R is a programming language and an analytics tool, different from any other programming language. The R language finds application in data analysis, data visualization, statistics, and machine learning. Currently, data science and focusing on analyzing the data that exists and processing the data to find out information is required in every business and must be quick enough to beat the competition. R language is used in every field ranging from Finance, IT to social media and healthcare. There is a large amount of data generated, and to be able to analyze this data for improving and innovating is extremely important, and R as an open-source language makes this possible.
As we are all collecting data in large amounts, it is necessary to look for insights and patterns to make better decisions in the future, and analyzing large amounts of data for this purpose is highly time-consuming and cumbersome. Once all the data has been collected and patterns are identified, reading and understanding the data is of great importance and is highly complex but being able to visualize that data in graphical form and charts makes drawing inferences easier and error-free.
Why R language?
- Open Source Platform
- Has data visualization capabilities
- Being developed continuously as per the need
- Provides a variety of modelling and analytical tools
- Gives the best insight from a collection of data
- A complete statistical and decision-making tool
The packages that R offers continuously enhanced the abilities of R, and it was no longer tough to implement them. The machine learning algorithms can be implemented with R and also communication with other programming languages like Java, C++.
Packages in R
R offers a wide range of packages that continuously make it easier and efficient for the application of R and use it for different kinds of data and has also sped up the process of computation.
Let us take a look at packages that the R language offers
- Tidyverse – Let’s clean, process, model, and visualize the different data that you come across.
- Rtweet – Majorly used when performing sentiment analysis through Twitter
- R markdown – This lets you create reports directly in various formats and creates highly informative reports
- MLR – Machine learning R contains all Machine Learning Algorithms
- Reticulate – Allows the use of Python with the R language
These are a few packages that allow you to take full advantage of the R language, and programming in R eases the entire process from collection of data to identifying patterns, creating insights, and presenting it.
Data Types in R
It is the data that we require to be processed using the specific program. There are various types of data to deal with in any programming language, and we create a space in the memory for the same, for the language to recognize it and for the computer program to process it and perform the various operations. Every variable you enter is associated with a data type that allows the performing of the suitable operations on it.
Let us take a look at the data types that exist in R and what data is stored in them!
| Numeric | Numbers and Decimals |
| Integer | Numeric data without decimals |
| Character | Text, known as strings |
| Factors | Limited number of unique characters |
| Logical | True or False |
| Complex | Data with imaginary component |
The data we collect is often scattered. Now imagine having something that stores and organizes the data you collect and lets you modify and access it easily. That’s what data structures do. Once the data is segregated into the different data types, the data structures make it accessible and perform operations hassle-free. Let us now move to the next step and understand what data structures are when programming in R?
Data Structures in R
There are six types of Data Structures in R, that are used in data analysis
- Vectors
- Lists
- Data Frames
- Matrices
- Arrays
- Factors
These data structures are categorized based on their dimensions as homogeneous or heterogeneous.
| Homogeneous | Heterogeneous |
| Vectors | Lists |
| Factors | Data Frames |
| Matrices | |
| Arrays |
Since data types and data structures both categorize and organize the data collected, it might be confusing.
What is the difference between data types and data structures?
- Data type gives meaning to a particular variable; it defines the variable whereas data structures are a collection of different types of data, categorized as R-objects.
- Data type Categorizes values in the different types of data, and data structure categorizes the different types of data and not values.
Knowing that R language is used to analyze data, but where is it used? Let us look at a few applications where programming in R is required
Applications of R
Every field today lives on data; there is a continuous analysis of data to draw inferences and make decisions and which is why the R language is used in every sector to make well-informed decisions. R is used in domains of e-commerce for cross-selling of products; it tends to show additional products that the buyer might be interested in based on the purchase. R finds applications in social media for newer campaign strategies, customer engagement and performs sentiment analysis and behaviour analysis to understand the content that consumers are engaging with. R finds its applications in the IT sector too to understand the user activity on the web. Google uses the R language to provide auto-suggestions, ROI of campaigns, the performance of keywords, and so on. R is often used in the financial and banking sector to analyze risk modelling and factors.
Advantages of Programming in R
- When programming in R, the cleaning of messy data for convenient analysis of data happens through the wide variety of tools available that save time, that is often utilized in segregating the data
- Certain kinds of data lack clarity when analyzing in tabulated form, R creates a visual representation of data that makes it easier to infer insights
- R also connects Machine learning that makes future predictions easier and brings in automation in the entire process of algorithms
- R is specifically designed for analysis and hence provides a special edge to statistically analyze data
A deeper look into applications of R in times of Covid
In the times of the covid-19 pandemic, there is a large amount of data that is being analyzed for the understanding of patterns to prevent and treat. The pattern of rising cases in various locations, in the age groups, in people with comorbidities, the gender-wise cases, the recoveries, and deaths can be understood when programming in R. This helps in identifying the various reasons as to why this is happening and makes the process of decision making faster. These also perform trend analysis, making future predictions easier and understand the new waves that are coming in.
Future of R language
The amount of data being generated is so huge, and this is not only by bigger companies. Even the smaller companies are analyzing the data that is generated to serve their customers better. To provide better services to customers and offer them the information they are looking for is only increasing and becoming more personalized. With the R language, the visualization of this data is crisp and clear and of high quality, which makes the process non-cumbersome and saves time. There is increasing research being done to analyze patterns in every domain, and focus on sentiment and behaviour analysis is extremely important in the times of social media. Continuous development in the language and availability of a lot of packages makes it efficient and exceptional.
As we now know that R is being improved continuously to provide better analysis; learning this language will give you an edge in the field of programming. Also, the basics remain the same!
Do check out our course on Programming in R that will take you through the basics of R, covering concepts such as data types, control statements, data structures. It gives an understanding of the functions, factors, and data frames in R that creates a base for deeper learning and understanding of programming in R.
To gain deeper insights and understand the application of R in different domains of marketing, supply chain, finance, and social media. Get to work with real-time data from various industries and apply the learning you have gained from our PG Program in Data Science.
R Programming isn’t disappearing, because the data that exists and is continuously generating isn’t disappearing ever.
