pyreadr

A python package to read and write R RData and Rds files into/from pandas dataframes. It does not need to have R or other external dependencies installed.

This module is based on the librdata C library by Evan Miller and a modified version of the cython wrapper around librdata jamovi-readstat by the Jamovi team.

Detailed documentation on all available methods is in the Module documentation

If you would like to read SPSS, SAS or STATA files into python in an easy way, take a look to pyreadstat, a wrapper around the C library ReadStat

Dependencies
Installation
Usage
Known limitations
Change Log
People

Dependencies

The module depends on pandas, which you normally have installed if you got Anaconda (highly recommended.) If creating a new conda or virtual environment or if you don't have it in your base installation, you will have to install it manually before using pyreadr. Pandas is not selected as a dependency in the pip package, as that would install pandas with pip and many people would prefer installing it with conda.

In order to compile from source, you will need a C compiler (see installation) and cython (version >= 0.28).

librdata also depends on zlib; it was reported not to be installed on Lubuntu. If you face this problem intalling the library solves it.

Installation

Using pip

Probably the easiest way: from your conda, virtualenv or just base installation do:

pip install pyreadr

If you are running on a machine without admin rights, and you want to install against your base installation you can do:

pip install pyreadr --user

We offer pre-compiled wheels for python 3.5, 3.6 and 3.7 for Windows, linux and macOs.

Using conda

The package is also available in conda-forge for windows, mac and linux 64 bit, python 3.6 and 3.7. only.

In order to install:

conda install -c conda-forge pyreadr

From the latest sources

Download or clone the repo, open a command window and type:

python3 setup.py install

If you don't have admin privileges to the machine do:

python3 setup.py install --user

You can also install from the github repo directly (without cloning). Use the flag --user if necessary.

pip install git+https://github.com/ofajardo/pyreadr.git

You need a working C compiler and cython.

Usage

Basic Usage: reading files

Pass the path to a RData or Rds file to the function read_r. It will return a dictionary with object names as keys and pandas data frames as values.

For example, in order to read a RData file:

import pyreadr

result = pyreadr.read_r('test_data/basic/two.RData')

# done! let's see what we got
print(result.keys()) # let's check what objects we got
df1 = result["df1"] # extract the pandas data frame for object df1

reading a Rds file is equally simple. Rds files have one single object, which you can access with the key None:

import pyreadr

result = pyreadr.read_r('test_data/basic/one.Rds')

# done! let's see what we got
print(result.keys()) # let's check what objects we got: there is only None
df1 = result[None] # extract the pandas data frame for the only object available

Here there is a relation of all functions available. You can also check the Module documentation.

Function in this package	Purpose
read_r	reads RData and Rds files
list_objects	list objects and column names contained in RData or Rds file
write_rdata	writes RData files
write_rds	writes Rds files

Basic Usage: writing files

Pyreadr allows you to write one single pandas data frame into a single R dataframe and store it into a RData or Rds file. Other python or R object types are not supported. Writing more than one object is not supported.

import pyreadr
import pandas as pd

# prepare a pandas dataframe
df = pd.DataFrame([["a",1],["b",2]], columns=["A", "B"])

# let's write into RData
# df_name is the name for the dataframe in R, by default dataset
pyreadr.write_rdata("test.RData", df, df_name="dataset")

# now let's write a Rds
pyreadr.write_rds("test.Rds", df)

# done!

now you can check the result in R:

load("test.RData")
print(dataset)

dataset2 <- readRDS("test.Rds")
print(dataset2)

Reading selected objects

You can use the argument use_objects of the function read_r to specify which objects should be read.

import pyreadr

result = pyreadr.read_r('test_data/basic/two.RData', use_objects=["df1"])

# done! let's see what we got
print(result.keys()) # let's check what objects we got, now only df1 is listed
df1 = result["df1"] # extract the pandas data frame for object df1

List objects and column names

The function list_objects gives a dictionary with object names contained in the RData or Rds file as keys and a list of column names as values. It is not always possible to retrieve column names without reading the whole file in those cases you would get None instead of a column name.

import pyreadr

object_list = pyreadr.list_objects('test_data/basic/two.RData')

# done! let's see what we got
print(object_list) # let's check what objects we got and what columns those have

Reading timestamps and timezones

R Date objects are read as integers (number of days since Jan 1st 1970). As librdata does not give back attributes such as class we cannot convert them automatically to a python datetime.

