Question 1
n_dims <- sample(3:9, 1)
vec <- c(1:n_dims^2)
re_vec <- sample(vec)
revec_matrix <- matrix(data=re_vec,nrow=n_dims,ncol=n_dims)
print(revec_matrix)
## [,1] [,2] [,3] [,4] [,5]
## [1,] 12 9 10 7 14
## [2,] 16 13 19 4 21
## [3,] 2 5 20 24 3
## [4,] 1 25 8 23 17
## [5,] 6 18 22 15 11
trans_rvmx<- t(revec_matrix)
print(trans_rvmx) #changed by making rows columns and columns rows
## [,1] [,2] [,3] [,4] [,5]
## [1,] 12 16 2 1 6
## [2,] 9 13 5 25 18
## [3,] 10 19 20 8 22
## [4,] 7 4 24 23 15
## [5,] 14 21 3 17 11
eigen_vec <- eigen(x=trans_rvmx,symmetric=TRUE) #all of these values are doubles
typeof(eigen_vec$values) #double
## [1] "double"
typeof(eigen_vec$vectors) #double
## [1] "double"
Question 2
my_matrix <- matrix(data=runif(16),nrow=4,ncol = 4)
print(my_matrix)
## [,1] [,2] [,3] [,4]
## [1,] 0.9537999 0.63064915 0.4706896 0.9075425
## [2,] 0.7990782 0.15290807 0.6954724 0.4107923
## [3,] 0.2779337 0.06970169 0.3521289 0.1747413
## [4,] 0.8145608 0.21597537 0.4740964 0.9257668
my_logical <- c(0.5 < runif(1:100))
print(my_logical)
## [1] FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE TRUE
## [13] TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE FALSE
## [25] FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
## [37] FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE
## [49] FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE TRUE TRUE
## [73] FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE TRUE
## [85] TRUE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE
## [97] TRUE FALSE FALSE FALSE
my_letters <- sample(letters)
my_list <- list(my_matrix,my_logical,my_letters)
new_list <- list(my_list[[1]][2,2], my_list[[2]][2], my_list[[3]][2])
typeof(new_list[[1]]) #double
## [1] "double"
typeof(new_list[[2]]) #logical
## [1] "logical"
typeof(new_list[[3]]) #character
## [1] "character"
single_av <- c(new_list[[1]],new_list[[2]],new_list[[3]])
typeof(single_av) #now just a character
## [1] "character"
Question 3
my_unis <- runif(26, min=1, max=10)
my_letters <- sample(LETTERS)
data_frame <- data.frame(my_unis,my_letters)
data_frame[ ,1][sample(nrow(data_frame), 4)] <- NA
is.na(data_frame)
## my_unis my_letters
## [1,] FALSE FALSE
## [2,] FALSE FALSE
## [3,] FALSE FALSE
## [4,] FALSE FALSE
## [5,] FALSE FALSE
## [6,] FALSE FALSE
## [7,] FALSE FALSE
## [8,] FALSE FALSE
## [9,] FALSE FALSE
## [10,] FALSE FALSE
## [11,] FALSE FALSE
## [12,] FALSE FALSE
## [13,] FALSE FALSE
## [14,] FALSE FALSE
## [15,] FALSE FALSE
## [16,] FALSE FALSE
## [17,] TRUE FALSE
## [18,] FALSE FALSE
## [19,] FALSE FALSE
## [20,] TRUE FALSE
## [21,] TRUE FALSE
## [22,] FALSE FALSE
## [23,] FALSE FALSE
## [24,] FALSE FALSE
## [25,] FALSE FALSE
## [26,] TRUE FALSE
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
ABC_dataframe <- data_frame %>% arrange(data_frame[2])
print(ABC_dataframe)
## my_unis my_letters
## 1 2.102428 A
## 2 2.158331 B
## 3 3.198105 C
## 4 7.377216 D
## 5 1.037026 E
## 6 5.076838 F
## 7 7.362733 G
## 8 5.035376 H
## 9 5.509014 I
## 10 NA J
## 11 6.294616 K
## 12 NA L
## 13 5.760605 M
## 14 1.244039 N
## 15 8.923814 O
## 16 6.133197 P
## 17 2.105820 Q
## 18 4.048511 R
## 19 7.411266 S
## 20 NA T
## 21 NA U
## 22 4.409480 V
## 23 3.705343 W
## 24 3.496684 X
## 25 3.666021 Y
## 26 9.855381 Z
mean(ABC_dataframe[,1],na.rm=TRUE)
## [1] 4.814175