मैट्रिक्स का ट्रांसपोज़ और पुनःआकार निर्धारण

m <- matrix(1:12, nrow = 3, ncol = 4)
cat('Original (3x4):
'); print(m)

# Reshape to 4x3
dim(m) <- c(4, 3)
cat('Reshaped to (4x3):
'); print(m)

# Reshape to 6x2
dim(m) <- c(6, 2)
cat('Reshaped to (6x2):
'); print(m)

m <- matrix(c(10,20,30,40,50,60), nrow = 2, ncol = 3)
cat('Matrix:
'); print(m)

v <- as.vector(m)
cat('As vector (col-by-col):', v, '
')
cat('Is vector:', is.vector(v), '
')
cat('Is matrix:', is.matrix(v), '
')

v <- c(5, 10, 15, 20, 25, 30)
cat('Vector:', v, '
')

# as.matrix makes a 6x1 matrix
m_single_col <- as.matrix(v)
cat('Single-column matrix:
'); print(m_single_col)
cat('Dimensions:', dim(m_single_col), '
')

first_half <- matrix(1:6, nrow = 2, ncol = 3)
second_half <- matrix(7:12, nrow = 2, ncol = 3)

combined <- rbind(first_half, second_half)
cat('rbind result (4x3):
'); print(combined)
cat('Dimensions:', dim(combined), '
')

ids <- matrix(101:104, nrow = 4, ncol = 1)
scores_mat <- matrix(c(88,79,95,72, 92,85,78,90), nrow = 4)

full_data <- cbind(ids, scores_mat)
colnames(full_data) <- c('ID', 'Test1', 'Test2')
cat('cbind result:
'); print(full_data)

# Add a new student row with rbind
existing <- matrix(c(88,92,75, 79,85,90), nrow = 2, byrow = TRUE,
                   dimnames = list(c('Alice','Bob'), c('Math','Sci','Eng')))
new_student <- c(Math=95, Sci=78, Eng=82)
updated <- rbind(existing, Carol = new_student)
cat('Updated roster:
'); print(updated)

m <- matrix(c(1,2,3,4,5,6), nrow = 2)
cat('Original:
'); print(m)
cat('t(m):
');    print(t(m))
cat('t(t(m)):
'); print(t(t(m)))
cat('t(t(m)) == m:', all(t(t(m)) == m), '
')

# Wide: 3 students x 4 months scores
wide <- matrix(c(85,88,90,92, 78,80,79,83, 91,94,88,95),
               nrow = 3, byrow = TRUE)
rownames(wide) <- c('Alice', 'Bob', 'Carol')
colnames(wide) <- c('Jan', 'Feb', 'Mar', 'Apr')
cat('Wide format:
'); print(wide)

# Transposed: 4 months x 3 students
long_view <- t(wide)
cat('Transposed (months x students):
'); print(long_view)

# Demo all tools
v <- 1:6
m1 <- as.matrix(v)               # 6x1
dim(m1) <- c(2, 3)               # reshape to 2x3
m2 <- t(m1)                      # 3x2
m3 <- cbind(m2, c(10, 20, 30))   # 3x3
cat('Final matrix:
'); print(m3)

# Practical: merge data then compute column means
team_a <- matrix(c(82,78,91, 85,80,88), nrow = 2, byrow=TRUE)
team_b <- matrix(c(75,92,79, 88,84,93), nrow = 2, byrow=TRUE)
all_teams <- rbind(team_a, team_b)
colnames(all_teams) <- c('Speed', 'Accuracy', 'Strength')
cat('Combined teams:
'); print(all_teams)
cat('Average per skill:', colMeans(all_teams), '
')