This is my personal view and usage of the languages (R, Scala, and Python)
I recently answered the above question. I didn't phrase the question, but it's a good starting point. I typically stay away from language debates, but this one really interested me, as I have debated the question with myself a lot. I was researching this specific question because I wanted to know which language to use for my next data project. Here are my personal insights. Please let me know what you think!
I use R, Scala, and Python based on which is better-suited for my specific use cases. This is my personal view and usage of the languages.
Use R as a replacement for a spreadsheet. Together with RStudio, it makes a killer statistics, plotting, and data analytics application. You can take log files, parse them, graph them, pivot table them, filter them, etc. — and all with great support from RStudio. It’s a killer data analysis language and workspace. You should consider it as a replacement for spreadsheet workings.
Do you want to grep some lines from a text file? No problem! Just use dateLines <- grep(x = mylog, pattern = "--", value = TRUE). It’s a backfiring arrow and is easy to write once you know the command you need to use. It’s often very difficult to figure out the correct command to use; practice and note-taking are key. This requires time. Consider whether you have the time to commit to it. If not, just use it as your spreadsheet from time to time until you get better with it. Save a note or doc with useful R commands. You will find that with a few plotting commands, you can be a small king in its realm. This example of grep is only one of a million of abilities; RStudio will have you doing analytics like crazy on data.
If you have no time for the above, I still highly recommend that you install RStudio, use it from time to time, and get the hang of it. There is nothing like it that I know of that is so good for quick data analysis and statistics. Just give it a shot and try to replace your routine calculations and quick data manipulations tasks with it.
You can also move on and do machine learning in R. It has extremely powerful libraries for this (i.e. rpart, caret, e1071), and by all means, if you and your teams are fluent with it, feel free to use it. But personally, I would only use it for speculations and quick analysis or modeling. I stop there. It can be very quick, but this is when I turn to language #2: Python.
Use Python for small- to medium-sized data processing applications. Python introduced some type-checking in recent releases, which is awesome. Also, it's an interpreted language, so you have the great benefit of speed of programming. You just write your code and run. However, the caveat is that you don’t have the amazing compiler and features (the good ones, not the kitchen sink one) from Scala. As long as your project is small- to medium-sized, Python is a suitable option.
It's going to be very helpful as you utilize NLTK, matplotlib, numpy, and pandas — and you will have a great time using them. This will take you on the fast route to machine learning, with great examples bundled into the libraries.
I’m not saying you can't do this with R or Scala with great success — I’m just saying that for my personal use, this is the most intuitive way to do what I use it for.
Let's say that I want a quick analysis of CSV: I turn to R. If I want a bulletproof fast app to scale quickly, I use Scala. If my project is expected to be big and to involve many developers, I turn to language/framework #3: Java/Scala.
Use Scala or Java for larger robust projects to ease maintenance. While many would argue that Scala is bad for maintenance, I would argue that this is not necessarily the case. Java and Scala, with their mostly super-strongly typed and compiled features, are great languages for large-scale projects. You have Spark OpenNLP libraries for machine learning and big data. They are robust and they work at scale. It’s true that it will take you a longer time to code in them than in Python, but the maintenance and onboarding of new data will be easier — at least in my experience.
Data is modeled with case classes. It has proper function signatures, proper immutability, and proper separation of concerns.
While the above could be applied in any of these languages, it’s more natural with Scala/Java.
But if you don’t have the time or desire to work with them all, this is what I would do:
R: Good for research, plotting, and data analysis.
Python: Good for small- or medium-scale projects to build models and analyze data, especially for fast startups or small teams.
Scala/Java: Good for robust programming with many developers and teams; it has fewer machine learning utilities than Python and R, but it makes up for it with increased code maintenance.
It’s a challenge to learn them all. I’m still in this challenge, and it’s a true headache, but at the end, you benefit. If you want only one of them, I would consider the following:
Thanks for reading!
In this article, you'll learn to leverage the best of both ‘Python and R’ in a single project.
In this article, you'll learn to leverage the best of both ‘Python and R’ in a single project.
