#Assign to the variable n_dims a single random integer between 3 and 10.
n_dims <- as.integer(runif(1, min=3,max=10))
#Create a vector of consecutive integers from 1 to n_dims2.
New_Vec <- (1:(n_dims)^2)
# Use the sample function to randomly reshuffle these values.
New_Vec <- sample(New_Vec)
# create a square matrix with these elements and print
New_Matrix <- matrix(New_Vec, ncol = n_dims)
print(New_Matrix)## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 48 6 22 63 52 3 60 11
## [2,] 64 36 41 61 62 10 57 56
## [3,] 42 50 32 45 20 16 14 5
## [4,] 9 58 46 30 44 26 43 53
## [5,] 7 40 23 35 37 29 12 8
## [6,] 17 24 27 31 1 51 34 55
## [7,] 21 28 25 49 15 33 59 2
## [8,] 38 4 13 19 18 47 39 54#Find a function in r to transpose the matrix and print
T_Matrix <- t(New_Matrix)
print(T_Matrix)## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 48 64 42 9 7 17 21 38
## [2,] 6 36 50 58 40 24 28 4
## [3,] 22 41 32 46 23 27 25 13
## [4,] 63 61 45 30 35 31 49 19
## [5,] 52 62 20 44 37 1 15 18
## [6,] 3 10 16 26 29 51 33 47
## [7,] 60 57 14 43 12 34 59 39
## [8,] 11 56 5 53 8 55 2 54#calculate the sum and the mean of the elements in the first row and then the last row.
print(sum(T_Matrix[1,]))## [1] 246print(mean(T_Matrix[1,]))## [1] 30.75print(sum(T_Matrix[row = n_dims,]))## [1] 244print(mean(T_Matrix[row=n_dims,]))## [1] 30.5#read about the eigen() function and use it on your matrix. Figure out the type of values.
eigen_T <- eigen(T_Matrix)
print(typeof(eigen_T$values[1]))## [1] "complex"print(typeof(eigen_T$vectors[1,1]))## [1] "complex"Create a list with the following named elements
#my_matrix, which is a 4 x 4 matrix filled with random uniform values
my_matrix <- matrix(runif(16),nrow=4)
#my_logical which is a 100-element vector of TRUE or FALSE values. Do this efficiently by setting up a vector of random values and then applying an inequality to it.
my_logical<- runif(100)>.5
#my_letters, which is a 26-element vector of all the lower-case letters in random order.
my_letters<- sample(letters)
#Create a new list, which has the element[2,2] from the matrix, the second element of the logical vector, and the second element of the letters vector.
new_list <- list(my_matrix[2,2], my_logical[2], my_letters[2])
#Confirm underlying data types
typeof(new_list)## [1] "list"print(typeof(new_list[[1]]))## [1] "double"print(typeof(new_list[[2]]))## [1] "logical"print(typeof(new_list[[3]]))## [1] "character"#Combine the list and determine vector type
new_vector <- c(new_list[[1]], new_list[[2]], new_list[[3]])
print(typeof(new_vector))## [1] "character"Create a data frame with the two variables (= columns) and 26 cases (= rows) below:
#call the first variable my_unis and fill it with 26 random uniform values from 0 to 10
my_unis <-runif(26, min=0, max=10)
# call the second variable my_letters and fill it 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 the first variable, write a single line of R code to identify which rows have the missing values.
print(my_df[is.na(my_df$my_unis), ])## my_unis my_letters
## 1 NA R
## 10 NA M
## 11 NA X
## 17 NA Z#re-order the entire data frame to arrange the second variable in alphabetical order
my_df[order(my_df$my_letters), ]## my_unis my_letters
## 16 8.439341 A
## 21 8.726806 B
## 14 5.010916 C
## 20 9.585449 D
## 4 7.703134 E
## 2 7.672912 F
## 8 1.181397 G
## 18 4.998003 H
## 5 1.637224 I
## 7 4.569551 J
## 26 8.226695 K
## 19 4.430863 L
## 10 NA M
## 15 6.804063 N
## 12 3.553432 O
## 25 5.500267 P
## 3 4.524800 Q
## 1 NA R
## 6 7.681998 S
## 23 1.991878 T
## 22 6.492096 U
## 9 3.155066 V
## 24 6.716163 W
## 11 NA X
## 13 3.480109 Y
## 17 NA Z#Calculate the column mean for the first variable.
print(mean(my_df$my_unis, na.rm=TRUE))## [1] 5.549189# if(my_df$my_letters== sample(LETTERS, 4))
# {
# my_df$my_unis<-NA
# }
# replace(my_df$my_unis, NA)
# sample(my_df$my_unis, 4)