#Experiment-1 except 1 and 15 as they need input from user
#2
objects <- ls()
for (object in objects) {
cat("Object Name:", object, "\n")
cat("Class:", class(get(object)), "\n")
cat("Structure:\n")
print(str(get(object)))
cat("\n")
}
#3
sequence <- 20:50
mean_20_to_60 <- mean(20:60)
sum_51_to_91 <- sum(51:91)
cat("Sequence: ", sequence)
## Sequence: 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
cat("\nMean: ", mean_20_to_60)
##
## Mean: 40
cat("\nSum: ", sum_51_to_91)
##
## Sum: 2911
#4
random_vector <- sample(-50:50, 10, replace = TRUE)
cat("Random values: ", random_vector)
## Random values: 27 7 -13 26 36 37 28 39 -5 39
#5
fib <- numeric(10)
fib[1] <- 0
fib[2] <- 1
for (i in 3:10) {
fib[i] <- fib[i - 1] + fib[i - 2]
}
cat("Fibinoacci series: ", fib)
## Fibinoacci series: 0 1 1 2 3 5 8 13 21 34
#6
letters_lower <- letters[1:10]
letters_upper_last <- toupper(letters[17:26])
letters_upper_22_to_24 <- toupper(letters[22:24])
cat("Lowercase letters in the start: ", letters_lower)
## Lowercase letters in the start: a b c d e f g h i j
cat("\nUppercase letters in the end: ", letters_upper_last)
##
## Uppercase letters in the end: Q R S T U V W X Y Z
cat("\nUppercase letters in between: ", letters_upper_22_to_24)
##
## Uppercase letters in between: V W X
#7
vector <- c(2, 1, 0, 6, 5)#Random vector taken
max_value <- max(vector)
min_value <- min(vector)
cat("Max value: ", max_value)
## Max value: 6
cat("\nMin value: ", min_value)
##
## Min value: 0
#8
unique_string <- unique(strsplit("Visualisation", NULL)[[1]])
unique_vector <- unique(c(1, 2, 2, 3, 3, 3))
cat("Unique characters in string: ", unique_string)
## Unique characters in string: V i s u a l t o n
cat("\nUnique numbers in the vector: ", unique_vector)
##
## Unique numbers in the vector: 1 2 3
#9
a <- c(1, 2, 3)
b <- c(4, 5, 6)
c <- c(7, 8, 9)
result <- matrix(c(a, b, c), nrow=3, byrow=FALSE)
cat("Combined Matrix: ", result)
## Combined Matrix: 1 2 3 4 5 6 7 8 9
#10
random_list <- rnorm(65)
occurrences <- table(random_list)
cat("List: ", random_list)
## List: -0.3263863 -2.04803 0.9137257 -0.2555159 0.9363121 0.0392561 -0.3196928 0.09440307 2.285322 -0.6585349 -1.004089 1.210694 -1.652925 -1.100675 -0.8160821 -0.9544835 -0.5852272 -1.887455 0.170629 0.3950414 0.2979762 0.4639108 0.4605013 0.02128202 -0.0409731 -2.34163 -1.522685 0.76013 -0.5791727 0.739704 0.5847004 -2.094869 0.5120813 1.74189 1.111894 -0.5026745 0.6394856 0.2984305 -2.523878 -0.4560295 0.2938852 2.093212 -0.9852411 -0.4066922 -1.101112 0.3074798 0.2986948 -1.800739 -0.7178571 2.208895 1.692517 -1.97711 -1.637764 1.688493 -0.7705347 -0.4411474 1.384195 0.1599661 0.5959678 0.3722479 0.7349761 1.128492 0.7725992 -0.0556442 -1.661389
cat("\nOccurrences: ", occurrences)
