary copying
of the data from the external vectors into R vectors. The values
are cached, so that the data is not copied every time the symbol
is accessed.
Subsetting
In the following code snippets, x is a DataFrame.
x[i,j,drop]: Behaves very similarly to the
[.data.frame method, except i can be a
logical Rle object and subsetting by matrix indices
is not supported. Indices containing NA's are also not
supported.
x[i,j] <- value: Behaves very similarly to the
[<-.data.frame method.
x[[i]]: Behaves very similarly to the
[[.data.frame method, except arguments j
and exact are not supported. Column name matching is
always exact. Subsetting by matrices is not supported.
x[[i]] <- value: Behaves very similarly to the
[[<-.data.frame method, except argument j
is not supported.
Combining
In the following code snippets, x is a DataFrame.
-
rbind(...): Creates a new DataFrame by
combining the rows of the DataFrame objects in
.... Very similar to rbind.data.frame, except
in the handling of row names. If all elements have row names, they
are concatenated and made unique. Otherwise, the result does not
have row names. The return value inherits its metadata from
the first argument.
-
cbind(...): Creates a new DataFrame by
combining the columns of the DataFrame objects in
.... Very similar to cbind.data.frame. The
return value inherits its metadata from the first argument.
Author(s)
Michael Lawrence
See Also
-
DataFrame-utils for common operations on DataFrame objects.
-
RectangularData and SimpleList which DataFrame extends
directly.
-
TransposedDataFrame objects.
Examples
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score <- c(1L, 3L, NA)
counts <- c(10L, 2L, NA)
row.names <- c("one", "two", "three")
df <- DataFrame(score) # single column
df[["score"]]
df <- DataFrame(score, row.names = row.names) #with row names
rownames(df)
df <- DataFrame(vals = score) # explicit naming
df[["vals"]]
# arrays
ary <- array(1:4, c(2,1,2))
sw <- DataFrame(I(ary))
# a data.frame
sw <- DataFrame(swiss)
as.data.frame(sw) # swiss, without row names
# now with row names
sw <- DataFrame(swiss, row.names = rownames(swiss))
as.data.frame(sw) # swiss
# subsetting
sw[] # identity subset
sw[,] # same
sw[NULL] # no columns
sw[,NULL] # no columns
sw[NULL,] # no rows
## select columns
sw[1:3]
sw[,1:3] # same as above
sw[,"Fertility"]
sw[,c(TRUE, FALSE, FALSE, FALSE, FALSE, FALSE)]
## select rows and columns
sw[4:5, 1:3]
sw[1] # one-column DataFrame
## the same
sw[, 1, drop = FALSE]
sw[, 1] # a (unnamed) vector
sw[[1]] # the same
sw[["Fertility"]]
sw[["Fert"]] # should return 'NULL'
sw[1,] # a one-row DataFrame
sw[1,, drop=TRUE] # a list
## duplicate row, unique row names are created
sw[c(1, 1:2),]
## indexing by row names
sw["Courtelary",]
subsw <- sw[1:5,1:4]
subsw["C",] # partially matches
## row and column names
cn <- paste("X", seq_len(ncol(swiss)), sep = ".")
colnames(sw) <- cn
colnames(sw)
rn <- seq(nrow(sw))
rownames(sw) <- rn
rownames(sw)
## column replacement
df[["counts"]] <- counts
df[["counts"]]
df[[3]] <- score
df[["X"]]
df[[3]] <- NULL # deletion
Example output
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, basename, cbind, colMeans, colSums, colnames,
dirname, do.call, duplicated, eval, evalq, get, grep, grepl,
intersect, is.unsorted, lapply, lengths, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind,
rowMeans, rowSums, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which, which.max, which.min
Attaching package: 'S4Vectors'
The following object is masked from 'package:base':
expand.grid
[1] 1 3 NA
[1] "one" "two" "three"
[1] 1 3 NA
Fertility Agriculture Examination Education Catholic
Courtelary 80.2 17.0 15 12 9.96
Delemont 83.1 45.1 6 9 84.84
Franches-Mnt 92.5 39.7 5 5 93.40
Moutier 85.8 36.5 12 7 33.77
Neuveville 76.9 43.5 17 15 5.16
Porrentruy 76.1 35.3 9 7 90.57
Broye 83.8 70.2 16 7 92.85
Glane 92.4 67.8 14 8 97.16
Gruyere 82.4 53.3 12 7 97.67
Sarine 82.9 45.2 16 13 91.38
Veveyse 87.1 64.5 14 6 98.61
Aigle 64.1 62.0 21 12 8.52
Aubonne 66.9 67.5 14 7 2.27
