R VS Python in Data science: The winner is ...

Source: Internet
Author: User

R VS Python in Data science: The winner is ...

In the "Best" data Science tools game, R and Python have their own pros and cons. The choice between the two depends on the use of the background, the need to learn spending and other tools that are often used

Martijn Theuwissen published in Datacamp.

At Datacamp, students often ask us about their daily data analysis tasks using R or Python. Although we mainly offer interactive R tutorials, we always answer this question depending on the type of data analysis challenge they face.
Both R and Python are popular statistical programming languages. The function of R is developed by statisticians (think of R's powerful data visualization capabilities), and Python is often praised for its easy-to-understand syntax.

In this article, we focus on the differences between R and Python, and how they occupy a position in the data science and statistical world. Let's say you prefer visual presentation, make sure to check out the corresponding infographic "Data Science War: R vs Python".

Introduction to R

Ross Ihaka and Robert gentleman created the open source language R as an implementation of the S programming language in 1995. The aim is to develop a language that provides better and more humane mathematical analysis, statistics and graphical models. Initially, R was primarily used in academia and research, The rest of the recent work has also started with R. This makes R the fastest growing statistical language in the world.

The main advantage of R is its strong community, supported by mailing lists, user contributed documents and a very active stack overflow team. There is also Cran, a huge resource library where users can easily contribute R packages. These packages are a collection of r functions and data, Get immediate access to the latest technology and features without having to start from scratch.

Finally, suppose you are an experienced program ape, learning R may not be difficult. However, as a person who has just started to learn, you may feel very tangled. Fortunately, there are a lot of current learning resources that you can consult.

Introduction to Python

Python was created by Guido van Rossum 1991, emphasizing efficiency and readability of the code.

Programs that want deep data analysis or applied statistics ape some python for the primary user of statistics.

The closer you work in the project environment. The more likely you are to prefer python. It is a flexible language that focuses on readability and simplicity, and its learning curve is lower.

Similar to R, Python has the same package.

PyPI is the index of the Python package. A library made up of user contributions.

Just like R,python has a great community, but it's a little scattered, because it's a universal language. However, data science in Python is rapidly claiming to be more dominant in the Python world: applications that are expected to be growing and more innovative scientific data will see their origins here.

R and Python: Common volume

On the network. You can find a lot more than R and Python to use and popularize the number. Although these numbers often tell us that these two languages are very well developed in the entire ecosystem of computer science, this is very difficult to cross-reference.

The main reason for such a situation is. You will only find R in the data Science environment, and on the one hand, Python is widely used in many fields as a common language. such as network development. This tends to favor ranking results in Python. And the result is some negative effect.

When and how do I use r?

R is primarily used when data analysis tasks require individual servers to calculate or analyze independently. Exploratory work is great, handy for almost any type of data analysis, and often provides you with the tools you need, thanks to a lot of packages and easy-to-use tests. High-speed start-up and execution. R can even be part of a big data solution.

When starting to use R, a good first step is to install the wonderful IDE RStudio. Once this is done. We recommend that you take a look at the following popular packages:

dplyr. plyr and data.table manipulate packages easily,
stringr operation string,
zoo handles regular and unscheduled time series,
ggvis, lattice, and GGPLOT2 visualization data,
caret machine learning
When and how do I use Python?

Python can be used when your data analysis task needs to integrate a Web application, or if the statistical code needs to be included in the production database. As a fully fledged programming language, it is a great tool for implementing algorithms for production use.

In the past, the data analysis of Python packages is a problem. This has improved significantly over the years. Be sure to install numpy/scipy (scientific calculations) and pandas (data processing) so that Python can be used for data analysis. Also see matplotlib making graphics and Scikit-learn for machine learning.

Unlike R,python there is no understanding of the "best" IDE.

We recommend that you use Spyder,ipython Notebook and rodeo. See which one is best for your needs.

R and Python: the number of data sciences

Let's say you look at recent polls. Focusing on the programming language for data analysis, R is often the obvious winner. Suppose you pay particular attention to the field of Python and r data analysis, and you get a similar pattern.

Despite the above figures, more and more people are moving from R to Python. In addition More and more people are using both at the same time.

This is also in line with our recommendation to students.

Suppose you're going to start a career in data science. It is best to be fluent in two languages. Recruitment trends show that demand for both skills is constantly being added and wages are well above average.

R: Strengths and weaknesses

Pro: One picture beats more than words

Visual data is often easier to understand than individual raw data.

R and visualization are a perfect match. Some of the must-see visualization packages are GGPLOT2. Ggvis. Googlevis and Rcharts.

Pro:r Eco-System

R has a rich ecosystem that includes cutting-edge packages and active communities.

The package can be cran. Get on Bioconductor and GitHub. You can search for all packages in rdocumentation.

Pro: Using R in Data science

R is developed by statisticians for statistical experts.

They are able to communicate ideas and ideas through R code and packages. You don't necessarily need a computer science background.

In addition, people outside academia are using r more and more.

Pro/con:r very slow.

R has been developed to make life easier for statisticians. Not the life of your computer.

Although R may perform slowly due to poorly written code, there are multiple packages to improve R performance: Pqr,renjin and Fastr,riposte, and so on.

Con:r has a steep learning curve.

The R learning curve is very complex, especially if you are a GUI from statistical analysis. If you are unfamiliar with it, it can be time consuming to find a package.

Python: Strengths and weaknesses

Pro:ipython Notebook

IPython notebook more easy to work with Python and data. You can easily share notebooks with coworkers without having to install whatever they need. This greatly reduces the overhead of organizing code, output, and gaze files.

This will allow you to spend a lot of other time doing the actual work.

Pro: Universal language

Python is an easy and intuitive universal language. This gives it a relatively flat learning curve that can add the speed at which you write a program. In short, you need less time to write code!

In addition The Python test frame is a built-in, ground-required test frame that inspires good test coverage. This ensures that your code can be reused and reliable.

Pro: A multi-purpose language

Python brings together people from different backgrounds.

As a generic. Easy to understand the language that statisticians can very easily learn, you can build a single tool to integrate each part of your workflow.

Pro/con: Visualization

When you select data analysis software. Visualization is an important criterion. Although Python has some good visual libraries, such as Seaborn. Bokeh and Pygal, there are too many options to choose from. In addition, compared to R. Visualization is generally more cumbersome and results are not always pleasing to the eye.

Con:python is a challenger.

Python is the challenger to R. It does not provide an alternative to the hundreds of indispensable r packages. Although it is catching up, it is unclear whether this will give up R?

Who's the winner?

It is up to you to decide. As a data scientist, choosing the language that best meets your needs is your job. Some questions can help you:

What problem do you want to solve?
What is the net cost of learning a language?
What tools are frequently used in your field?
What are the other available tools and how are these related tools used daily? 

Hope to be helpful to you!

R VS Python in Data science: The winner is ...

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