# Vectors and arrays¶

Beside functions and environments, most of the objects an R user is interacting with are vector-like. For example, this means that any scalar is in fact a vector of length one.

The class `Vector` has a constructor:

```>>> x = robjects.Vector(3)
```
class rpy2.robjects.Vector(o)[source]

Vector(seq) -> Vector.

The parameter ‘seq’ can be an instance inheriting from rinterface.SexpVector, or an arbitrary Python object. In the later case, a conversion will be attempted using conversion.py2rpy().

R vector-like object. Items can be accessed with:

• the method “__getitem__” (“[” operator)

• the delegators rx or rx2

items()[source]

iterator on names and values

property names

Names for the items in the vector.

sample(n: int, replace: bool = False, probabilities: = None)[source]

Draw a random sample of size n from the vector.

If ‘replace’ is True, the sampling is done with replacement. The optional argument ‘probabilities’ can indicate sampling probabilities.

## Creating vectors¶

Creating vectors can be achieved either from R or from Python.

When the vectors are created from R, one should not worry much as they will be exposed as they should by `rpy2.robjects`.

When one wants to create a vector from Python, either the class `Vector` or the convenience classes `IntVector`, `FloatVector`, `BoolVector`, `StrVector` can be used.

class rpy2.robjects.vectors.BoolVector(obj)[source]

Vector of boolean (logical) elements BoolVector(seq) -> BoolVector.

The parameter ‘seq’ can be an instance inheriting from rinterface.SexpVector, or an arbitrary Python sequence. In the later case, all elements in the sequence should be either booleans, or have a bool() representation.

class rpy2.robjects.vectors.IntVector(obj)[source]

Vector of integer elements IntVector(seq) -> IntVector.

The parameter ‘seq’ can be an instance inheriting from rinterface.SexpVector, or an arbitrary Python sequence. In the later case, all elements in the sequence should be either integers, or have an int() representation.

tabulate(nbins=None)[source]

Like the R function tabulate, count the number of times integer values are found

class rpy2.robjects.vectors.FloatVector(obj)[source]

Vector of float (double) elements

FloatVector(seq) -> FloatVector.

The parameter ‘seq’ can be an instance inheriting from rinterface.SexpVector, or an arbitrary Python sequence. In the later case, all elements in the sequence should be either float, or have a float() representation.

class rpy2.robjects.vectors.StrVector(obj)[source]

Vector of string elements

StrVector(seq) -> StrVector.

The parameter ‘seq’ can be an instance inheriting from rinterface.SexpVector, or an arbitrary Python sequence. In the later case, all elements in the sequence should be either strings, or have a str() representation.

factor() [source]

Construct a factor vector from a vector of strings.

class rpy2.robjects.vectors.ListVector(obj)[source]

R list (vector of arbitray elements)

ListVector(iterable) -> ListVector.

The parameter ‘iterable’ can be:

• an object with a method items(), such for example a dict, a rpy2.rlike.container.TaggedList, an rpy2.rinterface.SexpVector of type VECSXP.

• an iterable of (name, value) tuples

static from_length(length)[source]

Create a list of given length

Sequences of date or time points can be stored in `POSIXlt` or `POSIXct` objects. Both can be created from Python sequences of `time.struct_time` objects or from R objects.

class rpy2.robjects.vectors.POSIXlt(obj)[source]

Bases: `rpy2.robjects.vectors.POSIXt`, `rpy2.robjects.vectors.ListVector`

Representation of dates with a 11-component structure (similar to Python’s time.struct_time).

POSIXlt(seq) -> POSIXlt.

The constructor accepts either an R vector or a sequence (an object with the Python sequence interface) of time.struct_time objects.

class rpy2.robjects.vectors.POSIXct(obj)[source]

Bases: `rpy2.robjects.vectors.POSIXt`, `rpy2.robjects.vectors.FloatVector`

Representation of dates as seconds since Epoch. This form is preferred to POSIXlt for inclusion in a DataFrame.

POSIXlt(seq) -> POSIXlt.

The constructor accepts either an R vector floats or a sequence (an object with the Python sequence interface) of time.struct_time objects.

static isrinstance(obj) bool[source]

Is an R object an instance of POSIXct.

iter_localized_datetime()[source]

Iterator yielding localized Python datetime objects.

static sexp_from_datetime(seq)[source]

return a POSIXct vector from a sequence of datetime.datetime elements.

New in version 2.2.0: Vectors for date or time points

### FactorVector¶

R’s factors are somewhat peculiar: they aim at representing a memory-efficient vector of labels, and in order to achieve it are implemented as vectors of integers to which are associated a (presumably shorter) vector of labels. Each integer represents the position of the label in the associated vector of labels.