Avenches 68.9 60.7 19 12 4.43
Cossonay 61.7 69.3 22 5 2.82
Echallens 68.3 72.6 18 2 24.20
Grandson 71.7 34.0 17 8 3.30
Lausanne 55.7 19.4 26 28 12.11
La Vallee 54.3 15.2 31 20 2.15
Lavaux 65.1 73.0 19 9 2.84
Morges 65.5 59.8 22 10 5.23
Moudon 65.0 55.1 14 3 4.52
Nyone 56.6 50.9 22 12 15.14
Orbe 57.4 54.1 20 6 4.20
Oron 72.5 71.2 12 1 2.40
Payerne 74.2 58.1 14 8 5.23
Paysd'enhaut 72.0 63.5 6 3 2.56
Rolle 60.5 60.8 16 10 7.72
Vevey 58.3 26.8 25 19 18.46
Yverdon 65.4 49.5 15 8 6.10
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
Martigwy 70.5 78.2 12 6 98.96
Monthey 79.4 64.9 7 3 98.22
St Maurice 65.0 75.9 9 9 99.06
Sierre 92.2 84.6 3 3 99.46
Sion 79.3 63.1 13 13 96.83
Boudry 70.4 38.4 26 12 5.62
La Chauxdfnd 65.7 7.7 29 11 13.79
Le Locle 72.7 16.7 22 13 11.22
Neuchatel 64.4 17.6 35 32 16.92
Val de Ruz 77.6 37.6 15 7 4.97
ValdeTravers 67.6 18.7 25 7 8.65
V. De Geneve 35.0 1.2 37 53 42.34
Rive Droite 44.7 46.6 16 29 50.43
Rive Gauche 42.8 27.7 22 29 58.33
Infant.Mortality
Courtelary 22.2
Delemont 22.2
Franches-Mnt 20.2
Moutier 20.3
Neuveville 20.6
Porrentruy 26.6
Broye 23.6
Glane 24.9
Gruyere 21.0
Sarine 24.4
Veveyse 24.5
Aigle 16.5
Aubonne 19.1
Avenches 22.7
Cossonay 18.7
Echallens 21.2
Grandson 20.0
Lausanne 20.2
La Vallee 10.8
Lavaux 20.0
Morges 18.0
Moudon 22.4
Nyone 16.7
Orbe 15.3
Oron 21.0
Payerne 23.8
Paysd'enhaut 18.0
Rolle 16.3
Vevey 20.9
Yverdon 22.5
Conthey 15.1
Entremont 19.8
Herens 18.3
Martigwy 19.4
Monthey 20.2
St Maurice 17.8
Sierre 16.3
Sion 18.1
Boudry 20.3
La Chauxdfnd 20.5
Le Locle 18.9
Neuchatel 23.0
Val de Ruz 20.0
ValdeTravers 19.5
V. De Geneve 18.0
Rive Droite 18.2
Rive Gauche 19.3
Fertility Agriculture Examination Education Catholic
Courtelary 80.2 17.0 15 12 9.96
Delemont 83.1 45.1 6 9 84.84
Franches-Mnt 92.5 39.7 5 5 93.40
Moutier 85.8 36.5 12 7 33.77
Neuveville 76.9 43.5 17 15 5.16
Porrentruy 76.1 35.3 9 7 90.57
Broye 83.8 70.2 16 7 92.85
Glane 92.4 67.8 14 8 97.16
Gruyere 82.4 53.3 12 7 97.67
Sarine 82.9 45.2 16 13 91.38
Veveyse 87.1 64.5 14 6 98.61
Aigle 64.1 62.0 21 12 8.52
Aubonne 66.9 67.5 14 7 2.27
Avenches 68.9 60.7 19 12 4.43
Cossonay 61.7 69.3 22 5 2.82
Echallens 68.3 72.6 18 2 24.20
Grandson 71.7 34.0 17 8 3.30
Lausanne 55.7 19.4 26 28 12.11
La Vallee 54.3 15.2 31 20 2.15
Lavaux 65.1 73.0 19 9 2.84
Morges 65.5 59.8 22 10 5.23
Moudon 65.0 55.1 14 3 4.52
Nyone 56.6 50.9 22 12 15.14
Orbe 57.4 54.1 20 6 4.20
Oron 72.5 71.2 12 1 2.40
Payerne 74.2 58.1 14 8 5.23
Paysd'enhaut 72.0 63.5 6 3 2.56
Rolle 60.5 60.8 16 10 7.72
Vevey 58.3 26.8 25 19 18.46
Yverdon 65.4 49.5 15 8 6.10
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
Martigwy 70.5 78.2 12 6 98.96
Monthey 79.4 64.9 7 3 98.22
St Maurice 65.0 75.9 9 9 99.06
Sierre 92.2 84.6 3 3 99.46
Sion 79.3 63.1 13 13 96.83
Boudry 70.4 38.4 26 12 5.62
La Chauxdfnd 65.7 7.7 29 11 13.79
Le Locle 72.7 16.7 22 13 11.22
Neuchatel 64.4 17.6 35 32 16.92
Val de Ruz 77.6 37.6 15 7 4.97
ValdeTravers 67.6 18.7 25 7 8.65
V. De Geneve 35.0 1.2 37 53 42.34
Rive Droite 44.7 46.6 16 29 50.43
Rive Gauche 42.8 27.7 22 29 58.33
Infant.Mortality
Courtelary 22.2
Delemont 22.2
Franches-Mnt 20.2
Moutier 20.3
Neuveville 20.6
Porrentruy 26.6
Broye 23.6
Glane 24.9
Gruyere 21.0
Sarine 24.4
Veveyse 24.5
Aigle 16.5
Aubonne 19.1
Avenches 22.7
Cossonay 18.7
Echallens 21.2
Grandson 20.0
Lausanne 20.2
La Vallee 10.8
Lavaux 20.0
Morges 18.0
Moudon 22.4
Nyone 16.7
Orbe 15.3
Oron 21.0
Payerne 23.8
Paysd'enhaut 18.0
Rolle 16.3
Vevey 20.9
Yverdon 22.5
Conthey 15.1
Entremont 19.8
Herens 18.3
Martigwy 19.4
Monthey 20.2
St Maurice 17.8
Sierre 16.3
Sion 18.1
Boudry 20.3
La Chauxdfnd 20.5
Le Locle 18.9
Neuchatel 23.0
Val de Ruz 20.0
ValdeTravers 19.5
V. De Geneve 18.0
Rive Droite 18.2
Rive Gauche 19.3
DataFrame with 47 rows and 6 columns
Fertility Agriculture Examination Education Catholic
<numeric> <numeric> <integer> <integer> <numeric>
Courtelary 80.2 17 15 12 9.96
Delemont 83.1 45.1 6 9 84.84
Franches-Mnt 92.5 39.7 5 5 93.4
Moutier 85.8 36.5 12 7 33.77
Neuveville 76.9 43.5 17 15 5.16
... ... ... ... ... ...
