R for Data Analysis, Statistics and Data Science

Install R on Windows by downloading the latest Windows installer, accept defaults, and create a desktop shortcut; explore the R GUI and console before proceeding to RStudio.

Install RStudio, an integrated development environment for writing and executing R, after ensuring R is installed; download from rstudio.com for Windows, macOS, or Ubuntu/Fedora, then run and open RStudio.

Explore the RStudio interface, an integrated development environment for R. Navigate the text editor, console, environment, history, files, plots, packages, and help.

Explore how the R help and examples facilities let you access official documentation with help.start or ?function, and view examples for length and sum.

Learn to customize the look and feel of RStudio through global options. Adjust appearance, font, editor theme, and default working directory, then apply changes optionally.

Explore general R functions for workspace management: get and setwd for the working directory, ls, rm, and history; inspect packages with library and dot lib paths; practice with assignments.

Learn to write your first R program using the R GUI, the R console, and R Studio, print hello R, save scripts, and understand statements and arguments.

Learn to write and run R scripts in RStudio, set the working directory, create and save R scripts, and print text with the print function, noting R is case sensitive.

Learn to write your first R program, set a working directory, create and run scripts in RStudio, and perform math operations like addition, subtraction, exponential operator, modulus, and integer division.

Explore how to declare and assign variables in R, using <-, =, or assign, with alphanumeric names, and inspect class, type, and mode for numeric, integer, complex, logical, character types.

Create vectors in R with c() and replicate elements, and inspect their atomic types using typeof(). Generate sequences with the colon operator and seq(), noting vectors are one-dimensional and single-typed.

Learn to create logical vectors in R by applying relational operators to vectors, yielding true or false results; find indices using which and extract matching elements.

Create and manipulate factors in R, converting vectors to factors, inspecting levels like male and female, and counting occurrences with table for self-describing data.

Learn to create and manipulate matrices in R with the matrix function, control rows, columns, and byrow, then use diag to build and extract diagonals.

Explore how in R you create lists as one dimensional, heterogeneous data structures using list() or c(), containing elements of types (integers, characters, numerics) and vectors, with double bracket indexing.

Learn how data frames store data in rows and columns using vectors, with each column a single type. Create data frames with data.frame() and import data via read.table or read.csv.

Create data frames in R by combining id, name, and marks vectors, then compare with lists to show columns versus rows.

Learn how to index and subset a data frame in R, using square brackets, c function, and the dollar and double-bracket operators to access and update rows, columns, and elements.

Import data from text and csv files using read.table and read.csv in R. Control file paths, headers, separators, end rows, and skip to load the desired data.

Identify missing values in vectors and data frames using is.na and complete.cases, then remove them to obtain complete data in a frame with id, temperature, event, and humidity.

Learn how to use the dplyr package to manipulate data frames and data tables in R, with fast, intuitive functions and simple installation steps.

Learn to use the select function from the dplyr package to subset a data frame by selecting specific columns, with examples of column indexes, names, ranges, and exclusions.

Learn to use the dplyr filter function to extract rows from a data frame, then select columns and apply conditions like murders > 100 and population < 10 million.

Sort data frames with dplyr arrange by the murders column to order observations ascending or descending, then use head, tail, and select to view states and population.

Learn how to rename columns in a data frame with dplyr's rename function, display updated names, and select renamed columns such as EBV to observation and martyrs to homicide.

Learn to use dplyr mutate to add or modify columns in data frames, create derived ratios, and selectively display transformed data with select and names in R for data analysis.

Apply the group_by and summarize functions to a data frame, grouping by region to compute sum, mean, max, and median of the models.

Explore the dplyr pipeline operator in R, chaining operations on data frames—arrange, select, filter—using the pipe to pass results left to right, and extract top rows.

Explore data visualization in R by creating bar plots to compare population and murders across states, customize axes, colors, titles, and build stacked bar plots.

Create and customize a histogram in R using stock price data, loading the library, selecting the price column, and adjusting breaks to 20 bins with labels, color, and border.

Master plotting in r with the plot function to create scatter and line plots from a data frame, selecting population and murders, and customize labels, title, color, and shapes.