##
## Occurrences: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#we can also use round here which might give a better expected result
random_list <- round(rnorm(65))
occurrences <- table(random_list)
cat("List: ", random_list)
## List: 2 0 -2 -2 -2 1 0 1 1 1 0 0 1 0 0 1 0 0 -1 0 -2 0 0 0 1 2 -2 0 -1 0 -1 1 -2 0 1 -1 0 -1 -2 0 -1 1 0 0 -1 -1 1 1 0 -1 1 0 1 -1 0 0 1 -1 0 1 1 0 2 0 -1
cat("\nOccurrences: ", occurrences)
##
## Occurrences: 7 12 26 17 3
#11
data <- read.csv("C:/Users/bmpav/OneDrive/Desktop/revenuefinal.csv")
print(data)
## SerialNumber Names Rank Marketcap
## 1 1 Shree Cement #1344 $12.13 B
## 2 2 Shriram Transport Finance #1513 $10.42 B
## 3 3 National Mineral Development Corporation #1914 $7.65 B
## 4 4 ACC #2394 $5.59 B
## 5 5 Siemens India #985 $18.02 B
## 6 6 Rain Industries #5410 $0.71 B
## 7 7 ICICI Lombard #1721 $8.78 B
## 8 8 Reliance Infrastructure #4963 $1.02 B
## 9 9 IDFC FIRST Bank #2098 $6.80 B
## 10 10 Chambal Fertilisers #4144 $1.84 B
## 11 11 Adani Ports & SEZ #609 $29.80 B
## 12 12 Madras Rubber Factory #2053 $6.98 B
## 13 13 Apollo Tyres #2938 $3.95 B
## 14 14 Cipla #1257 $13.30 B
## 15 15 Indian Railway Finance #670 $27.35 B
## 16 16 Torrent Power #2334 $5.78 B
## 17 17 Dr. Reddy's #1383 $11.72 B
## 18 18 Aurobindo Pharma #1829 $8.11 B
## 19 19 National Fertilizers #5361 $0.74 B
## 20 20 Hindustan Aeronautics #787 $23.36 B
## 21 21 Indus Towers #1995 $7.30 B
## 22 22 IndusInd Bank #1191 $14.15 B
## 23 23 Hindustan Zinc #1080 $15.93 B
## 24 24 Power Finance Corp #1048 $16.64 B
## 25 25 Oil India #2486 $5.25 B
## 26 26 Bank of India #2016 $7.20 B
## 27 27 Bajaj Finance #339 $52.66 B
## 28 28 Aditya Birla Capital #2504 $5.18 B
## 29 29 Indian Bank #1954 $7.46 B
## 30 30 Patanjali Foods #2076 $6.88 B
## 31 31 Ambuja Cements #1247 $13.39 B
## 32 32 TVS Motor #1444 $11.10 B
## 33 33 Hero MotoCorp #1490 $10.68 B
## 34 34 LTIMindtree #924 $19.58 B
## 35 35 Asian Paints #542 $34.03 B
## 36 36 Max Financial Services #3062 $3.64 B
## 37 37 Bajaj Auto #716 $25.88 B
## 38 38 Adani Power #733 $25.21 B
## 39 39 New India Assurance #2695 $4.58 B
## 40 40 Vodafone Idea #1756 $8.55 B
## 41 41 Powergrid Corporation of India #669 $27.47 B
## 42 42 Titan Company #453 $40.27 B
## 43 43 Ashok Leyland #2289 $5.99 B
## 44 44 Sun Pharmaceutical #464 $39.50 B
## 45 45 General Insurance Corporation of India #1960 $7.44 B
## 46 46 Jindal Stainless #2470 $5.33 B
## 47 47 DMart #623 $29.24 B
## 48 48 UPL #2600 $4.85 B
## 49 49 Punjab National Bank #1210 $13.86 B
## 50 50 Tech Mahindra #1102 $15.52 B
## 51 51 Union Bank of India #1313 $12.51 B
## 52 52 Adani Wilmar #2430 $5.49 B
## 53 53 Tata Power #1198 $14.03 B
## 54 54 Petronet LNG #2647 $4.69 B
## 55 55 InterGlobe Aviation #1258 $13.29 B
## 56 56 Hindustan Unilever #239 $68.70 B
## 