如何简化计算度假屋公寓价格的冗长R代码？

你的代码核心是根据区域、年份、季节三个维度匹配价格，这种多维度规则最适合用**查找表（Lookup Table）**实现，不仅代码更简洁，后续修改价格、新增年份/区域也更方便。

步骤1：创建价格规则查找表 #

先把所有价格规则整理成一个数据框，每个规则一行，清晰明了：

library(tidyverse)

price_lookup <- tribble(
  ~region,        ~year, ~season,       ~price_night,
  "ost",          2018,  "hochsaison",  180,
  "ost",          2018,  "mittelsaison",160,
  "ost",          2018,  "nebensaison", 140,
  "west",         2018,  "hochsaison",  180,
  "west",         2018,  "mittelsaison",160,
  "west",         2018,  "nebensaison", 140,
  "sued",         2018,  "hochsaison",  100,
  "sued",         2018,  "mittelsaison",80,
  "sued",         2018,  "nebensaison", 60,
  "ost.west",     2018,  "hochsaison",  360,
  "ost.west",     2018,  "mittelsaison",320,
  "ost.west",     2018,  "nebensaison", 280,
  "sued.ost",     2018,  "hochsaison",  280,
  "sued.ost",     2018,  "mittelsaison",240,
  "sued.ost",     2018,  "nebensaison", 200,
  "sued.west",    2018,  "hochsaison",  280,
  "sued.west",    2018,  "mittelsaison",240,
  "sued.west",    2018,  "nebensaison", 200,
  "gesamtes_haus",2018,  "hochsaison",  460,
  "gesamtes_haus",2018,  "mittelsaison",400,
  "gesamtes_haus",2018,  "nebensaison", 340,
  "ost",          2022,  "hochsaison",  240,
  "ost",          2022,  "mittelsaison",210,
  "ost",          2022,  "nebensaison", 170,
  "west",         2022,  "hochsaison",  240,
  "west",         2022,  "mittelsaison",210,
  "west",         2022,  "nebensaison", 170,
  "sued",         2022,  "hochsaison",  120,
  "sued",         2022,  "mittelsaison",100,
  "sued",         2022,  "nebensaison", 80,
  "ost.west",     2022,  "hochsaison",  480,
  "ost.west",     2022,  "mittelsaison",420,
  "ost.west",     2022,  "nebensaison", 340,
  "sued.ost",     2022,  "hochsaison",  360,
  "sued.ost",     2022,  "mittelsaison",310,
  "sued.ost",     2022,  "nebensaison", 250,
  "sued.west",    2022,  "hochsaison",  360,
  "sued.west",    2022,  "mittelsaison",310,
  "sued.west",    2022,  "nebensaison", 250,
  "gesamtes_haus",2022,  "hochsaison",  600,
  "gesamtes_haus",2022,  "mittelsaison",520,
  "gesamtes_haus",2022,  "nebensaison", 420
)

步骤2：转换原数据格式 #

原数据中，区域（ost/west等）和季节（hochsaison等）都是宽格式（每个类别一列），需要转成长格式才能和查找表匹配：

ue_processed <- ue %>%
  # 提取当前行对应的有效区域（仅保留值>0的区域）
  pivot_longer(cols = c(ost, west, sued, ost.west, sued.ost, sued.west, gesamtes_haus),
               names_to = "region", values_to = "region_val") %>%
  filter(region_val > 0) %>%
  select(-region_val) %>%
  # 提取当前行对应的有效季节（仅保留值>0的季节）
  pivot_longer(cols = c(hochsaison, mittelsaison, nebensaison),
               names_to = "season", values_to = "season_val") %>%
  filter(season_val > 0) %>%
  select(-season_val)

步骤3：匹配价格并合并回原数据 #

把处理后的数据和查找表合并，得到对应价格，再合并回原数据：

ue <- ue %>%
  left_join(
    ue_processed %>% left_join(price_lookup, by = c("region", "year", "season")),
    by = names(ue)[!names(ue) %in% c("region", "season")]
  ) %>%
  # 处理无匹配规则的情况，默认价格为0
  mutate(price_night = replace_na(price_night, 0))

为什么这样更好？ #

• 可读性强：查找表集中展示所有规则，一眼就能看懂各维度对应的价格
• 易维护：新增年份、区域或修改价格，直接修改查找表即可，无需改动主逻辑
• 减少错误：避免重复编写case_when条件时出现的语法疏漏（比如你原代码中2018年最后一个条件后缺失逗号）

内容的提问来源于stack exchange，提问作者Sofia