Data Analytics using R programming

Explore data analytics with R programming: what data is and how raw facts become information and knowledge through filtering useful data, with examples like attendance records and timestamps.

Collect data to support fact-based decisions with solid evidence about sales trends and Facebook boosting. Analyze underperformance, demand and supply, and customer needs to guide new applications and improved processes.

Classifies data into categorical and numerical types; explains nominal versus ordinal data with examples such as gender, color, education level, and customer satisfaction.

Identify numerical data as discrete or continuous, using examples like number of students or dice rolls for discrete data, and temperature, height, or time for continuous data.

Explore the difference between analytics and analysis, and learn how analytics supports fact-based decisions with charts and reports, while analysis converts raw data into useful information.

Collect and clean data, extract, transform, and load into unified formats; categorize as structured or unstructured, then analyze for patterns and trends, and share insights via auto-updating dashboards.

Explore data analysis workflows, including cleaning, transforming, modeling, and visualizing data, with automated models that run on new data; contrast traditional modeling with machine learning training and prediction.

Explore the four classifications of data analytics—descriptive, diagnostic, predictive, and prescriptive—and how they transform historical and current data into patterns, root causes, future forecasts, and actionable decisions.

Define the target, collect relevant data, clean and filter it, then analyze patterns to reveal gender-based product preferences and present insights with charts and dashboards.

Explore data analytics with the R programming language, focusing on its use in statistical analysis and machine learning algorithms, its faster runtime, and its role in comparison to Python.

R is an open-source language for data analytics with a vast package ecosystem on CRAN, cross-platform across Windows, Mac, and Linux, offering advanced statistics and visualization, though not beginner-friendly.

Install the RStudio desktop IDE on ubuntu 18 plus or debian 10 plus by downloading the linux package, using the terminal to install, then launching the gui via show applications.

Learn to install r on ubuntu terminal using apt commands, update the system, install r-base, open r, and quit the session after using version 3.6.3.

Navigate the RStudio GUI overview, mastering the console, environment, history, plots, and packages panels, and use the help section and full-screen shortcut (ctrl+shift+1) for efficient coding.

Learn to create and run R scripts in the GUI, open new files with ctrl+shift+n, and execute single lines or blocks by line using ctrl+enter, even without saving.

Save your R program with the .R extension, edit it in vim, and run it from the terminal in Ubuntu, including navigating directories and handling spaces with backslashes.

Learn how dot r data and dot r history manage your R workspace, loading saved variables and command history automatically, while the text editor copies lines to the console.

Learn how variables act as containers in R, storing values with dynamic typing and no explicit data types, and explore different assignment styles using =, <-, or -> for consistency.

Explore data types in R, including integer, numeric (instead of float), and character values. Understand boolean (logical) values like true and false, and how to check types with class of.

Explore how to print and concatenate in R using print and cat. The lecture demonstrates printing variables, combining text with values, and using newline with cat for formatted output.

Use ls to list variables in the workspace or global environment, and rm to remove a variable by name; after removal, C disappears even when accessed via an alias.

Learn R variable naming: start with a letter, may include numbers; underscores in middle are allowed; dots can substitute underscores, but dot marks a private variable, avoid spaces or keywords.

Explore special keywords in R, including null, inf, NaN, and NA, and learn how reserved words like next and repeat guide control flow in R.

Explore scalar values, vector values, and matrices with rows and columns in R, and learn about arrays, lists, and factors for data analytics.

Explore vectorization in r by using the c function to combine values into a single vector, enforce a single data type, and perform operations in parallel.

Demonstrates implicit coercion in R by showing automatic type conversion among integer, numeric, complex, and logical vectors, with examples of the L suffix, class checks, and warnings.

Explore creating multi-value vectors in R using c to form collection of integers, strings, or numbers, and understand implicit coercion rules that promote logical to numeric to complex.

Explore the core operators in R, including arithmetic, null, and assignment operators, highlighting equal and forward arrow forms, and introduce miscellaneous operators for practical programming.

Explore arithmetic operators in R by performing vector operations, including addition, subtraction, multiplication, division, modulus, quotient, and power, while noting vector recycling and errors when lengths differ.

Explore relational operators in R by comparing values across a vector, yielding true or false for each value, and learn how vectorization speeds up operations over loops.

Explore how logical operators in R work, including and, or, not, bitwise operations. Learn how true and false map to numbers, and how & vs && differ in vector evaluation.

Explore miscellaneous operators in R, including the colon for sequences, the range function, and the %in% operator to test membership, with notes on 1-based indexing and inclusive ranges.

Explore R basics: assign variables with left-to-right or right-to-left operators, avoid equals, and distinguish numeric and integer types, using class and typeof to check data type.

Explore conditional statements in R, using if, else, and else if to control flow with bracketed conditions and brace-delimited bodies, including nesting and indentation.

Explore how the R switch statement routes output using numeric cases, handling user input, and note that without a default, non-matching cases may print nothing or raise errors.

Explore loops in R with for, while, and repeat, using break and continue. See for syntax with i in a range or vector, printing i and last-value behavior.

Master the while loop in R programming by setting a condition, printing n, and incrementing until the condition holds false. Use break at 100 and next to skip iterations.

Learn how the repeat loop controls execution and how to exit with break or escape. Compare repeat with while and use conditions to stop the loop.

Learn how to read user input with readLine and prompts, perform explicit coercion with as.integer, as.numeric, and as.character, and use length and nchar to inspect vector size and character length.

Explore getting user input part 2 in Swift using readLine, prompting for name, age, and address, with single or combined inputs and later age-to-integer conversion.

Learn to check if a pattern appears in each vector element with grepl and grep in R. See how grepl yields booleans, and grep returns indices, with case sensitivity noted.

Compare print, cat, and paste in R to see how they handle multiple variables and strings. Paste returns a value; cat prints without returning anything, while print supports structure.

Explore string manipulation in r using toupper, tolower, substr, and format, including standardizing input cases, extracting substrings, and controlling numeric output with precision.

Master vector indexing in R by accessing single or multiple values with square brackets, using 1:end sequences, custom index lists, and negative indices to exclude items.

Master vector indexing in R by numeric and named keys, handling out-of-range access, missing values (NA), and updating or expanding vectors with key-value pairs.

Explore operations in R, including sum, maximum, and minimum, and handle missing values with na.rm set to true. Sort vectors in ascending or descending order by setting decreasing to true.

Master the rep function in R to repeat vector elements, control repetition with times, and tailor output with length.out for concise, ordered sequences.

Learn to read user input and print results in R, use curly braces to run lines, inspect memory with ls and print objects, and implement a factorial calculator.

Implement a script to sum the squares of n numbers using a vector and for loop. Print the result and build a prime checker using a loop and a flag.