Val de Ruz 77.6 37.6 15 7 4.97
ValdeTravers 67.6 18.7 25 7 8.65
V. De Geneve 35 1.2 37 53 42.34
Rive Droite 44.7 46.6 16 29 50.43
Rive Gauche 42.8 27.7 22 29 58.33
Infant.Mortality
<numeric>
Courtelary 22.2
Delemont 22.2
Franches-Mnt 20.2
Moutier 20.3
Neuveville 20.6
... ...
Val de Ruz 20
ValdeTravers 19.5
V. De Geneve 18
Rive Droite 18.2
Rive Gauche 19.3
DataFrame with 47 rows and 6 columns
Fertility Agriculture Examination Education Catholic
<numeric> <numeric> <integer> <integer> <numeric>
Courtelary 80.2 17 15 12 9.96
Delemont 83.1 45.1 6 9 84.84
Franches-Mnt 92.5 39.7 5 5 93.4
Moutier 85.8 36.5 12 7 33.77
Neuveville 76.9 43.5 17 15 5.16
... ... ... ... ... ...
Val de Ruz 77.6 37.6 15 7 4.97
ValdeTravers 67.6 18.7 25 7 8.65
V. De Geneve 35 1.2 37 53 42.34
Rive Droite 44.7 46.6 16 29 50.43
Rive Gauche 42.8 27.7 22 29 58.33
Infant.Mortality
<numeric>
Courtelary 22.2
Delemont 22.2
Franches-Mnt 20.2
Moutier 20.3
Neuveville 20.6
... ...
Val de Ruz 20
ValdeTravers 19.5
V. De Geneve 18
Rive Droite 18.2
Rive Gauche 19.3
DataFrame with 47 rows and 0 columns
DataFrame with 47 rows and 0 columns
DataFrame with 0 rows and 6 columns
DataFrame with 47 rows and 3 columns
Fertility Agriculture Examination
<numeric> <numeric> <integer>
Courtelary 80.2 17 15
Delemont 83.1 45.1 6
Franches-Mnt 92.5 39.7 5
Moutier 85.8 36.5 12
Neuveville 76.9 43.5 17
... ... ... ...
Val de Ruz 77.6 37.6 15
ValdeTravers 67.6 18.7 25
V. De Geneve 35 1.2 37
Rive Droite 44.7 46.6 16
Rive Gauche 42.8 27.7 22
DataFrame with 47 rows and 3 columns
Fertility Agriculture Examination
<numeric> <numeric> <integer>
Courtelary 80.2 17 15
Delemont 83.1 45.1 6
Franches-Mnt 92.5 39.7 5
Moutier 85.8 36.5 12
Neuveville 76.9 43.5 17
... ... ... ...
Val de Ruz 77.6 37.6 15
ValdeTravers 67.6 18.7 25
V. De Geneve 35 1.2 37
Rive Droite 44.7 46.6 16
Rive Gauche 42.8 27.7 22
[1] 80.2 83.1 92.5 85.8 76.9 76.1 83.8 92.4 82.4 82.9 87.1 64.1 66.9 68.9 61.7
[16] 68.3 71.7 55.7 54.3 65.1 65.5 65.0 56.6 57.4 72.5 74.2 72.0 60.5 58.3 65.4
[31] 75.5 69.3 77.3 70.5 79.4 65.0 92.2 79.3 70.4 65.7 72.7 64.4 77.6 67.6 35.0
[46] 44.7 42.8
[1] 80.2 83.1 92.5 85.8 76.9 76.1 83.8 92.4 82.4 82.9 87.1 64.1 66.9 68.9 61.7
[16] 68.3 71.7 55.7 54.3 65.1 65.5 65.0 56.6 57.4 72.5 74.2 72.0 60.5 58.3 65.4
[31] 75.5 69.3 77.3 70.5 79.4 65.0 92.2 79.3 70.4 65.7 72.7 64.4 77.6 67.6 35.0
[46] 44.7 42.8
DataFrame with 2 rows and 3 columns
Fertility Agriculture Examination
<numeric> <numeric> <integer>
Moutier 85.8 36.5 12
Neuveville 76.9 43.5 17
DataFrame with 47 rows and 1 column
Fertility
<numeric>
Courtelary 80.2
Delemont 83.1
Franches-Mnt 92.5
Moutier 85.8
Neuveville 76.9
... ...
