n_dims <-sample(3:10, 1)
myvec <- 1:(n_dims^2)
print(myvec)
sample(myvec)
myMat <- matrix(data=myvec,nrow=4)
print(myMat)
t(myMat)
print(myMat)
# reordered so the numbers are going from left to right rather than vertical
eigen(myMat)
# an Eigenvalue tells us about variance in the data. Each eigenvalue tells us how spread out the data is from one another. Every data point has an eigenvector (which is why there are 16 for my matrix) and those are where the eigenvalue comes from. All the values are decimals to very very small decimals
typeof(eigen(myMat))
# these values are a "list"# 4x4 matrix with random uniform values (my_matrix)
my_matrix <- matrix(runif(16),nrow=4)
# 100 element vector of TRUE/FALSE values (my_logical)
my_logical<- runif(100)>0.5
# 26 element vector of all lower case letters in a random order (my_letters)
my_letters<- sample(letters)
# list with element [2,2] from the matrix, second element of logical and second
# element of letters (new_list)
new_list <- list(my_matrix[2,2], my_logical[2], my_letters[2])
print(new_list)
#Confirm underlying data types
typeof(new_list)
print(typeof(new_list[[1]]))
print(typeof(new_list[[2]]))
print(typeof(new_list[[3]]))
# combine the list together and figure out vector type
new_vector <- c(new_list[[1]], new_list[[2]], new_list[[3]])
print(typeof(new_vector))create data frame with two variables (=columns) and 26 cases (=rows) below:
# call the first variable my_unis, fill with 26 random values from 0 to 10
my_unis <- runif(26,min=0,max=10)
# my_letters fill with 26 capital letters in random order
my_letters <- sample(LETTERS)
my_DF <- data.frame(my_unis, my_letters)
# for the first variable, use a single line of code in R to select 4 random
# rows and replace the numerical values in those rows with NA.
my_DF[sample(1:26,4, 1]<-NA
# for first variable write a line to find which rows have the NA
print(my_DF[is.na(my_DF$my_unis, ]))
# reorder entire DF to arrange 2nd variable alphabetically
my_DF[order(my_DF$my_letters), ]
#Calculate column mean for first variable
print(mean(my_DF$my_unis, na.rm=TRUE))