For example, the following vector of labels

 a b a b b c

will become

 1 2 1 2 2 3

and

 a b c
```>>> sv = ro.StrVector('ababbc')
>>> fac = ro.FactorVector(sv)
>>> print(fac)
 a b a b b c
Levels: a b c
>>> tuple(fac)
(1, 2, 1, 2, 2, 3)
>>> tuple(fac.levels)
('a', 'b', 'c')
```

Since a `FactorVector` is an `IntVector` with attached metadata (the levels), getting items Python-style was not changed from what happens when gettings items from a `IntVector`. A consequence to that is that information about the levels is then lost.

```>>> item_i = 0
>>> fac[item_i]
1
```

Getting the level corresponding to an item requires using the `levels`,:

```>>> fac.levels[fac[item_i] - 1]
'a'
```

Warning

Do not forget to subtract one to the value in the `FactorVector`. Indexing in Python starts at zero while indexing R starts at one, and recovering the level for an item requires an adjustment between the two.

When extracting elements from a `FactorVector` a sensible default might be to use R-style extracting (see Extracting items), as it preserves the integer/string duality.

class rpy2.robjects.vectors.FactorVector(obj)[source]

Vector of ‘factors’.

FactorVector(obj,

levels = rinterface.MissingArg, labels = rinterface.MissingArg, exclude = rinterface.MissingArg, ordered = rinterface.MissingArg) -> FactorVector

obj: StrVector or StrSexpVector levels: StrVector or StrSexpVector labels: StrVector or StrSexpVector (of same length as levels) exclude: StrVector or StrSexpVector ordered: boolean

property isordered

are the levels in the factor ordered ?

iter_labels()[source]

Iterate the over the labels, that is iterate over the items returning associated label for each item

property nlevels

number of levels

## Extracting items¶

Extracting elements of sequence/vector can become a thorny issue as Python and R differ on a number of points (index numbers starting at zero / starting at one, negative index number meaning index from the end / everything except, names cannot / can be used for subsettting).

In order to solve this, the Python way and the R way were made available through two different routes.

### Extracting, Python-style¶

The python `__getitem__()` method behaves like a Python user would expect it for a vector (and indexing starts at zero).

```>>> x = robjects.r.seq(1, 5)
>>> tuple(x)
(1, 2, 3, 4, 5)
>>> x.names = robjects.StrVector('abcde')
>>> print(x)
a b c d e
1 2 3 4 5
>>> x
1
>>> x
5
>>> x[-1]
5
```

### Extracting, R-style¶

Access to R-style extracting/subsetting is granted though the two delegators rx and rx2, representing the R functions [ and [[ respectively (See the note below about [[, and \$).

In short, R-style extracting has the following characteristics:

• indexing starts at one (while Python indexing starts at zero).

• the argument to subset on can be a vector of

• integers (negative integers meaning exlusion of the elements)

• booleans

• strings (whenever the vector has names for its elements)

```>>> print(x.rx(1))
 1
>>> print(x.rx('a'))
a
1
```

R can extract several elements at once:

```>>> i = robjects.IntVector((1, 3))
>>> print(x.rx(i))
 1 3
>>> b = robjects.BoolVector((False, True, False, True, True))
>>> print(x.rx(b))
 2 4 5
```

When a boolean extract vector is of smaller length than the vector, is expanded as necessary (this is know in R as the recycling rule):

```>>> print(x.rx(True))
1:5
>>> b = robjects.BoolVector((False, True))
>>> print(x.rx(b))
 2 4
```

In R, negative indices are understood as element exclusion.

```>>> print(x.rx(-1))
2:5
>>> i = robjects.IntVector((-1, -3))
>>> print(x.rx(i))
 2 4 5
```

That last example could also be written:

```>>> i = - robjects.IntVector((1, 3)).ro
>>> print(x.rx(i))
 2 4 5
```

This extraction system is quite expressive, as it allows a very simple writting of very common tasks in data analysis such as reordering and random sampling.

```>>> from rpy2.robjects.packages import importr
>>> base = importr('base')
>>> x = robjects.IntVector((5,3,2,1,4))
>>> o_i = base.order(x)
>>> print(x.rx(o_i))
 1 2 3 4 5
>>> rnd_i = base.sample(x)
>>> x_resampled = x.rx(o_i)
```

R operators are vector operations, with the operator applied to each element in the vector. This can be used to build extraction indexes.

```>>> i = x.ro > 3 # extract values > 3
>>> i = (x.ro >= 2 ).ro & (x.ro <= 4) # extract values between 2 and 4
```

(More on R operators in Section Operators).

