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.

Install the rstudio integrated development environment by first ensuring r is installed, then download the desktop installer from the official rstudio site and follow os-specific setup steps.

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.

Explore the built-in help and examples facilities in R. Learn to use help, the question mark, and the example function to access official documentation, function details, and package references.

Change the look and feel of RStudio via global options in the tools menu, adjusting font, font size, and editor theme to suit your preferences; this is optional.

Explore essential R fundamentals: getwd and setwd for directories, ls for objects, assignments with <-, rm to remove objects, history to review commands, library and .libPaths for packages and paths.

Learn to write your first R program using the R GUI or console, print hello R, save work to a file, and prepare for R Studio scripts.

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.

Use the hash symbol to add single-line comments in R scripts, documenting statements ignored by the interpreter; R does not support multi-line comments, so prefix each line with a hash.

Learn how to use R as a calculator to perform basic and complex arithmetic, master operator precedence and parentheses, and explore inbuilt mathematical functions in the R console.

Discover R's built-in mathematical functions for data, including abs, ceiling, floor, trunk, round, sqrt, exp, log, pi, e, and sin, cos, tan; learn about console output and preparing for variables.

R stores values in memory via variables assigned with (<-), creating objects like amount with 1000 in the environment, and follows name rules—letters, digits, dot or underscore, with case sensitivity.

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.

Develop skills in R by creating and updating variables for amount, rate, and time, compute interest with the formula amount times rate divided by one hundred times time.

Explore how to assign values in R using equals, the left assignment operator <-, the right assignment operator ->, and the assign function, including multi-variable assignments and right-to-left evaluation.

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.

Explore reading user input in R using scan and read line functions, handling multiple values, numeric vs character data, and prompt messages, with print outputs and common pitfalls.

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.

Explore the five atomic data types in R: numeric, integer, complex, logical, and character, understanding memory allocation, variable types, and how the class function and type function guide allowable operations.

Explore numeric types in R, where decimal values are floating point numbers called numerics stored as the numeric class. Use is.numeric and is.integer to confirm numeric types.

Learn how R represents integer values with the L suffix, differentiates integers from numerics, and uses is.integer, is.numeric, and as.integer to create and coerce integer objects.

Explore the complex data type in R by assigning imaginary numbers like three plus four i to an object, then check its class and type and verify with is.complex.

Explore the logical type in R, using true/false or T/F as boolean values, with class, type, and is.logical checks, and note A as a missing value.

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.

Convert integers, numerics, complex numbers, and characters to logical values in R with as.logical, where non-zero values yield true, zero yields false, and 'true'/'false' strings yield corresponding logical values.

Explore converting various data types to character values in R using the as.character function, converting numeric, integer, complex, and logical values, and verifying with class.

Explore how operators perform operations on operands and values, including assignment, automatic, relational, and logical operators, and how they assign values to variables.

Explore six relational operators in R: <, <=, >, >=, ==, !=. Use them on numbers, floating point values, and strings, observing true/false results and dictionary-order comparisons for strings.

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.

Create and manipulate vectors in R using c(), sequence, and replicate, explore atomic types (double, integer, logical, character, complex, raw), and use type(), length(), and vector initialization.

Learn how to subset vectors in R by indexing with square brackets, using 1-based, negative, and logical vectors, combining colon sequences, c() indexing, seq_along, length, and handling NA.

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 vector methods and operations in R, including length, sum, prod, rev, sort with ascending and decreasing options, and compute dot, cross, and outer products.

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.

Explore how to create and use logical vectors in R by applying relational operators on vectors, generating true/false results, and retrieving indexes with which or by subsetting with [].

Explore how to create vectors, apply mathematical functions such as abs, ceiling, floor, round, trunc, sqrt, exp, log, and factorial in R, with practical examples.

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.

Explore how the replicate function duplicates elements in R by a specified times argument, including numbers, characters, and sequences; distinguish times from each by applying to a sequence.

Learn how to create matrices in R with the matrix function, specifying rows and columns. Discover column-wise vs row-wise construction, and use dim and length to inspect matrices.

Learn to use diag() in R to construct and extract diagonal matrices, including 3x3 examples with specified values or sequences and matrix creation.

Create a three by three matrix with the matrix function, then assign rownames and colnames using vectors and the assignment operator.

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.

Create and manipulate 3 by 3 matrices in R using the matrix function and row-wise filling, then perform element-wise operations and use transpose, inverse, and solve for matrix computations.

Create a 3x3 matrix in R and compute row sums, column sums, row means, and column means. Apply the apply function to perform row-wise or column-wise calculations.

Create and manipulate lists in R, a one-dimensional heterogeneous data structure that can hold elements of different types and vectors, using the list function and access them with double brackets.

Explore list subsetting in R using single and double square brackets to access and modify elements. Learn how lists contain vectors and how 1-based indexing and named elements work.

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.

Learn how factors represent categorical data in R with the factor function, view levels, and count occurrences using table, illustrating why factors are better than integers.

Explore how data frames organize data as a list of vectors with uniform length, supporting different column types and factors, and import data from text and csv files.

Create data frames in R by combining id, name, and marks vectors into a data frame with columns as attributes. Compare data frames with lists to show observation handling advantages.

Learn how to access and update data frame elements in R using indexing, including row and column selection with square brackets, negative indexes, and the dollar and double-bracket operators.

Use the subset function to filter a data frame by conditions like marks greater than 60, and learn how to select columns with the select argument.

Learn to build a data frame from id, name, and marks vectors, then append rows with rbind and add a new column with cbind, ensuring equal vector lengths.

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.

Explore how to identify and remove missing values in vectors and data frames in R using is.na, any, and complete.cases, creating clean data sets.