Learn Data Science & Biostatistics with R and RStudio

In this lecture, we will start with simple hands-on examples in R. The goal is to help you feel comfortable and build confidence before moving to detailed concepts.

In this lecture, you will learn how to import Excel data into R easily. We will practice step by step so you can start working with real datasets.

In this lecture, you will learn how to create basic graphs in R. This will help you visualize your data and understand patterns easily.

In this lecture, we will create simple summary tables to understand the data. You will learn how to quickly explore variables and basic statistics.

Understand how to use arithmetic operators for calculations and logical operators for comparisons in R. These are the building blocks for writing and testing your first R code.

Learn about the basic data structures in R, including vectors, matrices, lists, and data frames. Understanding these will give you a strong foundation for data analysis.

Find out where to access the essential R scripts and datasets used in this course. These files will help you follow along with examples and practice on your own.

R is continuously improving through regular updates. As a result, some code in the course may occasionally show errors when you run it on a newer version of R. I actively maintain and update the code to match the latest R versions. If you experience any issues, please send me a message and I will help you as soon as possible.

Learn how to import data from Excel, Stata, SPSS, and CSV files into RStudio. This video will help you get your datasets ready for analysis quickly and efficiently.

Learn how to connect OneDrive with your data analysis tools and easily import files directly from your OneDrive folder. This lesson covers setup, permissions, and best practices for seamless data access.

Learn how to import data directly from your My Documents folder without writing the full file path. This lesson shows quick and efficient methods to access your data easily and save time.

Learn how to load a new dataset into RStudio and explore its structure using the codebook. This helps you understand variables, values, and prepare for analysis.

Learn how to manage your dataset by selecting specific variables, keeping the ones you need, and dropping unnecessary ones. This helps organize your data for analysis.

Learn how to assign and manage value labels for your variables in R. This makes your data easier to understand and interpret during analysis.

Labeling variables is an essential step in preparing datasets for data science, statistics, and public health research. Clear and meaningful variable labels make your data easier to understand, share, and publish. In this course, you will learn how to use the apply_labels() function in R to assign descriptive labels to your dataset variables.

Learn how to use the pipe (%>%) function in R to write cleaner, more readable code. This helps you chain multiple commands together efficiently.

Learn how to convert variables into numeric format in R. This is essential for performing calculations and statistical analysis correctly.

Learn how to keep only the variables you need and drop the ones you don’t. This helps streamline your dataset and makes analysis more efficient.

Learn how to reorder variables in your dataset. Organizing the sequence of variables makes your data easier to view, manage, and analyze.

Discover how to efficiently reorder variables when working with datasets that have a large number of variables. This makes navigation and analysis much smoother.

Learn how to keep specific values or cases and drop the ones you don’t need. This skill is essential for cleaning your dataset and focusing only on the relevant observations.

Learn how to sort your dataset in both ascending and descending order. Sorting makes it easier to explore data patterns and organize your results.

Learn how to rename variables in R to make your dataset clearer and easier to work with. Clear variable names improve readability and understanding during analysis.

Learn how to simplify your workflow by combining multiple tasks—keeping, ordering, filtering, and renaming variables—into a single command using the pipe operator in R.

Learn how to explore and check the structure of your dataset in R. Understanding data types and formats is the first step in proper data validation and cleaning.

Learn how to detect missing values in your dataset. Identifying missing data is the first step toward cleaning and preparing your data for analysis.

Learn how to check the range of your variables and create subsets of your dataset. These techniques help ensure your data is clean and ready for analysis.

Learn how to identify and manage outliers in your dataset. Handling outliers properly helps maintain data accuracy and improves the quality of your analysis.

Learn how to create new variables in R using existing data. Generating variables helps you customize your dataset for deeper analysis and insights.

Learn how to transform existing categorical variables into new categories. This is useful for merging groups, simplifying data, and preparing datasets for analysis.

Learn how to convert numeric variables into categorical variables in R. This is useful for grouping data, creating meaningful categories, and preparing for analysis.

Learn how to apply a logarithmic transformation to your variables in R. Log transformation helps normalize skewed data and improves the accuracy of analysis.

Learn how to create dummy (indicator) variables from categorical data in R. Dummy variables are essential for regression and other statistical analyses.

Review how to perform multiple variable transformations—such as generating, recoding, and creating dummy variables—using the pipe (%>%) operator. Learn to streamline your workflow with a single, readable command.

By the end of this practice session, students will be able to import datasets, assign value labels to variables, and perform basic variable transformations in R. This exercise reinforces hands-on skills for managing and preparing data efficiently.

In this video, you will learn how to save your cleaned and managed data in R so you don’t have to redo your work every time. We cover saving multiple objects with RData.

By the end of this video, you’ll know how to save and load your data efficiently, making your workflow in RStudio faster and easier. Perfect for beginners and anyone working with data analysis in R.

In this video, you will learn how to create a quick and professional summary of your cleaned data using the gtsummary package in R. We cover how to summarize numeric and categorical variables, calculate means, medians, and percentages, and generate clear tables ready for reports or presentations.

This method is perfect for checking your data quickly and presenting results in a professional format. Save time and make your data analysis workflow in RStudio more efficient with gtsummary.

Learn how to create histograms in R using ggplot2 to visualize the distribution of your data clearly and effectively.

Learn how to overlay a normal distribution curve on your histogram using ggplot2. This helps compare your data distribution with the normal distribution.

Learn how to save your ggplot2 histograms as JPEG files in a folder. This helps you keep and share your visualizations easily.

The boxplot is one of the most useful tools for visualizing the distribution of a continuous variable. It helps you quickly understand the spread, median, quartiles, and outliers in your dataset.

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Learn how to add titles, axis labels, and annotations to your boxplots in R using ggplot2. Proper labeling makes your plots clear and professional.

Learn how to change the background, colors, and themes of your boxplots in R using ggplot2. Customizing visuals makes your plots more appealing and easier to interpret.

Learn how to create boxplots that compare a continuous variable across different categories using ggplot2. This helps visualize differences and distributions between groups.

Learn how to create boxplots that display a continuous variable across two categorical variables using ggplot2. This allows you to analyze interactions and group comparisons effectively.

Learn how to enhance your boxplots by applying nature-inspired color palettes in ggplot2. Attractive colors improve visualization and interpretation of your data.

Learn how to save your ggplot2 boxplots directly into a specific folder on your computer. This makes organizing and sharing your plots easy and efficient.