Learn Data Science & Machine Learning with R from A-Z

Install and set up R and RStudio, understand their open-source data science capabilities, and access CRAN for packages as you begin end-to-end data analysis.

Explore the R basics in this studio introduction, interact with the console, perform calculations, save variables, and access help to begin data science and machine learning with R.

Learn to manage files in R Studio by creating and saving R scripts (.R), using notebooks with markdown and chunks, and organizing work into projects and folders.

Explore the RStudio interface, its panes—files, plots, packages, help, viewer—and the console and terminal, and learn to swap layouts for efficient coding.

Explore the tidyverse ecosystem of R packages that share a common philosophy and grammar for data manipulation, visualization, and data structures, guided by Hadley Wickham.

Explore two essential resources for data science and machine learning with R: the free online 'Are for data science' and 'An introduction to statistical learning,' ideal for beginners.

Discover core data types in R: character, numeric, integer, and logical—and how atomic vectors, lists, matrices, data frames, and tibbles structure and manipulate data, including missing values and coercion.

Explore basic data types in R, including characters, numeric, integer, and logical, using the console and the class function to understand type and coercion.

Learn how vectors in R are homogeneous collections, created with vector or c(), and examined with length, class, and str while exploring logical, character, numeric, and integer types.

Learn the practical use of vectors in R by creating ranges with the colon operator and the sequence function, perform element-wise operations, and practice indexing and length-based operations.

Explore missing values in R, using NA, across numeric, logical, and character vectors; learn to detect them with is.na and any, and consult RStudio documentation for practical handling.

Learn how coercion converts values between types in vectors, both implicitly by context and explicitly with as.character, as.numeric, and as.logical, causing mixed types to become a single type.

Learn how to name vector elements in R using the names function, create named vectors like x with names a, b, c, and use the letters vector to index data.

Extend existing vectors by concatenating them with other vectors to form longer sequences. Learn to perform vector-based operations, such as mean, sum, min, and max, including handling missing values.

Learn how to create and manipulate matrices in R using matrix, cbind, and rbind, control filling by rows or columns, and set row and column names.

Explore how lists in R store multiple data types, including nested lists, and learn to create, name, access list elements with [ and [[, and apply family functions to them.

Discover data frames as a core data structure in R, with columns as vectors, uniform length, and named attributes; explore iris and other built-in datasets and viewing tools in RStudio.

Create data frames in R by defining columns and vectors, reference columns and rows, and compare datasets like iris and empty cars to prepare for data manipulation and EDA.

Learn to work with data frames using helper functions—head, tail, dim, str, and names—to inspect structure, dimensions, and column names, and apply the apply family for column-wise analysis.

Learn how tibbles extend data frames in R's tidyverse, enabling safer printing, flexible column naming, and seamless creation with as_tibble and tribble for data science.

Explore intermediate R concepts, from data types to functional programming with tidyverse, and master packages, CRAN, and documentation for reproducible data science.

Learn how relational operators test numeric vectors in R, performing element-wise comparisons for greater than, less than, and equal to, and use sum or any to detect true values.

Explore how logical operators and, or, and not work in R by testing all true/false combinations, applying them to vectors, and inverting results for clear boolean logic.

Explore conditional statements using if else, relational operators, and logical operators to control code flow, compare values, and print outcomes.

Demonstrate how a for loop iterates over vector V of first four capital letters, assigns each to I, and prints it, contrasting with lapply and unlist to create a vector.

Explore built-in and user-defined functions in R, learn how to set default arguments, pass inputs, and package reusable code for data science tasks.

Install and manage R packages from CRAN with install.packages, load them into scripts, and install multiple packages with a vector. Use help and vignettes for deeper understanding.

Learn practical techniques for working with factors in R, converting string vectors to factors, defining and ordering levels, and using forcats to order by frequency.

Learn to manage dates and times in R with the package that simplifies date types, time zones, today and now, and creating dates from strings or numbers.

Explore functional programming with lapply and apply in R to apply a function to each element, replace loops, return lists, and flatten results with unlist.

