Assign to the variable ‘n_dims’ a single random integer between 3 and 10.
# generate random integer between 3 and 10
n_dims <- sample(3:10, size = 1)
# generate vector from 1 to n_dims^2
vec <- (1:n_dims^2)
print(vec)## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25# reshuffle vector
vec <- sample(vec)
length(vec) # length = 16, therefore 4 x 4 matrix## [1] 25# even better: populate with n_dims in order to avoid errors
# turn vector into a square matrix
m <- matrix(data = vec, nrow = n_dims)
print(m)## [,1] [,2] [,3] [,4] [,5]
## [1,] 10 5 16 8 20
## [2,] 11 25 15 9 2
## [3,] 19 12 1 7 18
## [4,] 21 4 3 17 22
## [5,] 24 6 23 14 13# transpose matrix
m <- t(m)
print(m)## [,1] [,2] [,3] [,4] [,5]
## [1,] 10 11 19 21 24
## [2,] 5 25 12 4 6
## [3,] 16 15 1 3 23
## [4,] 8 9 7 17 14
## [5,] 20 2 18 22 13 # order of rows and columns have changed
# calculate the sum and the mean of the elements in the first row and then the last row.
sum_r1 <- sum(m[1,])
mean_r1 <- mean(m[1,])
sum_r4 <- sum(m[4,])
mean_r4 <- mean(m[4,])
# read about the eigen() function and use it on your matrix
# Computes eigenvalues and eigenvectors of numeric (double, integer, logical) or complex matrices.
# an eigen vector is a vector associated with a set of linear equations
# an eigen value is the factor by which an eigen vector is squished
# these functions can only be used with square matrices
eigen_m <- eigen(m)
print(eigen_m)## eigen() decomposition
## $values
## [1] 65.421051 20.383392 -17.070470 -10.011788 7.277815
##
## $vectors
## [,1] [,2] [,3] [,4] [,5]
## [1,] -0.5690302 -0.19964825 0.24599989 0.79161144 -0.2148128
## [2,] -0.3080851 0.87280608 0.16645709 0.09936043 0.3686910
## [3,] -0.4172747 -0.02069582 -0.85876638 -0.43143138 -0.5180994
## [4,] -0.3632771 -0.02988438 -0.09257509 0.06026773 0.6688115
## [5,] -0.5245949 -0.44387918 0.40709490 -0.41678963 -0.3196664# look carefully at the elements of $values and $vectors in the output. What kind of numbers are these?
# $values numbers are unreal numbers (they end with i)
# $vectors are also small unreal numbers
typeof(eigen_m$values)## [1] "double"# outputs complex numbers
typeof(eigen_m$vectors)## [1] "double"# again, outputs complexCreate a list with the following named elements:
# my_matrix, which is a 4 x 4 matrix filled with random uniform values
random_nums <- runif(16)
my_matrix <- matrix(data = random_nums, 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_vec <- runif(100)
my_logical <- my_vec > runif(1)
# my_letters, which is a 26-element vector of all the lower-case letters in random order.
my_letters <- sample(letters[1:26])
# Then, complete the following steps:
# 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])
# use the typeof() function to confirm the underlying data types of each component in this list
typeof(new_list)## [1] "list"str(new_list)## List of 3
## $ : num 0.323
## $ : logi TRUE
## $ : chr "v"# outputs type is list. str() gives number, logical, and character
# combine the underlying elements from the new list into a single atomic vector with the c() function.
vec_list <- c(my_matrix[2,2], my_logical[2], my_letters[2])
# what is the data type of this vector?
typeof(vec_list)## [1] "character"str(vec_list)## chr [1:3] "0.32292015873827" "TRUE" "v"# r coerces vector into charactersCreate 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, 0, 10)
# call the second variable my_letters and fill it with 26 capital letters in random order.
my_letters <- sample(LETTERS[1:26])
dat <- data.frame(my_unis, my_letters)
print(dat)## my_unis my_letters
## 1 1.1522146 C
## 2 8.2008660 I
## 3 2.2225648 P
## 4 5.1080171 U
## 5 4.3194782 J
## 6 6.8093590 O
## 7 9.9204941 G
## 8 2.9072497 M
## 9 0.4328099 H
## 10 7.2046386 V
## 11 8.8033642 S
## 12 5.0032932 N
## 13 7.4222484 A
## 14 6.4164776 E
## 15 8.5919918 W
## 16 3.3733168 F
## 17 5.4141033 Z
## 18 4.5486523 K
## 19 9.1727826 T
## 20 3.5823671 Q
## 21 6.2165137 Y
## 22 4.3976318 B
## 23 9.2931086 L
## 24 4.6005944 R
## 25 8.1190920 D
## 26 6.3145966 X# creates data frame with two variables
# 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.
rows <- sample(length(my_unis), size = 4)
dat[rows,1] <- NA
# for the first variable, write a single line of R code to identify which rows have the missing values.
which(is.na(dat$my_unis))## [1] 6 8 12 15# re-order the entire data frame to arrange the second variable in alphabetical order
dat <- dat[order(dat$my_letters),]
print(dat)## my_unis my_letters
## 13 7.4222484 A
## 22 4.3976318 B
## 1 1.1522146 C
## 25 8.1190920 D
## 14 6.4164776 E
## 16 3.3733168 F
## 7 9.9204941 G
## 9 0.4328099 H
## 2 8.2008660 I
## 5 4.3194782 J
## 18 4.5486523 K
## 23 9.2931086 L
## 8 NA M
## 12 NA N
## 6 NA O
## 3 2.2225648 P
## 20 3.5823671 Q
## 24 4.6005944 R
## 11 8.8033642 S
## 19 9.1727826 T
## 4 5.1080171 U
## 10 7.2046386 V
## 15 NA W
## 26 6.3145966 X
## 21 6.2165137 Y
## 17 5.4141033 Z# calculate the column mean for the first variable.
mean(dat$my_unis, na.rm = TRUE)## [1] 5.737997