R: المُشغِّلات في لغة R

# 5 Sales at each branch(10,000 yuan)
sales <- c(120, 150, 80, 200, 175)

# All calculations are completed with a single line of code
total_sales <- sum(sales)                          # Total Sales 725
avg_sales <- mean(sales)                           # Average 145
profit <- sales * 0.3                              # Profit Vector
high_sales <- sales[sales > avg_sales]             # Branches with Above-Average Performance
profit_int <- profit %/% 1                         # Integer Profit(Integer Division)

cat("Total Sales:", total_sales, "10,000 yuan\n")
cat("Average Sales:", avg_sales, "10,000 yuan\n")
cat("Total Profit(Integer):", sum(profit_int), "10,000 yuan\n")
cat("Above-average sales at branch locations:", high_sales, "\n")

# 7 / 2 = 3.5(Ordinary Division)
7 / 2
# [1] 3.5

# 7 %/% 2 = 3(Integer Division,Round down)
7 %/% 2
# [1] 3

# 7 %% 2 = 1 (Modulo, Remainder)
7 %% 2
# [1] 1

# Practical Application: Convert seconds to "hr:min:s"
total_seconds <- 3725
hours <- total_seconds %/% 3600
minutes <- (total_seconds %% 3600) %/% 60
seconds <- total_seconds %% 60
cat(hours, "hr", minutes, "min", seconds, "s\n")
# 1 hr 2 min 5 s

# Vector + Scalar(Scalar"Broadcast"Go to each element)
c(1, 2, 3) + 10
# [1] 11 12 13

# Vector + Vector(Element-wise Operations)
c(1, 2, 3) + c(10, 20, 30)
# [1] 11 22 33

# When the lengths are different,"Loop"
c(1, 2, 3, 4) + c(10, 20)
# Warning: longer object length is not a multiple of shorter object length
# [1] 11 22 13 24

# Single = It is an assignment
x = 5

# Double equal sign == It is a comparison
x == 5
# [1] TRUE

# Character by ASCII Code Comparison
"apple" < "banana"
# [1] TRUE  ← 'a' Coding < 'b'

# Numeric characters cannot be compared directly for size.(Character-by-character comparison,Not by value)
"9" > "10"
# [1] TRUE  <- '9' (0x39) > '1' (0x31), Not a numerical comparison!

# The Correct Way to Do It:Convert to a number
as.numeric("9") > as.numeric("10")
# [1] FALSE

# NA The results of the comparison are NA(No TRUE/FALSE)
NA == 5
# [1] NA

# Decision NA Must use is.na()
is.na(NA)
# [1] TRUE

# NaN Comparison of
NaN == NaN
# [1] NA  <- NaN == NaN is also NA!

x <- c(TRUE, TRUE, FALSE, FALSE)
y <- c(TRUE, FALSE, TRUE, FALSE)

# Elemental Level AND(Vectorization)
x & y
# [1]  TRUE FALSE FALSE FALSE

# Elemental Level OR
x | y
# [1] TRUE TRUE TRUE FALSE

# if Used in the conditions &&(Check only the first element)
if (x > 0 && x < 10) {
  cat("x is between 0-10\n")
}

# Used in vector logic &(Check all elements)
c(1, 2, 11) > 0 & c(1, 2, 11) < 10
# [1]  TRUE  TRUE FALSE

# Find 18-30 Adults aged
ages <- c(15, 22, 28, 35, 40, 19)
adults <- ages[ages >= 18 & ages <= 30]
adults
# [1] 22 28 19

# Identify the failing or perfect scores
scores <- c(58, 60, 75, 100, 45, 90)
special <- scores[scores < 60 | scores == 100]
special
# [1]  58 100  45

# Is the element in the vector?
c(1, 2, 3) %in% c(2, 4, 6)
# [1] FALSE  TRUE FALSE  <- 1, 3 Not here; 2 is

# Practical Application: Filter "Beijing, Shanghai, and Guangzhou" City
cities <- c("Beijing", "Shanghai", "Shenzhen", "Guangzhou", "Hangzhou")
target <- c("Beijing", "Shanghai", "Guangzhou")
cities[cities %in% target]
# [1] "Beijing" "Shanghai" "Guangzhou"

# Basic Multiplication(Elemental Level)
matrix(1:4, 2, 2) * matrix(1:4, 2, 2)
#      [,1] [,2]
# [1,]    1    9
# [2,]    4   16

# Matrix Multiplication(Linear Algebra)
matrix(1:4, 2, 2) %*% matrix(1:4, 2, 2)
#      [,1] [,2]
# [1,]    7   15
# [2,]   10   22

# Without parentheses(By priority)
1 + 2 * 3
# [1] 7  <- 2*3=6, then +1

# Add parentheses(Clarify Intent)
(1 + 2) * 3
# [1] 9

# Be sure to use parentheses in complex expressions.
# Find 18-65 yrs
ages <- c(15, 25, 70, 30)
adults <- ages[ages >= 18 & ages <= 65]   # ✅ Adding parentheses makes it clearer

# ============================================
# Comprehensive Analysis of Branch Sales
# Features:Analysis Using Various Operators 5 Sales Data by Branch
# ============================================

# 1. Prepare data
sales <- c(Beijing = 120, Shanghai = 150, Shenzhen = 80, Guangzhou = 200, Hangzhou = 175)
cat("Raw Sales Data(10,000 yuan):\n")
print(sales)

# 2. Arithmetic Operations:Total Sales,Average,Highest,Lowest
total <- sum(sales)                  # Sum
avg <- mean(sales)                   # Average
cat("\nTotal Sales:", total, "10,000 yuan\n")
cat("Average Sales:", avg, "10,000 yuan\n")
cat("Top Sales:", max(sales), "10,000 yuan\n")
cat("Minimum Sales:", min(sales), "10,000 yuan\n")

# 3. Arithmetic Operations:Profit(30% Profit Margin)
profit_rate <- 0.3
profit <- sales * profit_rate        # Vector × Scalar
cat("\nTotal Profit:", sum(profit), "10,000 yuan\n")

# 4. Comparison + Logic:Identify branches that perform above average
is_above_avg <- sales > avg           # Comparison:Which ones are above average?
cat("\nBranches with Above-Average Performance:")
print(sales[is_above_avg])

# 5. Comparison + Logic:Identify low-performing stores(< 1,000,000)
is_low <- sales < 100
cat("\nLow-Sales Stores(< 1,000,000):")
print(sales[is_low])

# 6. Integer Operations:Round Profit to the Nearest Whole Number + Grouping
profit_int <- profit %/% 1            # Integer Division(Round down)
cat("\nRound Profit to the Nearest Whole Number:")
print(profit_int)

# 7. %in% Filter
target_cities <- c("Beijing", "Shanghai", "Guangzhou")
selected <- sales[names(sales) %in% target_cities]
cat("\nSales in Target Cities:")
print(selected)

Raw Sales Data(10,000 yuan):
   Beijing    Shanghai    Shenzhen    Guangzhou    Hangzhou
   120     150      80     200     175

Total Sales: 725 10,000 yuan
Average Sales: 145 10,000 yuan
Top Sales: 200 10,000 yuan
Minimum Sales: 80 10,000 yuan

Branches with Above-Average Performance:
Guangzhou   Hangzhou
200    175

Total Profit: 217.5 10,000 yuan

Low-Sales Stores(< 1,000,000):
Shenzhen
  80