如何为观测数据批量匹配种群规模？替代大量if-else的R语言方案

我有一份观测数据列表，还有一张记录各物种不同阶段种群规模（small、medium、large）的对照表，需要给观测数据添加种群规模字段。

观测数据（实际数据更长） #

observations <- data.frame("species" = c("Rana temporaria","Rana temporaria", "Bufo bufo", "Bufo bufo"),
                           "stage" = c("adult","egg","adult","adult"),
                           "count" = c(20,40,100,35),
                           "population" = c(NA,NA,NA,NA))

种群规模对照表（实际数据更长） #

population <- data.frame("species" = c("Rana temporaria","Bufo bufo","Bufo bufo"),
                         "stage" = c("adult","adult", "egg"),
                         "small" = c("1-5","1-5","0-100"),
                         "medium" = c("6-10","6-50", "101-500"),
                         "large" = c(">10",">50",">500"))

目前我通过逐个编写if语句来实现匹配，但效率极低，示例代码如下：

if(observations$species[1]=="Rana temporaria" & 
   observations$stage[1] == "adult" & 
   observations$count[1] >= 6 & 
   observations$count[1] >= 10) {
  observations$population[1] = "medium"
}

请问有没有更高效的替代方案？

解决方案 #

步骤1：预处理对照表，将区间转为数值阈值

首先需要把对照表中的区间字符串转换成可计算的数值范围，方便后续批量匹配：

# 加载dplyr和tidyr包
library(dplyr)
library(tidyr)

# 处理population表，拆分区间为最小值和最大值
population_processed <- population %>%
  pivot_longer(cols = c(small, medium, large), names_to = "size_class", values_to = "range") %>%
  mutate(
    min_val = as.numeric(sub("-.*", "", range)),
    max_val = as.numeric(sub(".*-", "", range)),
    # 处理带大于号的区间
    max_val = ifelse(grepl(">", range), Inf, max_val),
    min_val = ifelse(grepl(">", range), as.numeric(sub(">", "", range)), min_val)
  ) %>%
  select(species, stage, size_class, min_val, max_val)

步骤2：关联数据并批量匹配规模类别

用left_join关联观测数据和预处理后的对照表，通过条件筛选匹配对应的规模标签：

# 匹配符合条件的规模类别
matched_size <- observations %>%
  left_join(population_processed, by = c("species", "stage")) %>%
  filter(count >= min_val & count <= max_val) %>%
  select(species, stage, count, size_class) %>%
  rename(population = size_class)

# 合并回原始数据，保留匹配不到的NA值
final_observations <- observations %>%
  left_join(matched_size, by = c("species", "stage", "count")) %>%
  mutate(population = coalesce(population.y, population.x)) %>%
  select(-population.x, -population.y)

结果验证

运行后final_observations的输出结果：

内容的提问来源于stack exchange，提问作者Patrick Marti