Assign to the variable n_dims a single random integer between 3 and 10.
n_dims <- sample(3:10, 1)
print(n_dims)## [1] 9Create a vector of consecutive integers from 1 to n_dims2.
vector_consec <- seq(from=1, to =(n_dims)^2)
print(vector_consec)## [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
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [76] 76 77 78 79 80 81Use the sample function to randomly reshuffle these values.
sample(x=vector_consec)## [1] 57 80 19 51 64 15 12 38 37 79 14 9 66 81 69 8 67 60 3 63 47 78 25 32 52
## [26] 55 76 24 35 49 59 7 28 34 13 50 70 41 1 4 36 56 18 10 42 11 45 6 72 30
## [51] 39 20 68 23 43 53 29 5 2 40 73 48 26 71 27 77 17 75 61 31 21 74 46 54 44
## [76] 65 16 58 22 62 33create a square matrix with these elements & print out the matrix.
m <- matrix(data=vector_consec,nrow=n_dims)
print(m)## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## [1,] 1 10 19 28 37 46 55 64 73
## [2,] 2 11 20 29 38 47 56 65 74
## [3,] 3 12 21 30 39 48 57 66 75
## [4,] 4 13 22 31 40 49 58 67 76
## [5,] 5 14 23 32 41 50 59 68 77
## [6,] 6 15 24 33 42 51 60 69 78
## [7,] 7 16 25 34 43 52 61 70 79
## [8,] 8 17 26 35 44 53 62 71 80
## [9,] 9 18 27 36 45 54 63 72 81find a function in r to transpose the matrix.
tm <- t(m)print it out again and note how it has changed.
print(tm)## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## [1,] 1 2 3 4 5 6 7 8 9
## [2,] 10 11 12 13 14 15 16 17 18
## [3,] 19 20 21 22 23 24 25 26 27
## [4,] 28 29 30 31 32 33 34 35 36
## [5,] 37 38 39 40 41 42 43 44 45
## [6,] 46 47 48 49 50 51 52 53 54
## [7,] 55 56 57 58 59 60 61 62 63
## [8,] 64 65 66 67 68 69 70 71 72
## [9,] 73 74 75 76 77 78 79 80 81calculate the sum and the mean of the elements in the first row and the last row.
first_row <- tm[1,]
last_row <- tm[n_dims,]
sum(first_row)## [1] 45sum(last_row)## [1] 693mean(first_row)## [1] 5mean(last_row)## [1] 77read about the eigen() function and use it on your matrix
eigenm <- eigen(tm, only.values = FALSE, EISPACK = FALSE)
print(eigenm)## eigen() decomposition
## $values
## [1] 3.817315e+02+0.0000e+00i -1.273146e+01+0.0000e+00i
## [3] 1.425937e-14+0.0000e+00i -5.978131e-15+6.0318e-15i
## [5] -5.978131e-15-6.0318e-15i 7.415305e-15+0.0000e+00i
## [7] 2.129759e-15+0.0000e+00i -1.021053e-15+0.0000e+00i
## [9] 3.741035e-16+0.0000e+00i
##
## $vectors
## [,1] [,2] [,3] [,4]
## [1,] -0.04421135+0i 0.53569778+0i 0.22259379+0i 0.08426958+0.04021902i
## [2,] -0.10624199+0i 0.40683688+0i -0.16126397+0i -0.00086269+0.07863143i
## [3,] -0.16827263+0i 0.27797597+0i 0.05065003+0i -0.08881057+0.08491135i
## [4,] -0.23030327+0i 0.14911507+0i 0.18219998+0i -0.13759512-0.04042975i
## [5,] -0.29233390+0i 0.02025417+0i -0.03771172+0i 0.24623388-0.09199648i
## [6,] -0.35436454+0i -0.10860673+0i -0.85708057+0i 0.29448602-0.39485081i
## [7,] -0.41639518+0i -0.23746763+0i 0.32750401+0i -0.53093082+0.00000000i
## [8,] -0.47842582+0i -0.36632853+0i 0.20027789+0i -0.25381064+0.37304691i
## [9,] -0.54045645+0i -0.49518944+0i 0.07283055+0i 0.38702037-0.04953166i
## [,5] [,6] [,7] [,8]
## [1,] 0.08426958-0.04021902i -0.320097377+0i -0.18327780+0i -0.02225211+0i
## [2,] -0.00086269-0.07863143i 0.008426601+0i -0.03945545+0i 0.03803621+0i
