R Programming Past Paper PDF

R Programming Introduction of R programming language: Introduction, Features of R, Data types in R: numeric, arithmetic, assignment, Operators, Data Objects in R :Arrays, Lists,vectors, Matrices and Data Frames, Factors Conditions and Loops: if, Switch, while, for , repeatloops, Strings handling in...

R Programming Introduction of R programming language: Introduction, Features of R, Data types in R: numeric, arithmetic, assignment, Operators, Data Objects in R :Arrays, Lists,vectors, Matrices and Data Frames, Factors Conditions and Loops: if, Switch, while, for , repeatloops, Strings handling in R, Calling Functions, Writing Functions,Exceptions, Date&Timings and Visibility, Packaging in R. 12 hours Reading and writing files: Reading Tabular Data, Commands to Extract Rows and Columns, working with CSV files: reading, writing, analysis, working with JSON Files: reading, writing, Working with XML Files: reading, writing. 12 hours R as a set of statistical tables: Statistics And Probability, Process of Descriptive Analysis, Average, Variance, Standard Deviation in R, Mean, Median and Mode in R, Covariance and Correlation in R, Probability distributions in R:Normal distributions, binomial distributions. 8 hours Statistical testing and modeling in R: Hypothesis testing in R, components of hypothesis test, testing means, testing proportions, testing categorical variables, errors and power. 8 hours Advanced graphics in R: Plotting commands-high level and low level, Graphics parameters list, Device drivers, Dynamic graphics, plot customization, plotting regions and margin,R Histogram, Bar chart, Pie chart, Scatter plots examples. 12 hours 1. Write an R program for different types of data structures in R. 2. Write an R program that includes variables, constants, and data types. 3. Write an R program that includes different operators, control structures, default values for arguments, returning complex objects. 4. Write an R program for quick sort implementation. 5. Write a R program for calculating cumulative sums, and products minima,maxima 6. Write an R program for finding stationary distribution of markov chains. 7. Write an R program that includes linear algebra operations on vectors and matrices. 8. Write a R program for any visual representation of an object with creating graphs using graphic functions: Plot(), Hist(), Linechart(), Pie(), Boxplot(), Scatterplots(). 9. Write an R program for with any dataset containing data frame objects, indexing and sub setting data frames, and employ manipulating and analyzing data. 10. Write a program to create an any application of Linear Regression in multivariate context for predictive purpose. Formative Assessment for Practical Assessment Occasion/type Marks Program Writing Any One Program 10 Marks Execution 10 Marks viva 05 Marks Total 25 Marks  A Brief History of R R is a programming language and software environment primarily used for statistical computing and data analysis. Here’s a brief history of its development: 1. Origins (1990s): R was created by Ross Ihaka and Robert Gentleman as a programming language to teach introductory statistics at the University of Auckland, New Zealand. It was developed as an open-source implementation of the S programming language, which was created at Bell Laboratories in the 1970s. It was released in the early 1990s under the GNU public license 2. Initial Release (1995): The first official version of R was released in 1995. It aimed to provide a user-friendly environment for data analysis and visualization, leveraging the capabilities of S while being freely available. 3. Growth and Adoption (2000s): Throughout the early 2000s, R gained popularity in academia and research due to its extensibility and the growing number of packages available through CRAN (Comprehensive R Archive Network), which was established in 2002. 4. CRAN and Packages: CRAN became a central repository for R packages, allowing users to easily share and access a wide range of statistical tools. The number of packages grew rapidly, enhancing R’s capabilities in various fields, including bioinformatics, finance, and machine learning. over 19,000 packages available. CRAN is the public repository for R packages, which are the fundamental unit of shareable code in R. 5. R Foundation (2003): The R Foundation for Statistical Computing was established to support the development of R and promote its use. The foundation oversees the development of R, organizes conferences, and provides funding for various initiatives. 6. R in Industry (2010s): R began to see increased adoption in industry, particularly in data science, analytics, and business intelligence. Its integration with big data tools and frameworks further broadened its applicability. 7. RStudio (2011): The introduction of RStudio, an integrated development environment (IDE) for R, made it easier for users to write and debug R code, significantly enhancing the user experience. 8. Modern Developments: In recent years, R has continued to evolve, with improvements in performance, usability, and interoperability with other languages (like Python). It remains a leading choice for statisticians, data scientists, and researchers. Today, R is recognized as one of the top programming languages for data analysis and visualization, with a strong community and a rich ecosystem of packages and resources.  Robert Gentleman, co-originator of R Ross Ihaka, co-originator of R Features of R Programming: 1. Interpreted Language: R is executed line-by-line, which allows for interactive data analysis and rapid prototyping. This can make debugging easier since you can test code in small increments. 2. Dynamically Typed: In R, you don’t need to declare the data type of a variable explicitly. The type is determined at runtime, which provides flexibility but can lead to type-related errors if not managed carefully. 