R Programming for Data Science: From Basics to Advanced

Explore basic arithmetic in R, including addition, division, exponentiation with the power operator, and sqrt; build and manipulate vectors with c(), and use seq, rep, and indexing.

Explore vectorized operations in R, from squaring and adding vectors to applying functions and filtering data with recycling rules, Ifelse, and sapply to boost performance and readability.

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 advanced operations with matrices and arrays in R, including 2x2 matrix creation, multiplication with percent and asterisk, transposition, eigen decomposition for PCA, and 3d arrays with apply.

Master logical operators in R to evaluate conditions, filter scores, and subset data frames with and, or, not, any, and all through practical examples.

Explore data manipulation with factors in R, converting character vectors to factors, inspecting levels, creating frequency tables and bar plots, and using factors in linear models.

Create lists and data frames in R by combining numbers, characters, a matrix, and logicals, and access elements with brackets or the dollar sign to filter adults aged over twenty.

Learn to create, access, modify, and analyze data frames in R, including filtering, sorting, summarizing, and exporting to CSV for data science workflows.

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.

Define and check object classes in R with the class function; demonstrate coercion, including implicit via concatenation and explicit via as.integer and as.character, across vectors, lists, and data frames.

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

Explore how R interprets function calls through argument matching rules, including positional and named arguments, defaults, ellipsis, and masking, with examples from sample vector, lm analysis, and calculate area.

Explore control flow mechanisms in R, including if-else, loops, break, next, and switch, to write dynamic, condition-driven code and efficient repetitive tasks.

Master advanced looping techniques in R—nested loops, vectorization, and the apply family—computing matrix products across mat one and mat two lists with lapply, outer, and map from the Pas package.

Define and reuse functions in R by using the assignment operator, arguments, default values, and return statements to compute areas, statistics, plots, and Fibonacci sequences.

Discover how specialized functions extend R's capabilities, using default arguments, multi-value returns, and ellipses for flexibility to automate data normalization, visualization, and Monte Carlo simulations.

Explore exception handling in R by distinguishing warnings from errors, using try with silent and suppress warnings to build robust Fibonacci indexing over vectors, keeping programs running gracefully.

Measure execution time in R with systemtime and proc time, compute a million-sum and the first 30 Fibonacci numbers, and track progress with a progress bar.

Learn to read and write files in R with read.table and read.csv, inspect data using head and glimpse, save plots as PNG, and manage the working directory.

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.

Explore ggplot2 in R to create scatter plots of horsepower versus miles per gallon, color and facet by cylinder, add smoothing, and export or make interactive plots with Plotly.

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.

Explore data visualization techniques in R, from bar plots and histograms to scatter plots, box plots, and pie charts, using the Mtcars dataset and ggplot2 to reveal patterns and insights.

Master advanced scatterplot analysis in R, visualizing relationships with regression lines, correlations, and scatterplot matrices, and exploring interactive visuals with ggplot2 and plotly.

Explore advanced scatter plot customization in R, from base plots with custom titles, axis labels, limits, colors, and grid to ggplot2 visuals with gradients, legends, and interactivity via Plotly.

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.

Master colors and plots in R by generating hex colors from RGB, building a custom palette, and creating 2D/3D plots and contours with iris and mtcars.

Explore statistics and data analysis with R, transforming raw data into insights through data frames, histograms, confidence intervals, and t tests, including central limit theorem, mean, median, and standard deviation.

Master measures of centrality and variation in R, including mean, median, mode, range, variance, and standard deviation, and visualize two sets of test scores with a ggplot2 boxplot.

Explore probability concepts from definitions to conditional probability, intersections and unions, and compute mean and variance with illustrative R code and visualizations.

Explore random variables and their discrete and continuous distributions, using R to define outcomes, visualize probability distributions, and simulate binomial, Poisson, and normal models.

Explore common probability distributions—binomial, Poisson, normal, geometric, and exponential—through practical R code that computes probabilities, simulates scenarios, and visualizes results for data driven decisions.

Master advanced probabilistic techniques in R, from manipulating normal, binomial, Poisson, geometric, and exponential distributions to computing probabilities with CDFs, bootstrapping for confidence intervals, and illustrating the central limit theorem.

Explore sampling distributions and confidence intervals with R. Simulate 1000 samples of size 30 from a normal population, analyze sample means, and illustrate standard error and 95% confidence intervals.

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.

Formulate null and alternative hypotheses, test means with a two-tailed t test, and interpret p values, confidence intervals, normality assumptions, and power analysis to determine sample size.

Apply one-way anova to compare mean weight loss across three diet groups in R, visualize with ggplot2, perform tukey hsd, and check assumptions with levene's test and qq plots.

Explore how two-way anova analyzes two factors and their interaction to influence a dependent variable, with r code for data generation, visualizing with ggplot2, and performing post-hoc and assumption checks.

Explore simple linear regression by fitting an lm model in R to relate hand span (explanatory variable) to height (response variable), and interpret intercept and slope, R-squared, and prediction intervals.

Explore categorical predictors in regression using one-hot encoding and dummy variables; compare managers and interns to engineers with lm, and interpret coefficients, R-squared, and years of experience, plus ggplot2 visuals.

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.

Explore high order interactions in regression, including two-way and three-way effects, using a nuclear dataset to model costs, visualize results, and interpret complex relationships.

Explore linear regression fundamentals in r by simulating data, fitting an lm model, and using residuals and qq plot diagnostics. Assess normality, homoscedasticity, and examine cook's distance and vifs.

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.