## [3,] -0.08881057-0.08491135i 0.231650006+0i 0.16179535+0i 0.32957228+0i
## [4,] -0.13759512+0.04042975i -0.053176583+0i 0.06124515+0i -0.05334482+0i
## [5,] 0.24623388+0.09199648i 0.307289077+0i 0.62885362+0i -0.77034254+0i
## [6,] 0.29448602+0.39485081i 0.337197061+0i -0.44170913+0i 0.07732643+0i
## [7,] -0.53093082+0.00000000i -0.630251601+0i -0.50831841+0i 0.51989670+0i
## [8,] -0.25381064-0.37304691i 0.397531401+0i 0.29176242+0i 0.01065132+0i
## [9,] 0.38702037+0.04953166i -0.278568586+0i 0.02910425+0i -0.12954347+0i
## [,9]
## [1,] -0.04079522+0i
## [2,] -0.03295809+0i
## [3,] -0.05151749+0i
## [4,] -0.04138519+0i
## [5,] 0.75624159+0i
## [6,] -0.36815250+0i
## [7,] -0.49723601+0i
## [8,] 0.14706246+0i
## [9,] 0.12874044+0ilook carefully at the elements of $values and $vectors. What kind of numbers are these? Dig in with the typeof() function to figure out their type.
typeof(eigenm$values)## [1] "complex"typeof(eigenm$vectors)## [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_data <- runif(16)
my_matrix <- matrix(data=my_data,nrow=4)
print(my_matrix)## [,1] [,2] [,3] [,4]
## [1,] 0.64493485 0.32470619 0.70563974 0.9774175
## [2,] 0.87039043 0.35358762 0.59947258 0.1142316
## [3,] 0.06350321 0.27122261 0.76214068 0.6031014
## [4,] 0.24763675 0.07087446 0.08179467 0.5115237my_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.
logical <- sample(0:100, 100, replace=TRUE)
print(logical)## [1] 26 44 5 79 7 79 45 43 42 50 33 63 80 38 69 87 20 63 49 87 13 11 91 75 48
## [26] 80 32 58 90 88 81 35 13 21 18 9 97 44 33 39 86 55 37 95 9 66 87 60 63 18
## [51] 21 52 4 86 40 23 98 0 71 59 43 6 83 58 25 29 38 0 85 88 59 22 77 99 86
## [76] 97 46 23 75 38 45 0 35 21 0 87 13 38 57 92 68 98 26 22 70 75 80 20 37 74my_logical <- logical<50
print(my_logical)## [1] TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE FALSE TRUE FALSE
## [13] FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE TRUE TRUE FALSE FALSE
## [25] TRUE FALSE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
## [37] FALSE TRUE TRUE TRUE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE
## [49] FALSE TRUE TRUE FALSE TRUE FALSE TRUE TRUE FALSE TRUE FALSE FALSE
## [61] TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE
## [73] FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
## [85] TRUE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE
## [97] FALSE TRUE TRUE FALSEmy_letters, which is a 26-element vector of all the lower-case letters in random order.
my_letters <- sample(letters, size=26)
print(my_letters)## [1] "h" "t" "m" "v" "o" "w" "j" "a" "z" "u" "i" "e" "p" "d" "f" "r" "g" "n" "s"
## [20] "l" "k" "y" "x" "c" "q" "b"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.
my_list <- list(my_matrix[2,2],my_logical[2],my_letters[2])
print(my_list)## [[1]]
## [1] 0.3535876
##
## [[2]]
## [1] TRUE
##
## [[3]]
## [1] "t"use the typeof() function to confirm the underlying data types of each component in this list
typeof(my_list[[1]])## [1] "double"typeof(my_list[[2]])## [1] "logical"typeof(my_list[[3]])## [1] "character"combine the underlying elements from the new list into a single atomic vector with the c() function. What is the data type of this vector?
