Python vs R for Data Science projects
Data science is a central part of the job for several growing numbers of people. Emphasis on analytics-driven decisions, powerful computing, and increased data availability in business has made it a heyday for data science. According to a recent IBM report, there were 2.35 million openings for data analytics jobs in the US in 2015. It is further estimated that the number will spike to 5 million by 2022.
The most popular programming tools used for data science work are R and Python. It is hard to pick one of the two amazingly flexible data analytics languages. Both are open source and free and were developed in the early 1990s- Python for data science serves as a general-purpose programming language and R for data science serves for statistical analysis. For anyone interested in working with large datasets, machine learning, or developing complex data visualizations, they are extremely useful.
A brief review of Python and R history
Python
Python was released in 1989 emphasizing efficiency and readability. It is an object-oriented programming language which means that collects data and codes them into objects that can modify and interact with one another. Scala, C++, Java are other programming language examples. This sophisticated programming language allows developers and data scientists to execute tasks with code readability, modularity, and better stability. Data science holds a very small portion within this diverse language.
R
R was developed in 1992 and was preferred by most data science professionals for years. It is a procedural language that works by breaking down a programming task into a series of subroutines, procedures, and steps. This is beneficial when it comes to building data models because it makes it easy to understand how complex operations are carried out; however, it is often at the expense of code readability and performance. However, lack of key features and slower performance like web frameworks and unit testing are common reasons that data science professionals prefer to look elsewhere.
Process of data science
Let us have a deeper look at these two languages regarding their use in the data pipeline, including:
1. Data collection
2. Data exploration
3. Data modeling
4. Data visualization
1. Data collection
Python
This language supports all kinds of different formats and is considered to be the best programming language for data science. One can work with comma-separated value documents (CSV) or can play with JSON sources from the web. SQL tables can be imported directly into codes. Data science professionals using Python can create datasets. The library offered by this programming language allows data scientists to take data from different websites within a line of code.
R
This programming language allows data importing from CSV, Excel, and text files into R. Files built-in SPSS format or Minitab can be turned into R data frames as well. However, R is not versatile enough to grab information from the web like Python is.
2. Data Exploration
Python
To get insights from data, data scientists use Pandas, the data analysis library for Python. This holds a large amount of data without any of the lag that comes from Excel. Individuals require data science skills to define and redefine Pandas data frames several times throughout a project
R
R for data science is used to do numerical and statistical analysis of large data sets, so its no surprise that data science professionals have many options while exploring data with R. Apart from machine learning, random number generation, signal processing, and statistical processing, one will have to depend on third-party libraries for heavier work.
3. Data modeling
Python
These programming languages have standard libraries for data modeling including Numpy for numerical modeling analysis and SciPy for scientific calculations and computing.
R
For specific modeling evaluation in R, data scientists sometimes have to rely on packages outside R’s core functionality. But there are certain specific packages known as the Tidyverse which makes it easy to visualize, manipulate and report on data.
4. Data visualization
Python
This area is not the strength of Python, however, the Matplotib library can be used for generating charts and graphs. Also, the seaborn library allows one to draw more informative and attractive graphics in Python.
R
R was built to demonstrate the statistical analysis results, with the base graphics model allowing users to easily create basic plots and charts.
Conclusion
Python is a versatile, powerful language that programmers can use for a variety of tasks in data and computer science. R programming language, on the other hand, is designed for data evaluation that is popular in the data science community. Understanding R is important if a user wants to make it far in data science. Learning both of these programming languages will only improve users as data scientists.
