dplyr left_join小于,大于条件

Question

这个问题在某种程度上与问题有关,问题是在非平凡的标准上有效地合并两个数据框,并检查日期是否在r中的两个日期之间.我在这里发布的请求该功能是否存在: GitHub问题

我希望加入两个数据帧dplyr::left_join().我用来加入的条件是小于,大于ie,<=和>.是否dplyr::left_join()支持这种功能？或者只=在这些键之间使用操作符.这很容易从SQL运行(假设我在数据库中有数据帧)

这是一个MWE:我有一个公司年(fdata)的两个数据集,而第二个是每五年发生一次的调查数据.因此,对于fdata两个调查年份之间的所有年份,我加入相应的调查年度数据.

id <- c(1,1,1,1,
        2,2,2,2,2,2,
        3,3,3,3,3,3,
        5,5,5,5,
        8,8,8,8,
        13,13,13)

fyear <- c(1998,1999,2000,2001,1998,1999,2000,2001,2002,2003,
       1998,1999,2000,2001,2002,2003,1998,1999,2000,2001,
       1998,1999,2000,2001,1998,1999,2000)

byear <- c(1990,1995,2000,2005)
eyear <- c(1995,2000,2005,2010)
val <- c(3,1,5,6)

sdata <- tbl_df(data.frame(byear, eyear, val))

fdata <- tbl_df(data.frame(id, fyear))

test1 <- left_join(fdata, sdata, by = c("fyear" >= "byear","fyear" < "eyear"))

我明白了

Error: cannot join on columns 'TRUE' x 'TRUE': index out of bounds 

除非left_join能处理这个条件,但我的语法遗漏了什么？

Answer 1

data.table 从v 1.9.8开始添加非equi连接

library(data.table) #v>=1.9.8
setDT(sdata); setDT(fdata) # converting to data.table in place

fdata[sdata, on = .(fyear >= byear, fyear < eyear), nomatch = 0,
      .(id, x.fyear, byear, eyear, val)]
#    id x.fyear byear eyear val
# 1:  1    1998  1995  2000   1
# 2:  2    1998  1995  2000   1
# 3:  3    1998  1995  2000   1
# 4:  5    1998  1995  2000   1
# 5:  8    1998  1995  2000   1
# 6: 13    1998  1995  2000   1
# 7:  1    1999  1995  2000   1
# 8:  2    1999  1995  2000   1
# 9:  3    1999  1995  2000   1
#10:  5    1999  1995  2000   1
#11:  8    1999  1995  2000   1
#12: 13    1999  1995  2000   1
#13:  1    2000  2000  2005   5
#14:  2    2000  2000  2005   5
#15:  3    2000  2000  2005   5
#16:  5    2000  2000  2005   5
#17:  8    2000  2000  2005   5
#18: 13    2000  2000  2005   5
#19:  1    2001  2000  2005   5
#20:  2    2001  2000  2005   5
#21:  3    2001  2000  2005   5
#22:  5    2001  2000  2005   5
#23:  8    2001  2000  2005   5
#24:  2    2002  2000  2005   5
#25:  3    2002  2000  2005   5
#26:  2    2003  2000  2005   5
#27:  3    2003  2000  2005   5
#    id x.fyear byear eyear val

您还可以foverlaps通过更多的努力在1.9.6中使用它.

Answer 2

这看起来像是一个包含fuzzyjoin地址的任务.包的各种功能看起来和工作类似于dplyr连接功能.

在这种情况下,其中一个fuzzy_*_join功能将适合您.dplyr::left_join和之间的主要区别在于fuzzyjoin::fuzzy_left_join您提供了在match.fun参数匹配过程中使用的函数列表.请注意,by参数仍然与其中的相同left_join.