3. Duck Typing: This concept means that the type of an object is determined by its behavior (methods and properties) rather than its explicit declaration. This allows for more flexible and polymorphic code, enabling users to write functions that can operate on different types of data as long as they support the necessary operations. 4. Extensibility: R's functionality can be significantly expanded through packages. The Comprehensive R Archive Network (CRAN) hosts thousands of packages, which cover a wide array of statistical techniques and data manipulation tools. 5. Statistical Analysis: R was specifically designed for statistical computing, making it a powerful tool for data analysis. It includes a vast number of built-in functions for statistical tests, modeling, and data visualization, as well as advanced capabilities for time series analysis, linear and nonlinear modeling, and more. Overall, R’s design and features make it a favorite among statisticians, data scientists, and researchers for Applications of R Programming R is widely used across various domains due to its powerful capabilities in data analysis, statistical computing, and visualization. Here are some common applications of R programming:  1. Data Analysis and Statistical Computing R was specifically developed for statistical analysis, making it ideal for tasks like data summarization, statistical modeling, and hypothesis testing. Common statistical methods supported include regression analysis, time series analysis, and ANOVA. Analysts use R for exploratory data analysis (EDA) to understand data patterns and distributions. 2. Data Visualization R is well-known for its ability to produce high-quality, customizable visualizations. Libraries like ggplot2, lattice, and plotly are used to create a wide variety of charts, including bar graphs, scatter plots, line charts, and heatmaps. Allows for interactive visualizations using packages such as Shiny and plotly, enabling users to create interactive web dashboards. 3. Machine Learning and Predictive Analytics R supports numerous machine learning algorithms for tasks such as classification, regression, clustering, and dimensionality reduction. Packages like caret, randomForest, xgboost, and mlr are commonly used for developing predictive models and tuning hyperparameters. It is also used for natural language processing (NLP) and recommendation systems. 4. Bioinformatics and Genomics R is a popular tool in bioinformatics for analyzing and visualizing biological data. The Bioconductor project provides specialized packages for analyzing genetic data, such as DNA sequencing and gene expression data. Used in genome-wide association studies (GWAS), sequence alignment, and other genomic research tasks. 5. Finance and Economics In finance, R is used for risk analysis, portfolio management, time series forecasting, and option pricing. Economists use R for econometric modeling, macroeconomic analysis, and simulation studies. Packages like quantmod and TTR facilitate financial modeling and technical analysis. 6. Social Sciences R is used in social science research for survey data analysis, psychometrics, and demographic studies. It supports text mining and sentiment analysis, helping researchers analyze social media content, interview transcripts, and survey responses. Commonly applied in network analysis to study social networks and relationships. 7. Clinical Trials and Healthcare R is frequently used in the pharmaceutical and healthcare industries for clinical trial data analysis, including survival analysis and dose-response modeling. Regulatory bodies, such as the FDA, accept R-based submissions for clinical trial data. It is used for medical statistics, epidemiology studies, and health economics analysis.  8. Environmental Science and Ecology Environmental scientists use R for modeling climate data, ecological studies, and environmental monitoring. Useful for analyzing geospatial data, species distribution modeling, and biodiversity assessments. Packages like sp, raster, and vegan help in spatial data analysis and multivariate ecological analysis. 9. Marketing and Business Intelligence Businesses use R for customer segmentation, market basket analysis, sales forecasting, and churn prediction. Helps in analyzing consumer behavior, optimizing marketing campaigns, and measuring the effectiveness of advertising efforts. Techniques like cluster analysis and predictive modeling are commonly used for customer analytics. 10. Geospatial Data Analysis R supports geospatial data manipulation and visualization with packages like sf, sp, and raster. Useful for mapping, spatial statistics, and geographic information system (GIS) applications. Allows for creating and analyzing spatially-referenced data such as maps and spatial datasets. 11. Text Mining and Natural Language Processing (NLP) R can handle text data for tasks like sentiment analysis, text classification, topic modeling, and keyword extraction. Packages such as tm, text2vec, and quanteda facilitate the processing of large amounts of text data. Used in social media analysis, document classification, and information retrieval. 12. Operations Research and Optimization R is used for solving optimization problems, such as linear programming, integer programming, and simulation modeling. Packages like lpSolve and ROI provide tools for solving complex mathematical optimization problems. Applicable in supply chain management, logistics, and decision-making. 13. Web Scraping and Data Acquisition R is capable of scraping data from websites using packages like rvest, httr, and RCurl. Useful for collecting data from web sources, APIs, or structured web pages. Facilitates automated data collection for subsequent analysis. Web scraping extracts underlying HTML code and, with it, data stored in a database. Many large websites, like Google, Twitter, Facebook, StackOverflow, etc. have API’s that allow you to access their data in a structured format. 14. Reproducible Research and Reporting R supports the creation of reproducible research reports, dynamic documents, and presentations. Packages like knitr and rmarkdown allow for combining code, output, and narrative text in the same document. Helps in generating professional-quality reports, presentations, books, and even interactive web content.  