R Programming for Data Analysis & Data Visualization

Explore data types in R, including character, numeric, and logical, and how to store them. Understand data structures, data frames and matrices, and factors for analysis and machine learning.

Explore atomic vectors in R by creating numeric, logical, and character vectors with c(), inspecting length and type, and handling missing values.

Learn how to create and manipulate numeric vectors in R, perform element-wise operations (addition, subtraction, multiplication), and use length and scalar multiplication to analyze data.

Learn how to create and manipulate lists in R with list(), combining different data types (character, numeric), and access elements using [ ] vs [[ ]].

Data frame is a table with rows and columns, where columns are variables and rows are observations, built from vectors or lists; create frames and use apply for means.

Explore matrices as a data structure for numeric values, learning row and column structure, type constraints, and operations like binding, addition, and element wise multiplication.

Explore how to access elements and subset different data structures in R, including vectors, matrices, lists, and data frames, using single and double brackets and the dollar operator.

Learn how to control flow in R with the if-else statement. This lecture covers the syntax, conditions, and braces, plus a simple x example.

Explore how the for loop controls iterations, printing values from 1 to 10 and iterating over numeric and character vectors, with nested loops for matrices by rows and columns.

Initialize a counter and use a while loop to increment and print values while the counter is less than 10, illustrating condition-driven repetition and do while as an alternative.

Learn how the break statement stops a for loop in R when a condition is met, preventing further iterations and output.

Learn how for loops control iteration in R, applying conditions to continue or skip values, and compare break versus next to shape flow from 1 to 5.

Create a custom function in R to add two numbers, print the result, and illustrate using vectors to compare actual versus predicted values with root mean square error.

Explain the return function in R, showing how to return a value from a function, employ conditional and early returns, and return multiple values with lists or vectors.

Learn data manipulation in R by applying select, filter, arrange, mutate, and summarize to a flights data set, including grouping for aggregation across 21 variables and 27,496 observations.

Learn how to use the select function in a pipeline to pick or drop columns, and how to sample rows to create a representative subset of a data frame.

Learn to use mutate to create new columns in a data frame, such as average speed (distance divided by time), by adding variables without altering the original data structure.

Learn to filter rows in R with the filter function, using conditions on numeric and character variables, including distance greater than 1000 and day of week values.

Learn to use the summarize function in R to aggregate data and compute min, max, and mean values, handle missing data with filtering, and apply the group function.

Group by month and flight number to compute average departure delay, using summarize after grouping, handle missing values, and compare delays across months and flights.

Explore how to visualize data in R by identifying numerical and categorical variables, importing data, inspecting data structure, and converting variables to factors for effective plotting.

Plot a histogram for a single numerical variable to reveal its distribution, using age as an example. Adjust the x axis and explore the density alternative with genome_histograms in R.

Create a scatterplot of two numerical variables in R, mapping x and y axes, labeling axes, and adjusting point size, transparency, and color to reveal relationships and trends.

Explore how to read a box plot for a single numerical variable and compare distributions across categories, identifying minimum value, maximum value, quartiles, median, and mean.

Learn to create a categorical bar plot in R that compares smokers and nonsmokers across races, displaying distributions with filled rectangular bars and using fill rather than color.

Explore a two numerical and one categorical scatter plot in R, coloring data points by race using as.factor to reveal patterns across categories.

Explore how to create a scatterplot with two numerical variables and two categorical variables, using color and shape to reveal interactions, clusters, and patterns in data.

Demonstrates using facet_wrap to include more than five variables, both categorical and numerical, and visualize distributions by race and smoking status with density plots and histograms.