R for Data Analysis, Statistics and Data Science

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

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 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 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.

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.

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.

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.

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.

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.

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.