15. Gaming and Simulation R can be used for simulation modeling, Monte Carlo simulations, and stochastic processes. Applicable in scenarios where random sampling and probabilistic modeling are required, such as game theory. R’s flexibility and specialized capabilities make it an ideal choice for professionals across various fields, particularly those involving data science, analytics, and research. Real-Life Use Cases of R Language R applications are not enough until you don’t know how people/companies are using the R programming language. 1. Facebook – Facebook uses R to update status and its social network graph. It is also used for predicting colleague interactions with R. 2. Ford Motor Company – Ford relies on Hadoop. It also relies on R for statistical analysis as well as carrying out data-driven support for decision making. 3. Google – Google uses R to calculate ROI(Return of investment) on advertising campaigns and to predict economic activity and also to improve the efficiency of online advertising. 4. Foursquare (Foursquare is the industry's leading geospatial technology platform, designed to help businesses make smarter decisions and create more engaging customer) – R is an important stack behind Foursquare’s famed recommendation engine. 5. John Deere – Statisticians at John Deere use R for time series modeling and also geospatial analysis in a reliable and reproducible way. The results are then integrated with Excel and SAP. 6. Microsoft – Microsoft uses R for the Xbox matchmaking service and also as a statistical engine within the Azure ML framework. Xbox services provides a matchmaking service called SmartMatch. It groups players based on player information and the matchmaking request for the players who want to play together. Matchmaking is server based. This means that players provide a request to the service, and then they're notified when a match is found. 7. Mozilla – It is the foundation behind the Firefox web browser and uses R to visualize web activity. 8. New York Times – R is used in the news cycle at The New York Times to crunch data and prepare graphics before they go for printing. 9. Thomas Cook (Tour Packages)– Thomas Cook uses R for prediction and also Fuzzy Logic Systems to automate price settings of their last-minute offers. 10. National Weather Service – The National Weather Service uses R at its River Forecast Centers. Thus, it is used to generate graphics for flood forecasting. 11. Twitter – R is part of Twitter’s Data Science toolbox for sophisticated statistical modeling. 12. Trulia – Trulia, the real-estate analysis website uses R for predicting house prices and local crime rates. 13. ANZ Bank – ANZ, the fourth largest bank in Australia uses R for its credit risk analysis. Advantages R is a powerful programming language and software environment widely used for statistical computing, data analysis, and graphical representation. Some of the key advantages of using R: 1. Statistical Analysis and Data Manipulation R is designed specifically for statistical analysis, making it ideal for tasks like hypothesis testing, regression analysis, and predictive modeling. Provides a wide range of statistical functions and libraries that simplify complex data analysis. 2. Data Visualization Offers extensive tools for creating high-quality visualizations, including graphs, plots, and charts. Libraries such as ggplot2, plotly, and lattice enable advanced and customizable data visualization.  Supports interactive visualization for web applications. 3. Open Source and Free R is open source, meaning it is free to use and can be modified to suit individual needs. Encourages a strong community of contributors who continuously develop new packages and update existing ones. 4. Extensive Package Ecosystem Thousands of packages are available through the Comprehensive R Archive Network (CRAN) and Bioconductor, covering various fields like finance, biology, machine learning, and more. The package ecosystem provides functions and tools for almost any data-related task. 5. Active Community and Support R has a large, active user community, which provides support through forums, mailing lists, and social media. Abundant tutorials, blogs, and online courses help users at all levels learn and improve their R skills. 6. Data Wrangling and Cleaning Offers powerful libraries like dplyr, tidyr, and data.table for efficient data manipulation, cleaning, and transformation. Allows for complex data wrangling tasks to be done in a few lines of code. Data wrangling : The process of transforming raw data into a format that is easier to analyze and access. 7. Machine Learning and AI Capabilities R supports various machine learning algorithms and techniques, such as classification, clustering, and regression. Packages like caret, randomForest, xgboost, and mlr facilitate the development of machine learning models. 8. Integration with Other Languages and Tools Can easily integrate with other programming languages like Python, C++, Java, and SQL, allowing for flexibility in projects. Supports data import from various formats (CSV, Excel, databases, etc.) and export to different formats. 9. Reproducible Research R supports reproducible research through packages like knitr and rmarkdown, which allow users to create dynamic reports that combine code, output, and text. Facilitates the creation of professional reports, presentations, and even web applications using Shiny. 10. Cross-Platform Compatibility R works on different operating systems, including Windows, macOS, and Linux. Code written in R is portable across platforms, making it convenient for collaboration.  11. Big Data Handling R can be used with big data tools like Apache Hadoop and Apache Spark through packages such as sparklyr. Provides tools for parallel computing, making it suitable for large datasets and high-performance tasks. 