## Course Objectives - Develop a solid understanding of R programming fundamentals. - Master data manipulation and statistical analysis using R. - Learn to create professional visualizations and reports using R's powerful packages. - Gain proficiency in using R for real-world data science, machine learning, and automation tasks. - Understand best practices for writing clean, efficient, and reusable R code. ## Weekly Breakdown ### Week 1: Introduction to R and Environment Setup #### Topics: - [**Overview of R: History, popularity, and use cases in data analysis.**](https://spinncode.com/designs/4YeO2Ldh): Discover the fundamentals of R programming, its history, and popularity in data analysis. Learn how R's flexibility, community support, and free open-source nature have made it a top choice for data analysis, visualization, and machine learning, used by companies like Google, Microsoft, and The Wall Street Journal. Understand its various use cases and real-world applications in statistics, business intelligence, and data science. - [**Setting up the R environment: Installing R and RStudio.**](https://spinncode.com/designs/WalDhtGb): Get started with R programming by learning how to install R and RStudio on different operating systems, including Windows, macOS, and Linux. Follow step-by-step guides to verify successful installation and set up your R environment. Master the basics of R programming with these essential tools. - [**Introduction to RStudio interface and basic usage.**](https://spinncode.com/designs/7EfVUZUP): Master R programming with a comprehensive guide to RStudio interface and basic usage. Learn how to navigate RStudio's panes and menus, create a new project, set the working directory, load libraries, and more. Discover essential shortcuts, tips, and tricks to boost your productivity in RStudio. - [**Basic syntax of R: Variables, data types, and basic arithmetic operations.**](https://spinncode.com/designs/QnEGWwS3): Master the basics of R programming, including variables, data types, and basic arithmetic operations, to build a strong foundation for data analysis and visualization. Learn how to assign values to variables, work with different data types, and perform calculations using R's straightforward syntax. #### Lab: - [**Install R and RStudio, and write a simple script performing basic mathematical operations.**](https://spinncode.com/designs/hZAdFoXD) #### Lab Summary: Learn how to set up your R programming environment by installing R and RStudio, and practice writing a simple script that performs basic mathematical operations. Discover best practices for coding in R, including using comments and descriptive variable names. Gain hands-on experience running your first R script and interpreting the results. ### Week 2: Data Types and Structures in R #### Topics: - [**Understanding R’s data types: Numeric, character, logical, and factor.**](https://spinncode.com/designs/c8tqc5pH): Mastering R Programming starts with understanding its fundamental data types: numeric, character, logical, and factor. Correctly identifying and utilizing these data types is essential for efficient data cleaning, preprocessing, and analysis. - [**Introduction to data structures: Vectors, lists, matrices, arrays, and data frames.**](https://spinncode.com/designs/JibEkf9H): Master fundamental data structures in R, including vectors, lists, matrices, arrays, and data frames, and learn how to create, manipulate, and use them to store and analyze data effectively. Understand the key attributes of each data structure and best practices for using them to manage data in R. This knowledge will help you work efficiently with data in R and set the stage for more advanced data analysis techniques. - [**Subsetting and indexing data in R.**](https://spinncode.com/designs/H4GFztR2): Master R programming with techniques for subsetting and indexing data. Learn how to isolate specific parts of your data using positive and negative indexing, and practice subsetting vectors, matrices, and data frames with various examples. - [**Introduction to R’s built-in functions and how to use them.**](https://spinncode.com/designs/J3zxdvyg): Explore R's built-in functions for data analysis, visualization, and manipulation. Learn how to use mathematical, statistical, and data manipulation functions to streamline your workflow and improve result accuracy. Discover best practices for utilizing these functions effectively. #### Lab: - [**Create and manipulate vectors, matrices, and data frames to solve data-related tasks.**](https://spinncode.com/designs/Ba3cFmsC) #### Lab Summary: Master the fundamentals of R programming by learning to create and manipulate vectors, matrices, and data frames. Explore various operations such as indexing, subsetting, concatenation, matrix multiplication, addition, and aggregation to work with data in R. ### Week 3: Control Structures and Functions in R #### Topics: - [**Using control flow in R: if-else, for loops, while loops, and apply functions.