##Question 1 1A
x = c(10, 20, 30, 44, 55, 10, 30, 50, 32, 30, 46)
x## [1] 10 20 30 44 55 10 30 50 32 30 46z = append(x,'26',after=10)
print(z)## [1] "10" "20" "30" "44" "55" "10" "30" "50" "32" "30" "26" "46"1B
sum = sum (x) / 11
sum## [1] 32.454551C
set.seed (1)
x1= rnorm(n= 12, mean= 5, sd= 1)
x1= round(x1, 1)
set.seed (2)
x2= rnorm(n= 12, mean= 5, sd= 1)
x2= round(x2, 1)
y = rep(c('A','B','C'),each = 4)
DFAssg1 = data.frame (y,
z,
x1,
x2
)
print(DFAssg1)## y z x1 x2
## 1 A 10 4.4 4.1
## 2 A 20 5.2 5.2
## 3 A 30 4.2 6.6
## 4 A 44 6.6 3.9
## 5 B 55 5.3 4.9
## 6 B 10 4.2 5.1
## 7 B 30 5.5 5.7
## 8 B 50 5.7 4.8
## 9 C 32 5.6 7.0
## 10 C 30 4.7 4.9
## 11 C 26 6.5 5.4
## 12 C 46 5.4 6.01D
min(z, z= c('A','B','C'))## [1] "10"1E
mean(c(DFAssg1$x1, DFAssg1$x2))## [1] 5.28751F
1G
Is_high_value<- x1 > mean(x1)
DFAssg1$Is_high_value## NULL1H
unique(z)## [1] "10" "20" "30" "44" "55" "50" "32" "26" "46"1I i) Write only ONE LINE of code that returns the count of how many elements in x are greater than 30.
sum(x > 30) ## [1] 5##Question 2 2A
die1 <-sample(6, 1000, replace=TRUE)
die1## [1] 1 6 1 4 3 6 1 6 5 6 6 3 1 5 5 6 6 2 2 3 4 3 1 1 5 1 2 4 5 6 5 4 2 5 6 5 2
## [38] 6 4 4 4 4 1 2 2 6 6 3 5 3 6 5 5 1 5 6 1 2 1 5 4 1 6 1 5 3 1 2 6 5 3 1 4 1
## [75] 2 1 4 4 1 4 6 1 5 6 4 6 3 5 4 5 5 1 5 2 3 3 1 6 1 3 5 3 6 3 6 1 4 2 2 5 4
## [112] 6 3 1 1 3 5 4 4 6 2 5 1 5 4 5 3 2 1 3 5 1 4 2 2 4 5 2 4 6 5 6 6 6 4 3 5 3
## [149] 5 2 2 6 3 5 2 4 3 2 6 2 5 6 4 4 3 3 6 3 6 1 6 6 1 6 3 6 6 5 6 6 3 2 4 3 3
## [186] 3 6 2 2 4 1 4 2 3 1 6 1 1 4 5 2 2 3 2 3 5 3 4 3 5 5 4 5 2 6 2 4 4 6 6 2 6
## [223] 1 5 3 4 5 2 5 4 5 4 6 2 4 1 6 1 2 4 5 2 6 3 2 5 6 6 3 2 6 5 1 1 5 4 5 3 2
## [260] 3 3 3 1 6 5 2 3 3 5 5 4 4 4 5 3 6 2 1 5 3 6 3 5 1 3 4 4 6 3 3 2 1 1 1 1 3
## [297] 3 4 5 5 4 5 2 4 6 4 4 3 2 1 5 3 6 6 6 2 4 2 3 5 5 3 4 6 3 1 3 2 3 6 6 4 1
## [334] 5 2 1 6 5 2 3 2 5 6 6 5 4 4 2 4 2 1 4 1 1 6 2 4 2 4 5 1 5 6 2 3 1 3 1 2 3
## [371] 4 2 3 5 6 1 2 1 6 6 5 1 3 5 1 4 1 4 2 5 1 2 5 4 1 1 4 6 1 5 4 1 1 1 5 3 5
## [408] 5 6 1 5 2 2 4 3 6 3 4 1 3 6 3 3 2 6 6 5 5 4 6 3 1 6 4 6 6 3 5 5 3 4 3 5 3
## [445] 2 1 5 6 6 2 2 2 2 6 6 6 2 5 5 2 5 6 2 4 1 4 6 6 4 1 6 4 6 3 1 4 1 5 6 5 2
## [482] 4 5 3 5 6 4 2 5 2 2 1 4 6 6 1 3 5 6 1 6 1 6 1 6 5 6 1 2 4 4 2 1 6 2 2 5 1
## [519] 3 1 3 1 3 6 6 6 