n_dims <- sample(3:10,1,replace = TRUE)
rep(n_dims)## [1] 5n_dims <- sample(3:10,1,replace = TRUE)
# single random integer between 3 and 10
# different function for integer values, whole numbers
# replace true means re sample it the number changes
n_dims2 <- 1:n_dims^2
# create a vector of consecutive integers from 1 to n_dims^2
n_dims3 <- sample(n_dims2)
# use sample function to randomly reshuffle values
m <- matrix(data=n_dims3,nrow= n_dims, ncol = n_dims)
# create a square matrix with these elements
print(m)## [,1] [,2] [,3] [,4] [,5]
## [1,] 23 5 20 14 25
## [2,] 10 8 9 21 6
## [3,] 16 17 3 22 11
## [4,] 15 1 13 7 24
## [5,] 2 19 4 12 18# print out the matrix
# Notes: Long list of values
tmatrix <- t(m)
# find a function in r to transpose matrix
print(tmatrix)## [,1] [,2] [,3] [,4] [,5]
## [1,] 23 10 16 15 2
## [2,] 5 8 17 1 19
## [3,] 20 9 3 13 4
## [4,] 14 21 22 7 12
## [5,] 25 6 11 24 18# print it out again and note how it has changed
#flips it, so if a number is at position 1,3 it will be moves to 3,1
# if a number is at 3,3 it will stay
x <- sum(tmatrix[1,])
y <- mean(tmatrix[1,])
# Sum and mean of 1st row
x2 <- sum(tmatrix[length(n_dims),])
y2 <- mean(tmatrix[length(n_dims),])
# Sum and mean of last row
# calculate the sum and the mean of the elements in 1st and last row
answer <- eigen(tmatrix)
# eigen() function in matrix
values <- answer$values
answer <- answer$vectors
# $values and $vectors in output
typeof(values)## [1] "complex"typeof(answer)## [1] "complex"# type is double
# typeof() function to figure out typemy_matrix <- matrix(sample(1:10, 16, replace = TRUE), nrow = 4)
# my_matrix
my_setLogical <- sample(1:20,100, replace = TRUE)
x <- my_setLogical > 10
#my_logical
my_letters <- sample(letters)
#my_letters
newlist <- rbind(my_matrix[2,2],my_setLogical[2],my_letters[2])
#newList
typeof(newlist)## [1] "character"# is a character
#typeof()
y <- c(newlist)
typeof(y)## [1] "character"# it is a character
#combineCreate a data frame with the two variables (= columns) and 26 cases (= rows) below:
my_unis <- sample(0:10,26,replace = TRUE)
my_letters <- sample(LETTERS)
rep(my_unis)## [1] 2 7 3 6 0 7 10 3 0 3 5 7 0 1 0 1 1 6 3 5 3 1 8 0 7
## [26] 5datax <- data.frame(my_unis, my_letters)
#Data frame
rep(datax)## $my_unis
## [1] 2 7 3 6 0 7 10 3 0 3 5 7 0 1 0 1 1 6 3 5 3 1 8 0 7
## [26] 5
##
## $my_letters
## [1] "X" "Z" "B" "G" "J" "P" "U" "W" "L" "K" "O" "D" "E" "I" "H" "Y" "T" "Q" "V"
## [20] "S" "N" "M" "R" "F" "C" "A"datax$my_unis[sample(1:26, 4)] <- NA
#first variable
missing <- which(is.na(datax$my_unis))
#missing row code
print(missing)## [1] 2 13 16 20datanew <- datax[order(datax$my_letters), ]
#Reorder data frame for second variable in alphabetical order
rep(datanew)## $my_unis
## [1] 5 3 7 7 NA 0 6 0 1 0 3 0 1 3 5 7 6 8 NA 1 10 3 3 2 NA
## [26] NA
##
## $my_letters
## [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S"
## [20] "T" "U" "V" "W" "X" "Y" "Z"meancolumn <- mean(datax$my_unis, na.rm = TRUE)
# Calc column mean for first variable
rep(meancolumn)## [1] 3.681818