基于最小值和最大值查找R数据帧

我正在寻找一种将R中的数据集连接到查找数据集的简单方法。查找数据集具有最小值、最大值和标签，如下所示

library(dplyr)

min_age <- c(0, .5, 1, 5, 17, 35, 50, 75)
max_age <- c(.5, 1, 5, 17, 35, 50, 75, 125)
age_lbl <- c("< 6 months", "6 months - 1 year", "1 year - 5 years", 
             "5 years - 17 years", "17 years - 35 years", "35 years - 50 years"
             , "50 years - 75 years", "> 75 years")
age_lbl <- as.factor(age_lbl)
lkp_df <- data.frame(min_age = min_age, max_age = max_age, age_grp_lb = age_lbl)

我有一个迂回的处理方法

# Introduce dummy column to obtain a cartesian
lkp_df <- lkp_df %>%
  mutate(join = 1)

set.seed(6789)
pat_id <- seq(1001, 1075)
pat_age <- runif(75, 0, 95)

pat_df <- data.frame(pat_id, pat_age) %>%
  mutate(pat_age_yrs = as.integer(pat_age),
         pat_age_mths = as.integer(pat_age * 12)) %>%
  # Introduce dummy column to obtain a cartesian
  mutate(join = 1) %>%
  # Create a cartesian product with the join column
  inner_join(lkp_df) %>%
  # Filter to keep only required records
  filter(pat_age >= min_age & pat_age < max_age) %>%
  # Keep only necessary columns
  select(pat_id, pat_age, pat_age_yrs, pat_age_mths, age_grp_lb)

#引入伪列以获得笛卡尔坐标
lkp_df%
变异（join=1）
种子集（6789）
个人识别码%
#使用联接列创建笛卡尔乘积
内部连接（lkp\U df）%>%
#筛选以仅保留所需的记录
过滤器（帕特年龄>=最小年龄&帕特年龄<最大年龄）%>%
#只保留必要的列
选择（个人id、个人年龄、个人年龄、个人年龄、个人年龄、个人年龄、个人年龄、年龄）

有人能提出更好的方法来处理类似的情况吗。提前谢谢。

---

MasOrgR.

如果间隔是连续的，就像这里，你也可以考虑<代码>剪切< /代码>。@ Jaap，谢谢你的链接。fuzzyjoin和data.table方法非常好。它从来没有出现在我的搜索，因为我使用的关键字。

head(pat_df)   
pat_id   pat_age 
1001     14.397769
1002     66.694280
1003     53.628013
1004     58.782156
1005      5.032531
1006     16.430463

# Introduce dummy column to obtain a cartesian
lkp_df <- lkp_df %>%
  mutate(join = 1)

set.seed(6789)
pat_id <- seq(1001, 1075)
pat_age <- runif(75, 0, 95)

pat_df <- data.frame(pat_id, pat_age) %>%
  mutate(pat_age_yrs = as.integer(pat_age),
         pat_age_mths = as.integer(pat_age * 12)) %>%
  # Introduce dummy column to obtain a cartesian
  mutate(join = 1) %>%
  # Create a cartesian product with the join column
  inner_join(lkp_df) %>%
  # Filter to keep only required records
  filter(pat_age >= min_age & pat_age < max_age) %>%
  # Keep only necessary columns
  select(pat_id, pat_age, pat_age_yrs, pat_age_mths, age_grp_lb)