Val de Ruz 77.6
ValdeTravers 67.6
V. De Geneve 35
Rive Droite 44.7
Rive Gauche 42.8
DataFrame with 47 rows and 1 column
Fertility
<numeric>
Courtelary 80.2
Delemont 83.1
Franches-Mnt 92.5
Moutier 85.8
Neuveville 76.9
... ...
Val de Ruz 77.6
ValdeTravers 67.6
V. De Geneve 35
Rive Droite 44.7
Rive Gauche 42.8
[1] 80.2 83.1 92.5 85.8 76.9 76.1 83.8 92.4 82.4 82.9 87.1 64.1 66.9 68.9 61.7
[16] 68.3 71.7 55.7 54.3 65.1 65.5 65.0 56.6 57.4 72.5 74.2 72.0 60.5 58.3 65.4
[31] 75.5 69.3 77.3 70.5 79.4 65.0 92.2 79.3 70.4 65.7 72.7 64.4 77.6 67.6 35.0
[46] 44.7 42.8
[1] 80.2 83.1 92.5 85.8 76.9 76.1 83.8 92.4 82.4 82.9 87.1 64.1 66.9 68.9 61.7
[16] 68.3 71.7 55.7 54.3 65.1 65.5 65.0 56.6 57.4 72.5 74.2 72.0 60.5 58.3 65.4
[31] 75.5 69.3 77.3 70.5 79.4 65.0 92.2 79.3 70.4 65.7 72.7 64.4 77.6 67.6 35.0
[46] 44.7 42.8
[1] 80.2 83.1 92.5 85.8 76.9 76.1 83.8 92.4 82.4 82.9 87.1 64.1 66.9 68.9 61.7
[16] 68.3 71.7 55.7 54.3 65.1 65.5 65.0 56.6 57.4 72.5 74.2 72.0 60.5 58.3 65.4
[31] 75.5 69.3 77.3 70.5 79.4 65.0 92.2 79.3 70.4 65.7 72.7 64.4 77.6 67.6 35.0
[46] 44.7 42.8
NULL
DataFrame with 1 row and 6 columns
Fertility Agriculture Examination Education Catholic
<numeric> <numeric> <integer> <integer> <numeric>
Courtelary 80.2 17 15 12 9.96
Infant.Mortality
<numeric>
Courtelary 22.2
$Fertility
[1] 80.2
$Agriculture
[1] 17
$Examination
[1] 15
$Education
[1] 12
$Catholic
[1] 9.96
$Infant.Mortality
[1] 22.2
DataFrame with 3 rows and 6 columns
Fertility Agriculture Examination Education Catholic
<numeric> <numeric> <integer> <integer> <numeric>
Courtelary 80.2 17 15 12 9.96
Courtelary 80.2 17 15 12 9.96
Delemont 83.1 45.1 6 9 84.84
Infant.Mortality
<numeric>
Courtelary 22.2
Courtelary 22.2
Delemont 22.2
DataFrame with 1 row and 6 columns
Fertility Agriculture Examination Education Catholic
<numeric> <numeric> <integer> <integer> <numeric>
Courtelary 80.2 17 15 12 9.96
Infant.Mortality
<numeric>
Courtelary 22.2
DataFrame with 1 row and 4 columns
Fertility Agriculture Examination Education
<numeric> <numeric> <integer> <integer>
Courtelary 80.2 17 15 12
[1] "X.1" "X.2" "X.3" "X.4" "X.5" "X.6"
[1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15"
[16] "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30"
[31] "31" "32" "33" "34" "35" "36" "37" "38" "39" "40" "41" "42" "43" "44" "45"
[46] "46" "47"
[1] 10 2 NA
NULL
S4Vectors documentation built on Dec. 11, 2020, 2:02 a.m.
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Description Details Constructor Accessors Coercion Subsetting Combining Author(s) See Also Examples
Description
The DataFrame class extends the RectangularData virtual
class supports the storage of any type of object (with length
and [ methods) as columns.
Details
On the whole, the DataFrame behaves very similarly to
data.frame, in terms of construction, subsetting, splitting,
combining, etc. The most notable exception is that the row names are
optional. This means calling rownames(x) will return
NULL if there are no row names. Of course, it could return
seq_len(nrow(x)), but returning NULL informs, for
example, combination functions that no row names are desired (they are
often a luxury when dealing with large data).
As DataFrame derives from Vector, it is
possible to set an annotation string. Also, another
DataFrame can hold metadata on the columns.
For a class to be supported as a column, it must have length
and [ methods, where [ supports subsetting only by
i and respects drop=FALSE. Optionally, a method may be
defined for the showAsCell generic, which should return a
vector of the same length as the subset of the column passed to
it. This vector is then placed into a data.frame and converted
to text with format. Thus, each element of the vector should be
some simple, usually character, representation of the corresponding
element in the column.
Constructor
-
DataFrame(..., row.names = NULL, check.names = TRUE, stringsAsFactors)Constructs a
DataFramein similar fashion todata.frame. Each argument in...is coerced to aDataFrameand combined column-wise. No special effort is expended to automatically determine the row names from the arguments. The row names should be given inrow.names; otherwise, there are no row names. This is by design, as row names are normally undesirable when data is large. Ifcheck.namesisTRUE, the column names will be checked for syntactic validity and made unique, if necessary.To store an object of a class that does not support coercion to
DataFrame, wrap it inI(). The class must still have methods forlengthand[.The
stringsAsFactorsargument is ignored. The coercion of column arguments to DataFrame determines whether strings become factors. -
make_zero_col_DFrame(nrow)Constructs a zero-column DFrame object with
nrowrows. Intended for developers to use in other packages and typically not needed by the end user.