57 57 Canara Bank #1547 $10.15 B
## 58 58 Kotak Mahindra Bank #434 $42.29 B
## 59 59 Chennai Petroleum #4527 $1.43 B
## 60 60 UltraTech Cement #534 $34.57 B
## 61 61 ITC #241 $68.42 B
## 62 62 Bank of Baroda #1193 $14.13 B
## 63 63 Axis Bank #479 $38.71 B
## 64 64 Jindal Steel & Power #1720 $8.79 B
## 65 65 ICICI Prulife #1779 $8.43 B
## 66 66 Redington India #4323 $1.62 B
## 67 67 Samvardhana Motherson #1640 $9.40 B
## 68 68 HDFC Life #1135 $14.98 B
## 69 69 Wipro #605 $29.84 B
## 70 70 Bajaj Finserv #576 $31.23 B
## 71 71 HCL Technologies #350 $50.51 B
## 72 72 Adani Enterprises #461 $39.70 B
## 73 73 Steel Authority of India #2305 $5.91 B
## 74 74 SBI Life Insurance #1050 $16.63 B
## 75 75 Housing Development Finance Corporation #285 $60.86 B
## 76 76 Grasim Industries #1057 $16.46 B
## 77 77 Maruti Suzuki India #501 $37.38 B
## 78 78 Mahindra & Mahindra #756 $24.47 B
## 79 79 Coal India #635 $28.87 B
## 80 80 GAIL #1271 $13.08 B
## 81 81 Vedanta #1375 $11.78 B
## 82 82 Bharti Airtel #182 $82.09 B
## 83 83 Infosys #179 $83.69 B
## 84 84 ICICI Bank #177 $85.20 B
## 85 85 HDFC Bank #90 $143.30 B
## 86 86 JSW Steel #769 $24.02 B
## 87 87 NTPC Limited #509 $36.73 B
## 88 88 Larsen & Toubro #299 $59.43 B
## 89 89 Hindalco Industries #1120 $15.26 B
## 90 90 Tata Steel #906 $19.93 B
## 91 91 Tata Consultancy Services #74 $165.88 B
## 92 92 State Bank of India #258 $65.80 B
## 93 93 Rajesh Exports #4702 $1.24 B
## 94 94 Tata Motors #519 $35.71 B
## 95 95 Hindustan Petroleum #1986 $7.35 B
## 96 96 Bharat Petroleum #1327 $12.34 B
## 97 97 Oil & Natural Gas #525 $35.43 B
## 98 98 Indian Oil #778 $23.81 B
## 99 99 Life Insurance Corporation of India (LIC) #237 $68.75 B
## 100 100 Reliance Industries #48 $220.32 B
## Share.Price Categories Revenue Country
## 1 $336.31 $2.28 B India
## 2 $27.76 💳 Financial services $2.29 B India
## 3 $2.61 ⛏️ Mining $2.30 B India
## 4 $29.69 Cement $2.30 B India
## 5 $50.60 $2.33 B India
## 6 $2.11 🧪 Chemicals $2.35 B India
## 7 $17.84 🏦 Insurance $2.38 B India
## 8 $2.59 🔋 Electricity $2.50 B India
## 9 $0.96 🏦 Banks $2.54 B India
## 10 $4.44 🧪 Chemicals $2.77 B India
## 11 $13.79 ⚓ Ports $2.81 B India
## 12 $1,647 Tires $2.90 B India
## 13 $6.23 Tires $2.99 B India
## 14 $16.48 💊 Pharmaceuticals $3.02 B India
## 15 $2.09 🚂 Railways $3.13 B India
## 16 $12.04 🔋 Electricity $3.20 B India
## 17 $69.54 💊 Pharmaceuticals $3.23 B India
## 18 $13.85 💊 Pharmaceuticals $3.23 B India
## 19 $1.51 🌱 Fertilizer companies $3.28 B India
## 20 $34.94 ✈️ Aircraft manufacturers $3.36 B India
## 21 $2.71 📡 Telecommunication $3.40 B India
## 22 $18.19 🏦 Banks $3.50 B India
## 23 $3.77 $3.52 B India
## 24 $5.04 💳 Financial services $3.59 B India
## 25 $4.85 🛢 Oil&Gas $3.63 B India
## 26 $1.58 🏦 Banks $3.63 B India