**](https://spinncode.com/designs/L27maU76): Master the fundamentals of control flow in R programming, including if-else statements, for loops, while loops, and apply functions, to write more efficient and modular code. Explore examples, syntax, and best practices for each structure. Practice with different data types and scenarios to become proficient in R programming. - [**Writing custom functions in R: Arguments, return values, and scope.**](https://spinncode.com/designs/NP1DQDDc): Mastering custom functions in R programming to simplify and make code more readable. Learn how to declare, write, and use functions with arguments and return values, and understand the function scope and its applications. With this knowledge, you can create reusable and efficient code in R. - [**Anonymous functions and lambda functions in R.**](https://spinncode.com/designs/mJOPAyLS): Mastering R Programming with anonymous and lambda functions, concise ways to define functions for one-time use or simple operations, and knowing when to apply them. - [**Best practices for writing reusable functions.**](https://spinncode.com/designs/RliWb7dk): Learn how to write reusable functions in R programming, essential for efficient and effective coding. Discover best practices for creating self-contained, modular, and maintainable functions, including input validation, error handling, and well-structured documentation. #### Lab: - [**Write programs using loops and control structures, and create custom functions to automate repetitive tasks.**](https://spinncode.com/designs/DwNi2m9o) #### Lab Summary: Mastering R Programming: Control Structures and Functions. Learn how to write efficient R programs using loops, control structures, and custom functions to automate repetitive tasks and increase productivity. Discover best practices for function creation and explore the various types of loops and control structures available in R. ### Week 4: Data Import and Export in R #### Topics: - [**Reading and writing data in R: CSV, Excel, and text files.**](https://spinncode.com/designs/DN7HqiB7): Discover how to efficiently read and write data in R using various file formats including CSV, Excel, and text files with built-in functions and additional packages such as 'readr' and 'readxl', and learn to adapt this knowledge for practical application in your own R programs. Effective data import and export are crucial skills for any R programmer to master. - [**Using `readr` and `readxl` for efficient data import.**](https://spinncode.com/designs/1yp4TJCt): Boost your data analysis skills by leveraging the `readr` and `readxl` packages in R, which offer faster and more efficient ways to import datasets, supporting various file formats and Excel files. Master key features, best practices, and examples to improve your data import tasks. - [**Introduction to working with databases in R using `DBI` and `RSQLite`.**](https://spinncode.com/designs/3WlMKlYK): Learn how to work with databases in R using the DBI and RSQLite packages, covering database connection, creation, and querying, along with best practices for secure and efficient database management. Discover how to interact with SQLite databases, write parameterized queries, and handle database errors in R. - [**Handling missing data and data cleaning techniques.**](https://spinncode.com/designs/fvsbvrcj): Mastering Data Import and Export in R requires handling missing data and applying effective data cleaning techniques. In R, missing values can be detected using `is.na()` and `is.nan()` functions, while techniques like deletion, imputation, and interpolation can be employed to handle missing values. #### Lab: - [**Import data from CSV and Excel files, perform basic data cleaning, and export the cleaned data.**](https://spinncode.com/designs/H38sM9Wh) #### Lab Summary: Learn to import data from CSV and Excel files into R, perform basic data cleaning, and export the cleaned data. This lab topic covers essential data cleaning operations, including checking for missing values, removing duplicates, and handling inaccuracies, providing practical experience working with real-world datasets and R. ### Week 5: Data Manipulation with dplyr and tidyr #### Topics: - [**Introduction to the `dplyr` package for data manipulation.**](https://spinncode.com/designs/pnX3xqlU): Mastering data analysis with R begins with effective data manipulation, and the dplyr package is a key tool for achieving this. Covered here are dplyr's key features, grammar-based syntax, and essential concepts like tibbles, pipe operators, and verbs. Explore the basics of using dplyr for data manipulation, along with practical examples and additional resources for further learning. - [**Key `dplyr` verbs: `filter()`, `select()`, `mutate()`, `summarize()`, and `group_by()`.