Big data and data science are today's two most popular and widely practiced words. Businesses have to make correct decisions for growth, which nowadays are made based on analysis of the relevant data collected. Data science provides good insights for making decisions through various tools like programming languages and many developed algorithms. Machine learning and deep learning utilize suitably trained models for the above purpose but require efficient programming languages that can give these models the power of prediction and visualization. The most popular languages in practice today include R, Python, MATLAB, etc. Each has many inbuilt capabilities to perform various data analysis and visualization tasks. R has its own uniqueness because of which it is equally popular and finds widespread application in data science. You can explore Data Science online courses in India to learn more about the use of different programming languages in Data Science.
In this article, we will explore various capabilities, some advantages, and disadvantages, along with potential projects in which R has its own place.
What is R in Data Science?
Let us look at a few important things about R in data science:
- The R language is an open-source language that can be used for free and is compatible with different operating systems and platforms.
- Since R is open-source software, it has a strong community of developers and users who contribute to the development of R.
- R, a programming language, provides objects, operators, and functions that allow users to explore, model, and visualize data.
- It can handle big data and perform data analysis and statistical modeling.
- R provides an environment for statistical analysis. It offers statistical and graphical capabilities. This means R can be used for classification, clustering, statistical tests, and linear and nonlinear modeling.
Why do we use R for Data Science?
With the emergence of Big Data, the Data Science field has become one of the most popular fields today. Companies possess valuable data, and there is a strong need to leverage the information present in this data to generate meaningful insights for decision-making. A proper and detailed data analysis is required to generate these insights with the help of several tools. Like Python, R is a popular programming language for data analysis, processing, transformation, and visualization. To know more, check out the data science courses, the majority of the topics covered in the Data Science Bootcamp review are useful for developing abilities, and end-to-end projects expose you to a real-world working environment as well.
How is R Used in Data Science?
To consider using R programming for data science, the focus is on the statistical and graphical capabilities of the R language. To study R for data science, one has to learn how to execute statistical studies and create data visualizations. The statistical functions in R allow easy data cleaning, importing, and analyzing. R can be used in RStudio, an Integrated Development Environment (IDE) for the language, which allows authoring and dealing with software packages easier. RStudio offers the required graphic accessibility and adds a syntax-highlighting editor to support the code execution. To consider using R programming for data science, the focus is on the statistical and graphical capabilities of the R language. To study R for data science, one has to learn how to execute statistical studies and create data visualizations. The statistical functions in R allow easy data cleaning, importing, and analyzing. R can be used in RStudio, an Integrated Development Environment (IDE) for the language, which allows authoring and dealing with software packages easier. RStudio offers the required graphic accessibility and adds a syntax-highlighting editor to support the code execution.
Features of R - Data Science
There are several reasons to use R for Data Science, such as -
- Although the academic community has used R until the recent past, it is now used in industries. R is commonly preferred by many statisticians and data scientists interested in developing statistical models to solve complex real-world problems.
- R can be used to perform complex statistical modeling. Additionally, R supports operations on arrays, matrices, and vectors.
- There are many R packages for Data Science that are suitable for different domains such as astronomy, biology, etc.
- R offers the option of interfacing the code with database management systems for data extraction purposes.
- R is well-known for its visualization libraries that allow the development of aesthetic graphs with interactivity. Moreover, it is possible to develop web applications with embedded visualizations using R Shiny that provides users with a high level of interactivity.
- Furthermore, there are several options for advanced data analytics, like building machine learning models for prediction, image processing, etc.
Essentials of R Programming
1. Data Types and Objects in R (with example)
In R, there are six basic data types: logical, numeric, integer, complex, character and raw. Let's discuss each of these R data types one by one.
- Logical Data Type: It is also known as the Boolean data type. It can only have two values: TRUE and FALSE.
- Numeric Data Type: It represents all real numbers with or without decimal values.
- Integer Data Type: It specifies real values without decimal points. We use the suffix L to specify integer data.
- Complex Data Type: It is used to specify purely imaginary values in R. We use the suffix to specify the imaginary part.
- Character Data Type: It is used to specify character or string values in a variable.
- Raw Data Type: It specifies values as raw bytes. We can use the following methods to convert character data types to raw data types and vice-versa:
- charToRaw() - converts character data to raw data
- rawToChar() - converts raw data to character data
R consists of several data objects to perform various functions. There are six types of objects in R Programming. They include vector, list, matrix, array, factor, and data frame.
- Vectors: They are one of the basic data objects. There are six atomic types of vectors: logical, integer, character, raw, double, and complex.
- Lists: These are data objects that contain various types of elements, including strings, numbers, vectors, and a nested list inside it. It can also consist of matrices or functions as elements. It can be created with the help of the list() function.
