Class 2

String

Pattern Matching

DNA_seq = 'ATCGATCGATCGATCGATCGATCGATCGATCGATCG'
pattern = 'ATC'

matches = grepl(pattern, DNA_seq)
print(matches)

[1] TRUE

matches = grepl('ABCD', DNA_seq)
print(matches)

[1] FALSE

Split

genes = 'gene1,gene2,gene3'
split_genes = strsplit(genes, ',')
print(split_genes)

[[1]]
[1] "gene1" "gene2" "gene3"

genes = 'gene1 gene2 gene3'
split_genes = strsplit(genes, ' ')
print(split_genes)

[[1]]
[1] "gene1" "gene2" "gene3"

paragraph = "This is an example. We are going to split. And show the output. "
p_split = strsplit(paragraph, "\\. ")
print(p_split)

[[1]]
[1] "This is an example"    "We are going to split" "And show the output"  

Reverse

library(stringi)
s1 = 'ABCDEFG'
rev_s1 = stri_reverse(s1)
print(rev_s1)

[1] "GFEDCBA"

Number of Occurances

DNA_seq = 'ATCGATCGATCGATCGATCGATCGATCGATCGATCG'
pattern = 'ATC'

locations = gregexpr(pattern, DNA_seq)
locations = unlist(locations)
print(locations)

[1]  1  5  9 13 17 21 25 29 33

num_occ = sum(locations != -1)
print(num_occ)

[1] 9

Vector

Create

ages = c(20, 25, 65, 18, 45)
print(ages)

[1] 20 25 65 18 45

Indexing

ages[-1]

[1] 25 65 18 45

Append

ages_extended = c(ages, 100)
ages_extended

[1]  20  25  65  18  45 100

ages_extended = c(ages, c(100, 50, 20, 10))
ages_extended

[1]  20  25  65  18  45 100  50  20  10

Append

ages = c(ages, 100)
ages

[1]  20  25  65  18  45 100

ages = c(ages, c(100, 50, 20, 10))
ages

 [1]  20  25  65  18  45 100 100  50  20  10

slice or subset

print(ages)

 [1]  20  25  65  18  45 100 100  50  20  10

print(ages[3:6])

[1]  65  18  45 100

print(ages[2:7])

[1]  25  65  18  45 100 100

print(ages[2:20])

 [1]  25  65  18  45 100 100  50  20  10  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA

Length

print(length(ages))

[1] 10

Insert in a specific position

print(ages)

 [1]  20  25  65  18  45 100 100  50  20  10

print(ages[1:2])

[1] 20 25

print(ages[3:10])

[1]  65  18  45 100 100  50  20  10

ages_extended = c(ages[1:2], 99, ages[3:10])
ages_extended

 [1]  20  25  99  65  18  45 100 100  50  20  10

Vector conditional

print(ages)

 [1]  20  25  65  18  45 100 100  50  20  10

eligible_participant = ages[ages < 50]
eligible_participant

[1] 20 25 18 45 20 10

eligible_participant = ages[ages <= 50]
eligible_participant

[1] 20 25 18 45 50 20 10

eligible_participant = ages[ages > 50]
eligible_participant

[1]  65 100 100

age_25 = ages[ages >= 25]
age_25

[1]  25  65  45 100 100  50

age_25_50 = age_25[age_25 <= 50]
age_25_50

[1] 25 45 50

ages[ages >= 25 & ages <= 50]

[1] 25 45 50

Sort

sort(ages)

 [1]  10  18  20  20  25  45  50  65 100 100

sort(ages, TRUE)

 [1] 100 100  65  50  45  25  20  20  18  10

Simple statistics

print(max(ages))

[1] 100

print(min(ages))

[1] 10

print(mean(ages))

[1] 45.3

print(median(ages))

[1] 35

print(sum(ages))

[1] 453

print(sd(ages))

[1] 33.46325

print(var(ages))

[1] 1119.789

print(quantile(ages))

    0%    25%    50%    75%   100% 
 10.00  20.00  35.00  61.25 100.00 

Correlation

length(ages)

[1] 10

scores = c(10, 30, 40, 50, 39, 10, 30, 40, 50, 39)
length(scores)

