Well, to pick and decide which is better. And which one you should choose between R and Python for Machine learning or Data science projects is typical unless you find out some differences. So not to worry, you have landed on the best resource; we’ll explore here the Pros and Cons of R and python language along with differences.
R and python both are famous open-source, free languages. Statisticians developed r language, so it provides an environment for data scientists by providing many necessary libraries by default. In comparison, Python is a general programming language. Thus, it can work out for data sciences, machine learning, web scraping, developing back-ends, and for many other projects.
Table of Contents
- 1 R vs Python Differences
- 2 R vs Python for Data science
- 3 R vs Python for Machine Learning
- 4 R vs python – FAQs
- 5 Conclusion
R vs Python Differences
|Parameter||R language||Python language|
|Prime objective||Statistics & data analysis||General programming|
|Users||Statisticians and Degree Holders||Developers & Programmers|
|Flexibility in uses||Provides an Environment for Data science||New algorithms and modules can be developed quickly.|
|Beginner-friendly||This required knowledge to understand libraries- Syntaxes are challenging to understand||Easy to learn, basic programming knowledge required to hands on it.|
|Increase in popularity||4.23% in 2018||21.69% in 2018|
|Program Integration limits||It can run and plot graphs locally||It can integrate programs and graph into a web app|
|Best feature||Best for analyzing data quickly||Best for overall data science tasks|
|The capacity of Handling Datasets||Large Datasets||Large Data sets|
|Recommended IDEs||Rstudio||A scope of IDEs to choose from Spyder, Ipython Notebook|
|Resources for Data science||ggplot2, caret, zoo, etc||pandas, scipy, TensorFlow, caret, etc|
|Drawbacks Or Cons||Many dependencies between the available Library, the high learning curve||Libraries and tools are not so-called perfectly match|
A beginner should be using python in 2021. There are various other tasks related to data science, which python covers in an appropriate manner. On the other R is a language that relatively undertakes almost every feature. However, it has limits. You wouldn’t go off and create your custom algorithm. You are bound when collecting data from the web. Neither are you allowed to manipulate the data reports into apps. If you try to build a custom algorithm under any circumstances, you need to learn many different languages. That means the learning curve will get at its peak.
With all this, python seems to be a mature and recommended choice. Not to mention python is also known as the most straightforward programming language to learn. You can use python distros such as Anaconda, which carries a lot of data science tools.
Python has now become the most popular programming language. Even many data scientists now prefer python to R. Even though the R language is suitable for the start-up. Still, the technology has been flourishing day by day, And where R language lacks. Mainly, R basic Structure and security is a matter of concern.
R vs Python for Data science
The short answer for this is; R and python both are convenient for data science. The only difference I came across in them is that python offers so many programming related applications. In contrast, R especially comes up with some useful libraries that make it a unique choice for data science. That is to say. Python uses are extendable.
There are some leading data science subtasks. It would be beneficial to understand the differences by dividing them alone.
- Data Collection- Collect data
- Data Exploration
- Data Visualization
Python is more robust in data collection since it can grab out data from the internet with a few code lines. It is also remarkable here python is easy, so you would not find any difficulties while collecting data. Moreover, it can work on CSVs files and import data from SQL tables.
R is not as powerful as python to collect data. However, it works more effectively with CSVs file datasets. Recently to resolve R data collecting limitations, some packages have been developed. Rvest performs necessary web scraping, while magrittr will clean it up and figure out the information. Lastly, even though R has these features yet not as useful as python.
Once the data is on the ground, you can now get insights from it in python. Panda is a tool that can let you go through an extensive data set in a matter of minutes. Using this tool, you can display, sort data as per your vision.
R is ahead in basic data exploring. It shouldn’t be shocking because R has the nature of statisticians. It let on to build probability distributions, apply mathematical and diversity of statistical inquiries to your data. However, to perform advanced data exploration methods, you may need to use third-party libraries.
Python provides many tools for visualizing data. Primarily, the IPython Notebook that comes with Anaconda has a lot of robust possibilities to visualize data. To generate Basic graphs in python, you can use the Matplotlib library, or plot.ly is also a good option.
R also has pretty much solid tools for plotting graphics, such as ggplot2 for more advanced graphs plotting. It is worth mentioning here, R is positively known by its graph plotting functionality. Many of the data scientists consider that R is easier to plot graphs than python even advanced ones.
R for data science?
R is on the top when it comes to data science. You don’t need to be a programming expert to get started with it. Thus R is mainly popular among mathematicians. It has a wide range of handy materials and is capable of handling large data sets. Most importantly, you can quickly start up your project using R as you’ll get almost everything on the ground.
R was leading ahead back in 2015, Due to its libraries, and the top of that, it puts together an environment for data science.
- dplyr, plyr, and data.table to manipulate packages,
- stringr to employ strings,
- zoo to work complicated time series.
- ggvis, lattice, and ggplot2 to visualize data and caret for machine learning.
