Master R for Statistics and Data Science

Learn how to use the assignment operator to store calculations in variables like x, y, and z, and reuse them for totals such as profit across months.

Learn to use the assignment operator to create and update variables in R, then perform subsequent calculations with x and y as demonstrated in a hands-on coding exercise.

Learn to store multiple numbers in vectors using R's c() function, creating examples like a profit vector and a weights vector, and compute totals from vector data.

Create and assign a vector in R using c() with elements 10, 20, and 30, then run the test to confirm a vector of length three.

Use the colon operator to create a vector of one through 100, assign it to my vector, and verify the result with a test.

Use the seq function in R to create vectors from one to fifteen by specifying from, to, and by, and explore length.out for custom lengths.

Learn to use seq to create a vector from 1 to 9 by twos, assign it to my vector with the assignment operator, and confirm success by running the test.

Practice creating a vector from 0 to 10 with exactly six elements using seek and by in R, illustrating a step-by-two sequence and confirming results with a test.

Use the rep function to create a vector by repeating the sequence 1, 2 three times, forming my vector with the assignment operator. Show how x and times improve readability.

Repeat each element of the sequence 1,2 using rep with each to produce 111222, using 1:2 to create the vector and specifying each = 3 to avoid misinterpreting as times.

Learn vectorized operations in R by applying operations to all elements, performing element-wise adds of x and y, and recycling shorter vectors to match lengths.

Utilize vectorized operations in R by adding a flat tax of 2 to a prices vector (15, 20, 25), yielding (17, 22, 27), and test the result.

Convert Celsius to Fahrenheit with a vectorized operation in R. Multiply the c(0, 20, 100) temperatures by 1.8 and add 32 to obtain 32, 68, and 212.

Add two weight vectors to compute the total weights of each set of twins, illustrating vector addition and its use for handling large data sets.

Learn to use common vector functions in R, such as length, sum, and mean, and explore median, standard deviation, variance, and five number summaries for heights.

Compute the mean, median, sum, and length of a five-element vector x = c(5, 8, 10, 3, 7) in R and verify the results.

Practice calculating variance and standard deviation in R using var() and sd() on a vector of production measurements, preparing for later statistics topics.

Use summary to obtain a five-number summary for a vector, including min, max, mean, median, and quartiles, demonstrated with last-quarter sales data in R.

Learn how to handle missing data in R by using na.rm to compute the mean of available weights. Remove the NA values to obtain the average.

Learn to subset vectors in R using square brackets, the colon for ranges, and the c function to select elements, while indexing starts at one and minus signs remove items.

Learn booleans in R using comparison operators like greater than, less than, equal to and double equals, and treat true as 1 and false as 0 for counting.

Apply boolean subsetting to filter vectors by comparison criteria, using true/false vectors to select elements meeting conditions such as less than eight or at least fifty.

Create matrices from vectors in R using matrix(), set rows or columns, and fill column-wise by default or use byrow to fill row-wise, a behavior shown alongside cbind and rbind.

Learn to subset matrices in R with square brackets, extracting elements by row and column, selecting entire rows or columns, and removing columns using minus notation while preserving matrix structure.

Master matrix subsetting with booleans to filter rows by height and weight, using boolean criteria and dplyr for clean subset selection.

learn to use r's apply to operate on matrices, computing row means, row sums, and column statistics like col means and col sums with a chosen function.

Master data frames in R to store multiple columns with different data types, such as names and heights, avoiding all numeric quotes. Access with the dollar sign and data.frame.