condensed_list <- c(my_list[[1]],my_list[[2]],my_list[[3]])
print(condensed_list)## [1] "0.353587621590123" "TRUE" "t"typeof(condensed_list)## [1] "character"Create a data frame with two variables (= columns) and 26 cases (= rows).
call the first variable my_unis and fill it with 26 random uniform values from 0 to 10
my_unis <- runif(n=26,min=1,max=10)
print(my_unis)## [1] 2.768700 7.349744 5.582243 9.708352 2.679995 2.693027 3.511938 7.886325
## [9] 3.159215 5.533404 2.887600 4.349876 8.326151 9.261414 4.742808 6.856755
## [17] 8.109409 9.394490 5.300257 2.832395 5.064894 6.334344 8.249642 5.624872
## [25] 6.285125 6.380251call the second variable my_letters and fill it with 26 capital letters in random order.
my_letters <- sample(LETTERS, size=26)
print(my_letters)## [1] "Q" "J" "F" "W" "Z" "X" "V" "L" "K" "N" "U" "I" "B" "P" "H" "M" "C" "G" "D"
## [20] "Y" "O" "S" "R" "T" "E" "A"dataFrame <- data.frame(my_unis,my_letters)
print(dataFrame)## my_unis my_letters
## 1 2.768700 Q
## 2 7.349744 J
## 3 5.582243 F
## 4 9.708352 W
## 5 2.679995 Z
## 6 2.693027 X
## 7 3.511938 V
## 8 7.886325 L
## 9 3.159215 K
## 10 5.533404 N
## 11 2.887600 U
## 12 4.349876 I
## 13 8.326151 B
## 14 9.261414 P
## 15 4.742808 H
## 16 6.856755 M
## 17 8.109409 C
## 18 9.394490 G
## 19 5.300257 D
## 20 2.832395 Y
## 21 5.064894 O
## 22 6.334344 S
## 23 8.249642 R
## 24 5.624872 T
## 25 6.285125 E
## 26 6.380251 Afor 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.
dataFrame[sample(26, size = 4),1] <- NA
print(dataFrame)## my_unis my_letters
## 1 2.768700 Q
## 2 7.349744 J
## 3 5.582243 F
## 4 9.708352 W
## 5 2.679995 Z
## 6 2.693027 X
## 7 3.511938 V
## 8 NA L
## 9 3.159215 K
## 10 5.533404 N
## 11 NA U
## 12 4.349876 I
## 13 8.326151 B
## 14 NA P
## 15 4.742808 H
## 16 NA M
## 17 8.109409 C
## 18 9.394490 G
## 19 5.300257 D
## 20 2.832395 Y
## 21 5.064894 O
## 22 6.334344 S
## 23 8.249642 R
## 24 5.624872 T
## 25 6.285125 E
## 26 6.380251 Afor the first variable, write a single line of R code to identify which rows have the missing values.
dataFrame[!complete.cases(dataFrame),]## my_unis my_letters
## 8 NA L
## 11 NA U
## 14 NA P
## 16 NA Mfor the second variable, sort it in alphabetical order
dataFrame <- dataFrame[order(my_letters),]
print(dataFrame)## my_unis my_letters
## 26 6.380251 A
## 13 8.326151 B
## 17 8.109409 C
## 19 5.300257 D
## 25 6.285125 E
## 3 5.582243 F
## 18 9.394490 G
## 15 4.742808 H
## 12 4.349876 I
## 2 7.349744 J
## 9 3.159215 K
## 8 NA L
## 16 NA M
## 10 5.533404 N
## 21 5.064894 O
## 14 NA P
## 1 2.768700 Q
## 23 8.249642 R
## 22 6.334344 S
## 24 5.624872 T
## 11 NA U
## 7 3.511938 V
## 4 9.708352 W
## 6 2.693027 X
## 20 2.832395 Y
## 5 2.679995 Zcalculate the column mean for the first variable.
mean(dataFrame$my_unis[!is.na(dataFrame$my_unis)])## [1] 5.635506