Accessors
In the following code snippets, x is a DataFrame.
dim(x): Get the length two integer vector indicating in the first and second element the number of rows and columns, respectively.dimnames(x),dimnames(x) <- value: Get and set the two element list containing the row names (character vector of lengthnrow(x)orNULL) and the column names (character vector of lengthncol(x)).
Coercion
as(from, "DataFrame"): By default, constructs a newDataFramewithfromas its only column. Iffromis amatrixordata.frame, all of its columns become columns in the newDataFrame. Iffromis a list, each element becomes a column, recycling as necessary. Note that for theDataFrameto behave correctly, each column object must support element-wise subsetting via the[method and return the number of elements withlength. It is recommended to use theDataFrameconstructor, rather than this interface.as.list(x): Coercesx, aDataFrame, to alist.as.data.frame(x, row.names=NULL, optional=FALSE): Coercesx, aDataFrame, to adata.frame. Each column is coerced to adata.frameand then column bound together. Ifrow.namesisNULL, they are retrieved fromx, if it has any. Otherwise, they are inferred by thedata.frameconstructor.NOTE: conversion of
xto adata.frameis not supported ifxcontains anylist,SimpleList, orCompressedListcolumns.as(from, "data.frame"): Coerces aDataFrameto adata.frameby callingas.data.frame(from).as.matrix(x): Coerces theDataFrameto amatrix, if possible.as.env(x, enclos = parent.frame()): Creates an environment fromxwith a symbol for eachcolnames(x). The values are not actually copied into the environment. Rather, they are dynamically bound usingmakeActiveBinding. This prevents unnecessary copying of the data from the external vectors into R vectors. The values are cached, so that the data is not copied every time the symbol is accessed.
Subsetting
In the following code snippets, x is a DataFrame.
x[i,j,drop]: Behaves very similarly to the[.data.framemethod, exceptican be a logicalRleobject and subsetting bymatrixindices is not supported. Indices containingNA's are also not supported.x[i,j] <- value: Behaves very similarly to the[<-.data.framemethod.x[[i]]: Behaves very similarly to the[[.data.framemethod, except argumentsjandexactare not supported. Column name matching is always exact. Subsetting by matrices is not supported.x[[i]] <- value: Behaves very similarly to the[[<-.data.framemethod, except argumentjis not supported.
Combining
In the following code snippets, x is a DataFrame.
-
rbind(...): Creates a newDataFrameby combining the rows of theDataFrameobjects in.... Very similar torbind.data.frame, except in the handling of row names. If all elements have row names, they are concatenated and made unique. Otherwise, the result does not have row names. The return value inherits its metadata from the first argument. -
cbind(...): Creates a newDataFrameby combining the columns of theDataFrameobjects in.... Very similar tocbind.data.frame. The return value inherits its metadata from the first argument.
Author(s)
Michael Lawrence
See Also
-
DataFrame-utils for common operations on DataFrame objects.
-
RectangularData and SimpleList which DataFrame extends directly.
-
TransposedDataFrame objects.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | score <- c(1L, 3L, NA)
counts <- c(10L, 2L, NA)
row.names <- c("one", "two", "three")
df <- DataFrame(score) # single column
df[["score"]]
df <- DataFrame(score, row.names = row.names) #with row names
rownames(df)
df <- DataFrame(vals = score) # explicit naming
df[["vals"]]
# arrays
ary <- array(1:4, c(2,1,2))
sw <- DataFrame(I(ary))
# a data.frame
sw <- DataFrame(swiss)
as.data.frame(sw) # swiss, without row names
# now with row names
sw <- DataFrame(swiss, row.names = rownames(swiss))
as.data.frame(sw) # swiss
# subsetting
sw[] # identity subset
sw[,] # same
sw[NULL] # no columns
sw[,NULL] # no columns
sw[NULL,] # no rows
## select columns
sw[1:3]
sw[,1:3] # same as above
sw[,"Fertility"]
sw[,c(TRUE, FALSE, FALSE, FALSE, FALSE, FALSE)]
## select rows and columns
sw[4:5, 1:3]
sw[1] # one-column DataFrame
## the same
sw[, 1, drop = FALSE]
sw[, 1] # a (unnamed) vector
sw[[1]] # the same
sw[["Fertility"]]
sw[["Fert"]] # should return 'NULL'
sw[1,] # a one-row DataFrame
sw[1,, drop=TRUE] # a list
## duplicate row, unique row names are created
sw[c(1, 1:2),]
## indexing by row names
sw["Courtelary",]
subsw <- sw[1:5,1:4]
subsw["C",] # partially matches
## row and column names
cn <- paste("X", seq_len(ncol(swiss)), sep = ".")