- Matrices: They are used to arrange elements in a two-dimensional layout. They contain elements of the same data type. They usually contain numeric values in order to perform mathematical operations.
- Array: It is used to store data in more than just two dimensions. It is used to store multi-dimensional data in the required format. It can be created with the help of an array() function.
- Factors: They are the data objects used to categorize and store data as levels. They can be strings or integers, which are extremely useful in data analytics for statistical modeling. It can also be created using the factor() function.
- Data frame: It is a two-dimensional data structure wherein each column consists of the value of one variable, and each row consists of a value set from each column.
2. Control Structures (Functions) in R
In order to control the execution of the expressions flow in R, we make use of the control structures. These control structures are also called loops. There are eight types of control structures: if, if-else, for, while, next, return, nested loops, repeat and break.
- If condition: This condition structure determines whether or not the expression given in parentheses is true. If true, the statements' execution continues.
- If-else condition: It is identical to the ‘if condition,’ except that when the test expression in the ‘if condition’ fails, the statements in the ‘else condition’ are performed.
- For loop: It is a loop or sequence of statements that are performed until an exit condition is fulfilled.
- while loop: It is another type of loop that iterates until a condition is satisfied. Before running the loop's content, the testing expression is checked.
- next statement: It is used to skip the current iteration without executing the preceding statements and to continue the next iteration cycle without completing the loop.
- return statement: It is used to return the result of a function that has been performed and to return control to the calling function.
- Nested loops: They are quite similar to basic loops. Nested loops are loops inside loops. Nested loops are also used to modify the matrix.
- Repeat and break statement: A repeat loop is a loop that can be iterated many times, but there is no exit condition to escape the loop. As a result, the break statement is used to end the loop. To exit the loop, we can use the break statement.
Most Common R Libraries for Data Science
You can find several R packages and libraries to perform different tasks in Data Science.
Here are some of the Best Add-On Packages for R as recommended by RStudio -
1. Database packages
- The DBI package to integrate R with DBMS (Database Management Systems).
- Packages RMySQL and RSQLite provide database drivers for loading and reading data from a database.
2. Visualization packages
- The ggplot2 makes it easy to create visually appealing plots and graphics.
- The ggmap is an R package that helps with spatial data as it allows downloading map areas from Google Maps and later integrating them into ggplot visualizations.
- The shiny package helps you create web apps.
3. Data Manipulation and Analysis packages
- The dplyr package allows summarizing, connecting, and rearranging the datasets.
- The stringr package provides user-friendly tools to deal with character strings and regular expressions.
- The lubridate package helps to work efficiently with the date and time entries in the dataset.
- The DataExplorer package is used in exploratory data analysis (EDA), feature engineering, and data reporting.
4. Machine Learning and Deep Learning packages
- The randomForest and caret packages can be used for training classification and regression models.
- The deepnet package provides a toolkit in R for deep learning. Similarly, popular frameworks Keras and Tensorflow can be used in R.
Additionally, the dev tools package helps to develop custom packages in R.
Applications of R for Data Science
R has a variety of applications in Data Science. When considering R in Data Science, it is widely used in many sectors to improve the effectiveness of services and processes in collaboration with data scientists and r data analysts.
The fields where R is used extensively make it a very popular tool.
- Google: R is a popular option at Google for performing various analytical procedures. R is used by the Google Flu Trends project to examine trends and patterns in flu-related queries.
- Facebook: R is widely used by Facebook for social network analytics to get insights about user behavior and to develop correlations between them.
- IBM: R is also used by IBM to provide various analytical solutions. R has been used in an open computing platform 'IBM Watson.'
- Uber: Uber uses the 'shiny' package for R to access its charting components, i.e., for building interactive web applications in R with embedded visual graphics.
- Research and Development: R is popular in the academic community for carrying out research and development work for its statistical computing and graphics handling abilities used to clean, analyze, and graph big data. R supports various powerful libraries that can help transform and analyze the data and visualize it with a few lines of code.
- Artificial Intelligence and Machine Learning: For AI and ML applications, R enables data scientists to work with different types of datasets and train models better by efficiently handling outliers as well as data mining. R assists machine learning application development by providing extensive statistical and prediction support.
- Production, Operations, and Manufacturing: R allows for improving the overall effectiveness and efficiency of production and industrial projects. Analyzing the production data offers more efficient techniques to minimize expenses, boost efficiency, meet deadlines, and simplify operations. Thus, R helps to reduce costs and increase profit and productivity within manufacturing or production processes. By properly assigning time and duties to employees, R can assist in splitting work across human assets leading to a better workplace and human resource management.
- Business Analytics and Analysis: R assists organizations, irrespective of their size, by performing statistical analysis and trend analysis to predict future issues and challenges and identify opportunities for development while helping mitigate present risks and losses. Using R, businesses can make better business decisions through comprehensive exploratory data analysis of historical and business data and meaningful data visualizations.