12. Statistical Reporting and Publication-Quality Outputs Produces publication-quality graphs and tables for academic and professional reports. Capable of exporting graphics to various formats (PDF, PNG, JPEG, etc.). 13. R Foundation (2003): The R Foundation for Statistical Computing was established to support the development of R and promote its use. The foundation oversees the development of R, organizes conferences, and provides funding for various initiatives R's advantages make it a popular choice in academia, research, and industry for data science, analytics, and statistical computing tasks. Disadvantages of using R: 1. Memory Management Limitations R stores objects in memory (RAM), which can cause performance issues with very large datasets. Handling large data may require special packages (like data.table) or integration with big data tools (e.g., Apache Spark). 2. Steep Learning Curve The syntax and concepts in R can be challenging for beginners, especially those without a background in programming or statistics. Requires familiarity with various functions and packages, which may feel overwhelming at first. 3. Slower Execution Speed R is generally slower than compiled languages like C++ or Java because it is an interpreted language. While there are ways to improve performance (e.g., using C/C++ integration or parallel computing), it can still lag behind other languages for computationally intensive tasks. 4. Less Suitable for Software Development R is primarily designed for statistical analysis and data visualization, rather than general-purpose software development. Its use cases are limited outside of data science, making it less versatile than languages like Python or JavaScript. 5. Poor Handling of 3D Graphics While R excels at 2D plotting, it has limited capabilities for creating 3D visualizations. Although some packages (like rgl and plotly) support 3D graphics, they may not be as robust as similar libraries in other languages. 6. Dependency Issues R relies heavily on packages, and some packages may not be well-maintained or have compatibility issues with newer versions of R.  Package conflicts or outdated libraries can lead to problems when installing or using specific functions. 7. Weak Object-Oriented Programming (OOP) Support R does support OOP, but its implementation is not as strong or conventional as in other programming languages like Python, Java, or C++. This may make it less suitable for developers who prefer or need a strong OOP approach. 8. Security Limitations R is not considered a secure programming language for developing web applications, especially when compared to more robust languages like Java or C#. Lacks built-in security features, making it less suitable for applications requiring high levels of data security. 9. Inconsistent Documentation While R has extensive documentation, the quality can be inconsistent across different packages. Some packages may lack detailed explanations or examples, making it difficult to understand how to use them effectively. 10. Less Support for Mobile App Development R is not typically used for developing mobile applications. Lacks frameworks and libraries specifically designed for mobile app development compared to languages like Swift (for iOS) or Kotlin (for Android). 11. Difficulty in Debugging Error messages in R can be cryptic and difficult to understand for beginners. Debugging in R is not as straightforward as in some other programming languages, which can be frustrating when dealing with complex code. 12. Limited GUI Support R has fewer options for building graphical user interfaces (GUIs) compared to other languages. Although tools like Shiny exist for creating web-based interactive applications, native desktop GUI development is not R's strong suit. Despite these disadvantages, R remains a valuable tool for data analysis, statistical computing, and data visualization, especially when used in the right context.  R Language Comments : The line with a hash mark (#), and anything that comes thereafter will be ignored by the interpreter. For example, executing the following in the console does nothing but return you to the prompt: # This is a comment in R... Comments can also appear after valid commands. 1+1 # This works out the result of one plus one! 2 Working Directory An active R session always has a working directory associated with it. Unless you explicitly specify a file path when saving or importing data files, R will use this working directory by default. To check the location of the working directory, use the getwd function. getwd() "C:/Users/Admin/OneDrive/Documents" File paths are always enclosed in double quotation marks, and R uses forward slashes, not backslashes, when specifying folder locations. You can change the default working directory using the function setwd as follows: setwd("C:/Users/Admin/OneDrive/Documents/2024") Installing Packages There are thousands of contributed packages not included with the typical R installation; to make them loadable in R, you must first download and install them from a repository (usually CRAN). The easiest way to do this is by using the install.packages function directly at the R prompt (for this you need an Internet connection). For example, install.packages("ks") The console will show running output as the procedure completes. You need to install a package only once; thereafter it will be available for your R installation. You can then load your installed package (like ks) in any newly opened instance of R with a call to library. Updating Packages The maintainers of contributed packages periodically provide version updates to fix bugs and add functionality. Every so often, you might want to check for updates to your collection of installed packages. From the R prompt, a simple execution of the following will attempt to connect to your set package repository (defaulting to CRAN), looking for versions of all your installed packages that are later than those you currently have. update.packages()  Assigning Variables In R, you can assign values to variables using the assignment operators: o