If you are into Data Science, the two programming languages that immediately come to mind are R and Python. However, instead of considering them as two options, more often than not, we end up comparing the two. R and Python, are excellent tools in their own right but are very often conceived as rivals. If you type
R vs Python , in your Google search bar, you instantly get a plethora of resources on topics which talk about the supremacy of one over the other.
One of the reasons for such an outlook is because people have divided the Data Science field into camps based on the choice of the programming language they use. There is an R camp and a Python camp and history is a testimony to the fact that camps cannot live in harmony. Members of both the camps fervently believe that their choice of language is superior to the other. So, in a way, divergence doesn’t lie with the tools but with the people using those tools.
There are people in the Data Science community who are using both Python and R, but their percentage is small. On the other hand, there are a lot of people who are committed to only one programming language but wished they had access to some of the capabilities of their adversary. For instance, R users sometimes yearn for the object-oriented capacities that are native to Python and similarly, some Python users long for the wide range of the statistical distributions that are available within R.
The figure above shows the results of the survey conducted by Red Monk in the third quarter of 2018. These results are based on the popularity of the languages on Stack Overflow as well as on Github and clearly show that both R and Python are rated quite high. Therefore, there is no inherent reason as to why we cannot work with both of them on the same project. Our ultimate goal should be to do better analytics and derive better insights and choice of a programming language should not be a hindrance in achieving that.
Let’s have a look at the various aspects of these languages and what’s good and not so good about them.
Since its release in 1991, Python has been extremely popular and is widely used in data processing. Some of the reasons for its wide popularity are:
However, Python doesn’t have specialized packages for statistical computing, unlike R.
R’s first release came in 1995 and since then it has gone on to become one of the most used tools for data science in the industry.
Performance wise R is not the fastest language and can be a memory glutton sometimes when dealing with large datasets.
Could we utilize the statistical prowess of R along with the programming capabilities of Python? Well, when we can easily embed SQL code within either R or Python script, why not blend R and Python together?
There are basically two approaches by which we can use both Python and R side by side in a single project.
PypeR provides a simple way to access R from Python through pipes. PypeR is also included in Python’s Package Index which provides a more convenient way for installation. PypeR is especially useful when there is no need for frequent interactive data transfers between Python and R. By running R through pipe, the Python program gains flexibility in sub-process controls, memory control, and portability across popular operating system platforms, including Windows, GNU Linux and Mac OS
pyRserve uses Rserve as an RPC connection gateway. Through such a connection, variables can be set in R from Python, and also R-functions can be called remotely. R objects are exposed as instances of Python-implemented classes, with R functions as bound methods to those objects in a number of cases.
rpy2 runs embedded R in a Python process. It creates a framework that can translate Python objects into R objects, pass them into R functions, and convert R output back into Python objects. rpy2 is used more often since it is one which is being actively developed.
One advantage of using R within Python is that we would able to use R’s awesome packages like ggplot2, tidyr, dplyr et al easily in Python. As an example let’s see how we can easily use ggplot2 for mapping in Python.
You may want to have a look at the following resources for more in-depth review of rpy2:
We can run R scripts in Python by using one of the alternatives below:
This package implements an interface to Python via Jython. It is intended for other packages to be able to embed python code along with R.
rPython is again a Package Allowing R to Call Python. It makes it possible to run Python code, make function calls, assign and retrieve variables, etc. from R.
SnakeCharmR is a modern overhauled version of rPython. It is a fork from ‘rPython’ which uses ‘jsonlite’ and has a lot of improvements over rPython.
PythonInR makes accessing Python from within R very easy by providing functions to interact with Python from within R.
The reticulate package provides a comprehensive set of tools for interoperability between Python and R. Out of all the above alternatives, this one is the most widely used, more so because it is being aggressively developed by Rstudio. Reticulate embeds a Python session within the R session, enabling seamless, high-performance interoperability. The package enables you to reticulate Python code into R, creating a new breed of a project that weaves together the two languages.
The reticulate package provides the following facilities:
Some great resources on using the reticulate package are:
Both R and Python are quite robust languages and either one of them is actually sufficient to carry on the Data Analysis task. However, there are definitely some high and low points for both of them and if we could utilize the strengths of both, we could end up doing a much better job. Either way, having knowledge of both will make us more flexible thereby increasing our chances of being able to work in different environments.
Interfacing R and Python — Andrew Collier
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