**](https://spinncode.com/designs/9DE8Diu5): Master common data manipulation tasks in R using dplyr's key verbs: filter() for subsetting rows, select() for choosing columns, mutate() for adding or modifying columns, summarize() for aggregation, and group_by() for grouping data. Combine these verbs to efficiently perform complex data manipulation tasks. - [**Data reshaping with `tidyr`: Pivoting and unpivoting data using `gather()` and `spread()`.**](https://spinncode.com/designs/1zTZI7oX): Master data manipulation using the tidyr package in R. Learn to transform and reshape data from wide to long formats and vice versa using the gather() and spread() functions. - [**Combining datasets using joins in `dplyr`.**](https://spinncode.com/designs/dtKx7m2x): Combining datasets is a crucial operation in data analysis, enabling you to merge data from different sources. Learn how to use 'dplyr' in R to perform different types of joins such as inner joins, left joins, right joins, and full outer joins, and explore example use cases. #### Lab: - [**Perform complex data manipulation tasks using `dplyr` and reshape data using `tidyr`.**](https://spinncode.com/designs/TI86anFG) #### Lab Summary: Perform complex data manipulation using dplyr verbs and tidyr package. Learn how to chain multiple verbs, join multiple tables, and reshape data from wide to long format and vice versa. Master data manipulation techniques to apply to real-world scenarios. ### Week 6: Statistical Analysis in R #### Topics: - [**Descriptive statistics: Mean, median, mode, variance, and standard deviation.**](https://spinncode.com/designs/EMPNKc3E): Mastering descriptive statistics in R, including measures of central tendency (mean, median, mode) and variability (variance, standard deviation), and how to calculate these using various R functions and packages, such as mean(), median(), getmode(), var(), and sd(). This foundational knowledge helps gain insight into a dataset and informs further analysis. - [**Performing hypothesis testing: t-tests, chi-square tests, and ANOVA.**](https://spinncode.com/designs/epfeRYXk): Master the concepts of hypothesis testing in R, including t-tests, chi-square tests, and ANOVA, to make inferences about a population based on a sample of data. Learn how to perform these tests using the t.test(), chisq.test(), and aov() functions in R, with practical examples and external resources for further learning. - [**Introduction to correlation and regression analysis.**](https://spinncode.com/designs/3RRrwDZ9): Gain a deeper understanding of correlation and regression analysis in R, including how to measure the relationship between variables, perform simple and multiple linear regression, and interpret model results for informed decision-making. Explore practical applications and common pitfalls to consider when working with these statistical techniques. - [**Using R for probability distributions: Normal, binomial, and Poisson distributions.**](https://spinncode.com/designs/4rsat3Qj): Master the use of R for probability distributions, including Normal, Binomial, and Poisson, to calculate probabilities, generate random variables, and visualize distribution curves. Learn key R functions and characteristics for each distribution, with hands-on examples and exercises to reinforce understanding. #### Lab: - [**Perform statistical analysis on a dataset, including hypothesis testing and regression analysis.**](https://spinncode.com/designs/1eF01W1K) #### Lab Summary: Master the basics of statistical analysis in R programming, including hypothesis testing and regression analysis, through hands-on exercises using the mtcars dataset. Learn to perform t-tests, chi-square tests, and ANOVA, as well as linear and multiple regression. Apply statistical concepts to real-world datasets and interpret the results. ### Week 7: Data Visualization with ggplot2 #### Topics: - [**Introduction to the grammar of graphics and the `ggplot2` package.**](https://spinncode.com/designs/O3vLoA4b): Learn how to effectively communicate complex data insights with data visualization using the grammar of graphics and the ggplot2 package in R. Discover the components of the grammar of graphics and the key features of ggplot2, including its layered grammar, consistent syntax, and customizability. - [**Creating basic plots: Scatter plots, bar charts, line charts, and histograms.**](https://spinncode.com/designs/pgBMZfW4): Learn to create basic data visualizations in R using the ggplot2 package. Discover how to build effective scatter plots, bar charts, line charts, and histograms to communicate insights from your data, and explore key concepts and takeaways for each type of plot. - [**Customizing plots: Titles, labels, legends, and themes.