R Programming: R for Data Science and Data Analytics A-Z™

Learn the basic syntax of R, including variables, operators, functions, comments, and libraries, with examples of adding two numbers and reading user input via readLine and print.

Learn how to define variables in R, follow naming rules, avoid reserved words, and work with constants—numeric, integer, complex, and character—using type of function to identify types.

Explore the core data types in R, including character, numeric, logical, and complex, and learn how vectors, lists, matrices, arrays, data frames, and factors organize data for analysis.

Explore arithmetic, relational, and logical operators in R with vector examples. Understand assignment and element-wise operations to perform vector calculations.

Master decision branching in R using if, if-else, and multi-condition flows with logical and relational operators. Study switch statements and practical examples like fever checks and even-odd tests.

Master loop operations in R, including for, while, and repeat loops, to iterate over lists, data frames, and vectors, and sum numbers from 1 to 200.

Learn string construction rules and core string manipulation in R, including case conversion, replacement, formatting, and splitting, plus creating and converting character vectors with c and as.character.

Define and call functions in R, pass and return values, use default arguments, and explore built-in general, statistical, and math functions, including Fahrenheit to Celsius and squaring examples.

Master creating, indexing, and modifying vectors in R, using numeric, logical, and character types; apply colon and sequence functions, perform logical and condition indexing, and recycle and sort values, descending.

Learn to create and manipulate lists in R, combining vectors of different types, access elements by position or name, modify or delete members, and inspect structure.

Learn to construct and manipulate matrices in R, including indexing, naming, modifying elements, and removing columns, with element-wise arithmetic, transpose, cbind, rbind, and apply for row- or column-wise summaries.

Discover how to create and manipulate multi-dimensional arrays in R, access elements by row, column, and dimension, assign names, and apply element-wise and apply-based operations.

Master data frames in R by constructing them with data.frame, accessing with indexing and the dollar operator, binding or expanding frames with rbind or cbind, and exploring built-in data sets.

Explore categorical data in R using factors. Learn how to create factors with the factor function, manage levels, order data, and convert strings to factors in data frames.

Master export and import of csv data in R, using working directory management and data from local and web sources. Select drama with high popularity and export to csv.

Import excel data in R Studio with the read excel package, from installation to setting the working directory, and preview age and employee datasets with range, skip, and na handling.

Import json data in R using json and jsonlite, set the working directory, install and load packages, and convert nested json into a flattened data frame for analysis.

Learn to import and parse XML data in R, convert to a data frame, and analyze book attributes using subset, unique, and basic statistics.

Connect to external data via the My School database from studio, and configure the ODBC driver. Run queries on the world database to explore country data for the Asia continent.

Master data visualization in R by creating plots with the plot function, including histograms, density plots, dot plots, and line and scatter plots, plus legends and export options.

Explore multiple R graphs for data visualization, including strip charts, box plots, violin plots, bar and pie charts, area plots, time series, and interactive maps with leaflet.

Explore graphics with ggplot2 by mapping data frames to aesthetics and using geoms such as bar, pie, area, histogram, scatter, line, and box plots, guided by the grammar of graphics.

Master data pre-processing with functions in R: compute mean and median, measure dispersion with standard deviation, explore normal distribution, and apply correlation and tests using dnorm, pnorm, qnorm, and rnorm.

Explore correlation and hypothesis testing using scatter plots and R functions, revealing positive and negative relationships, t tests, and g squared tests with examples from mpg, disp, and exercise data.

Learn how missing values are defined, categorized, and treated in data preprocessing, including deletion, mean/median imputation, and a complete example using the mice package with air quality data.

Explore outlier detection and treatment in R using the interquartile range method, box plots, and the Irish data set, and learn how outliers affect regression results.

Learn the simple linear regression algorithm, its equation, and predicting a continuous target from a single predictor. Explore the intercept and slope with real data examples.

Explore multiple linear regression, predicting sales from YouTube, Facebook, and newspaper data using three predictors. Learn model coefficients, significance testing, and practical predictive analysis.

Explore logistic regression for binary classification in R, fitting models with glm, predicting outcomes, and evaluating performance via confusion matrices, roc curves, and auc on heart disease data.