以下是一个例子.我使用的功能来匹配顷>=并<为fyear到byear和fyear到eyear的比较,分别.该

library(fuzzyjoin)

fuzzy_left_join(fdata, sdata, 
             by = c("fyear" = "byear", "fyear" = "eyear"), 
             match_fun = list(`>=`, `<`))

Source: local data frame [27 x 5]

      id fyear byear eyear   val
   (dbl) (dbl) (dbl) (dbl) (dbl)
1      1  1998  1995  2000     1
2      1  1999  1995  2000     1
3      1  2000  2000  2005     5
4      1  2001  2000  2005     5
5      2  1998  1995  2000     1
6      2  1999  1995  2000     1
7      2  2000  2000  2005     5
8      2  2001  2000  2005     5
9      2  2002  2000  2005     5
10     2  2003  2000  2005     5
..   ...   ...   ...   ...   ...

Answer 3

用一个filter.(但请注意,这个答案并没有产生正确的答案LEFT JOIN;但是MWE会给出正确的结果INNER JOIN.)

在dplyr若问合并两个表没有什么合并,所以在下面,我做一个虚拟变量两个表为此目的,然后过滤,然后放下包不开心dummy:

fdata %>% 
    mutate(dummy=TRUE) %>%
    left_join(sdata %>% mutate(dummy=TRUE)) %>%
    filter(fyear >= byear, fyear < eyear) %>%
    select(-dummy)

请注意,如果您在PostgreSQL中执行此操作(例如),查询优化器会通过dummy以下两个查询说明来查看变量:

> fdata %>% 
+     mutate(dummy=TRUE) %>%
+     left_join(sdata %>% mutate(dummy=TRUE)) %>%
+     filter(fyear >= byear, fyear < eyear) %>%
+     select(-dummy) %>%
+     explain()
Joining by: "dummy"
<SQL>
SELECT "id" AS "id", "fyear" AS "fyear", "byear" AS "byear", "eyear" AS "eyear", "val" AS "val"
FROM (SELECT * FROM (SELECT "id", "fyear", TRUE AS "dummy"
FROM "fdata") AS "zzz136"

LEFT JOIN 

(SELECT "byear", "eyear", "val", TRUE AS "dummy"
FROM "sdata") AS "zzz137"

USING ("dummy")) AS "zzz138"
WHERE "fyear" >= "byear" AND "fyear" < "eyear"


<PLAN>
Nested Loop  (cost=0.00..50886.88 rows=322722 width=40)
  Join Filter: ((fdata.fyear >= sdata.byear) AND (fdata.fyear < sdata.eyear))
  ->  Seq Scan on fdata  (cost=0.00..28.50 rows=1850 width=16)
  ->  Materialize  (cost=0.00..33.55 rows=1570 width=24)
        ->  Seq Scan on sdata  (cost=0.00..25.70 rows=1570 width=24)

并使用SQL更干净地完成它会得到完全相同的结果:

> tbl(pg, sql("
+     SELECT *
+     FROM fdata 
+     LEFT JOIN sdata 
+     ON fyear >= byear AND fyear < eyear")) %>%
+     explain()
<SQL>
SELECT "id", "fyear", "byear", "eyear", "val"
FROM (
    SELECT *
    FROM fdata 
    LEFT JOIN sdata 
    ON fyear >= byear AND fyear < eyear) AS "zzz140"


<PLAN>
Nested Loop Left Join  (cost=0.00..50886.88 rows=322722 width=40)
  Join Filter: ((fdata.fyear >= sdata.byear) AND (fdata.fyear < sdata.eyear))
  ->  Seq Scan on fdata  (cost=0.00..28.50 rows=1850 width=16)
  ->  Materialize  (cost=0.00..33.55 rows=1570 width=24)
        ->  Seq Scan on sdata  (cost=0.00..25.70 rows=1570 width=24)

Answer 4

dplyr v1.1.0现在包括执行像这样的非等值连接的功能，其语法几乎与您尝试过的语法完全相同。对于具有许多部分匹配的数据，这将比使用fuzzyjoin或filter过度包含连接之后的步骤性能更高。

# Relies on dplyr >=1.1.0, released Jan 2023
library(dplyr)
left_join(fdata, sdata, join_by(fyear >= byear,fyear < year))