• Install R on Windows and macOS operating systems.
• Install RStudio on Windows and macOS operating systems.
• Install Rtools for Windows systems.

• Create and organize files for project data and R scripts
• Set the working directory in RStudio to manage and conduct data analysis projects effectively.

• Understand how to install, update, and load external R packages (install.packages() and library()).
• Leverage the tidyverse suite of packages (such as dplyr and ggplot2) to streamline data manipulation and visual data exploration.
• Introduction to the gtsummary package to generate publication-ready summary statistics and analytical tables easily.

• Manually create and enter data (vectors, and data frames) using native R syntax.
• Understand fundamental data structures (vectors, and data frames).
• Import data sets from various file formats (such as Excel, and SPSS files) into RStudio.

• Identify and use basic R data types (numeric dbl, character chr, and factor fct).
• Understand core R syntax rules, naming conventions, and code formatting.
• Select, rename, labelling (Gender:0=Male, 1=Female) and create new variables within a dataset: BMI variable creation example.
• Write clear code comments and call built-in R functions effectively.

• Summarize continuous datasets using measures of central tendency (mean, median) and dispersion (variance, standard deviation, interquartile range).
• Compute basic summary statistics grouped by categorical factors (e.g., summary statistics by gender).
• Utilize Base R summary functions (summary(), str(), quantile(), sd(), mean()) to inspect datasets effectively.

• Construct primary data visualizations (scatter plots, histograms, and bar charts) for a sample of 50 patients using Base R functions.
• Build publication-ready graphics using the ggplot2 package syntax.
• Customize titles, labels, colors, and visual themes to communicate data insights effectively.

• Create publication-ready summary tables for descriptive statistics using tbl_summary().
• Integrate inferential statistics (Chi-square tests, t-tests) into summary tables using add_p().
• Display multivariable regression model outcomes (Linear and Logistic Regression) cleanly using tbl_regression().
• Customize labels, statistics, and formatting for academic manuscript export.

• Leverage Gemini AI to streamline and assist your R coding workflow.
• Formulate effective natural language prompts to generate R scripts, particularly using tidyverse and ggplot2 syntax.
• Utilize AI assistance to rapidly debug code, troubleshoot console errors, and optimize existing data analysis pipelines.
• Interpret complex statistical outputs and error messages by consulting Gemini for clear, step-by-step explanations.