Get started with R for Data Science

1. GET STARTED WITHFOR R DATA SCIENCE © Copyright 2023. United States Data Science Institute. All Rights Reserved us dsi .org

2. In modern-day businesses, no organization can afford to ignore the importance of data science. By leveraging the power of data science, many companies have reached new heights that used to be impossible once. By properly analyzing the data, these organizations are able to make a more accurate data-driven decision that not only helps to improve their business operations but also enhances their customer experience. World Economic Forum The recent ranking released by the 2023 ranking the fastest growing jobs has ranked the data science jobs 5 on the list. Also, the data science market is witnessing a rapid growth rate and it is expected to reach a market value of $501 billion by 2032, as reported by Precedence Research. in May th With the growing amount of data, where millions of terabytes of data are generated every day, businesses are looking for skilled data science professionals who can process this enormous amount of data for their organization’s best interest. And it is the perfect time to step into this domain for a successful career ahead. When it comes to performing data science tasks, there are several programming languages and tools that come in handy such as Python, R, Scala, Java, SQL, etc. Here we will guide you about one of the most popular languages used for Data Science – the R programming language. So, if you are a beginner, then this document is for you to learn everything you need to know about R. © Copyright 2023. United States Data Science Institute. All Rights Reserved us dsi 1 .org

3. What is R? R is among the very popular programming languages that several data science professionals use specifically for data analysis and statistical computing. This programming language was created by Ross Ihaka and Robert Gentleman from the University of Auckland, New Zealand in the 1990s. And since then, R has gained immense popularity in the data science community. As this language offers extensive libraries and robust statistical capabilities, it has managed to garner a huge user base since its inception. Getting started with Rstudio So, now that you have downloaded and installed R on your system, it’s time to get started with RStudio. It is a popular Integrated Development Environment that makes working with R more user-friendly. Even though R can be used from the command line, many data science professionals prefer using IDEs i.e. RStudion for their work. Rstudio can be downloaded from their official website https://www.rstudio.com/products/rstudio/dow nload/. After installing this, you will see a user- friendly interface where you get access to panels for writing code, viewing data, and visualizing results. How to Install R? Installing R is very easy. It is available for all major platforms including Windows, MacOS, and Linux. Before you can start using this software, you can download it from the Comprehensive R Archive Network (CRAN) website (https://cran.r- project.org/) and install it in your system. Ensure you have downloaded the latest version to make use of the latest features. You will get the installation instructions for your operating system on this website as well. Basic concepts of R: So now let us get started with the basics of R and its components. The first thing to understand is, that R is an object-oriented language which means every operation you perform in R is around objects. These objects also known as building blocks of R, are: © Copyright 2023. United States Data Science Institute. All Rights Reserved usdsi.org 2

4. Variables In R, you can assign values to variables using the assignment operator <- or =. For example: This assigns the value 10 to the variable ‘x’. Data Structures Data Structure refers to the nouns of Programming in R and data items of different types are organized into data structures. These data structures can take the form of Vectors – they are one-dimensional arrays that can hold multiple values of the same data type Data frames - Two-dimensional tables used to store data, with rows and columns, similar to spreadsheets Lists - Containers that can hold elements of different data types Ÿ Ÿ Ÿ Functions Another thing that makes R a popular programming language is its built-in functions that can perform various functions. For example, you can use the ‘mean()’ function to calculate the mean of a vector of numbers: © Copyright 2023. United States Data Science Institute. All Rights Reserved usdsi.org 3

