Data Science: R Programming Complete Diploma

Learn how variables act as containers to store strings and numbers, created when assigned with the left arrow operator, and named with descriptive, case-sensitive rules.

Declare variables with meaningful names and assign values using the left arrow operator. View results in the console and environment panel, and identify data types with the built-in type function.

Master the paste built-in function to concatenate text and variables in R, using the console and control enter, see outputs like 32 from 12+20 and how to clear the environment.

Understand how variables manage strings and numbers, including why concatenation can error. Master single-line multi-variable assignment and naming rules: start with a letter, allow digits, periods, and underscores.

Explore data types in the R programming language, including numeric, integer, complex, character, and boolean, and learn how variables can change type or value without declaration.

Explore numeric, integer, and complex data types in r, assigning 19.6 as numeric and 200 with the L suffix as integer; use class to reveal each type.

Explore the character data type in R, using quotes and the left-arrow assignment. See how boolean values are represented and how to check variable types in the environment panel.

Learn how comments improve code readability in R programming, using hash-based single-line and multi-line comments to describe logic and safely test alternatives without affecting execution.

Explore numeric, integer, and complex numbers in R. Create numeric values with or without decimals, integers using a capital L, and complex numbers with real and imaginary parts.

Learn how to convert numbers to numeric, integer, and complex using built-in functions like as.numeric, as.integer, and as.complex.

Explore core math operations in R, including addition, subtraction, multiplication, and division, and use built-in functions like max, min, sqrt, abs, ceiling, and floor to process numbers and sequences.

Explore strings and the character data type by creating text with quotes, assigning to variables, printing with cat, measuring length, testing presence, and using escape sequences.

Explore creating strings in R with double or single quotes, assign them using = or <-, and see identical console output for both quote styles.

Explore creating a multi-line string data type, assign it to a variable, understand escape characters at line ends, and print or display it in the console using the cat function.

Master using the built-in length function to count characters in strings, including multi-line strings, by referencing the string variable and viewing the console output.

Use the grill built in function to check if a character appears in a string or multi-line string, returning a boolean value in the console.

Learn how to escape inner double quotes in strings using backslashes in R, including placing backslashes before each quote, printing the string, and creating new lines or backspaces within strings.

Learn boolean values, true or false, how comparisons produce them, assign them to variables, and use them to control if statements and conditional flow.

Explore logical values and boolean results in R by evaluating comparisons such as greater than, less than, and equals, using variables X and Y and checking outputs in the console.

Learn the four operator types in R programming: arithmetic, assignment, comparison, and logical, with real-time examples of operations on variables and values.

Apply arithmetic operators to X and Y (12 and 10) in R to perform addition, subtraction, multiplication, division, exponent, remainder, and integer division.

Explore assignment operators in R, using local and global assignment operators to set variable values, switch scope from local to global, and even change the operator direction.

Explore comparison operators in the auto programming language using X=10 and Y=12, including greater than, less than, equal to, not equal to, and their true or false results.

Learn how the logical and, or, and not operators in R produce true or false outcomes: and requires both terms true, or requires one true, and not inverts truth.

Learn to create vectors in the programming language using the c function to combine items like London, Cairo, and Berlin into a cities vector, and explore basic vector operations.

Create two string vectors, countries and fruits, with the c() function, and print them. Create a numeric vector new_numbers with 10, 20, 30, 40 and display it.

Use colon operator to create numeric vectors and ranges like 10 to 20 with endpoints. Explore decimal sequences such as 2.5 to 7.5 and boolean vectors with true and false.

Discover how to use the length built-in function to count items in vectors and how to sort vectors alphabetically or numerically using the sort built-in function in R.

Learn to access and modify items in an R vector using index numbers, with indices starting at one, including first, second, and third elements, and negative indexing to exclude items.

Learn to repeat elements of a numeric vector in R using the built-in rep function, adjusting each and times parameters to duplicate 10, 20, 30, and 40 as shown.

Learn to create sequences in R with the seq function using from, to, and by; starting from 1 to 50 by 5 excludes 50, while starting from 0 includes 50.

Learn how lists work in the arab programming language: an ordered, changeable collection of values of different types, accessed by 1-based indices and created with the list function.

Use the length function to get a list’s size, the in operator to check item presence, and the append function to add items at the end or after an index.

Remove items by index with the minus sign to delete from lists, create new lists like new numbers, and combine two lists with the built in function.

Define matrices as two-dimensional datasets with rows and columns, and create them with a built-in function in R to perform operations like addition, subtraction, multiplication, and division.

Create a matrix from a vector using the matrix function, specify rows and columns, and print the result; demonstrate numeric and string matrices with London, Oxford, Cairo, and Austin.

Access matrix items with square brackets by specifying row and column indices, then extract specific rows, columns, or multiple rows using the built-in function.

Learn to extend matrices with the CBN function to add columns and the urban function to add rows, and remove rows and columns using minus signs.

Use the diagram function to get the rows and columns of a matrix in R, and use the length function to verify a 2 by 2 matrix has four elements.

Explore arrays in R, a data structure for holding multi-dimensional data. Learn how to create arrays with the built-in array function and specify dimensions using the dim parameter.

Create a one-dimensional array in R by assigning the range 1 to 35 to a variable and printing it. Use the array function with dim to define the 1-D array.

Create a multidimensional array in R by using the built-in function to set 5 rows, 4 columns, and 3 arrays, producing a three-dimensional array.

Access items in a multidimensional array in R by indexing rows, columns, and matrix levels, using square brackets and the c() function to retrieve elements or entire rows and columns.

Learn to check item presence in a multidimensional array in R with the %in% operator, returning true or false. Use dim and length to get rows, columns, and matrices.

Create a data frame in R by combining vectors for players, numbers, and height, view its columns and first rows, and generate descriptive statistics with the summary function.

Access any value in a data frame by index or column name using single, double square brackets, or the dollar sign, and practice selecting columns in real examples.

Discover how to add new rules (rows) and columns to a data frame in R using the urban function, and remove rows or columns via index-based operations, with practical examples.

Discover how to determine a data frame’s dimensions in R using built-in functions, including length for the column count and separate row and column helpers, demonstrated on a three-by-three dataset.

Learn to vertically concatenate two data frames using the urban function with two arguments and a new variable, then horizontally join with the Sibande or Seaborn function and view results.