## 27 $85.26 💳 Financial services $3.70 B India
## 28 $1.99 🏦 Insurance $3.71 B India
## 29 $5.54 🏦 Banks $3.76 B India
## 30 $19.03 🍴 Food $3.77 B India
## 31 $6.74 Cement $3.85 B India
## 32 $23.36 🏍 Motorcycle Manufacturers $3.93 B India
## 33 $53.45 🏍 Motorcycle Manufacturers $4.18 B India
## 34 $66.12 💼 Professional services $4.27 B India
## 35 $35.49 🎨 Paint & Coating $4.29 B India
## 36 $10.57 🏦 Insurance $4.62 B India
## 37 $91.42 $4.98 B India
## 38 $6.54 🔋 Electricity $5.09 B India
## 39 $2.78 🏦 Insurance $5.11 B India
## 40 $0.18 📡 Telecommunication $5.14 B India
## 41 $2.95 🔋 Electricity $5.37 B India
## 42 $45.37 ⌚ Luxury goods $5.39 B India
## 43 $2.04 🏭 Manufacturing $5.39 B India
## 44 $16.46 💊 Pharmaceuticals $5.52 B India
## 45 $4.24 🏦 Insurance $5.57 B India
## 46 $6.48 🚚 Transportation $5.72 B India
## 47 $44.93 🛍️ Retail $5.87 B India
## 48 $6.47 🧪 Chemicals $5.93 B India
## 49 $1.26 🏦 Banks $6.35 B India
## 50 $15.91 $6.39 B India
## 51 $1.69 🏦 Banks $6.55 B India
## 52 $4.22 🍴 Food $6.59 B India
## 53 $4.39 🔋 Electricity $6.84 B India
## 54 $3.09 🛢 Oil&Gas $7.06 B India
## 55 $34.45 ✈️ Airlines $7.36 B India
## 56 $29.22 🍴 Food $7.45 B India
## 57 $5.60 🏦 Banks $7.56 B India
## 58 $21.28 🏦 Banks $8.00 B India
## 59 $9.63 🛢 Oil&Gas $8.05 B India
## 60 $119.96 Cement $8.26 B India
## 61 $5.48 🚬 Tobacco $8.44 B India
## 62 $2.73 🏦 Banks $8.53 B India
## 63 $12.55 🏦 Banks $8.83 B India
## 64 $8.62 🔩 Steel producers $8.91 B India
## 65 $5.85 🏦 Insurance $9.44 B India
## 66 $2.08 $10.53 B India
## 67 $1.39 🚘 Automotive Suppliers $10.64 B India
## 68 $6.97 🏦 Insurance $11.00 B India
## 69 $5.72 💼 Professional services $11.12 B India
## 70 $19.62 💳 Financial services $11.26 B India
## 71 $18.65 💼 Professional services $12.76 B India
## 72 $34.82 🏙 Conglomerate $12.82 B India
## 73 $1.43 🔩 Steel producers $13.12 B India
## 74 $16.62 🏦 Insurance $14.15 B India
## 75 $32.87 🏦 Banks $14.22 B India
## 76 $24.99 $14.87 B India
## 77 $118.90 🚗 Automakers $15.31 B India
## 78 $19.68 🚗 Automakers $15.69 B India
## 79 $4.69 ⛏️ Mining $15.84 B India
## 80 $1.99 🛢 Oil&Gas $17.05 B India
## 81 $3.17 ⛏️ Mining $17.43 B India
## 82 $13.97 📡 Telecommunication $17.73 B India
## 83 $20.22 💼 Professional services $18.55 B India
## 84 $24.13 🏦 Banks $18.63 B India
## 85 $56.61 🏦 Banks $20.39 B India
## 86 $9.82 🔩 Steel producers $20.97 B India
## 87 $3.79 🔋 Electricity $21.44 B India
## 88 $43.24 🏗 Construction $24.48 B India
## 89 $6.83 $26.05 B India
## 90 $1.61 🔩 Steel producers $28.19 B India
## 91 $45.85 💼 Professional services $28.90 B India
## 92 $7.37 🏦 Banks $36.81 B India
## 93 $4.23 🛍️ Retail $39.91 B India
## 94 $9.77 🚗 Automakers $43.65 B India
## 95 $5.18 🛢 Oil&Gas $51.52 B India
## 96 $5.71 🛢 Oil&Gas $54.11 B India
## 97 $2.82 🛢 Oil&Gas $78.08 B India
## 98 $1.73 🛢 Oil&Gas $94.87 B India
## 99 $10.87 🏦 Insurance $97.94 B India
## 100 $32.56 🏙 Conglomerate $106.19 B India
#12
