R Programming for Data Science

Download and install R from CRAN across Linux, macOS, or Windows, run the installer with defaults, and learn to open the R console.

Explore the RStudio interface, an R integrated development environment, and learn to write and run code, view results, and manage environment, history, files, plots, packages, and working directory.

Create and run your first R script in RStudio by setting the working directory, creating a new script, and using print to display text; note R is case sensitive.

Discover how keywords and reserved words determine syntax in R, with case-sensitive predefined meanings that cannot be used as variables or identifiers, illustrated by the break example.

Discover how to display output in R with print and cat. Use print to show values with an index, and cat to concatenate multiple arguments for a clean console display.

Compare statically typed languages with explicit data type declarations to dynamically typed ones like R, where types are determined at runtime and variables can have different types as values change.

Explain the character data type in R, show strings in single or double quotes, check class and type, and coerce numbers to characters with the as dot character function.

Discover how R converts values between data types using as.numeric, as.integer, as.complex, as.logical, and as.character. Learn the rules of each conversion and start with numeric type conversions.

Learn to convert values to numeric in R using as.numeric, including integers, complex numbers (imaginary parts discarded), booleans (true to 1, false to 0), and numeric strings.

Convert values to integer in R using the as.integer function, covering numeric, complex, logical, and character inputs, with decimals truncated and non-numeric characters yielding missing values.

Learn how to convert numeric, integer, logical, and character values to the complex data type in R using as complex and as dot complex, showing real and imaginary parts.

Explore how logical operators and, or, and not work on booleans and numbers in R, including how 0 represents false and non-zero values represent true.

Learn the matching operator in R programming for data science, which checks if elements are present in a vector, returning true or a boolean vector for multiple checks.

Explore vector arithmetic in R for data science by performing element-wise operations on vectors with scalars and other vectors, including addition, multiplication, and square roots, with round-robin behavior and warnings.

Explore implicit and explicit coercion in R, where a vector with mixed classes auto-coerces to a common type, and as functions perform explicit conversions between integer, numeric, character, and logical.

Learn to generate random numbers in R for simulation, exploring normal, Poisson, and binomial distributions, and customize with mean and standard deviation to produce ten values.

Learn to generate numeric sequences in R using the colon operator and the seq function, covering start and end, the by or step parameter, reverse order, and floating point sequences.

Learn to subset matrices in R using row and column indices, access full rows or columns, modify elements, remove rows or columns with negative indices, and retrieve diagonal elements.

Explore how to create and combine matrices in R using rbind() and cbind(), bind vectors as rows or columns, and print the resulting matrices.

Name list elements and access them with the dollar or subset operators to retrieve elements by name, such as Ida and scores, and use c for index-based subsetting.

Explore how to concatenate lists in R by combining vectors into a student list and an age list, and view the before-and-after results.

This lecture demonstrates the data frame edit() function in R by opening a tabular window to edit data frames, including updating the third row of marks to 85.25.