**](https://spinncode.com/designs/9eIGVtSb): Customize your ggplot2 visualizations with ease by adding informative titles, labels, legends, and themes to effectively convey data insights. Learn various techniques to enhance plot appearance and readability, and discover best practices for creating high-quality visualizations. - [**Creating advanced visualizations: Faceting, adding annotations, and custom scales.**](https://spinncode.com/designs/xs0YNjRn): Create advanced data visualizations in R with ggplot2 by using faceting to compare relationships between different groups, adding annotations to highlight trends, and creating custom scales to emphasize specific patterns in your data. Learn how to use Facet_wrap(), facet_grid(), and annotate() functions to create informative and complex visualizations. #### Lab: - [**Use `ggplot2` to create and customize a variety of visualizations, including scatter plots and bar charts.**](https://spinncode.com/designs/Pf8xkamO) #### Lab Summary: Learn to create and customize various data visualizations using ggplot2, including scatter plots and bar charts. Discover how to add layers, colors, labels, and themes to enhance the appearance and informativeness of your plots. Master the fundamentals of data visualization in R with this in-depth guide to ggplot2. ### Week 8: Advanced Data Visualization Techniques #### Topics: - [**Creating interactive visualizations with `plotly` and `ggplotly`.**](https://spinncode.com/designs/pjNa4cXW): Master interactive data visualization in R using plotly and ggplotly, and learn how to create web-based interactive plots that can be shared easily. Discover how to convert existing ggplot2 plots to interactive plots and customize them using various options. - [**Time series data visualization in R.**](https://spinncode.com/designs/hItGKBud): Mastering time series data visualization in R using ggplot2, seasonal decomposition, and ggfortify to extract trends and patterns, and creating interactive visualizations with plotly. - [**Using `leaflet` for creating interactive maps.**](https://spinncode.com/designs/npxOBbEl): Learn to create interactive maps with the leaflet package in R, exploring its features, functions, and best practices for adding markers, polygons, and customizing appearances, to effectively visualize geospatial data. Discover how to add interactive features, legends, and pop-ups to enhance user experience. - [**Best practices for designing effective visualizations for reports and presentations.**](https://spinncode.com/designs/ZV4C5q5e): Master effective data visualization techniques to inform, engage, and persuade your audience. Learn best practices such as knowing your audience, keeping it simple, choosing the right visualization, and using color effectively. Improve your R programming skills with hands-on examples using ggplot2 and plotly. #### Lab: - [**Develop interactive visualizations and build a dashboard using `plotly` or `shiny`.**](https://spinncode.com/designs/7AZMBOor) #### Lab Summary: Mastering interactive data visualization in R with `plotly` and `shiny` involves learning how to create engaging, web-based charts and build intuitive dashboards. Key techniques include customizing chart layouts, adding interactive tools, and integrating user inputs to create dynamic, real-time visualizations. ### Week 9: Working with Dates and Times in R #### Topics: - [**Introduction to date and time classes: `Date`, `POSIXct`, and `POSIXlt`.**](https://spinncode.com/designs/b6NCPu5o): Mastering dates and times in R requires understanding the three primary date and time classes: `Date`, `POSIXct`, and `POSIXlt`. Knowing the differences between these classes is essential for performing date and time-related operations in R, such as converting between classes and working with both dates and times. - [**Performing arithmetic operations with dates and times.**](https://spinncode.com/designs/PmH4Geh1): Mastering date and time arithmetic in R is essential for data analysts, and involves performing operations with 'Date', 'POSIXct', and 'difftime' objects using the '+' and '-' operators. Learn how to add, subtract, and calculate differences between dates and times based on the object class. Mastering these skills can simplify working with time-related data in R. - [**Using the `lubridate` package for easier date manipulation.**](https://spinncode.com/designs/BzO96v2s): Master the use of dates and times in R with the lubridate package, a powerful and intuitive tool that simplifies complex date-time manipulation tasks, offering easy-to-use functions, consistent documentation, and improved performance. Learn how to install, load, and utilize key functions in lubridate, and explore practical applications in data cleaning, aggregations, and custom date-time formats. - [**Working with time series data in R.