colnames(sw) <- cn
colnames(sw)
rn <- seq(nrow(sw))
rownames(sw) <- rn
rownames(sw)
## column replacement
df[["counts"]] <- counts
df[["counts"]]
df[[3]] <- score
df[["X"]]
df[[3]] <- NULL # deletion
|
Example output
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, basename, cbind, colMeans, colSums, colnames,
dirname, do.call, duplicated, eval, evalq, get, grep, grepl,
intersect, is.unsorted, lapply, lengths, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind,
rowMeans, rowSums, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which, which.max, which.min
Attaching package: 'S4Vectors'
The following object is masked from 'package:base':
expand.grid
[1] 1 3 NA
[1] "one" "two" "three"
[1] 1 3 NA
Fertility Agriculture Examination Education Catholic
Courtelary 80.2 17.0 15 12 9.96
Delemont 83.1 45.1 6 9 84.84
Franches-Mnt 92.5 39.7 5 5 93.40
Moutier 85.8 36.5 12 7 33.77
Neuveville 76.9 43.5 17 15 5.16
Porrentruy 76.1 35.3 9 7 90.57
Broye 83.8 70.2 16 7 92.85
Glane 92.4 67.8 14 8 97.16
Gruyere 82.4 53.3 12 7 97.67
Sarine 82.9 45.2 16 13 91.38
Veveyse 87.1 64.5 14 6 98.61
Aigle 64.1 62.0 21 12 8.52
Aubonne 66.9 67.5 14 7 2.27
Avenches 68.9 60.7 19 12 4.43
Cossonay 61.7 69.3 22 5 2.82
Echallens 68.3 72.6 18 2 24.20
Grandson 71.7 34.0 17 8 3.30
Lausanne 55.7 19.4 26 28 12.11
La Vallee 54.3 15.2 31 20 2.15
Lavaux 65.1 73.0 19 9 2.84
Morges 65.5 59.8 22 10 5.23
Moudon 65.0 55.1 14 3 4.52
Nyone 56.6 50.9 22 12 15.14
Orbe 57.4 54.1 20 6 4.20
Oron 72.5 71.2 12 1 2.40
Payerne 74.2 58.1 14 8 5.23
Paysd'enhaut 72.0 63.5 6 3 2.56
Rolle 60.5 60.8 16 10 7.72
Vevey 58.3 26.8 25 19 18.46
Yverdon 65.4 49.5 15 8 6.10
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
Martigwy 70.5 78.2 12 6 98.96
Monthey 79.4 64.9 7 3 98.22
St Maurice 65.0 75.9 9 9 99.06
Sierre 92.2 84.6 3 3 99.46
Sion 79.3 63.1 13 13 96.83
Boudry 70.4 38.4 26 12 5.62
La Chauxdfnd 65.7 7.7 29 11 13.79
Le Locle 72.7 16.7 22 13 11.22
Neuchatel 64.4 17.6 35 32 16.92
Val de Ruz 77.6 37.6 15 7 4.97
ValdeTravers 67.6 18.7 25 7 8.65
V. De Geneve 35.0 1.2 37 53 42.34
Rive Droite 44.7 46.6 16 29 50.43
Rive Gauche 42.8 27.7 22 29 58.33
Infant.Mortality
Courtelary 22.2
Delemont 22.2
Franches-Mnt 20.2
Moutier 20.3
Neuveville 20.6
Porrentruy 26.6
Broye 23.6
Glane 24.9
Gruyere 21.0
Sarine 24.4
Veveyse 24.5
Aigle 16.5
Aubonne 19.1
Avenches 22.7
Cossonay 18.7
Echallens 21.2
Grandson 20.0
Lausanne 20.2
La Vallee 10.8
Lavaux 20.0
Morges 18.0
Moudon 22.4
Nyone 16.7
Orbe 15.3
Oron 21.0
Payerne 23.8
Paysd'enhaut 18.0
Rolle 16.3
Vevey 20.9
Yverdon 22.5
Conthey 15.1
Entremont 19.8
Herens 18.3
Martigwy 19.4
Monthey 20.2
St Maurice 17.8
Sierre 16.3
Sion 18.1
Boudry 20.3
La Chauxdfnd 20.5
Le Locle 18.9
Neuchatel 23.0
Val de Ruz 20.0
ValdeTravers 19.5
V. De Geneve 18.0
Rive Droite 18.2
Rive Gauche 19.3
Fertility Agriculture Examination Education Catholic
Courtelary 80.2 17.0 15 12 9.96
Delemont 83.1 45.1 6 9 84.84
Franches-Mnt 92.5 39.7 5 5 93.40
Moutier 85.8 36.5 12 7 33.77
Neuveville 76.9 43.5 17 15 5.16
Porrentruy 76.1 35.3 9 7 90.57
Broye 83.8 70.2 16 7 92.85
Glane 92.4 67.8 14 8 97.16
Gruyere 82.4 53.3 12 7 97.67
Sarine 82.9 45.2 16 13 91.38
Veveyse 87.1 64.5 14 6 98.61
Aigle 64.1 62.0 21 12 8.52
Aubonne 66.9 67.5 14 7 2.27
Avenches 68.9 60.7 19 12 4.43
Cossonay 61.7 69.3 22 5 2.82
Echallens 68.3 72.6 18 2 24.20
Grandson 71.7 34.0 17 8 3.30
Lausanne 55.7 19.4 26 28 12.11
La Vallee 54.3 15.2 31 20 2.15
Lavaux 65.1 73.0 19 9 2.84
Morges 65.5 59.8 22 10 5.23
