n_dims <- sample(3:10,size=1) # random integer between 3-10
print(n_dims)## [1] 3my_seq <- 1:n_dims^2 # sequence from 1 to n_dims^2
print(my_seq)## [1] 1 2 3 4 5 6 7 8 9seq_shuffle <- sample(x=my_seq) # randomly reshuffle these values
print(seq_shuffle)## [1] 9 5 6 4 1 8 2 7 3seq_matrix <- matrix(data=seq_shuffle,nrow=n_dims) # creating a square matrix with these values
print(seq_matrix)## [,1] [,2] [,3]
## [1,] 9 4 2
## [2,] 5 1 7
## [3,] 6 8 3Transposing a matrix swaps the rows with the columns
transpose_matrix <- t(seq_matrix) # transposing this matrix
print(transpose_matrix)## [,1] [,2] [,3]
## [1,] 9 5 6
## [2,] 4 1 8
## [3,] 2 7 3sum(transpose_matrix[1,]) # sum of first row## [1] 20mean(transpose_matrix[1,]) # mean of first row## [1] 6.666667sum(transpose_matrix[n_dims,]) # sum of last row## [1] 12mean(transpose_matrix[n_dims,]) # mean of last row## [1] 4eigen(transpose_matrix) # using eigen()## eigen() decomposition
## $values
## [1] 14.931570 -5.558319 3.626749
##
## $vectors
## [,1] [,2] [,3]
## [1,] 0.7927761 0.005446303 0.8191681
## [2,] 0.4583392 -0.774669397 -0.2789202
## [3,] 0.4017851 0.632342994 -0.5011657eigen(transpose_matrix)$values # looking at $values## [1] 14.931570 -5.558319 3.626749eigen(transpose_matrix)$vectors # looking at $vectors ## [,1] [,2] [,3]
## [1,] 0.7927761 0.005446303 0.8191681
## [2,] 0.4583392 -0.774669397 -0.2789202
## [3,] 0.4017851 0.632342994 -0.5011657typeof(eigen(transpose_matrix)$values) # type of $values## [1] "double"typeof(eigen(transpose_matrix)$vectors) # type of $vectors## [1] "double"my_matrix <- matrix(data=runif(16),nrow=4) # creating my_matrix
print(my_matrix)## [,1] [,2] [,3] [,4]
## [1,] 0.2273021 0.8700357 0.9457997 0.23094772
## [2,] 0.2896987 0.7866773 0.5433780 0.28132805
## [3,] 0.9841563 0.9886634 0.2929972 0.07325231
## [4,] 0.3819385 0.9696800 0.7914918 0.27124610my_logical <- runif(100)<0.5 # creating my_logical
print(my_logical)## [1] TRUE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [13] TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE
## [25] TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE
## [37] TRUE FALSE TRUE TRUE FALSE TRUE TRUE FALSE FALSE FALSE TRUE FALSE
## [49] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE FALSE FALSE
## [61] FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE
## [73] TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE
## [85] FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE
## [97] FALSE TRUE TRUE FALSEmy_letters <- sample(letters) # creating my_letters
print(my_letters)## [1] "s" "d" "w" "f" "q" "j" "n" "v" "p" "o" "x" "m" "k" "z" "b" "u" "e" "l" "h"
## [20] "a" "t" "c" "r" "y" "g" "i"my_list <- list(my_matrix,my_logical,my_letters) # creating my_list
print(my_list)## [[1]]
## [,1] [,2] [,3] [,4]
## [1,] 0.2273021 0.8700357 0.9457997 0.23094772
## [2,] 0.2896987 0.7866773 0.5433780 0.28132805
## [3,] 0.9841563 0.9886634 0.2929972 0.07325231
## [4,] 0.3819385 0.9696800 0.7914918 0.27124610
##
## [[2]]
## [1] TRUE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [13] TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE
## [25] TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE
## [37] TRUE FALSE TRUE TRUE FALSE TRUE TRUE FALSE FALSE FALSE TRUE FALSE
## [49] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE FALSE FALSE
## [61] FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE
## [73] TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE
## [85] FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE
## [97] FALSE TRUE TRUE FALSE
##
## [[3]]
## [1] "s" "d" "w" "f" "q" "j" "n" "v" "p" "o" "x" "m" "k" "z" "b" "u" "e" "l" "h"
## [20] "a" "t" "c" "r" "y" "g" "i"new_list <- list(my_matrix[2,2],my_logical[2],my_letters[2]) # creating new list
print(new_list)## [[1]]
## [1] 0.7866773
##
## [[2]]
## [1] FALSE
##
## [[3]]
## [1] "d"typeof(new_list[[1]])## [1] "double"typeof(new_list[[2]])## [1] "logical"typeof(new_list[[3]])## [1] "character"atomic_vec <- c(new_list[[1]], new_list[[2]], new_list[[3]])
print(atomic_vec)## [1] "0.786677304422483" "FALSE" "d"typeof(atomic_vec)## [1] "character"my_unis <- sample(0:10,size=26,replace=TRUE) # create my_unis
print(my_unis)## [1] 4 6 4 5 8 5 10 4 1 1 5 6 5 1 6 0 9 7 4 8 10 10 10 3 0
## [26] 2my_letters <- sample(LETTERS) # create my_letters
print(my_letters)## [1] "S" "L" "F" "Z" "W" "C" "D" "J" "M" "G" "N" "U" "V" "O" "P" "K" "B" "X" "H"
## [20] "Q" "I" "R" "E" "Y" "T" "A"my_df <- data.frame(my_unis,my_letters) # create dataframe
print(my_df)## my_unis my_letters
## 1 4 S
## 2 6 L
## 3 4 F
## 4 5 Z
## 5 8 W
## 6 5 C
## 7 10 D
## 8 4 J
## 9 1 M
## 10 1 G
## 11 5 N
## 12 6 U
## 13 5 V
## 14 1 O
## 15 6 P
## 16 0 K
## 17 9 B
## 18 7 X
## 19 4 H
## 20 8 Q
## 21 10 I
## 22 10 R
## 23 10 E
## 24 3 Y
## 25 0 T
## 26 2 Amy_df[sample(1:nrow(my_df),size=4),1] = NA # replace 4 random rows of my_unis with NA
print(my_df)## my_unis my_letters
## 1 4 S
## 2 6 L
## 3 4 F
## 4 5 Z
## 5 8 W
## 6 5 C
## 7 NA D
## 8 NA J
## 9 1 M
## 10 1 G
## 11 5 N
## 12 6 U
## 13 5 V
## 14 1 O
## 15 6 P
## 16 0 K
## 17 9 B
## 18 NA X
## 19 4 H
## 20 NA Q
## 21 10 I
## 22 10 R
## 23 10 E
## 24 3 Y
## 25 0 T
## 26 2 Awhich(is.na(my_df$my_unis)) # which rows in my_unis have missing values## [1] 7 8 18 20new_df <- arrange(my_df,my_letters) # rearrange the dataframe alphabetically
print(new_df)## my_unis my_letters
## 1 2 A
## 2 9 B
## 3 5 C
## 4 NA D
## 5 10 E
## 6 4 F
## 7 1 G
## 8 4 H
## 9 10 I
## 10 NA J
## 11 0 K
## 12 6 L
## 13 1 M
## 14 5 N
## 15 1 O
## 16 6 P
## 17 NA Q
## 18 10 R
## 19 4 S
## 20 0 T
## 21 6 U
## 22 5 V
## 23 8 W
## 24 NA X
## 25 3 Y
## 26 5 Zmean(new_df$my_unis,na.rm=TRUE) # calculate the mean for the first variable after removing NAs## [1] 4.772727