**](https://spinncode.com/designs/5wY10Zno): Working with time series data is crucial in data analysis, especially in finance, economics, and weather forecasting. This tutorial covers creating and manipulating time series objects in R using the `ts()` function, handling missing values with `na.action()` and `na.replace()`, and performing basic analysis with `summary()`, `plot()`, `diff()`, and `acf()`. By mastering these skills, you'll be better equipped to analyze and make predictions from time series data in R. #### Lab: - [**Manipulate and analyze time series data, and perform operations on dates using `lubridate`.**](https://spinncode.com/designs/bJYJtyl8) #### Lab Summary: Discover the power of the `lubridate` package in R for efficient time series data manipulation and analysis. Learn how to extract dates, round down or up to specific intervals, and perform operations on dates using various functions. ### Week 10: Functional Programming in R #### Topics: - [**Introduction to functional programming concepts in R.**](https://spinncode.com/designs/4c0Oc8do): Discover the fundamentals of functional programming in R, and learn how to apply these concepts to data analysis tasks. This overview covers key concepts such as pure functions, immutable data, and higher-order functions, along with examples of how to use them in R. - [**Using higher-order functions: `apply()`, `lapply()`, `sapply()`, and `map()`.**](https://spinncode.com/designs/Njkwjgem): Using Higher-Order Functions in R for Effective Data Analysis: Learn how to apply functions like `apply()`, `lapply()`, `sapply()`, and `map()` to simplify data manipulation, analysis, and visualization in R. - [**Working with pure functions and closures.**](https://spinncode.com/designs/09WX5DiB): Mastering R programming involves understanding functional programming concepts like pure functions and closures. Pure functions offer benefits like easier testing, code modularity, and error handling, while closures promote encapsulation and modularity. Apply these concepts in data analysis and visualization to facilitate code reusability and maintainability. - [**Advanced functional programming with the `purrr` package.**](https://spinncode.com/designs/lodQaY0x): Master advanced functional programming concepts using the purrr package in R, covering mapping, reducing, accumulating, plucking, and invoking. Learn how to work with functions and data structures in R, simplifying your code and making it more readable. #### Lab: - [**Solve data manipulation tasks using `apply` family functions and explore the `purrr` package for advanced use cases.**](https://spinncode.com/designs/e1NE58Da) #### Lab Summary: Explore functional programming in R using the `apply` family of functions and the `purrr` package. Learn how to perform data manipulation tasks, including applying functions to lists, matrices, and data frames, and discover best practices for using these functions. ### Week 11: Building Reports and Dashboards with RMarkdown and Shiny #### Topics: - [**Introduction to RMarkdown for reproducible reports.**](https://spinncode.com/designs/zjmnJu8R): Discover RMarkdown, a powerful tool for creating reproducible reports in R, and master its basics. Learn how to integrate R code, outputs, and narrative text in a single document, and explore key benefits including reproducibility, consistency, and flexibility. Get hands-on guidance on creating dynamic documents with RMarkdown. - [**Integrating R code and outputs in documents.**](https://spinncode.com/designs/9eWfwzCl): Master R programming by integrating R code and outputs into documents with RMarkdown, enabling reproducibility, transparency, and automation. Learn the basics of RMarkdown, including creating R code chunks and specifying options for displaying results. Apply practical takeaways to create professional reports and dashboards, including tables and plots using the knitr package. - [**Introduction to `Shiny` for building interactive dashboards.**](https://spinncode.com/designs/YBrxjndO): Learn how to use Shiny to create interactive dashboards and web applications directly from R. This tutorial covers the basics of Shiny, including key concepts such as UI, server, and reactive, and guides you in building your first interactive app. - [**Deploying Shiny apps and RMarkdown documents.**](https://spinncode.com/designs/QmWOfTDU): Deploy and share your data analysis and visualization projects by learning the various methods for deploying Shiny apps and RMarkdown documents. This includes using Shiny Server, RStudio Connect, Shiny Apps.io, Docker, RPubs, GitHub Pages, and more. #### Lab: - [**Create a reproducible report using RMarkdown and build a basic dashboard with `Shiny`.**](https://spinncode.com/designs/1Ra1bxug) #### Lab Summary: Create interactive and dynamic reports using RMarkdown and build web-based dashboards with Shiny, while learning how to deploy them to various platforms for data analysis and visualization, and discover how to integrate R code and outputs into documents for reproducible results. ### Week 12: Introduction to Machine Learning with R #### Topics: - [**Overview of machine learning in R using the `caret` and `mlr3` packages.