Moudon 65.0 55.1 14 3 4.52
Nyone 56.6 50.9 22 12 15.14
Orbe 57.4 54.1 20 6 4.20
Oron 72.5 71.2 12 1 2.40
Payerne 74.2 58.1 14 8 5.23
Paysd'enhaut 72.0 63.5 6 3 2.56
Rolle 60.5 60.8 16 10 7.72
Vevey 58.3 26.8 25 19 18.46
Yverdon 65.4 49.5 15 8 6.10
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
Martigwy 70.5 78.2 12 6 98.96
Monthey 79.4 64.9 7 3 98.22
St Maurice 65.0 75.9 9 9 99.06
Sierre 92.2 84.6 3 3 99.46
Sion 79.3 63.1 13 13 96.83
Boudry 70.4 38.4 26 12 5.62
La Chauxdfnd 65.7 7.7 29 11 13.79
Le Locle 72.7 16.7 22 13 11.22
Neuchatel 64.4 17.6 35 32 16.92
Val de Ruz 77.6 37.6 15 7 4.97
ValdeTravers 67.6 18.7 25 7 8.65
V. De Geneve 35.0 1.2 37 53 42.34
Rive Droite 44.7 46.6 16 29 50.43
Rive Gauche 42.8 27.7 22 29 58.33
Infant.Mortality
Courtelary 22.2
Delemont 22.2
Franches-Mnt 20.2
Moutier 20.3
Neuveville 20.6
Porrentruy 26.6
Broye 23.6
Glane 24.9
Gruyere 21.0
Sarine 24.4
Veveyse 24.5
Aigle 16.5
Aubonne 19.1
Avenches 22.7
Cossonay 18.7
Echallens 21.2
Grandson 20.0
Lausanne 20.2
La Vallee 10.8
Lavaux 20.0
Morges 18.0
Moudon 22.4
Nyone 16.7
Orbe 15.3
Oron 21.0
Payerne 23.8
Paysd'enhaut 18.0
Rolle 16.3
Vevey 20.9
Yverdon 22.5
Conthey 15.1
Entremont 19.8
Herens 18.3
Martigwy 19.4
Monthey 20.2
St Maurice 17.8
Sierre 16.3
Sion 18.1
Boudry 20.3
La Chauxdfnd 20.5
Le Locle 18.9
Neuchatel 23.0
Val de Ruz 20.0
ValdeTravers 19.5
V. De Geneve 18.0
Rive Droite 18.2
Rive Gauche 19.3
DataFrame with 47 rows and 6 columns
Fertility Agriculture Examination Education Catholic
<numeric> <numeric> <integer> <integer> <numeric>
Courtelary 80.2 17 15 12 9.96
Delemont 83.1 45.1 6 9 84.84
Franches-Mnt 92.5 39.7 5 5 93.4
Moutier 85.8 36.5 12 7 33.77
Neuveville 76.9 43.5 17 15 5.16
... ... ... ... ... ...
Val de Ruz 77.6 37.6 15 7 4.97
ValdeTravers 67.6 18.7 25 7 8.65
V. De Geneve 35 1.2 37 53 42.34
Rive Droite 44.7 46.6 16 29 50.43
Rive Gauche 42.8 27.7 22 29 58.33
Infant.Mortality
<numeric>
Courtelary 22.2
Delemont 22.2
Franches-Mnt 20.2
Moutier 20.3
Neuveville 20.6
... ...
Val de Ruz 20
ValdeTravers 19.5
V. De Geneve 18
Rive Droite 18.2
Rive Gauche 19.3
DataFrame with 47 rows and 6 columns
Fertility Agriculture Examination Education Catholic
<numeric> <numeric> <integer> <integer> <numeric>
Courtelary 80.2 17 15 12 9.96
Delemont 83.1 45.1 6 9 84.84
Franches-Mnt 92.5 39.7 5 5 93.4
Moutier 85.8 36.5 12 7 33.77
Neuveville 76.9 43.5 17 15 5.16
... ... ... ... ... ...
Val de Ruz 77.6 37.6 15 7 4.97
ValdeTravers 67.6 18.7 25 7 8.65
V. De Geneve 35 1.2 37 53 42.34
Rive Droite 44.7 46.6 16 29 50.43
Rive Gauche 42.8 27.7 22 29 58.33
Infant.Mortality
<numeric>
Courtelary 22.2
Delemont 22.2
Franches-Mnt 20.2
Moutier 20.3
Neuveville 20.6
... ...
Val de Ruz 20
ValdeTravers 19.5
V. De Geneve 18
Rive Droite 18.2
Rive Gauche 19.3
DataFrame with 47 rows and 0 columns
DataFrame with 47 rows and 0 columns
DataFrame with 0 rows and 6 columns
DataFrame with 47 rows and 3 columns
Fertility Agriculture Examination
<numeric> <numeric> <integer>
Courtelary 80.2 17 15
Delemont 83.1 45.1 6
Franches-Mnt 92.5 39.7 5
Moutier 85.8 36.5 12
Neuveville 76.9 43.5 17
... ... ... ...
Val de Ruz 77.6 37.6 15
ValdeTravers 67.6 18.7 25
V. De Geneve 35 1.2 37
Rive Droite 44.7 46.6 16
Rive Gauche 42.8 27.7 22
DataFrame with 47 rows and 3 columns
Fertility Agriculture Examination
<numeric> <numeric> <integer>
Courtelary 80.2 17 15
Delemont 83.1 45.1 6
Franches-Mnt 92.5 39.7 5
Moutier 85.8 36.5 12
Neuveville 76.9 43.5 17
... ... ... ...