[1] 10

cor(ages, scores)

[1] -0.4010329

ages

 [1]  20  25  65  18  45 100 100  50  20  10

max(ages)

[1] 100

max(ages[2:5])

[1] 65

Data Frame

exam_score = data.frame(
  ID = c(1, 2, 3, 4, 5),
  Name = c("Alice", "Bob", "David", "John", "Jenny"),
  Age = c(20, 25, 30, 22, 18),
  score = c(100, 78, 90, 55, 81)
)


print(exam_score)

Row select

#vector[location]
#data[row,col]

exam_score[2:4, ]

exam_score[2, ]

Colunm select

exam_score[ , 3:4 ]

exam_score[ , 4 ]

[1] 100  78  90  55  81

Row and column select

exam_score[2:4, 3:4 ]

Column select

exam_score$Name

[1] "Alice" "Bob"   "David" "John"  "Jenny"

exam_score$Age

[1] 20 25 30 22 18

Row and column select

exam_score$Name

[1] "Alice" "Bob"   "David" "John"  "Jenny"

exam_score$Age

[1] 20 25 30 22 18

selected_segment = data.frame(
  Name = exam_score$Name,
  Age = exam_score$Age
)
selected_segment

exam_score[c(2, 3, 4), c(1, 4) ]

Conditional selection

exam_score[ exam_score$score > 80 & exam_score$score <= 90, ]

selected_ID = c(1, 3, 4)
exam_score[ exam_score$ID %in% selected_ID, ]

Add a new row

exam_score

new_row = c(10, 'Sam', 55, 89)

exam_score2 = rbind(exam_score, new_row)
exam_score2

Add a new column

Loaction = c('Dhaka', "Raj", "Barishal", "Dhaka", "Barishal", "Dhaka" )
exam_score2 = cbind(exam_score2, Loaction)
exam_score2

Condition

x = 90

if(x < 80) {
  print("Fail")
  print("Please improve")
} else {
  print("Pass")
  print("you are good")
}

[1] "Pass"
[1] "you are good"

Loop

for(i in 1:10) {
  print(i)
}

[1] 1
[1] 2
[1] 3
[1] 4
[1] 5
[1] 6
[1] 7
[1] 8
[1] 9
[1] 10

score = c(100, 78, 90, 55, 81)

for(s in score) {
  print(s)
}

[1] 100
[1] 78
[1] 90
[1] 55
[1] 81

Loop and condition

score = c(100, 78, 90, 55, 81)

for(s in score) {
  
  if(s >= 80) {
    cat(s, "Pass \n")
  } 
  else {
    cat(s, "Fail \n")
  }
  
  
}

100 Pass 
78 Fail 
90 Pass 
55 Fail 
81 Pass 

Loop example

genes = c("AGTC", "AGTCGA", "AAGTACAT")

for(gene in genes) {
  cat(gene, nchar(gene), '\n')
}

AGTC 4 
AGTCGA 6 
AAGTACAT 8 

Add a serial number

genes = c("AGTC", "AGTCGA", "AAGTACAT")

i = 1
for(gene in genes) {
  cat(i, gene, nchar(gene), '\n')
  i = i+1
}

1 AGTC 4 
2 AGTCGA 6 
3 AAGTACAT 8 

Add letters as serial

# A, B, C ... 

for(i in LETTERS) {
  print(i)
}

[1] "A"
[1] "B"
[1] "C"
[1] "D"
[1] "E"
[1] "F"
[1] "G"
[1] "H"
[1] "I"
[1] "J"
[1] "K"
[1] "L"
[1] "M"
[1] "N"
[1] "O"
[1] "P"
[1] "Q"
[1] "R"
[1] "S"
[1] "T"
[1] "U"
[1] "V"
[1] "W"
[1] "X"
[1] "Y"
[1] "Z"

LETTERS[2]

[1] "B"

genes = c("AGTC", "AGTCGA", "AAGTACAT")

i = 1
for(gene in genes) {
  cat(i, LETTERS[i], gene, nchar(gene), '\n')
  i = i+1
}

1 A AGTC 4 
2 B AGTCGA 6 
3 C AAGTACAT 8