Sample Programs Using Arrays in R

May 30, 2026

Sample Programs Using Arrays in R

Arrays are used to represent multidimensional homogeneous data. The following programs illustrate practical numerical applications of arrays suitable for undergraduate laboratories.

1. Student Marks Analysis Across Semesters

Problem

Marks of 3 students in 4 subjects over 2 semesters are stored in a three-dimensional array.

Find:

Total marks of each student in each semester.
Average marks in each semester.
Highest mark in the entire array.

Program


marks <- array(
  c(85,90,78,88,
    92,80,86,95,
    75,82,85,79,

    89,91,83,90,
    94,85,88,96,
    80,84,87,81),
  dim=c(3,4,2)
)

print(marks)

# Total marks of students in each semester
student_totals <- apply(marks, c(1,3), sum)

cat("Student Totals\n")
print(student_totals)

# Average marks in each semester
semester_avg <- apply(marks, 3, mean)

cat("Semester Averages\n")
print(semester_avg)

# Highest mark
cat("Highest Mark =", max(marks))

2. Monthly Sales Analysis

Problem

Sales of 3 products in 4 quarters over 2 years are stored in an array.

Find:

Total sales for each product.
Total sales for each year.
Product having maximum total sales.

Program


sales <- array(
  c(100,120,110,130,
    140,150,160,155,
    90,100,95,105,

    110,125,115,140,
    150,165,170,175,
    95,110,100,120),
  dim=c(3,4,2)
)

# Total sales of products
product_sales <- apply(sales,1,sum)

cat("Product Sales\n")
print(product_sales)

# Total sales of years
year_sales <- apply(sales,3,sum)

cat("Year Sales\n")
print(year_sales)

cat("Highest Selling Product =", which.max(product_sales))

3. Temperature Monitoring

Problem

Daily temperatures are recorded for 7 days during 3 weeks.

Find:

Average temperature each week.
Maximum temperature recorded.
Minimum temperature recorded.

Program


temp <- array(
  c(30,31,29,32,33,31,30,
    29,30,31,32,34,33,31,
    28,29,30,31,32,30,29),
  dim=c(7,3)
)

print(temp)

weekly_avg <- apply(temp,2,mean)

cat("Weekly Average Temperatures\n")
print(weekly_avg)

cat("Maximum Temperature =", max(temp), "\n")
cat("Minimum Temperature =", min(temp))

4. RGB Image Representation

Problem

Represent a small color image using a three-dimensional array and calculate average intensity in each channel.

Program


image <- array(
  c(
    255,0,0,255,
    0,255,255,0,

    100,120,130,140,
    150,160,170,180,

    50,60,70,80,
    90,100,110,120
  ),
  dim=c(2,4,3)
)

cat("Average Red Channel =",
    mean(image[,,1]), "\n")

cat("Average Green Channel =",
    mean(image[,,2]), "\n")

cat("Average Blue Channel =",
    mean(image[,,3]), "\n")

Concepts Demonstrated

Three-dimensional arrays
Slicing arrays
Image representation

5. Rainfall Analysis

Problem

Rainfall data for 12 months over 3 years are stored in an array.

Find:

Average rainfall each year.
Month having maximum rainfall.
Total rainfall over all years.

Program


rainfall <- array(
  sample(50:200,36),
  dim=c(12,3)
)

print(rainfall)

# Average rainfall of years
year_avg <- apply(rainfall,2,mean)

cat("Average Rainfall per Year\n")
print(year_avg)

# Maximum rainfall
cat("Maximum Rainfall =", max(rainfall), "\n")

# Total rainfall
cat("Total Rainfall =", sum(rainfall))

6.Population Data Analysis

Problem

Population of 4 cities over 5 years for 2 states is stored in a 3D array.

Find:

State-wise population.
City-wise average population.
Maximum population recorded.

Program


population <- array(
  sample(1000:5000,40),
  dim=c(4,5,2)
)

# State totals
state_pop <- apply(population,3,sum)

# City averages
city_avg <- apply(population,1,mean)

cat("State Population\n")
print(state_pop)

cat("City Average Population\n")
print(city_avg)

cat("Maximum Population =", max(population))

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Statistical Methods Lab ( R Language) PCCBL308 Semester 3 KTU BTech CB CU 2024 Scheme