R/S also have particularities, in which some see consistency issues. For example although the indexing starts at 1, indexing on 0 does not return an index out of bounds error but a vector of length 0:

```>>> print(x.rx(0))
integer(0)
```

Note

What about the R operator \$ ? In R, elements of a list can be extracted with [, or if only one element is wanted [[ or \$ (with [[ able to extract on index, that is position in the list, or on name while \$ can only extract on name).

In R, the 3 ways to extract one element out of a list are:

```> l <- list(a = 1:3, b = 4:6)
> l[]
 1 2 3
> l[["a"]]
 1 2 3
> l\$a
 1 2 3
```

With rpy2, it is looking like:

```>>> elt = l.rx2(1) # This is the R `[[`, so one-offset indexing
>>> elt = l.rx2('a')
```

## Assigning items¶

### Assigning, Python-style¶

Since vectors are exposed as Python mutable sequences, the assignment works as for regular Python lists.

```>>> x = robjects.IntVector((1,2,3))
>>> print(x)
 1 2 3
>>> x = 9
>>> print(x)
 9 2 3
```

In R vectors can be named, that is elements of the vector have a name. This is notably the case for R lists. Assigning based on names can be made easily by using the method `Vector.index()`, as shown below.

```>>> x = robjects.ListVector({'a': 1, 'b': 2, 'c': 3})
>>> x[x.names.index('b')] = 9
```

Note

`Vector.index()` has a complexity linear in the length of the vector’s length; this should be remembered if performance issues are met.

### Assigning, R-style¶

Differences between the two languages require few adaptations, and in appearance complexify a little the task. Should other Python-based systems for the representation of (mostly numerical) data structure, such a `numpy` be preferred, one will be encouraged to expose our rpy2 R objects through those structures.

The attributes rx and rx2 used previously can again be used:

```>>> x = robjects.IntVector(range(1, 4))
>>> print(x)
 1 2 3
>>> x.rx = 9
>>> print(x)
 9 2 3
```

For the sake of complete compatibility with R, arguments can be named (and passed as a `dict` or `rpy2.rlike.container.TaggedList`).

```>>> x = robjects.ListVector({'a': 1, 'b': 2, 'c': 3})
>>> x.rx2[{'i': x.names.index('b')}] = 9
```

## Missing values¶

Anyone with experience in the analysis of real data knows that some of the data might be missing. In S/Splus/R special NA values can be used in a data vector to indicate that fact, and `rpy2.robjects` makes aliases for those available as data objects `NA_Logical`, `NA_Real`, `NA_Integer`, `NA_Character`, `NA_Complex`.

```>>> x = robjects.IntVector(range(3))
>>> x = robjects.NA_Integer
>>> print(x)
 NA  1  2
```

The translation of NA types is done at the item level, returning a pointer to the corresponding NA singleton class.

```>>> x is robjects.NA_Integer
True
>>> x == robjects.NA_Integer
True
>>> [y for y in x if y is not robjects.NA_Integer]
[1, 2]
```

Note

`NA_Logical` is the alias for R’s NA.

Note

The NA objects are imported from the corresponding `rpy2.rinterface` objects.

## Operators¶

Mathematical operations on two vectors: the following operations are performed element-wise in R, recycling the shortest vector if, and as much as, necessary.

To expose that to Python, a delegating attribute `ro` is provided for vector-like objects.

Python

R

`+`

`+`

`-`

`-`

`*`

`*`

`/`

`/`

`**`

`**` or `^`

`~`

`!`

`or`

`|`

`and`

`&`

`<`

`<`

`<=`

`<=`

`==`

`==`

`!=`

`!=`

```>>> x = robjects.r.seq(1, 10)
>>> print(x.ro + 1)
2:11
```

Note

In Python, using the operator `+` on two sequences concatenates them and this behavior has been conserved:

```>>> print(x + 1)
  1  2  3  4  5  6  7  8  9 10  1
```

Note

The boolean operator `not` cannot be redefined in Python (at least up to version 2.5), and its behavior could not be made to mimic R’s behavior

## Names¶

`R` vectors can have a name given to all or some of the elements. The property `names` can be used to get, or set, those names.

```>>> x = robjects.r.seq(1, 5)
>>> x.names = robjects.StrVector('abcde')
>>> x.names
'a'
>>> x.names = 'z'
>>> tuple(x.names)
('z', 'b', 'c', 'd', 'e')
```

## `Array`¶

In R, arrays are simply vectors with a dimension attribute. That fact was reflected in the class hierarchy with `robjects.Array` inheriting from `robjects.Vector`.

class rpy2.robjects.vectors.Array(obj)[source]

An R array

property dim

Get or set the dimension of the array.

property dimnames: rpy2.rinterface_lib.sexp.Sexp

Return a list of name vectors (like the R function ‘dimnames’ does).

property names: rpy2.rinterface_lib.sexp.Sexp

Return a list of name vectors (like the R function ‘dimnames’ does).