- Finance: R is also preferred by financial institutions and businesses as it provides several statistical tools for different financial operations, such as risk measurement, credit risk modeling, market analysis, etc. It also assists in the creation of interactive visualizations and graphs for financial reporting. R can also be integrated with Hadoop to perform customer analysis and segmentation.
- Medicine And Healthcare: R can be used to perform several tasks in medical research, genetics, epidemiology, bioinformatics, and medicine. It is often used for exploratory research, from enabling patient and disease studies to chemical discoveries, especially during pre-clinical trials, to interact successfully with drug-safety data. R can also be used for analyzing genetic data and modeling epidemiological requirements.
- Social Media and Advertising: R has a wide range of applications in social media and advertising, from social media data mining to customer behavior research. The majority of the data in social media is unstructured. As a result, R is widely used to target, extract, and analyze this data in order to boost social media analytics for a variety of activities, such as customer segmentation, audience targeting, and generating relational graphs, which aids in successful marketing or advertising. R can be used in forecasting sales and in proposing and promoting items to clients via social media.
How to Install R / RStudio?
Note: R should be installed first, followed by RStudio.
To install R and RStudio on windows, go through the following steps:
A) Installing R on Windows
- Download the R installer from the CRAN R project website. Alternatively, you can find this link on the RStudio website.
- Select the ‘base’ option if you are installing R for the first time. This will download the latest R installer for Windows.
- Next, run the downloaded .exe file and follow the default installation instructions. Once installation is completed, you’ll see the following message -
Now that you have R-base installed, you can proceed with RStudio installation.
B) Installing RStudio on Windows
- You’ll need to download the installer for RStudio first. Here, you can find the recommended installer version for your system.
- Run the downloaded .exe file with default settings, and you’ll see the following message after completing the installation of RStudio.
- There! You have successfully installed both R and RStudio on your computer. You can launch the RStudio IDE from the shortcut or taskbar icon.
Here is how the RStudio IDE appears. You can create a New Script using the menu file option to begin working on R projects.
Data Science Projects That Use R
Several industries, such as banking, telecommunications, and media, use R for data science. Following are some real-world examples of data visualization in R.
- T-Mobile employs R to classify customer support texts in order to connect clients to an agent appropriately.
- Twitter tweets can be analyzed for text using R. The twitterR package supports text analytics and scraping of Twitter data.
- Google Analytics can be combined with R to perform statistical data analysis and build meaningful data visualizations. This can be achieved by installing the RGoogleAnalytics package.
- The Financial Times used R to create data visualizations purely using R and ggplot2 package for their featured articles such as "Is Russia-Saudi Arabia the worst World Cup game ever?"
- BBC uses data visualization in R to generate appealing graphics for its publications. BBC has developed an R package based on the bbplot package and an R cookbook to standardize their data visualization graphic creation process.
Top Reasons to Learn R for Data Science
R has many features suitable for solving different problems related to data science.
- It is open-source software.
- It can be used for suitable projects for machine learning and deep learning model building.
- It has a huge capability as a statistical tool.
- It is probably the best visualization tool for depicting insights through different graphs and charts.
Advantages and Disadvantages of R in Data Science
R offers many advantages in data science. Here are some of the most significant benefits of using R.
Advantages of R
- R is an open-source software platform that helps create interactive graphs and provides great visual alternatives, making it even more user-friendly.
- R has a big development community, various developer forums, and a very friendly community of R enthusiasts.
- R offers the interface from Github as well as an enormous catalog for use in data analysis and data mining.
- There are many powerful R libraries for Data Science. For example, the R package Shiny allows developers to build interactive web applications directly using R.
- RMarkdown allows R to support various dynamic and static output formats such as HTML, MS Word, and PDF.
Thus, R offers multiple benefits when used for Data Science, but there are a few disadvantages. These are outlined below.
Disadvantages of R
- R has a steep learning curve as the R syntax is quite different and hence, slightly challenging to learn compared to Python.
- R does not offer basic security measures which are essential for production-grade web applications.
- The performance of R is slower than Python or MATLAB, and it does perform memory management, i.e., R requires a lot of memory.
This article discussed the importance of the R programming language in Data Science. We explored how to install R for Windows. Statistics is required in all Data Science projects, and R provides many powerful libraries for analyzing Big Data. Further, we also looked at the several reasons why aspiring Data Science professionals need to invest in learning R. It is evident that the popularity of data science will continue to grow, leading to better employment opportunities in the AI domain. Hence, the requirement for R professionals will boost as well. In conclusion, investing in learning KnowledgeHut's Data Science courses in India can help aspiring young data science professionals and existing mid-career changers to transition better into a data science career.