Introduction of R language
R language is one of the most widely used primary languages specifically for machine learning and data science along with some data analysis and visualization room it was developed by Ross Ihaka and Robert Gentleman in 1993.
it is one of the best statistics to consider for data processing data cleaning data preparation and machine learning many foreign companies like Facebook, Google, uber, and almost all complete who work on data use R for one or more of their functionalities.
the benefit of R is it can handle a large amount of data and that’s why it can also work with big data systems so that it can leverage the functionality and features of big data for parallel processing and distributed storage.
it has series of packages functions which can help you to work on almost any kind of data and get the useful insight one of the most important things that I want to mention over here is our very easy to learn and implement and that is the region.
it is widely used in the industry there are multiple places where R can be used it can be used for statistical inference can be used for data analysis machine learning algorithm and not forget data visualization. we can use R anywhere where data is involved it can be data preparation or creating some predictive model creating some clustering or doing some predictions everywhere.
Features and uses of R language?
R can be used basically the use is in data analysis for the five regions first program means are is a clear and accessible programming tool and it helps you with a lot of packages where multiple functions features are involved to just name it and you will be having a predefined function available for data science.
it can also be used for the transformation right like our email collection of libraries designed specifically for data science you can transform your data from one language you know one type to another type very easily.
The help of R you can discover the insight from the data redefine your hypothesis concepts analyze them and make the business decisions once the analysis is done
you can even make models on top of the data with the help of our these models can be supervised models unsupervised models or any other advanced form of modeling techniques you can implement with the help of our you can also visualize your data with the help of our there is numerous package available in our which help you to visualize your data get the inside to pretend to the business and take the business decisions are can help you with all these things.
R is majorly used for various industries like academics, health care, government, consulting, and so on. It’s a good opportunity a good time to learn and grow in the data industry with the help of a programming language called R.
The popular programming language for machine learning data science and needless to say R has acquired at the second place yes definitely re very useful tool for all kinds of data analysis and if you are looking to start with data analysis advanced analytics machine learning data science is can be a very useful and easy tool to get started
There are some of the very famous use cases which industrial Legends are using
For Example, Facebook uses it for predictions right predictions whether the social post will be interrupted or not.
Banks basically ready for the risk you know analysis risk analysis
The real-state company used for the prediction of pricing and offers.
Twitter is using for statistical data analysis.
weather services you it for flood forecasting and weather forecasting and there is n number of companies which usually are for different purposes to sell or to solve different problems and you can be the one who can solve the next problem.
Why use R is used for data science?
Nowadays it’s possible to have a job in data science or statistics without knowing python programming or R languages and you should be an expert in both but you should be comfortable in both or at least very good in one and comfortable enough in the other that
you that you’re able to with you and also write some code also try to stay up to date with what’s happening in both communities in both languages because
I think that’s important as well try to also learn packages there are some out there that exist for both languages or that work the very same way. if you write your
models in one file and then you execute those models in R language or Python but the syntax that you write your models in is totally independent for inviting plus to bind their bindings for our Python but also for other languages matter start etc so try to learn these kinds of packages there are packages in Python that work very similarly as ggplot there are basically three implementations of ggplot2.
so if you learn those packages you would be able to use them in both languages.
what’s behind the statistics the analytics and also the visualizations that today’s brightest knowledge scientists and business leaders suppose to form powerful selections you will not invariably see it however it’s there it’s referred to as R language open supply R language the applied mathematics artificial language that knowledge specialists.
the planet overuse for everything from mapping broad social and selling trends on-line to developing the monetary and climate models that facilitate drive our economies and communities however what precisely is our and wherever did our begin well originally our started Newzealand with 2 professors,
United Nations agency needed a far better-applied mathematics platform for his or her students so that they created one shapely once the statistical languages them together with several others unbroken engaged on and victimization our making new tools for our and finding new applications for our every day due to this worldwide community effort R unbroken growing however thousands of user-created libraries designed to reinforce our practicality and crowd-sourced quality validation and support from the foremost recognized business leaders in every field,
that uses our that is nice as a result of our is the best at what it will holding specialists quickly and simply interpret move with and visualize knowledge showing the speedily growing community of R users worldwide and see however open supply our continues to form the longer term of applied mathematics analysis and knowledge science.
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