**](https://spinncode.com/designs/0TKU5KBX): Master machine learning in R using the caret and mlr3 packages. Discover the basics of machine learning and learn how to create and evaluate predictive models in R, covering both supervised and unsupervised learning tasks. - [**Supervised learning: Linear regression, decision trees, and random forests.**](https://spinncode.com/designs/JXabvzbU): Mastering supervised learning in R for data analysis involves understanding linear regression, decision trees, and random forests. Learn the strengths and weaknesses of each algorithm and how to implement them in R for accurate predictions. - [**Unsupervised learning: K-means clustering, PCA.**](https://spinncode.com/designs/DPa3WX6L): Learn how to apply unsupervised learning techniques in R, including K-means clustering to group similar data points into clusters and Principal Component Analysis (PCA) to reduce dataset dimensionality. Discover how to implement these techniques using R's built-in functions and explore example use cases with the iris and mtcars datasets. - [**Model evaluation techniques: Cross-validation and performance metrics.**](https://spinncode.com/designs/EUvfNuDY): Mastering machine learning model evaluation techniques is crucial for ensuring your models perform well on unseen data. This guide covers key concepts, including cross-validation and performance metrics such as mean squared error, R-squared, and accuracy, along with practical examples in R using the caret package. #### Lab: - [**Implement a simple machine learning model using `caret` or `mlr3` and evaluate its performance.**](https://spinncode.com/designs/IlHRqNTn) #### Lab Summary: Learn how to implement simple machine learning models using R packages 'caret' and 'mlr3', and evaluate their performance using metrics like accuracy and mean absolute error. This guide walks you through the process of building and assessing a model using the classic 'iris' dataset, providing a solid foundation for more complex machine learning projects. ### Week 13: Big Data and Parallel Computing in R #### Topics: - [**Introduction to handling large datasets in R using `data.table` and `dplyr`.**](https://spinncode.com/designs/Uvfew0O7): Mastering R Programming for large datasets is easier with the right tools. This topic introduces two popular packages, `data.table` and `dplyr`, which provide efficient data manipulation capabilities for handling big data. Learn about their key features, use hands-on examples to practice, and compare the strengths and weaknesses of each package to choose the best tool for your needs. - [**Working with databases and SQL queries in R.**](https://spinncode.com/designs/R6ak5ukM): Working with big data in R requires understanding how to interact with databases and write SQL queries. This topic covers the basics of database management systems, connecting to databases in R using ODBC drivers, and performing common SQL queries like SELECT, INSERT, UPDATE, and DELETE. By following best practices and using the right tools, you can manage and analyze large data sets efficiently and effectively in R. - [**Parallel computing in R: Using `parallel` and `foreach` packages.**](https://spinncode.com/designs/RHAhbDL9): Master parallel computing in R using the `parallel` and `foreach` packages to speed up computationally intensive tasks by distributing the workload across multiple CPU cores or machines. Learn key functions, best practices, and how to apply this knowledge to large datasets and complex tasks. - [**Introduction to distributed computing with `sparklyr` and Apache Spark.**](https://spinncode.com/designs/30FYrs7X): Discover how to process large datasets with efficiency using distributed computing. Learn the key concepts, benefits, and applications of distributed computing with sparklyr and Apache Spark, and how to perform data manipulation operations such as filtering, grouping, and sorting. Unlock the power of big data analytics and machine learning with this comprehensive guide to distributed computing in R. #### Lab: - [**Perform data analysis on large datasets using `data.table`, and implement parallel processing using `foreach`.**](https://spinncode.com/designs/kH2scrnF) #### Lab Summary: Efficiently analyze large datasets in R using the `data.table` package and implement parallel processing with `foreach`, significantly speeding up data analysis tasks on big data. ### Week 14: Debugging, Testing, and Profiling R Code #### Topics: - [**Debugging techniques in R: Using `browser()`, `traceback()`, and `debug()`.**](https://spinncode.com/designs/h4w3G03D): Master essential debugging techniques in R, including using `browser()` to step through code line by line, `traceback()` to identify the sequence of functions leading to errors, and `debug()` to enable debugging for specific functions. Learn how to apply these techniques to diagnose and resolve issues in your code more efficiently. - [**Unit testing in R using `testthat`.