However, R has no definite tool for implementing visualized data in a web app. That’s the reason python took it over. Python allows data visualization and highlights to implement the data into a web app through a backend.
You can use R so long as you want to collect data, visualize data, and have an excellent grasp of statistics.
R Pros for Data science
- Provides a Mature-environment for data science– R language is the top if you want to analyze data as quickly as possible. It is excellent for prototyping and statistical analysis.
- Rstudio marked as the best IDE– Many developers prefer R because of Rstudio. Read more on Best R IDEs.
R Cons for Data science
- R is a slow programming language– This issue is the leading cause of R losing its hard-earned reputation. It is being said about R it is dead because it is developed by Statisticians, not programmers.
- R’s documentation needs improvement– It is also a drawback of the R language. It does not have detailed documentation.
- Does not work with the website– R language, no doubt, compatible with all data science operations. However, it does not work with the website.
Python for Data Science?
Python basically lacked tools to utilize in data science before 2014. It has now improved a lot. The first and foremost advantage of python over R is that python can manipulate visualized data into a database, where we can implement and show it on the front-side of a backend.
Talking about its tools that come in handy when someone thinks of using python instead of R.
Numpy improved python by exceeding its uses. Using NumPy, you can do scientific computing, and Panda is for data manipulation. Both of these tools are the basic need for python to pull off projects related to data science.
Also, have a look at matplotlib to make a graphical interface of the data. It is not necessary, however, a great way to serve raw-data into graphs.
Moreover, python has many IDEs, so it is difficult to recommend one, Yet we can try one by one the following IDEs with python for your data science project: Spyder, Ipython, Notebook. Moreover, we have compared pycharm vs jupyter. It would be better to read this blog.
Python Pros for Data Science
- Python Extensive libraries– Being a popular language, you can find many useful resources linked with Python.
- Python is easy– Python is mostly preferred for its easy to code syntaxes. It is as easy as learning English. For the most part, python uses English words as syntaxes.
Python Cons for Data science
- Python is not as suitable for data visualization as R– Python does not provide an environment for data scientists to quickly start. Likewise, it makes python’s learning curve steeper than R. Since you have first to become familiar with Python rather than Statistics.
- Python does have a recommended IDE as Rstudio for data science– The Drawback may get filled up soon, as time passes. However, there should be a unique IDE, especially for Data science for Python.
R vs Python for Machine Learning
Machine learning tasks are generally required programming skills, so python is on the rise here. Some modules offer a robust algorithm feature for machine learning tasks, such as Pybrain and Tensor flow. On the other hand, as statisticians wrote r, it is a perfect choice for essential data management such as Labeling data, filling values, and filtering. Thus, the R language is not ideal for machine learning; it still has some packages to carry out basic ML tasks. The Caret Net are two packages or R on the top.
R for Machine learning?
Although R has many weak spots in machine learning, the world’s best data scientists still use it. Generally, machine learning requires programming skills of scripting languages C++, Java, Python, Ruby. Ultimately to understand the algorithm, if you don’t know any of the data scripting languages, then doing ML tasks with R will be troublesome.
Nonetheless, R has ways to do machine learning tasks, and truly it’ll not be a better choice than Python. Unless you need to configure a basic algorithm for your project, you may find it pre-written already, then you might face less trouble. Developing a custom one is a hard-pain.
Python for Machine learning?
We’ve mentioned earlier. Python is a good fit for machine learning tasks. Plus, it has a beginner-friendly learning curve so that you can precisely get what you indicate just right after you learn its basics. What makes python so significant for machine learning is its vast resources- you can search on google for more resources that will even cut down the complexity. And it has active and comprehensive community support is also appreciable. You will resolve your issue there.
You are either after to build a custom algorithm for your data science project or looking to play around with machine learning projects. Python is the way to go.
R vs python – FAQs
Why is Python better than R?
If we talk generally, there is a clear winner that is Python. Python is a programming language. It is composed of many useful tools, frameworks that all extend the uses of python language. Whereas R is limited, mostly used by data scientists, some essential tools make R a unique environment for data science.
Can Python replace R in data science?
The short answer for this YES. Python now can replace R. Along ago, R was the only choice for data scientists as it was introduced for this purpose. Back then, programmers or statisticians for visualizing data had to learn R in the first place. However, not python works side-by-side with R for data science. Even at many data-related tasks, python seems to be a better fit. That’s the reason python’s users are more truthful with python than R. And according to a Survey. Many R users are now turning towards Python.
First of all you should know- what your intentions are? if you think you just need to figure something out from a given data, like plotting or making graphs, and you have already collected data in an excel file. So you’re better using R. As it would not take much of your time to handle such tasks.
On the other hand, if you feel like your project may need to be associated with the internet, as for collecting data, you realize you need to do web scraping first or need to import data from a database, then you should use python. Moreover, using python, you would have better options to perform your task using advanced techniques.