Fahmida Faiza (assignment 1)

Q 1(a)

gene1 = "ACGTACGT"
gene2 = "ACG"

Q 1(b)

combined_var = paste(gene1, gene2, sep = "")
print(combined_var)

[1] "ACGTACGTACG"

Q 1(c)

sub = substr(gene1, 2, 5)
sub

[1] "CGTA"

Q 1(d)

gene1 = "ACGTACGT"
gene2 = "ACG"

grepl(gene2, gene1)

[1] TRUE

Q 2(a)

var1 = 40
var2 = 20

## Add
res_add = var1 + var2
print(res_add)

[1] 60

## substract
res_sub = var1 - var2
print(res_sub)

[1] 20

## multiply
res_mult = var1 * var2
print(res_mult)

[1] 800

## division
res_div = var1 / var2
print(res_div)

[1] 2

## power
res_pow = var1 ** var2
print(res_pow)

[1] 1.099512e+32

## module
res_mod = var1 %% var2
print(res_mod)

[1] 0

Q 3

scores = c(100, 90, 50, 20, 30, 88, 66, 75, 71, 91, 0, 45, 61)

for (i in 1:length(scores)){
 
s = scores[i]

if (s >= 80){
  cat(i, s, "A+\n")
} else if(s >= 75){
  cat(i, s, "A\n")
} else if(s >= 70){
  cat(i, s, "A-\n")
} else if(s >= 65){
  cat(i, s, "B+\n")
} else if(s >= 60){
  cat(i, s, "B\n")
} else if(s >= 55){
  cat(i, s, "B-\n")
} else if(s >= 50){
  cat(i, s, "C+\n")
} else if(s >= 45){
  cat(i, s, "C\n")
} else if(s >= 40){
  cat(i, s, "D\n")
} else{
  cat(i, s, "F\n")
}
}

1 100 A+
2 90 A+
3 50 C+
4 20 F
5 30 F
6 88 A+
7 66 B+
8 75 A
9 71 A-
10 91 A+
11 0 F
12 45 C
13 61 B

age = c(20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80)
age

 [1] 20 25 30 35 40 45 50 55 60 65 70 75
[13] 80

correlation

scores = c(100, 90, 50, 20, 30, 88, 66, 75, 71, 91, 0, 45, 61)

age = c(20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80)


cor(age, scores)

[1] -0.2658518

here age and scores have weak negative correlation

scores1 = scores[3:10]
scores1

[1] 50 20 30 88 66 75 71 91

age1 = age[3:10]
age1

[1] 30 35 40 45 50 55 60 65

cor(scores1, age1)

[1] 0.7361898

this time correlation is positive and slightly strong.