Learn to create and customize box plots in R to compare murders across regions by selecting data, grouping by region, and adjusting labels, color, border, and notches.

Create and display multiple plots in a single layout to compare state population and murders using bar plots and a scatter plot in R.

Explore qualitative, or categorical, data in R by creating character vectors and factors, examining levels, and using a table and summary to summarize shirt size data.

Visualize qualitative data in R by shaping categories into factors, building tables and levels, and rendering bar charts and pie charts with colors and labels to show proportions.

Handle quantitative data in R by numbers and structured data for analysis, distinguish it from qualitative data, and illustrate with numeric vectors such as songs, ratings, and stock prices.

Visualize quantitative data using histograms, box blocks, and a strip chart, and compare frequency and probability density with optional color and density overlays.

Visualize real-time stock prices and explore quantitative data in R by creating histograms and density plots, plotting a density function with a blue histogram and a red line.

Explore how min, max, sum, prod, and sort operate on quantitative data, using songs and ratings vectors to illustrate lengths, maxima, minima, totals, products, and ordering.

Compute the mean in R, and explore arithmetic, geometric, and harmonic means, then apply the sum and length functions to calculate the average of vector elements.

Compute the geometric mean in R by taking the nth root of the product of sample elements, and use log and exp for an efficient, overflow-safe calculation.

Learn to use geometric mean to calculate compounded annual growth rate from stock returns, shown with a 10%, -20%, 30% three-year example and an R vector and product function implementation.

Explore the harmonic mean as the reciprocal of the mean of reciprocals, noting nonzero values prevent undefined results, with a sample calculation and its use in rates and finance.

Explore median and mode as central tendency measures, learn how to compute median from sorted data, and note that mode may be unsuitable for statistical analysis in R.

Explore how outliers affect mean and median using a salary example, visualize with a box plot, and examine how trimming reveals the impact of extreme values.

Explore quartiles and quantiles as tools to describe data distribution, including the median and interquartile range, with practical R examples using a songs dataset.

Learn how variance and standard deviation measure data spread by deviations from the mean, and how the square root of variance reveals volatility in finance, with songs and ratings examples.

The lecture demonstrates loading real-time stock price data, reading price columns, and computing variance and standard deviation in R to compare volatility between GE and IBM stocks.

Explore correlation and covariance in finance, showing how stock prices move together, with positive, negative, and zero correlations, and compare Pearson and Spearman methods.

Analyze correlation coefficients and covariances of stock prices, comparing IBM, GE, and Coca-Cola datasets to show how relationships differ across pairs and quantify variation.

Explore how to analyze two-variable qualitative data by converting ratings and courses into factors, building contingency tables with table(), and visualizing with bar plots and mosaic plots.

Analyze bivariate quantitative data by exploring stock price over time using R, creating box plots and line graphs, and identifying max and min prices with their dates.

Explore multivariate data in a murders dataset by computing basic summaries, creating bar plots and scatter plots, and revealing correlations between population, murders, and gun murders.

Explore probability distribution as a statistical function describing possible values and their likelihoods for a random variable, including normal, uniform, and binomial distributions, with dice and R examples.

Explore uniform distribution, a probability distribution where all outcomes are equally likely, with discrete and continuous types, using examples such as coin flips and dice, and generating it in R.

Explore the normal distribution as a symmetric, mean-centered probability distribution with data concentrated near the mean, defined by mean and standard deviation, and illustrated with a bell curve.

Learn how p value assesses a null hypothesis in statistical testing, contrasts with the alternative, and applies a 0.05 significance level for 95% confidence.

Learn degrees of freedom as the maximum number of independent values, defined as the sample size minus one; a t-test example with 30 samples yields 29 degrees of freedom.

Explore how confidence level and confidence interval quantify uncertainty in sampling, with 95% and 99% levels shaping the range that likely contains the true population parameter.

Learn how inferential statistics use hypothesis testing to draw population conclusions from samples, comparing means with one-sample, two-sample, and paired tests, evaluating p-values against a significance level.

Learn how to perform chi square tests in R to assess independence and goodness of fit using contingency tables, p-values, and critical values.