नामित वेक्टर और नामित अनुक्रम

# Assign names after creation
temps <- c(22.1, 18.5, 25.3, 20.0)
cat('Before naming:', temps, '
')

names(temps) <- c('Mon', 'Tue', 'Wed', 'Thu')
cat('After naming:
')
print(temps)

# Retrieve names
cat('Days:', names(temps), '
')

scores <- c(Math = 88, Science = 92, English = 75, History = 81)

# Single bracket: keeps name
cat('Math score (with name):
')
print(scores['Math'])

# Double bracket: just the value
cat('Science score (value only):', scores[['Science']], '
')

# Multiple names
cat('STEM subjects:
')
print(scores[c('Math', 'Science')])

# setNames returns a named copy (does not modify original)
raw_values <- c(0.85, 0.72, 0.91, 0.68)
metrics <- setNames(raw_values, c('Precision', 'Recall', 'F1', 'Accuracy'))

cat('Original (unchanged):', raw_values, '
')
cat('Named metrics:
')
print(metrics)

# Best metric
best_name <- names(which.max(metrics))
cat('Best metric:', best_name, '=', metrics[best_name], '
')

prices <- c(apple = 1.20, bannana = 0.80, cherry = 3.50)  # typo!

# Fix a single name
names(prices)[2] <- 'banana'
cat('Fixed names:
')
print(prices)

# Rename all
names(prices) <- c('Apple', 'Banana', 'Cherry')  # capitalise
cat('Capitalised:
')
print(prices)

# Monthly average temperature with names
temps_monthly <- setNames(
  c(3.1, 4.2, 8.5, 13.0, 17.2, 21.0, 23.5, 23.0, 19.0, 13.5, 7.8, 4.0),
  month.abb
)
cat('Monthly temps:
')
print(temps_monthly)
cat('Summer avg:', mean(temps_monthly[c('Jun', 'Jul', 'Aug')]), '
')

population <- c(Tokyo = 37.4, Delhi = 32.9, Shanghai = 28.5,
                Dhaka = 22.5, Cairo = 21.3, Mumbai = 20.7)

# Select cities with population > 25 million
big_cities <- population[population > 25]
cat('Cities over 25M:
')
print(big_cities)

# Sort descending by population
cat('Ranked:
')
print(sort(population, decreasing = TRUE))

named_vec <- c(x1 = 10, x2 = 20, x3 = 30, x4 = 40)
cat('Named:
')
print(named_vec)

unnamed_vec <- unname(named_vec)
cat('Unnamed:
')
print(unnamed_vec)

# Check
cat('Has names after unname:', !is.null(names(unnamed_vec)), '
')

# Map month abbreviation to number of days
days_in_month <- c(Jan=31, Feb=28, Mar=31, Apr=30, May=31,
                   Jun=30, Jul=31, Aug=31, Sep=30, Oct=31,
                   Nov=30, Dec=31)

# Look up specific months
query <- c('Mar', 'Jul', 'Nov')
cat('Days in', query[1], ':', days_in_month[query[1]], '
')
cat('Days in', query[2], ':', days_in_month[query[2]], '
')
cat('Days in', query[3], ':', days_in_month[query[3]], '
')

# Complete workflow
rates <- c(USD = 1.0, EUR = 0.92, GBP = 0.79, JPY = 149.5)

# Convert 100 USD to each currency
amount_usd <- 100
converted <- amount_usd * rates
cat('100 USD in other currencies:
')
print(round(converted, 2))

# Named vector as grade lookup
grade_map <- c('90'='A', '80'='B', '70'='C', '60'='D')
student_score <- 80

# Find grade bracket
brackets <- c(90, 80, 70, 60)
threshold <- max(brackets[brackets <= student_score])
cat('Score:', student_score, '-> Grade:', grade_map[as.character(threshold)], '
')