**](https://spinncode.com/designs/KJJ9pwBj): **Mastering R Programming: Learn how to write unit tests in R using the `testthat` package to ensure your code works as expected, catch bugs early, and improve overall code quality. Discover key functions, best practices, and how to integrate `testthat` with RStudio.** - [**Profiling code performance with `Rprof` and `microbenchmark`.**](https://spinncode.com/designs/cX9a2dBu): Mastering code performance is crucial for efficient R programming. Learn how to use built-in R tool `Rprof` and the `microbenchmark` package to profile and benchmark your code, identifying bottlenecks and areas for improvement to optimize your R code. - [**Writing efficient R code and avoiding common performance pitfalls.**](https://spinncode.com/designs/QmJ5LR11): Master the art of writing efficient R code by avoiding common performance pitfalls and optimizing your programs. Learn how to identify and fix bottlenecks, minimize memory allocation, and use vectorized operations and efficient data structures. By following best practices and practical tips, you can significantly improve the speed and efficiency of your R code. #### Lab: - [**Write unit tests for R functions using `testthat`, and profile code performance to optimize efficiency.**](https://spinncode.com/designs/wHTOWevo) #### Lab Summary: Mastering R Programming: Learn how to write unit tests for R functions using `testthat` and optimize code performance with R's built-in profiling tools to ensure reliability and efficiency in your data analysis. By applying these techniques, you can catch bugs early and streamline your code to run faster. This tutorial provides step-by-step guidance on writing tests, profiling code, and optimizing performance, along with practical examples and best practices. ### Week 15: Version Control and Project Management in R #### Topics: - [**Introduction to project organization in R using `renv` and `usethis`.**](https://spinncode.com/designs/3gCYkpkM): Master a well-structured approach to R project development with renv and usethis, ensuring reproducibility, collaboration, and efficiency. Learn how to isolate project dependencies, manage libraries, and create a consistent workflow. Discover best practices for using these powerful R packages to streamline your project's organization. - [**Using Git for version control in RStudio.**](https://spinncode.com/designs/GdbXc894): Learn how to use Git for version control in RStudio to track changes to your R code, collaborate with others, and maintain a record of your project's history. Discover the benefits of using Git, including version control, collaboration, and backup, and follow step-by-step instructions for setting up and using Git in RStudio. Master basic Git commands and best practices to take your R programming skills to the next level. - [**Managing R dependencies with `packrat` and `renv`.**](https://spinncode.com/designs/m2YfhNy0): Learn how to manage R dependencies using `packrat` and `renv`, two popular tools that help maintain a stable and reproducible project environment. Discover key features of each tool, including self-contained environments and automatic package version management. Choose the best tool for your project needs, whether working solo or collaboratively. - [**Best practices for collaborative development and sharing R projects.**](https://spinncode.com/designs/8gyFhk1W): Mastering collaborative R development with best practices in version control, project organization, and dependency management to improve project maintainability and sharability. Learn how to use Git, RStudio Projects, and `renv` or `packrat` to streamline your workflow and share your projects effectively. Effective collaboration is crucial for project success. #### Lab: - [**Set up version control for an R project using Git, and manage dependencies with `renv`.**](https://spinncode.com/designs/cLldj8vQ) #### Lab Summary: Master version control in R using Git and manage project dependencies with `renv`. Learn how to set up a Git repository, create a `.gitignore` file, and use `renv` to restore and manage dependencies. Follow best practices for collaborative development with meaningful commit messages, feature branches, and version control hosts. ## Final Project - **Description:** Develop a data analysis project or a Shiny application that demonstrates the integration of multiple R programming concepts (e.g., data manipulation, visualization, machine learning, and reporting). The project should showcase best practices in R coding and data analysis workflows. - **Presentation:** Students will present their final projects, demonstrating the data analysis process, results, and decision-making insights. ## Grading Breakdown - **Assignments&Labs:** 40% - **MidtermProject:** 20% - **FinalProject:** 30% - **Participation&Quizzes:** 10%