## `Matrix`¶

A `Matrix` is a special case of `Array`. As with arrays, one must remember that this is just a vector with dimension attributes (number of rows, number of columns).

```>>> m = robjects.r.matrix(robjects.IntVector(range(10)), nrow=5)
>>> print(m)
[,1] [,2]
[1,]    0    5
[2,]    1    6
[3,]    2    7
[4,]    3    8
[5,]    4    9
```

Note

In R, matrices are column-major ordered, although the constructor `matrix()` accepts a boolean argument byrow that, when true, will build the matrix as if row-major ordered.

### Computing on matrices¶

Regular operators work element-wise on the underlying vector.

```>>> m = robjects.r.matrix(robjects.IntVector(range(4)), nrow=2)
>>> print(m.ro + 1)
[,1] [,2]
[1,]    1    3
[2,]    2    4
```

For more on operators, see Operators.

Matrix multiplication is available as `Matrix.dot()`, transposition as `Matrix.transpose()`. Common operations such as cross-product, eigen values computation , and singular value decomposition are also available through method with explicit names.

```>>> print( m.crossprod(m) )
[,1] [,2]
[1,]    1    3
[2,]    3   13
>>> print( m.transpose().dot(m) )
[,1] [,2]
[1,]    1    3
[2,]    3   13
```
class rpy2.robjects.vectors.Matrix(obj)[source]

An R matrix

property colnames

Column names

crossprod(m)[source]

crossproduct X’.Y

dot(m)[source]

Matrix multiplication

eigen()[source]

Eigen values

property ncol

Number of columns

property nrow

Number of rows

property rownames

Row names

svd(nu=None, nv=None, linpack=False)[source]

SVD decomposition. If nu is None, it is given the default value min(tuple(self.dim)). If nv is None, it is given the default value min(tuple(self.dim)).

tcrossprod(m)[source]

crossproduct X.Y’

transpose()[source]

transpose the matrix

### Extracting¶

Extracting can still be performed Python-style or R-style.

```>>> m = ro.r.matrix(ro.IntVector(range(2, 8)), nrow=3)
>>> print(m)
[,1] [,2]
[1,]    2    5
[2,]    3    6
[3,]    4    7
>>> m
2
>>> m
7
>>> print(m.rx(1))
 2
>>> print(m.rx(6))
 7
```

Matrixes are two-dimensional arrays, and elements can be extracted according to two indexes:

```>>> print(m.rx(1, 1))
 2
>>> print(m.rx(3, 2))
 7
```

Extracting a whole row, or column can be achieved by replacing an index number by True or False

Extract the first column:

```>>> print(m.rx(True, 1))
```

Extract the second row:

```>>> print(m.rx(2, True))
```

## `DataFrame`¶

Data frames are a common way in R to represent the data to analyze.

A data frame can be thought of as a tabular representation of data, with one variable per column, and one data point per row. Each column is an R vector, which implies one type for all elements in one given column, and which allows for possibly different types across different columns.

If we consider for example tre data about pharmacokinetics of theophylline in different subjects, the data table could look like this:

Subject

Weight

Dose

Time

conc

1

79.6

4.02

0.00

0.74

1

79.6

4.02

0.25

2.84

1

79.6

4.02

0.57

6.57

2

72.4

4.40

7.03

5.40

Such data representation shares similarities with a table in a relational database: the structure between the variables, or columns, is given by other column. In the example above, the grouping of measures by subject is given by the column Subject.

In `rpy2.robjects`, `DataFrame` represents the R class data.frame.

### Creating objects¶

Creating a `DataFrame` can be done by:

• Using the constructor for the class

• Create the data.frame through R

• Read data from a file using the instance method `from_csvfile()`

The `DataFrame` constructor accepts either an `rinterface.SexpVector` (with `typeof` equal to VECSXP, that is, an R list) or any Python object implementing the method `items()` (for example `dict` or `rpy2.rlike.container.OrdDict`).