Val de Ruz 77.6 37.6 15
ValdeTravers 67.6 18.7 25
V. De Geneve 35 1.2 37
Rive Droite 44.7 46.6 16
Rive Gauche 42.8 27.7 22
[1] 80.2 83.1 92.5 85.8 76.9 76.1 83.8 92.4 82.4 82.9 87.1 64.1 66.9 68.9 61.7
[16] 68.3 71.7 55.7 54.3 65.1 65.5 65.0 56.6 57.4 72.5 74.2 72.0 60.5 58.3 65.4
[31] 75.5 69.3 77.3 70.5 79.4 65.0 92.2 79.3 70.4 65.7 72.7 64.4 77.6 67.6 35.0
[46] 44.7 42.8
[1] 80.2 83.1 92.5 85.8 76.9 76.1 83.8 92.4 82.4 82.9 87.1 64.1 66.9 68.9 61.7
[16] 68.3 71.7 55.7 54.3 65.1 65.5 65.0 56.6 57.4 72.5 74.2 72.0 60.5 58.3 65.4
[31] 75.5 69.3 77.3 70.5 79.4 65.0 92.2 79.3 70.4 65.7 72.7 64.4 77.6 67.6 35.0
[46] 44.7 42.8
DataFrame with 2 rows and 3 columns
Fertility Agriculture Examination
<numeric> <numeric> <integer>
Moutier 85.8 36.5 12
Neuveville 76.9 43.5 17
DataFrame with 47 rows and 1 column
Fertility
<numeric>
Courtelary 80.2
Delemont 83.1
Franches-Mnt 92.5
Moutier 85.8
Neuveville 76.9
... ...
Val de Ruz 77.6
ValdeTravers 67.6
V. De Geneve 35
Rive Droite 44.7
Rive Gauche 42.8
DataFrame with 47 rows and 1 column
Fertility
<numeric>
Courtelary 80.2
Delemont 83.1
Franches-Mnt 92.5
Moutier 85.8
Neuveville 76.9
... ...
Val de Ruz 77.6
ValdeTravers 67.6
V. De Geneve 35
Rive Droite 44.7
Rive Gauche 42.8
[1] 80.2 83.1 92.5 85.8 76.9 76.1 83.8 92.4 82.4 82.9 87.1 64.1 66.9 68.9 61.7
[16] 68.3 71.7 55.7 54.3 65.1 65.5 65.0 56.6 57.4 72.5 74.2 72.0 60.5 58.3 65.4
[31] 75.5 69.3 77.3 70.5 79.4 65.0 92.2 79.3 70.4 65.7 72.7 64.4 77.6 67.6 35.0
[46] 44.7 42.8
[1] 80.2 83.1 92.5 85.8 76.9 76.1 83.8 92.4 82.4 82.9 87.1 64.1 66.9 68.9 61.7
[16] 68.3 71.7 55.7 54.3 65.1 65.5 65.0 56.6 57.4 72.5 74.2 72.0 60.5 58.3 65.4
[31] 75.5 69.3 77.3 70.5 79.4 65.0 92.2 79.3 70.4 65.7 72.7 64.4 77.6 67.6 35.0
[46] 44.7 42.8
[1] 80.2 83.1 92.5 85.8 76.9 76.1 83.8 92.4 82.4 82.9 87.1 64.1 66.9 68.9 61.7
[16] 68.3 71.7 55.7 54.3 65.1 65.5 65.0 56.6 57.4 72.5 74.2 72.0 60.5 58.3 65.4
[31] 75.5 69.3 77.3 70.5 79.4 65.0 92.2 79.3 70.4 65.7 72.7 64.4 77.6 67.6 35.0
[46] 44.7 42.8
NULL
DataFrame with 1 row and 6 columns
Fertility Agriculture Examination Education Catholic
<numeric> <numeric> <integer> <integer> <numeric>
Courtelary 80.2 17 15 12 9.96
Infant.Mortality
<numeric>
Courtelary 22.2
$Fertility
[1] 80.2
$Agriculture
[1] 17
$Examination
[1] 15
$Education
[1] 12
$Catholic
[1] 9.96
$Infant.Mortality
[1] 22.2
DataFrame with 3 rows and 6 columns
Fertility Agriculture Examination Education Catholic
<numeric> <numeric> <integer> <integer> <numeric>
Courtelary 80.2 17 15 12 9.96
Courtelary 80.2 17 15 12 9.96
Delemont 83.1 45.1 6 9 84.84
Infant.Mortality
<numeric>
Courtelary 22.2
Courtelary 22.2
Delemont 22.2
DataFrame with 1 row and 6 columns
Fertility Agriculture Examination Education Catholic
<numeric> <numeric> <integer> <integer> <numeric>
Courtelary 80.2 17 15 12 9.96
Infant.Mortality
<numeric>
Courtelary 22.2
DataFrame with 1 row and 4 columns
Fertility Agriculture Examination Education
<numeric> <numeric> <integer> <integer>
Courtelary 80.2 17 15 12
[1] "X.1" "X.2" "X.3" "X.4" "X.5" "X.6"
[1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15"
[16] "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30"
[31] "31" "32" "33" "34" "35" "36" "37" "38" "39" "40" "41" "42" "43" "44" "45"
[46] "46" "47"
[1] 10 2 NA
NULL
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