Foundational Chapters: Vectors in R


Vectors in R are typically assigned with <- and contain elements that consist of the same mode. Unlike in other programming languages, even if there is an individual element like a scalar, it is considered a one-element vector in R. Vectors are fundamental to data analysis in R because all data structures in R use vectors as their basis. Vectors are the basic foundation that is then expanded open whether it be through operations or functions. By using this vectorization, I believe it makes it easier to code. If you understand the main concepts of vectors, you can use that to understand how it is applied to the rest of your data and simplify the analysis process.

Comments

Post a Comment

Popular posts from this blog

myMean Error and Correction

Using the following vector and function in RStudio, I ran myMean(assignment2). assignment2 <- c(16, 18, 14, 22, 27, 17, 19, 17, 17, 22, 20, 22) myMean <- function(assignment2) { return(sum(assignment) / length(someData)) }    The output was "Error in myMean(assignment2) : object 'assignment' not found".   The function fails because assignment and someData are not assigned variables. These objects do not exist, so trying to pass them to the function myMean(assignment2) results in an error. To correctly return the mean of assignment2, the myMean function needs the variable assignment2 to replace assignment and someData. Corrected version of myMean, which returns 19.25. myMean <- function(assignment2) { return(sum(assignment2) / length(assignment2)) }   
Read more

Election Data Frame

Image
Errors in the original data set: a missing comma between Marco and Carly in Name, I fixed the spelling of Berine to Bernie, a missing comma between 62 and 51 in ABC, quotation marks around names had to be changed to " ", the variable names for ABC and CBS political poll results had spaces, which I changed to _. After fixing all the errors in the data set, I used the three variables Name, ABC_political_poll_results,  and CBS_political_poll_results to create the data frame. Since the data set is based on the 2016  presidential election, I named the data frame election_2016. Here is my corrected code in R: Name <- c("Jeb", "Donald", "Ted", "Marco", "Carly", "Hillary", "Bernie") ABC_political_poll_results <- c(4, 62, 51, 21, 2, 14, 15) CBS_political_poll_results <- c(12, 75, 43, 19, 1, 21, 19)  election_2016 <- data.frame(Name, ABC_political_poll_results, CBS_political_poll_results) election_2016 Af...
Read more

Doing Math on Matrices

I used the following values to find the inverse and determinants of two matrices. A = matrix(1:100, nrow=10) B = matrix(1:1000, nrow=10) First, I assigned the matrices in R.  A <- matrix(1:100, nrow=10) B <- matrix(1:1000, nrow=10) To obtain the determinant of each matrix, I used the det function. det(A) Result:  0 det(B) Result: Error in determinant.matrix(x, logarithm = TRUE, ...) :    'x' must be a square matrix Then, to obtain the inverse of each matrix, I used the solve function. solve(A) Result:  Error in solve.default(A) :    Lapack routine dgesv: system is exactly singular: U[3,3] = 0 solve(B) Result: Error in solve.default(B) : 'a' (10 x 100) must be square Looking at the matrices, we can see that A is a square matrix because it has the same number of rows and columns. There are 100 values in the matrix; dividing that by 10 rows gives me 10 columns. This makes matrix A a 10x10 square matrix. With B, there are 1000 values in the matrix, a...
Read more