R 与Python

R 编程语言和Python都广泛用于数据科学。两者都是非常有用的开源语言。

在本文中，我们将介绍以下主题：

• R 编程语言
• Python编程语言
• R编程和Python编程之间的区别
• R 编程和Python编程中的生态系统
• R编程和Python编程的优势
• 数据科学中的 R 和Python用法
• R 和Python中的示例

R 编程语言

R 语言用于机器学习算法、线性回归、时间序列、统计推断等。它是由 Ross Ihaka 和 Robert Gentleman 于 1993 年设计的。R 是一种开源编程语言，被广泛用作统计软件和数据分析工具。 R 通常带有命令行界面。 R 可在 Windows、Linux 和 macOS 等广泛使用的平台上使用。此外，R 编程语言是最新的尖端工具。

Python编程语言

Python是一种广泛使用的通用高级编程语言。它由 Guido van Rossum 于 1991 年创建，并由Python软件基金会进一步开发。它的设计强调代码的可读性，其语法允许程序员用更少的代码行来表达他们的概念。

R编程和Python编程之间的区别

以下是 R 和Python之间的一些主要区别：

Feature	R	Python
Introduction	R is a language and environment for statistical programming which includes statistical computing and graphics.	Python is a general purpose programming language for data analysis and scientific computing
Objective	It has many features which are useful for statistical analysis and representation.	It can be used to develop GUI applications and web applications  as well as with embedded systems
Workability	It has many easy to use packages for performing tasks	It can easily perform matrix computation as well as optimization
Integrated development environment	Various popular R IDEs are Rstudio, RKward, R commander, etc.	Various popular Python IDEs are Spyder, Eclipse+Pydev, Atom, etc.
Libraries and packages	There are many packages and libraries like ggplot2, caret, etc.	Some essential packages and libraries are Pandas, Numpy, Scipy, etc.
Scope	It is mainly used for complex data analysis in data science.	It takes a more streamlined approach for data science projects.

R 编程和Python编程中的生态系统

Python支持一个非常大的社区来实现数据科学的通用性。数据分析的最基本用途之一，主要是因为以数据为中心的Python包的奇妙生态系统。 Pandas 和 NumPy 就是其中之一，它们使导入、分析和可视化数据变得更加容易。

R Programming拥有丰富的生态系统，可用于标准机器学习和数据挖掘技术。它适用于大型数据集的统计分析，它为探索数据提供了许多不同的选项，它使 更容易使用概率分布，应用不同的统计测试。

Features	R	Python
Data collection	It is used for data analysts to import data from Excel, CSV, and text files.	It is used in all kinds of data formats including SQL tables
Data exploration	It optimized for statistical analysis of large datasets	You can explore data with Pandas
Data modeling	It supports Tidyverse and it became easy to import, manipulate, visualize and report on data	Use can you NumPy, SciPy, scikit-learn
Data visualization	You can use ggplot2 and ggplot tools to plots complex scatter plots with regression lines.	You can use Matplotlib, Pandas, Seaborn

R编程和Python编程的优势

Advantage
R Programming	Python Programming
It supports a large dataset for statistical analysis	General-purpose programming to use data analyse
Primary users are Scholar and R&D	Primary users are Programmers and developers
Support package like tidyverse, ggplot2, caret, zoo	Support packages like pandas, scipy, scikit-learn, TensorFlow, caret
Support RStudio and It has a wide range of statistics and general data analysis and visualization capabilities.	Support Conda environment with Spyder, Ipython Notebook

数据科学中的 R 和Python用法

Python和 R 编程语言在数据科学中最有用，它处理从数据源中识别、表示和提取有意义的信息，用于使用这些语言执行一些业务逻辑。它有一个流行的包 数据收集、数据探索、数据建模、数据可视化和静态分析。

R 和Python中的示例

两个数相加的程序

R

# R program to add two numbers
numb1 <- 8
numb2 <- 4
 
# Adding two numbers
sum <- numb1 + numb2
 
print(paste(The sum is", sum))

Python

# Python program to add two numbers
 
numb1 = 8
numb2 = 4
 
# Adding two numbers
sum = numb1 + numb2
 
# Printing the result
print("The sum is", sum)

输出：

The sum is 12