5 4 3 4 6 1 3 5 1 2 1 1 4 1 2 4 6 2 1 2 6 2 4 5 4 5 3 5 2
## [556] 4 4 5 4 6 6 5 6 3 5 1 4 3 5 4 2 3 2 1 2 2 6 4 2 4 5 1 3 1 6 1 3 4 4 6 5 3
## [593] 6 5 3 3 2 6 5 5 2 6 4 2 3 2 5 2 2 1 1 1 2 4 4 4 6 1 5 3 2 2 4 4 6 5 1 5 2
## [630] 2 6 5 4 1 4 5 4 6 2 4 5 5 5 5 3 5 3 6 4 3 4 5 5 3 4 4 6 6 6 5 5 5 2 6 6 6
## [667] 6 5 6 2 4 2 4 5 4 2 5 2 4 2 2 6 3 1 1 3 3 5 5 3 3 3 3 5 6 5 1 4 3 1 2 2 5
## [704] 5 6 2 1 1 5 5 6 4 2 6 6 4 3 5 1 3 5 4 6 5 3 4 3 1 4 5 1 5 4 4 1 4 4 4 4 1
## [741] 3 6 3 3 4 3 6 1 3 4 4 5 2 6 2 2 3 6 2 1 4 3 6 2 6 6 5 4 4 3 5 6 5 4 3 4 3
## [778] 5 3 4 6 4 1 1 5 5 5 5 1 6 1 4 3 4 1 5 2 2 2 1 5 5 5 6 2 2 5 6 1 5 5 6 5 2
## [815] 4 5 4 4 6 6 2 2 4 6 2 2 4 5 4 4 5 5 5 4 1 5 5 6 2 5 6 4 3 6 2 2 1 4 2 1 4
## [852] 5 1 3 1 3 4 6 2 1 2 5 2 3 4 4 5 6 1 6 3 6 4 6 4 2 5 4 6 6 3 4 1 6 2 6 5 4
## [889] 1 3 1 3 2 4 5 4 4 5 6 5 1 5 4 4 4 1 3 1 1 1 6 5 3 5 2 3 2 3 5 4 3 2 1 1 3
## [926] 5 6 4 4 4 1 4 2 4 2 4 3 3 5 6 2 5 2 5 5 4 5 2 4 2 6 5 4 6 6 6 4 6 3 1 1 1
## [963] 1 5 3 3 1 1 5 1 6 5 1 2 4 3 5 5 2 4 6 3 3 1 1 4 1 5 1 4 2 6 1 3 5 1 4 3 1
## [1000] 5die2 <-sample(6, 1000, replace=TRUE)
die2## [1] 1 3 1 2 3 4 6 3 4 2 4 3 3 3 5 6 6 5 2 6 4 1 4 2 1 2 4 1 6 6 5 2 4 1 4 6 2
## [38] 5 6 5 3 6 4 4 1 2 2 6 2 6 6 6 6 6 5 5 3 3 6 3 3 4 6 1 3 1 2 4 4 6 4 5 4 3
## [75] 1 5 1 3 1 4 6 1 2 2 5 2 1 4 2 3 5 6 3 3 6 5 3 3 5 5 1 1 4 1 3 3 2 6 2 5 5
## [112] 4 2 6 3 4 6 3 1 3 3 2 4 1 5 2 6 6 5 3 3 4 1 3 6 6 4 1 6 2 2 3 2 6 5 1 1 4
## [149] 2 5 5 6 6 2 2 6 6 2 5 3 6 4 3 5 2 5 3 5 5 1 3 6 4 6 1 6 4 5 5 3 1 2 5 4 1
## [186] 5 5 2 5 6 2 2 3 3 5 5 4 5 2 3 5 4 4 2 4 5 6 2 3 2 2 3 1 4 4 5 4 3 1 1 2 4
## [223] 5 2 3 2 6 6 2 6 6 1 1 1 3 5 6 5 2 2 6 6 4 6 5 3 6 1 2 5 2 6 3 1 3 1 5 2 6
## [260] 1 6 3 5 6 2 1 4 2 5 2 4 3 1 5 4 6 6 6 4 1 6 2 1 1 3 4 3 1 4 2 3 4 1 4 1 2
## [297] 3 2 2 2 1 4 5 4 4 6 5 2 3 3 2 2 5 5 2 3 3 4 4 2 1 1 1 3 4 1 1 6 3 1 4 2 1
## [334] 2 5 4 6 1 4 3 6 3 4 5 3 6 4 2 2 5 4 2 6 6 2 1 5 3 6 5 4 5 6 3 4 1 6 4 1 1
## [371] 5 1 1 2 3 1 1 1 6 3 6 3 1 1 5 2 3 5 2 5 1 6 2 3 4 3 2 4 3 4 1 1 3 1 1 5 6
## [408] 6 3 6 5 4 2 3 1 2 4 4 6 2 6 6 1 6 1 5 1 4 2 6 1 4 2 3 3 5 4 6 1 6 2 6 1 6
## [445] 1 3 2 3 1 5 2 3 1 1 1 5 3 6 1 6 1 6 4 1 1 2 3 6 4 6 1 4 6 2 5 1 3 1 1 1 1
## [482] 1 3 5 2 4 1 1 6 2 4 1 5 2 6 4 4 2 4 3 5 1 3 3 4 3 6 6 2 2 3 4 2 4 2 2 4 3
## [519] 