Master basic operators in R, including variables and negative numbers, exponentiation, rounding, and common functions such as trig, inverse trig, logs, and factorial.

Master vectors as objects in R by creating numeric, character, and boolean vectors with c or ranges, inspect with class, subset using square brackets and 1:3, and perform elementwise arithmetic.

Explore matrices and matrix operations in R by binding data with cbind and rbind, assigning column and row names with paste, and merging data sets while handling missing data.

Develop scatter plots in R with the Cars 93 data set, add regression lines and correlation information, and use pairs and order to compare variables by mpg city.

Learn to handle missing data in R by loading data with read.table, detecting and replacing missing values, and using na.omit and complete.cases for valid analyses.

Learn to build and interpret linear models in R using lm and glm, inspect residuals and coefficients, assess R-squared and p-values, and visualize with multi-plot diagnostics and optional intercept adjustments.

Learn to debug functions in R by flagging and stepping through code with debug and browser tools, including examining a mean function and tracing errors in loops and conditionals.