R Programming for Data Science: From Basics to Advanced

Explore matrices and arrays in R, from creating 2x3 and 3x2 matrices to 4x3x2 arrays, with indexing, dimension checks, and elementwise and matrix multiplication.

Explore special values in R—na, na.rm, NaN, Inf, -Inf, and NULL—and learn how they propagate in calculations and how empty strings differ from whitespace.

Explore how environments organize objects, variables, and functions in an R session, including global environments and package contexts, and how scoping rules govern accessibility.

Access built-in datasets with the data command and load iris for analysis. Visualize relationships such as horsepower vs mpg and ozone over months with ggplot2.

Learn the basics of plotting in R, creating basic scatter plots, customizing colors and point shapes, and using ggplot2 and plotly for interactive visualizations.

Generate two 100-observation samples with the Onom function to form x and y, then create scatterplots with base plot and ggplot2, adding a regression line and minimal theme.

Master the grammar of graphics in ggplot2 to build layered, customizable visualizations from scatterplots and bar plots to annotated, faceted charts using viridis color scales.

Explore constructing and interpreting confidence intervals for means and proportions, using standard error, z and t scores, and visualize results with ggplot2 for clear uncertainty in data.

Learn the components of hypothesis testing, including null and alternative hypotheses, z statistics, p-values, alpha, and one-tailed tests, and how to compute confidence intervals and perform power analyses.

Compare multiple regression in R, building on simple regression to predict an outcome with multiple predictors, interpreting coefficients and residuals, and assessing model validity with VIF, interaction terms, and ANOVA.

Assess linear regression validity in R by applying diagnostic methods, including residuals vs fitted plots, time series residuals, scale-location plots, QQ plots, and metrics like Cook's distance and VIF.

Choose a dataset such as US cereal data, Diamond Price Data, or Old Faithful Geyser data, then clean, explore, analyze, and visualize with ggplot2 and an interactive R Shiny app.