When it comes to choosing programming languages for data science, R vs Python are the two most popular choices that data scientists tend to gravitate towards.
For statistical analysis, R seems to be the better choice while Python provides a more general approach to data science. Both R and Python are considered state of the art in terms of programming language oriented towards data science. Knowing both of them is, of course, the ideal solution when tackling data science which may include some data analysis along with statistical analysis. Python is a strong contender when it comes to usability and easy to follow, readable syntax.
Is R Similar to Python
Python’s syntax is more similar to other languages than R’s syntax is. Python is considered to be “verbally readable” which leads to faster comprehension of the language. It is very similar to English syntax and hence many organizations use it in their production systems. Python is a full-fledged programming language that can be used to write simple programs or large, complex systems that scales easily. On the other hand, R is not verbally readable but is exceptional when it comes to statistical programming.
R vs Python For Statistics and Data Science
R is mainly used for statistical analysis while Python provides a more general approach to data science. R is flexible and supports both data and statistical analysis and new data and statistical analysis techniques are implemented in R before the commercial packages.
R in Python (R Within Python)
To simplify the transition from Python to R, it is possible to use RPy, a simple easy-to-use interface that allows one to enjoy the elegance of working in Python but having access to the rich graphical and statistical capabilities of R.
By including this line of code in a Python program:
From rpy2.robjects import r
It launches an execution of R in a Python process but maintains the communication between the original Python program. The Python class instance r includes various functions for remote execution of R commands including those involved with data produced by the Python program.
Data Collection: R vs Python
One of the challenges of a data scientist is to collect data in a data structure of some sort to perform cleanup, validation and verification before the data can be analyzed. To get data into a data structure, it is a requirement for a programming language to provide import functions to bring data in from csv files or from a database.
When comparing R vs Python, both languages have the ability to import from csv files and from various databases. With Python, the pandas library offers CSV parsing capabilities and used mainly to handle data and numerical analysis. With R, the base function read.csv() can be used.
Data Exploration: Difference between R and Python
Before a data scientist can understand and give insights about the data, a series of exploratory steps must be taken. For example, it is very useful information to determine the number of observations per category. In Python, the following code categorizes the data and provides a count for each category. In this case, we are counting the number of people per location.
In R, more code is required to do the same. Another exploratory question is to determine the number of missing data points. Again in Python, the code is trivial:
But in R, more code is necessary to perform the same operation.
Data Visualization in R vs Python
To gain a perspective of the data, data scientists utilize data visualization tools to provide valuable insight visually so that their audience can see the correlations and patterns in the data. In R, there are sophisticated visualization libraries included. There is a lattice graph package that enables the use of a trellis graph. Trellis graphs are useful when there is a relationship between the variables and when one or more are dependent on each other. One of the most widely used visualization packages in R is the ggplot2 which enables users to create sophisticated visualizations with little code using the Grammar of Graphics. The Grammar of Graphics is a general scheme which breaks up graphs into semantic components such as scales and layers.
With Python, the most widely used library is the matplotlib. It was designed to mimic MATLAB, a proprietary programming language developed in 1980’s to perform data visualization. With extreme power comes complex programming so developers provide ‘wrapper’ libraries on top of matplotlib to simplify coding. Seaborn is one of these libraries that produces beautiful graphs with little code.
Equal to R’s ggplot2 library, Python has its own implementation with ggplot. Bokeh is another library based on ggplot but enables the ability to create interactive, web-ready plots which can easily be exported as JSON objects. Bokeh also supports streaming and real-time data.
One of the problems plaguing data collections is missing data. In Python, the missingno library allows you to quickly gauge the completeness of a dataset with a visual summary instead of trudging through a table.
Big Data in R vs Python
When dealing with very large datasets, all programming languages becomes bogged down in performance and R is no exception. Basically, R keeps all of its objects in memory. This can become a problem with big data. Since objects are kept in memory, one solution is to increase the machine’s memory. R can address up to 8 TB of RAM when it runs on 64-bit machines.
Another alternative is to store R objects on hard disk and analyze the data off board. There are packages available that will allow you to do this but advance planning on your part is required because not all R datatypes can exist outside of in-memory space. Analyzing data on hard disk will allow parallel analysis in principle.
Python on the other hand works well with big data projects. Take the Dask library for example which helps in flexible parallel computing for analytic purpose. It works with large data collections like data frames, multi-dimensional lists and parallel arrays and with Python iterators, you can power through computation in memory and in a distributed environment.
Python also is compatible with Hadoop, the synonymous name to big data. The Pydoop package helps access to HDFS API and also helps writing Hadoop MapReduce programming to solve big data problems with minimal scripting.
Machine Learning in R vs Python
There are 2 phases in machine learning: Model Building and Prediction phase. Model building is typically performed as a batch process and the prediction phase are done in real time. The reason why it is a batch process is because it is a number crunching computation intensive process. Whereas the prediction phase happens in a flash. Both R and Python are equal in performance.
From a library standpoint, both R and Python have enormous libraries to support data visualization and data analysis. But because R is used mainly by academics, new algorithms are developed and released in new packages. This makes R more state-of-the-art than Python.
Python is known for its very readable syntax so it makes complex coding look simple. This is advantageous for machine learning and deep learning. With its extensive selection of machine learning-specific libraries and frameworks, it simplifies the development process and ultimately cuts down the development time.
OOP in R vs Python
When it comes to OOP, R is more functional while Python is more Object Oriented. With every new release, R is getting better in terms of object oriented support but it’s way behind compared to Python.
Data Structures in R and Python
The dataframe is available in both R and Python and is used mainly to collect observations. The dataframe in R is a built-in object whereas in Python, it must be imported from a package. Luckily, there is no performance difference when using a built-in object or importing from a package.
Data structures in R include:
- Data Frame
Data structures in Python include:
- Data Frame
Disclosure of Material Connection: Some of the links in the post above are “affiliate links.” This means if you click on the link and purchase the item, I will receive an affiliate commission. Regardless, I only recommend products or services I use personally and believe will add value to my readers.