6 5 3 4 4 6 2 5 1 1 6 3 5 2 2 1 6 4 2 3 1 6 4 5 5 2 2 2 6 1 5 5 4 4 5 5 2
## [556] 5 4 4 6 2 6 4 1 3 4 2 6 4 3 1 2 3 4 6 2 3 5 5 4 3 2 5 4 1 2 5 3 4 4 1 6 1
## [593] 5 3 1 1 6 1 2 2 4 3 1 6 2 3 3 6 2 1 2 6 6 6 3 2 2 5 5 4 3 4 1 6 1 4 3 2 3
## [630] 6 1 6 6 5 5 5 6 3 5 4 1 6 5 1 5 3 5 5 6 2 3 1 5 3 3 1 3 6 3 4 5 1 5 4 1 5
## [667] 3 1 2 2 6 1 2 3 2 5 1 6 1 2 2 2 4 5 2 2 3 2 4 5 5 6 1 6 3 6 3 5 5 5 2 2 6
## [704] 6 5 4 5 1 4 1 2 5 6 4 2 6 3 4 4 6 1 6 1 2 3 4 1 4 4 6 2 1 4 5 2 6 3 3 2 4
## [741] 6 4 2 3 2 5 4 5 5 5 1 1 5 4 3 4 2 5 4 2 3 2 3 6 3 3 2 4 4 4 5 1 6 4 6 1 1
## [778] 6 4 4 6 4 4 2 3 4 5 6 3 6 1 3 2 1 1 1 3 2 3 5 6 1 2 3 5 3 3 6 1 6 3 6 2 2
## [815] 3 4 1 5 6 2 3 1 6 2 6 3 2 6 5 2 3 6 6 3 4 3 4 4 1 5 1 3 5 2 5 5 3 5 6 4 3
## [852] 6 2 4 4 3 4 4 3 2 2 6 6 3 1 4 1 3 1 1 1 2 1 2 1 1 6 6 5 1 3 6 2 4 2 2 5 3
## [889] 3 5 2 4 5 3 6 4 3 1 1 4 2 5 6 4 4 2 3 6 1 6 5 2 6 3 3 5 4 1 4 1 3 1 5 2 5
## [926] 4 6 2 6 5 1 6 1 1 6 1 5 3 2 2 6 4 4 3 6 1 6 2 2 2 6 3 3 2 2 2 6 1 4 2 1 2
## [963] 6 6 4 6 1 3 3 1 4 3 4 2 6 2 2 4 1 5 4 2 6 6 3 3 2 5 6 3 4 6 2 5 5 2 4 4 4
## [1000] 22B
die1.sum <- sum(die1)
die1.sum## [1] 3626die2.sum <- sum(die2)
die2.sum## [1] 34542C
table <- table(die1, die2)
table## die2
## die1 1 2 3 4 5 6
## 1 33 27 26 26 23 21
## 2 21 36 25 24 20 25
## 3 27 22 26 27 21 24
## 4 32 28 34 30 26 29
## 5 34 36 28 25 28 40
## 6 25 31 28 29 25 38cumsum_table <- sum(table)
cumsum_table ## [1] 1000##Question 3 3A
sample_of_20 <- sample(x = 1:100, size = 20)
standardized_values <- (sample_of_20 - mean(sample_of_20)) / sd(sample_of_20)
standardized_values## [1] -0.43723211 0.06341534 -1.50528001 -1.33839753 -1.07138555 -1.23826804
## [7] -0.10346714 1.16483974 -0.97125606 -0.60411459 -0.23697313 0.69756879
## [13] 1.59873420 -0.40385561 1.09808675 1.06471025 0.23029783 1.53198121
## [19] -0.57073810 1.031333763B
sum_sample <- sum(sample_of_20)
count_sample <- (sample_of_20)
sum_sample / count_sample## [1] 27.939394 19.208333 922.000000 153.666667 65.857143 102.444444
## [7] 21.441860 11.382716 54.235294 32.928571 23.641026 13.761194
## [13] 9.808511 27.117647 11.670886 11.820513 17.396226 10.021739
## [19] 31.793103 11.9740263C
sum(( (standardized_values) - mean(standardized_values)) ^ 2) / (length(standardized_values) - 1)## [1] 1