Empty data.frame:

```>>> dataf = robjects.DataFrame({})
```

data.frame with 2 two columns (not that the order of the columns in the resulting `DataFrame` can be different from the order in which they are declared):

```>>> d = {'a': robjects.IntVector((1,2,3)), 'b': robjects.IntVector((4,5,6))}
>>> dataf = robject.DataFrame(d)
```

To create a `DataFrame` and be certain of the clumn order order, an ordered dictionary can be used:

```>>> import rpy2.rlike.container as rlc
>>> od = rlc.OrdDict([('value', robjects.IntVector((1,2,3))),
('letter', robjects.StrVector(('x', 'y', 'z')))])
>>> dataf = robjects.DataFrame(od)
>>> print(dataf.colnames)
 "letter" "value"
```

Creating the data.frame in R can otherwise be achieved in numerous ways, as many R functions do return a data.frame, such as the function data.frame().

Note

When creating a `DataFrame`, vectors of strings are automatically converted by R into instances of class `Factor`. This behavior can be prevented by wrapping the call into the R base function I.

```from rpy2.robjects.vectors import DataFrame, StrVector
from rpy2.robjects.packages import importr
base = importr('base')
dataf = DataFrame({'string': base.I(StrVector('abbab')),
'factor': StrVector('abbab')})
```

Here the `DataFrame` dataf now has two columns, one as a `Factor`, the other one as a `StrVector`

```>>> dataf.rx2('string')
<StrVector - Python:0x95fe5ec / R:0x9646ea0>
>>> dataf.rx2('factor')
<FactorVector - Python:0x95fe86c / R:0x9028138>
```

### Extracting elements¶

Here again, Python’s `__getitem__()` will work as a Python programmer will expect it to:

```>>> len(dataf)
2
>>> dataf
<Vector - Python:0x8a58c2c / R:0x8e7dd08>
```

The `DataFrame` is composed of columns, with each column being possibly of a different type:

```>>> [column.rclass for column in dataf]
['factor', 'integer']
```

Using R-style access to elements is a little richer, with the rx2 accessor taking more importance than earlier.

Like with Python’s `__getitem__()` above, extracting on one index selects columns:

```>>> dataf.rx(1)
<DataFrame - Python:0x8a584ac / R:0x95a6fb8>
>>> print(dataf.rx(1))
letter
1      x
2      y
3      z
```

Note that the result is itself of class `DataFrame`. To get the column as a vector, use rx2:

```>>> dataf.rx2(1)
<Vector - Python:0x8a4bfcc / R:0x8e7dd08>
>>> print(dataf.rx2(1))
 x y z
Levels: x y z
```

Since data frames are table-like structure, they can be thought of as two-dimensional arrays and can therefore be extracted on two indices.

```>>> subdataf = dataf.rx(1, True)
>>> print(subdataf)
letter value
1      x     1
>>> rows_i <- robjects.IntVector((1,3))
>>> subdataf = dataf.rx(rows_i, True)
>>> print(subdataf)
letter value
1      x     1
3      z     3
```

That last example is extremely common in R. A vector of indices, here rows_i, is used to take a subset of the `DataFrame`.

### Python docstrings¶

class rpy2.robjects.vectors.DataFrame(tlist)[source]

R ‘data.frame’.

cbind(*args, **kwargs)[source]

bind objects as supplementary columns

classmethod from_csvfile(path, header=True, sep=',', quote='"', dec='.', row_names=<rpy2.rinterface._MissingArgType object> [RTYPES.SYMSXP], col_names=<rpy2.rinterface._MissingArgType object> [RTYPES.SYMSXP], fill=True, comment_char='', na_strings=[], as_is=False)[source]

Create an instance from data in a .csv file.

Parameters
• path – string with a path

• sep – separator character

• quote – quote character

• row_names – column name, or column index for column names (warning: indexing starts at one in R)

• fill – boolean (fill the lines when less entries than columns)

• comment_char – comment character

• na_strings – a list of strings which are interpreted to be NA values

• as_is – boolean (keep the columns of strings as such, or turn them into factors)

iter_column()[source]

iterator across columns

iter_row()[source]

iterator across rows

property ncol

Number of columns. :rtype: integer

property nrow

Number of rows. :rtype: integer

rbind(*args, **kwargs)[source]

bind objects as supplementary rows

property rownames

Row names

to_csvfile(path, quote=True, sep=',', eol='\n', na='NA', dec='.', row_names=True, col_names=True, qmethod='escape', append=False)[source]

Save the data into a .csv file.

:param path : string with a path :param quote : quote character :param sep : separator character :param eol : end-of-line character(s) :param na : string for missing values :param dec : string for decimal separator :param row_names : boolean (save row names, or not) :param col_names : boolean (save column names, or not) :param comment_char : method to ‘escape’ special characters :param append : boolean (append if the file in the path is already existing, or not)