ฟังก์ชันหน้าต่าง: lag, lead, cumsum

library(dplyr)

# Monthly revenue — we want to compare each month to previous
df <- data.frame(
  month = 1:6,
  revenue = c(100, 120, 95, 140, 160, 130)
)
print(df)

library(dplyr)

df <- data.frame(
  month = 1:6,
  revenue = c(100, 120, 95, 140, 160, 130)
)

df %>%
  mutate(
    prev_revenue = lag(revenue),
    change = revenue - lag(revenue),
    pct_change = round(100 * (revenue - lag(revenue)) / lag(revenue), 1)
  )

library(dplyr)

df <- data.frame(
  month = 1:5,
  revenue = c(100, 120, 95, 140, 160)
)

df %>%
  mutate(
    next_month = lead(revenue),
    next_2_months = lead(revenue, n = 2),
    will_increase = revenue < lead(revenue)
  )

library(dplyr)

df <- data.frame(
  month = 1:6,
  revenue = c(100, 120, 95, 140, 160, 130)
)

df %>%
  mutate(
    running_total = cumsum(revenue),
    growth_factor = round(cumprod(1 + (revenue - 100) / 100), 3)
  )

library(dplyr)

df <- data.frame(
  day = 1:7,
  price = c(50, 52, 48, 55, 53, 58, 56)
)

df %>%
  mutate(
    all_time_high = cummax(price),
    all_time_low = cummin(price),
    drawdown_from_high = price - cummax(price)
  )

library(dplyr)

sales <- data.frame(
  region = c('East','East','East','West','West','West'),
  rep = c('Alice','Bob','Carol','Dave','Eve','Frank'),
  revenue = c(200, 150, 180, 120, 190, 160)
)

sales %>%
  group_by(region) %>%
  mutate(rank = row_number(desc(revenue))) %>%
  arrange(region, rank)

library(dplyr)

df <- data.frame(
  customer = paste0('C', 1:10),
  spend = c(50, 200, 75, 300, 125, 450, 25, 175, 100, 350)
)

df %>%
  mutate(
    quartile = ntile(spend, 4),
    decile = ntile(spend, 10)
  ) %>%
  arrange(spend)

library(dplyr)

df <- data.frame(
  student = c('Alice','Bob','Carol','Dave','Eve'),
  score = c(85, 92, 78, 95, 88)
)

df %>%
  mutate(
    pct_rank = round(percent_rank(score), 3),
    top_pct = round(1 - percent_rank(score), 3)
  ) %>%
  arrange(desc(score))

library(dplyr)

df <- data.frame(
  product = c('A','A','A','B','B','B'),
  month = c(1,2,3,1,2,3),
  sales = c(100, 120, 110, 200, 180, 220)
)

df %>%
  group_by(product) %>%
  mutate(
    mom_change = sales - lag(sales),
    cumulative = cumsum(sales),
    rank_in_product = row_number(sales)
  )

library(dplyr)

stock <- data.frame(
  date = as.Date(c('2024-01-01','2024-01-02','2024-01-03',
                   '2024-01-04','2024-01-05')),
  close = c(150.2, 152.8, 149.5, 155.1, 157.3)
)

stock %>%
  mutate(
    prev_close = lag(close),
    daily_return = round((close / lag(close) - 1) * 100, 2),
    running_high = cummax(close),
    distance_from_high = round(close - cummax(close), 2)
  )

library(dplyr)

df <- data.frame(
  week = 1:5,
  visitors = c(500, 620, 480, 700, 650)
)

df %>%
  mutate(
    prev_week = lag(visitors, default = 0),
    growth = visitors - lag(visitors, default = visitors[1])
  )

library(dplyr)

# Comprehensive window function example
data.frame(
  region = c('East','East','East','West','West','West'),
  month = c(1,2,3,1,2,3),
  sales = c(100, 130, 120, 200, 190, 210)
) %>%
  group_by(region) %>%
  mutate(
    mom_chg = sales - lag(sales),
    running_total = cumsum(sales),
    rank = row_number(desc(sales))
  ) %>%
  ungroup()