employee_data <- data.frame(
Name = c("John", "Alice", "Bob", "Emily", "David", "Smith", "Cumminis", "Head", "Lyon", "Strac"),
Age = c(30, 25, 35, 28, 32, 45, 24, 35, 86, 21),
Salary = c(50000, 60000, 70000, 55000, 75000, 100000, 18000, 29000, 34000, 150000)
)
print(employee_data)
## Name Age Salary
## 1 John 30 50000
## 2 Alice 25 60000
## 3 Bob 35 70000
## 4 Emily 28 55000
## 5 David 32 75000
## 6 Smith 45 100000
## 7 Cumminis 24 18000
## 8 Head 35 29000
## 9 Lyon 86 34000
## 10 Strac 21 150000
cat("\n\nSummary of data: \n", summary(employee_data))
##
##
## Summary of data:
## Length:10 Class :character Mode :character NA NA NA Min. :21.00 1st Qu.:25.75 Median :31.00 Mean :36.10 3rd Qu.:35.00 Max. :86.00 Min. : 18000 1st Qu.: 38000 Median : 57500 Mean : 64100 3rd Qu.: 73750 Max. :150000
#13
for (i in 1:100) {
if(i%%(4*2)==0)#multiple for both 2 and 4
{
print("DATA VISUALIZATION")
}else if(i%%4==0)#multiple of 2
{
print("VISUALIZATION")
}else if(i%%2==0)#multiple of 4
{
print("DATA")
}else {
print(i)
}
}
## [1] 1
## [1] "DATA"
## [1] 3
## [1] "VISUALIZATION"
## [1] 5
## [1] "DATA"
## [1] 7
## [1] "DATA VISUALIZATION"
## [1] 9
## [1] "DATA"
## [1] 11
## [1] "VISUALIZATION"
## [1] 13
## [1] "DATA"
## [1] 15
## [1] "DATA VISUALIZATION"
## [1] 17
## [1] "DATA"
## [1] 19
## [1] "VISUALIZATION"
## [1] 21
## [1] "DATA"
## [1] 23
## [1] "DATA VISUALIZATION"
## [1] 25
## [1] "DATA"
## [1] 27
## [1] "VISUALIZATION"
## [1] 29
## [1] "DATA"
## [1] 31
## [1] "DATA VISUALIZATION"
## [1] 33
## [1] "DATA"
## [1] 35
## [1] "VISUALIZATION"
## [1] 37
## [1] "DATA"
## [1] 39
## [1] "DATA VISUALIZATION"
## [1] 41
## [1] "DATA"
## [1] 43
## [1] "VISUALIZATION"
## [1] 45
## [1] "DATA"
## [1] 47
## [1] "DATA VISUALIZATION"
## [1] 49
## [1] "DATA"
## [1] 51
## [1] "VISUALIZATION"
## [1] 53
## [1] "DATA"
## [1] 55
## [1] "DATA VISUALIZATION"
## [1] 57
## [1] "DATA"
## [1] 59
## [1] "VISUALIZATION"
## [1] 61
## [1] "DATA"
## [1] 63
## [1] "DATA VISUALIZATION"
## [1] 65
## [1] "DATA"
## [1] 67
## [1] "VISUALIZATION"
## [1] 69
## [1] "DATA"
## [1] 71
## [1] "DATA VISUALIZATION"
## [1] 73
## [1] "DATA"
## [1] 75
## [1] "VISUALIZATION"
## [1] 77
## [1] "DATA"
## [1] 79
## [1] "DATA VISUALIZATION"
## [1] 81
## [1] "DATA"
## [1] 83
## [1] "VISUALIZATION"
## [1] 85
## [1] "DATA"
## [1] 87
## [1] "DATA VISUALIZATION"
## [1] 89
## [1] "DATA"
## [1] 91
## [1] "VISUALIZATION"
## [1] 93
## [1] "DATA"
## [1] 95
## [1] "DATA VISUALIZATION"
## [1] 97
## [1] "DATA"
## [1] 99
## [1] "VISUALIZATION"
#14
factor_vector <- factor(levels = c())
values <- c(1, 2, 3, 4, 5, 6, 7)
