R or Python? One of the most asked question by many data analytics aspirants while both languages are achieving prominence in the data analytics field.
R and Python both are open source programming languages with a large community. While both of these languages are under steady development. This is the reason why these languages add new libraries in their prospectus regularly. The major purpose of using R is a high-level programming language for statistical analysis and reporting, in other way Python is a general-purpose programming language, that provides a more general approach to data science.
For the beginner in a programming language can learn R without putting more effort.
R is built by statisticians, it has a variety of libraries for different tasks to do Statistical analysis and visualization. Likewise Python is one of the most popular programming languages for beginners with powerful libraries for different tasks like designing web, data analytics, etc. And hence the simplicity of Python language makes it more popular.
Furthermore, let us see in detail that which language is better for you.
R programming:
R is one of the most powerful programming language and software environment for statistical analysis and representing graphics reports. It is an open-source platform. For the reason that the main purpose of using R is that it can be used to implement a statistical approach such as linear and non-linear modeling.
IDE: The common IDE for R programming is RStudio.
RStudio is an Integrated Development Environment (IDE) which allows users to code and develop R based applications. R consists of countless libraries from data manipulation to data visualization which makes programming easier.
For more info on RStudio with basic Coding examples, we recommend our Article link below.
Easy To Use:
R is a user-friendly language that is mostly used for data analysis, statistics including graphical representation models. Even packages like ggplot2 and dplyr that extend the R features further.
For more about visualization, we recommend our Article link below.
R can be used to integrate with databases such as SQL server. It can also be used for machine learning algorithms, natural language processing.
Data structures:
R also supports data structures such as vectors, lists, matrices, arrays, factors and data frames.
For more understanding and the working of data structure in R, we recommend our below link blog.
Data structure
Other Advantages:
- People who adopt R, generally from fields such as research, data science, statistics.
- Statistical models can be written in an easy way with a few lines and some functionality can be written in many ways.
- R is easy to use complex statistical formulas. All statistical test models are available and easy to use.
Important libraries in R:
Popular Packages
- Data Manipulation:- dplyr, plyr, and data.table to manipulate data easily.
- Stringr to manipulate strings generally used for text manipulation.
- Zoo to work with regular or irregular time series or trend analysis.
- Ggplot2 and lattice to visualize data.
- Caret for the machine learning approach.
Flexibility: Easy to use the available library. i.e; ggplot2, dplyr etc.
Database size: R can handle the huge size (in GBs) of the dataset.
Learn to handle a huge dataset using data.table package in the below article.
Python Programming:
Python is a high-level most popular general-purpose programming language. It is an open-source platform. The python codes are easy to write, read, debug because of its code brevity.
Similar to R, Python is also an interpreted language. It is easy to give commands using the command line, Users can use command prompt to execute python scripts.
Learn Python step by step, the article link is given below.
Python for beginners
IDE: The most popular IDE for Python is Spyder, Jupyter notebook which is easily available in anaconda distributor.
To install Jupyternotebook and to work on python codes we recommend our below link blog.
Python can be used to integrate with databases such as MySQL. It can also be used for machine learning algorithms, natural language processing and many more.
Libraries like Matplotlib and Pandas and Numpy that extends the Python features further.
Data structures:
Python also supports data structures such as lists, dictionaries, and tuples.
For more understanding and the working of data structures in Python, we recommend our Article link below.
Other Advantages:
- People who adopt Python are developers, programmers, data scientists.
- Codes can be written in an easy way because of its nice syntax and any functionality can be written in the same way in python.
- Python is flexible for doing something complex that has never done before.
Important libraries in Python:
- Pandas to manipulate data easily.
- SciPy/NumPy for scientific computing.
- Matplotlib to make interactive graphs.
- Statsmodels to exploratory data analysis and estimate statical models.
- Scikit-learn to use machine learning algorithms.
Flexibility: Easy to build models from scratch. i.e., matrix computation and optimization, etc.
Database size: Python can handle the huge size (in GBs) of the dataset.
R vs python popularity.
R has been more popular among analysts and data scientists till in 2015-2016. Also in the last 2-3 years python gained a lot of popularity.
KDnuggets has done a survey to figure out the top tool among data analysts and data scientists professionals.
KDnuggets analytics for data science, data analytics and machine learning poll
R vs Python: Jobs and Salary
There is a tremendous demand for R and Python – data analytics, data scientists professionals in MNC’s like Google, Facebook, Microsoft, Musigma, Amazon, etc. The average annual salaries were $110,000 (R) and $95,000 (Python)
R vs Python conclusion:
Now you have got a brief comparison on R vs Python. You can use any one for data analysis and data science that best fits your needs.
consequently, both R and Python languages have their own strengths in Statistical analysis and model deployment.
These programming languages have a lot of similarities in terms of syntax. You can choose to work with any of them. Now you may come to know the strengths of these programming languages over each other and their approach.
Learn skills for data science and data analytics
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