Install R 4.3+ and RStudio, use the console and script editor, perform arithmetic and string operations, understand R's basic data types, and run your first complete R script.

Create and subset vectors, matrices, and lists; build data frames from scratch and from named vectors; use $ and [] subsetting; understand recycling and type coercion.

Use if/else/ifelse(), for and while loops, write named R functions with default parameters and return values, understand R's functional idioms (lapply, sapply, Map), and avoid loops when vectorization works.

Use filter(), select(), mutate(), arrange(), group_by() + summarise(), and the pipe operator to write readable data transformation pipelines.

Build scatter, histogram, bar, box, and line plots with ggplot2; customize labels, colors, and themes; use facet_wrap() for multi-panel views; and save charts to files.

Compute comprehensive descriptive statistics, work with probability distributions (normal, binomial), perform t-tests, chi-square, and correlation analysis, and interpret p-values and confidence intervals correctly.

Import CSV, JSON, and Excel files; export data in multiple formats; handle encoding and missing values during import; use read_csv vs read.csv correctly; and work with the readr and jsonlite packages.

Execute a complete EDA from raw data to report: handle missing values, detect outliers, compute grouped statistics, build a multi-panel visualization, run a hypothesis test, and output reproducible findings.