factor_vector <- append(factor_vector, values)
numeric_vector <- as.numeric(factor_vector)
vector_sum <- sum(numeric_vector)
vector_mean <- mean(numeric_vector)
cat("Vector Elements:", factor_vector, "\n")
## Vector Elements: 1 2 3 4 5 6 7
n <- 2
nth_elements <- factor_vector[seq(from = 1, to = length(factor_vector), by = n)]
cat("Sum of the vector:", vector_sum, "\n")
## Sum of the vector: 28
cat("Mean of the vector:", vector_mean, "\n")
## Mean of the vector: 4
cat("Every", n, "th element of the vector:", nth_elements, "\n")
## Every 2 th element of the vector: 1 3 5 7
#16
i <- 1
while (i^2 <= 4000) {
i <- i + 1
}
cat("First positive integer whose square to exceed 4000: ", i)
## First positive integer whose square to exceed 4000: 64
#17
numeric_vector <- c(1.3, 6.5, 0.9, 4.4, 3.4, 1.5)
complex_vector <- c(1 + 20i, 3 + 11i, 5 + 6i, 3 + 9i, 2 + 60i, 9 + 13i)
logical_vector <- c(TRUE, FALSE, TRUE, FALSE, TRUE, FALSE)
character_vector <- c("pavan", "vamshi", "sandeep", "harsha", "darshita", "rupa")
print(paste("Numeric Vector:", numeric_vector))
## [1] "Numeric Vector: 1.3" "Numeric Vector: 6.5" "Numeric Vector: 0.9"
## [4] "Numeric Vector: 4.4" "Numeric Vector: 3.4" "Numeric Vector: 1.5"
print(paste("Complex Vector:", complex_vector))
## [1] "Complex Vector: 1+20i" "Complex Vector: 3+11i" "Complex Vector: 5+6i"
## [4] "Complex Vector: 3+9i" "Complex Vector: 2+60i" "Complex Vector: 9+13i"
print(paste("Logical Vector:", logical_vector))
## [1] "Logical Vector: TRUE" "Logical Vector: FALSE" "Logical Vector: TRUE"
## [4] "Logical Vector: FALSE" "Logical Vector: TRUE" "Logical Vector: FALSE"
print(paste("Character Vector:", character_vector))
## [1] "Character Vector: pavan" "Character Vector: vamshi"
## [3] "Character Vector: sandeep" "Character Vector: harsha"
## [5] "Character Vector: darshita" "Character Vector: rupa"
#18
a <- c(1, 2, 3)
b <- c(4, 5, 6)
c <- c(7, 8, 9)
result <- matrix(c(a, b, c), nrow=3, byrow=FALSE)
cat("Combined Matrix: ", result)
## Combined Matrix: 1 2 3 4 5 6 7 8 9
#19
matrix <- matrix(c(4.3, 3.1, 8.2, 8.2, 3.2, 0.9, 1.6, 6.5), nrow = 4, byrow = TRUE)
print("Matrix:")
## [1] "Matrix:"
print(matrix)
## [,1] [,2]
## [1,] 4.3 3.1
## [2,] 8.2 8.2
## [3,] 3.2 0.9
## [4,] 1.6 6.5
#20
numeric_vector <- c(1, 2, 3, 4, 5, 6)
character_vector <- c("a", "b", "c", "d", "e", "f")
logical_vector <- c(TRUE, FALSE, TRUE, FALSE, TRUE, FALSE)
cat("Vector_1", numeric_vector, "\n")
## Vector_1 1 2 3 4 5 6
cat("Type:", typeof(numeric_vector), "\n\n")
## Type: double
cat("Vector_2:", character_vector, "\n")
## Vector_2: a b c d e f
cat ("Type:", typeof (character_vector), "\n\n")
## Type: character
cat("Vector_3:", logical_vector, "\n")
## Vector_3: TRUE FALSE TRUE FALSE TRUE FALSE
cat("Type:", typeof(logical_vector), "\n")
## Type: logical