5. Packages Packages in R refer to the collections of functions, data, and documentation. By installing and loading R packages, you can enhance the R’s functionality in doing specific data science tasks. The ‘install.packages()’ function installs packages, while the ‘library()’ function loads them. For example: This assigns the value 10 to the variable ‘x’. Data Manipulation in R Data manipulation is the most important part of data analysis and R offers several packages to perform data manipulation tasks. ‘dplyr’ and ‘tidyr’ are such packages that are used to perform the below-mentioned tasks easily: Data Import You need to import the data first before you can start working on it. R can work on various data formats such as CSV, Excel, and different databases. The ‘read.csv()’ function is commonly used for reading CSV files. For example: Data Exploration After the data has been imported, you can now explore these using different functions like ‘head()’, ‘tail()’, and ‘summary()’. These functions will help you provide a quick overview of your dataset. Data Filtering The ‘filter()’ function in ‘dplyr’ is used to subset data based on specific criteria. For example, if you want to filter data with age more than 30, then use the command © Copyright 2023. United States Data Science Institute. All Rights Reserved usdsi.org 4

6. Data Transformation Data transformation refers to the modification of variables or the creation of new ones. To perform this ‘mutate()’ function is commonly used in ‘dplyr’. Here is an example of a transform function: Data Aggregation It involves aggregation of data i.e. summarizing or grouping them to obtain insights. For example: Data Visualization in R The core of any data science project is Data Visualization which refers to creating beautiful and interactive visuals of the findings from the data analysis. With this technique, complex insights can be easily conveyed to stakeholders. With R, packages like ‘ggplot2’ can be used for data visualization. With this package, the following types of plots can be created: Scatter Plot It is used to visualize the relationship between two numerical values. © Copyright 2023. United States Data Science Institute. All Rights Reserved usdsi.org 5

7. Histogram Histograms are used to represent the distribution of single variables. Bar Chart It is suitable for visualizing categorical data. These are just only a few of the examples of how you can use ggplot2 for data visualization. You can explore it more and use your creativity to visualize your data in your own way. Since the R and its package offer great flexibility, you can customize the visualization for your findings. Statistical Analysis in R What distinguishes R from other programming languages is its statistical capabilities. And this makes it an ideal choice among data scientists. With the help of R, you can perform various statistical tests and analyses to derive meaningful insights from your data. Some common statistical functions in R include: T-tests – for comparing means of two groups ANOVA – for analysis of variance tests for comparing multiple groups Linear Regression – for building relationships between variables Correlation – it measures the strength and direction of the relationship between two numerical values Ÿ Ÿ Ÿ Ÿ © Copyright 2023. United States Data Science Institute. All Rights Reserved usdsi.org 6

8. Conclusion So, these are some basics of R that can help you get started with this programming language for data science. You must remember, that R is a comprehensive programming language and offers several functions and packages. It requires a lot of practice and working on several different types of data and projects to completely understand all its functionalities. The more you practice, the more you will concepts will get cleared. So, download it now, jump into online communities, dive into video tutorials, get enrolled in data science certification, and master this incredible tool for data science. © Copyright 2023. United States Data Science Institute. All Rights Reserved us dsi 7 .org

9. GROW BIG WITH DATA SCIENTIST EXPERTISE VIA About The United States Data Science Institute ® (USDSI ) is deemed a high-end and in-depth technical certification provider for Data Science Professionals and leads the global panorama in Data Science Organizational Transformation, Innovation, and Leadership. USDSI researches, designs, and certifies personnel who enter or engage in various emerging Data Science Majors. ® CERTIFICATIONS REGISTER NOW LOCATIONS Arizona Connecticut Illinois 1345 E. Chandler BLVD., Suite 111-D Phoenix, AZ 85048, info.az@usdsi.org Connecticut680 E Main Street #699, Stamford, CT 06901 info.ct@usdsi.org 1 East Erie St, Suite 525 Chicago, IL 60611 info.il@usdsi.org Singapore United Kingdom No 7 Temasek Boulevard#12-07 Suntec Tower One, Singapore, 038987 Singapore, info.sg@usdsi.org 29 Whitmore Road, Whitnash Learmington Spa, Warwickshire, United Kingdom CV312JQ info.uk@usdsi.org info@usdsi.org | www.usdsi.org © Copyright 2023. United States Data Science Institute. All Rights Reserved