Master data import and export in R using the Reidar package, with read_csfi and write_csfi to handle csv-like tabular data and export to Excel or web sources.

Explore how to connect to relational databases in R using the dbi package, create in-memory databases, list tables and fields, read and write data, run queries, and manage connections.

Explore data manipulation in R with tidy data, the pipe operator, and core verbs from dplyr and tidyr, enabling practical wrangling of real data with tidyverse tools.

Learn to recognize tidy data and distinguish it from messy formats, and convert datasets into a single-variable-per-column, single-observation-per-row, single-value-per-cell long format for easy manipulation.

Discover the pipe operator in R by loading the Margaretha package, then chain functions like mean, floor, and paste for readable, step-by-step data transformations.

Learn how to use dplyr's filter verb to select rows in tidy data, using a pipe workflow with the starwars dataset to practice conditional filtering by eye color.

Explore the select verb in the player package to choose, reorder, and remove columns using index ranges, names, or helper functions like everything, ends with, starts with, contains, and where.

Learn how the mutate verb in dplyr creates or updates columns, using BMI from mass and height and across to convert homeworld and species to factors.

Learn to use the arrange verb to reorder tidy data by height, in ascending or descending order, with unknowns at the bottom and rows rearranged together.

Explore the summarize verb in dplyr to compute means and counts, including per group summaries by cylinders and using across to apply functions across columns.

Transform wide data into tidy long formats with tidyr's pivot longer and pivot wider, aligning one variable per column and one observation per row for analysis.

Learn string manipulation in R with the stringr package, applying str_length, str_sub, str_detect, str_split, str_trim, str_squish, and str_pad to clean, transform, and analyze text data.

Learn to scrape real web data with rvest, extract IMDB top 250 titles, years, and ratings, and organize them into a tidy data frame for analysis.

Explore parsing JSON data in R using the Jason Light library, converting data frames, lists, and vectors to and from JSON, with options like simplify vector and prettify.

Explore data visualization in r using ggplot2 to map data to visuals, defining aesthetics, geoms, and layers, and choosing appropriate plots to reveal insights.

Learn to visualize data in R by creating your first ggplot scatterplot, mapping displacement to x and highway to y using the NPG data set, and layering aesthetics and geometry.

Explore building and customizing ggplot2 scatter plots in R, mapping displacement to x and highway mpg to y, while using color, size, alpha, and shape aesthetics to show class patterns.

Explore univariate plots in R using histograms, density, frequency polygons, and bar graphs to reveal a single variable's distribution.

Explore two-variable plots to visualize relationships between x and y variables, including scatterplots, box plots, violin plots, and density plots, with smoothing and regression for trend insight.

Layer and overlay plots in R using plus signs, add density and rug plots, and facet by class. Explore coord systems, including coord flip, polar coordinates, and map coordinates.

Learn to style and save data visualizations in R by applying themes, labeling axes and titles, and exporting plots as pdf, png, or jpeg.

Learn to generate dynamic client reports with R Markdown, using code chunks and inline results to produce HTML, PDF, or Word outputs, illustrated by iris data visualizations.

Learn how R Shiny is a complete web framework in R that lets data scientists build interactive, production-ready web apps and dashboards directly from R, without web technologies.

Create a basic R Shiny app with a responsive UI and server. Link a slider input to a renderPlot to generate a histogram from the faithful data.

Explore practical R Shiny examples using the iris dataset, building a reactive web app with select inputs, dynamic plots and tables, and learning iterative, lightweight UI design.

Discover how data collection, cleaning, and pipelines power data science, and how machine learning learns from training data to predict, recognize patterns, and drive applications.

Identify the three main machine learning categories: supervised learning, unsupervised learning, and reinforcement learning. Explain supervised uses labeled data, unsupervised uses unlabeled data, and note reinforcement is not the focus.

Develop machine learning workflows by mastering data preprocessing, including handling missing values, encoding categorical data, scaling features, and creating training and testing splits; explore recipes and tidy models in R.

Load r packages, inspect data with skim, and perform preprocessing steps such as feature engineering, missing value imputation, encoding, and scaling to build a reproducible recipe-based pipeline.