R datetime objects (POSIXct and POSIXlt) are internally stored as UTC timestamps, and may have additional timezone information if the user set it explicitly. If no timezone information was set by the user R uses the local timezone for display.

librdata cannot retrieve that timezone information, therefore pyreadr display UTC time by default, which will not match the display in R. You can set explicitly some timezone (your local timezone for example) with the argument timezone for the function read_r

import pyreadr

result = pyreadr.read_r('test_data/basic/two.RData', timezone='CET')

if you would like to just use your local timezone as R does, you can get it with tzlocal (you need to install it first with pip) and pass the information to read_r:

import tzlocal
import pyreadr

my_timezone = tzlocal.get_localzone().zone
result = pyreadr.read_r('test_data/basic/two.RData', timezone=my_timezone)

If you have control over the data in R, a good option to avoid all of this is to transform the POSIX object to character, then transform it to a datetime in python.

When writing these kind of objects pyreadr transforms them to characters. Those can be easily transformed back to POSIX with as.POSIXct/lt (see later).

What objects can be read and written

Data frames composed of character, numeric (double), integer, timestamp (POSIXct and POSIXlt), logical atomic vectors. Factors are also supported.

Tibbles are also supported.

Atomic vectors as described before can also be directly read, but as librdata does not give the information of the type of object it parsed everything is translated to a pandas data frame.

Only single pandas data frames can be written into R data frames.

More on writing files

For converting python/numpy types to R types the following rules are followed:

Python Type	R Type
np.int32 or lower	integer
np.int64, np.float	numeric
str	character
bool	logical
datetime, date	character
category	depends on the original dtype
any other object	character
column all missing	logical
column with mixed types	character

datetime and date objects are translated to character to avoid problems with timezones. These characters can be easily translated back to POSIXct/lt in R using as.POSIXct/lt. The format of the datetimes/dates is prepared for this but can be controlled with the arguments dateformat and datetimeformat for write_rdata and write_rds. Those arguments take python standard formatting strings.
Pandas categories are NOT translated to R factors. Instead the original data type of the category is preserved and transformed according to the rules. This is because R factors are integers and levels are always strings, in pandas factors can be any type and leves any type as well, therefore it is not always adecquate to coerce everything to the integer/character system. In the other hand, pandas category level information is lost in the process.
Any other object is transformed to a character using the str representation of the object.
Columns with mixed types are translated to character. This does not apply to column cotaining np.nan, where the missing values are correctly translated.
R integers are 32 bit. Therefore python 64 bit integer have to be promoted to numeric in order to fit.
A pandas column containing only missing values is transformed to logical, following R's behavior.
librdata writes Numeric missing values as NaN instead of NA. In pandas we only have np.nan both as NaN and missing value representation, and it will always be written as NaN in R.

Known limitations

As explained before, although atomic vectors can also be directly read, as librdata does not give the information of the type of object it parsed everything is translated to a pandas data frame.
R Date objects are read as integers (number of days since Jan 1st 1970). As librdata does not give back attributes such as class (type of object) we cannot convert them automatically to a python datetime.
POSIXct and POSIXlt objects in R are stored internally as UTC timestamps and may have in addition time zone information. librdata does not return time zone information and thefore the display of the tiemstamps in R and in pandas may differ.
Matrices and arrays are read, but librdata does not return information about the dimensions, therefore those cannot be arranged properly multidimensional numpy arrays. They are translated to pandas data frames with one single column.
Lists are not read.
Objects that depend on non base R packages (Bioconductor for example) cannot be read. The error code in this case is a bit obscure:

"ValueError: Unable to read from file"

Data frames with special values like arrays, matrices and other data frames are not supported.
The max size of a numeric vector is 2 ** 32 bytes (4GB), meaning 2 ** 30 elements for an Integer vector or 2 ** 29 elements for a Double Vector. If a vector in the dataframe is longer than this limit, a
"Unable to allocate memory" error (see this ) will arise.
librdata first de-compresses the file in memory and then extracts the data. That means you need more free RAM than the decompress file ocuppies in memory. RData and Rds files are highly compressed: they can occupy in memory easily 40 or even more times in memory as in disk. Take it into account in case you get a "Unable to allocate memory" error (see this )
When writing numeric missing values are translated to NaN instead of NA.
Writing is supported only for a single pandas data frame to a single R data frame. Other data types are not supported. Multiple data frames for rdata files are not supported.
RData and Rds files produced by R are (by default) compressed. Files produced by pyreadr are not compressed and therefore pretty bulky in comparison. Pyreadr writing is a relative slow operation compared to doint it in R.

Solutions to some of these limitations have been proposed in the upstream librdata issues (points 1-4 are addressed by issue 12, point 5 by issue 16 and point 7 by issue 17). However there is no guarantee that these changes will be made and there are no timelines either. If you think it would be nice if these issues are solved, please express your support in the librdata issues.

Change Log

A log with the changes for each version can be found here

People

Otto Fajardo - author, maintainer

Jonathon Love - contributor (original cython wrapper from jamovi-readstat and msvc compatible librdata)

katwre / pyreadr