Introduction to Biostatistics

• Biostatistics defined: Explain that biostatistics is the application of statistical methods to biological and medical data.
• Importance of biostatistics: Discuss how biostatistics plays a crucial role in generating insights, making evidence-based decisions, and advancing research in the life sciences field.
• Applications of biostatistics: Highlight various examples of biostatistical analyses in healthcare, epidemiology, genetics, and clinical trials.

Introduction to R

• R programming language: Introduce R as a popular statistical programming language widely used in biostatistics and data analysis.
• Advantages of R: Discuss the benefits of using R, such as its open-source nature, extensive statistical libraries, and active user community.
• Comparison with other software: Mention other statistical software packages like SPSS and SAS, but emphasize the flexibility and customization options offered by R.

Installing R and RStudio

• R installation: Provide step-by-step instructions on how to download and install R on different operating systems (Windows, macOS, Linux).
• RStudio installation: Explain the importance of using an integrated development environment (IDE) and guide students on installing RStudio, which enhances the programming experience.
• Verifying the installation: Show how to open RStudio and verify the successful installation.

Basic R Syntax and Data Structures

• R as a calculator: Demonstrate how R can be used as a calculator for basic arithmetic operations and mathematical functions.
• Variables in R: Introduce the concept of variables and how to assign values to variables in R. Explain the importance of choosing meaningful variable names.
• Data types: Explain the different data types in R, such as numeric, character, logical, and factors.
• Data structures: Introduce vectors, matrices, data frames, and lists as fundamental data structures in R.

Data Import and Cleaning in R

• Data import: Discuss the importance of data import and demonstrate how to load data from different file formats, such as CSV, Excel, and text files, using R functions like read.csv() and read.table().
• Exploring data: Introduce functions like head() and summary() to explore the loaded dataset and gain insights into its structure and summary statistics.
• Handling missing values: Explain the significance of missing data and discuss methods for identifying and dealing with missing values in the dataset using R.

Introduction to Data Visualization in R

• Principles of effective data visualization: Emphasize the importance of visualizing data to understand patterns, relationships, and distributions.
• Basic plotting functions: Introduce common plotting functions in R, such as plot(), hist(), and boxplot(), to create basic visualizations.
• ggplot2 package: Briefly mention the ggplot2 package, a powerful tool for creating more advanced and customizable visualizations in R.