Which one is more suitable for data analysis field, R or Python? Who has an advantage in certain situations? Or is one inherently better than the other in every way?
When we want to choose a programming language for data analysis, I believe most people will think of R and Python - but it is very difficult to choose one of these two very powerful and flexible data analysis languages. difficult.
I admit that I haven’t been able to choose the better one from these two favorite languages of data scientists. So, to keep things interesting, this article will go into some details about both languages and leave the decision-making up to the reader. It’s worth mentioning that there are many ways to learn about the pros and cons of both languages. However, in my opinion, there is actually a strong connection between the two languages.
Stack Overflow trend comparison
##The above chart shows the two trends since 2008 (when Stack Overflow was founded) changes in a language over time.
R and Python are competing fiercely in the data science space, let’s take a look at their respective platform shares and compare 2016 to 2017:
Next we will learn more about these two languages in terms of applicable scenarios, data processing capabilities, tasks, installation difficulty, and open tools.
Applicable scenarios
R is suitable for application scenarios where data analysis tasks require independent computing or a single server. Python serves as a glue language and is better used when data analysis tasks require integration with web applications or when a piece of statistical code needs to be inserted into a production database.
Task
When it comes to exploratory statistical analysis, R wins. It's great for beginners, and statistical models can be implemented in just a few lines of code. Python, as a complete and powerful programming language, is a powerful tool for deploying algorithms for production use.
Data processing capabilities
There is support for a large number of software packages and libraries for professional programmers as well as non-professional programmers, whether performing statistical tests or R language is handy for creating machine learning models.
Python was not particularly good at data analysis initially, but with the introduction of NumPy, Pandas and other extension libraries, it has gradually become widely used in the field of data analysis.
Development environment
For R language, you need to use R Studio. For Python, there are many Python IDEs to choose from, with Spyder and IPython Notebook being the most popular.
Popular software packages and libraries
The following is a list of the most popular software packages and libraries launched by R and Python for professional and non-professional programmers.
R: Popular software packages for professional programmers
dplyr, plyr and data table for data manipulation
for stringr for string operations
Periodic and irregular time series zoo
Data visualization tools ggvis, lattice and ggplot2
caret for machine learning
R: Popular packages for non-professional programmers
Rattle
R Commander
Deducer
These complete GUI packages enable powerful Data statistics and modeling functions.
Python: Popular libraries for professional programmers
for data analysis
pandas
for SciPy and NumPy for scientific computing
scikit-learn
Chart library for machine learningmatplotlibstatsmodels
Used to explore data, estimate statistical models, and perform statistical and unit tests
Python: a popular library for non-expert programmers
Orange Canvas 3.0 is an open source software package that follows the GPL license. It uses some commonly used Python open source libraries for scientific computing, including numpy, scipy and scikit-learn.
Detailed comparison between R and Python
As mentioned at the beginning of this article, there is a strong relationship between R and Python, and the two The language is becoming increasingly popular. It’s hard to say which one is better, and the integration of both is creating a lot of positive and collaborative waves in the data science community.
Summary
In fact, everyday users and data scientists can take advantage of both languages, since R users can run it in R through the rPython package Python code in R, and Python users can run R code in a Python environment through the RPy2 library.
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