What is R and its importance?
R is a very easy popular programming language. It is very easy language for beginners to learn and write.
It is basically used for arithmetical calculations and pictorial(picture) presentation.
The most common use of R programming is to analyze and visualize data.
R is mainly used for statistical(arithmetical) computing and design. It has brought about thorough developments in large data and data analytics. It is the most extensively used language in the whole world of data science! Some of the big shots in the industries like LinkedIn, Google, Yahoo, and Facebook, rely on R for many of their functions.
R is best popularly used statistics programming language. It is the first range of data scientists as well as supported through a dynamic as well as talented community of contributors. R is actually trained within colleges as well as utilized in mission crucial business applications.
Application of R
What can I do with R?
R can be used to perform a variety of tasks(functions) such as - built statistical models, data store and analyze data . Since data mining and data analysis are activities that need a variety of applications and different ideas to communicate so R is a perfect language to learn and very easy to use.
Why R is so popular?
R is the most prominent(famous) language in the world of Data Science. It is mainly used in examine data that is both assembled and disassembled . This has made R, the excellent language for doing statistical calculations(functions). R allows lot of different features that make it different from other Data Science languages.
1. To print the text in R, we use single or double quotes:
For example- “ Congratulations ! This is your first R program ”
2. To print numbers, just type the number (without any quotes):
For doing simple calculations, add numbers together (without any quotes):
For example- 9+7
Creating a function:-
For creating a function we use the
Pros and Cons of R
| Benefits: ||Limitations:|
- Powerful Graphics
- ML Operations
- Array of packages
- Cross Compatibility
- Poor security
- Low support
Difference between the R and Python programming language:
- These types of codes need much more maintence
- Applied in data analysis and statistical modelling
- Runs programs locally
- Not cut out for handling large data sets
- Some of the commonly used IDEs are Rstudio and R GUI
- Essential libraries include caret, tidyverse, ggplot2 etc
- R is inefficient as compared to python when it comes to speed
- It is not easy to learn for beginners
- It is best utilized for data visualization
- It comprises hundreds of packages or even technique to complete a same task
- It support only procedural programming for several functions and OOP for other functions.
- It can be more slowly as compared with python
- It makes it simple to utilize complicated statistical tests and mathematical calculations.
- These types of codes tend to be more rubust and can be easily maintained.
- Applied in data science, web development, and embedded systems
- Programs are integrated with web app for easy deployment
- Can easily handle large data sets
- Some of the commonly used IDEs are Jupyter, Spyder, Ipython etc
- Essential libraries include Numpy, pandas, scikit-learn, scipy, TensorFlow etc
- Python outshines R in terms of processing speed for majority cases
- Simple and readable code structure makes python easier to learn for beginners.
- It is utilized as the general purpose language for deployment as well as development
- It is best utilized for deep learning
- It's developed on the philosophy that there should be only one unique technique to perform it. Consequently, comprised few key packages to perform the task.
- It's the multi paradigm language that means it supports several paradigms. such as functional, object oriented, aspect- oriented and so on
- It is faster
- It is good to develop something new from scrtch. It is utilized for application development also.
Object in R:
Object in python:
dimension NumPy array
Dimension NumPy record array
dimension NumPy array
other NumPy array
Basic Syntax in R Programming Language:
var1 = “Simple Homework”
var2 <- “leftward homework” “rightward homework” -> var3
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