R 匹配数据帧,不包括最后一个非NA值和忽略顺序
我有两个数据帧:R 匹配数据帧,不包括最后一个非NA值和忽略顺序,r,join,data.table,dplyr,melt,R,Join,Data.table,Dplyr,Melt,我有两个数据帧: Partner<-c("Alpha","Beta","Zeta") COL1<-c("A","C","M") COL2<-c("B","D","K") COL3<-c("C","F",NA) COL4<-c("D",NA,NA) df1<-data.frame(Partner,COL1,COL2,COL3,COL4) lift<-c(9,10,11,12,12,23,12,24) RULE1<-c("B","B","D","A"
Partner<-c("Alpha","Beta","Zeta")
COL1<-c("A","C","M")
COL2<-c("B","D","K")
COL3<-c("C","F",NA)
COL4<-c("D",NA,NA)
df1<-data.frame(Partner,COL1,COL2,COL3,COL4)
lift<-c(9,10,11,12,12,23,12,24)
RULE1<-c("B","B","D","A","C","K","M","K")
RULE2<-c("A","A","C","B","A","M","T","M")
RULE3<-c("G","D","M","C" ,"M", "E",NA,NA)
RULE4<-c(NA,NA,"K","D" ,NA, NA,NA,NA)
df2<-data.frame(lift,RULE1,RULE2,RULE3,RULE4)
df1
Partner COL1 COL2 COL3 COL4
Alpha A B C D
Beta C D F NA
Zeta M K NA NA
df2
lift RULE1 RULE2 RULE3 RULE4
9 B A G NA
10 B A D NA
11 D C M K
12 A B C D
12 C A M NA
23 K M E NA
12 M T NA NA
24 K M NA NA
为清晰起见,请写出结果:
df3
第1行输出“否”,因为在Alpha Partner中找不到G,并且G之前的所有值都显示在Alpha Partner(B,A)中
第2行输出“是”,因为D出现在Alpha Partner中,D之前的所有值都出现在Alpha Partner(B,A)中
第3行输出“是”,因为D出现在Alpha Partner中,D之前的所有值都出现在Alpha Partner中(A、B、C)
第4行输出“否”,因为在Alpha Partner中找不到M,并且M之前的所有值都显示在Alpha Partner(C,A)中
第5行输出“否”,因为在Zeta Partner中找不到E,并且E之前的所有值都显示在Zeta Partner中(K,M)
第6行输出“否”,因为在Zeta Partner中找不到T,并且T之前的所有值都显示在Zeta Partner(M)中
第7行输出“是”,因为M出现在Zeta Partner中,并且M之前的所有值都出现在Zeta Partner(K)中
我认为这必须是一个加入或某种匹配,但不知道如何做到这一点
如果有人能帮我解决这个问题,这将是非常有帮助的
谢谢
这是一次尝试:
df1<-cbind(df1_id=1:nrow(df1),df1)
df2 <- cbind(df2_id=1:nrow(df2),df2)
d11 <- df1 %>% gather(Col, Value,starts_with("C")) #Long
d11 <- d11 %>% na.omit() %>%group_by(df1_id) %>% slice(-n()) #remove last non NA
d22 <- df2 %>% gather(Rule, Value,starts_with("R"))
res <- inner_join(d11,d22)
rm(d22)
rm(d11)
final<-cbind(df1[res$df1_id,],df2[res$df2_id,])
final$Exist <- apply(final, 1, FUN = function(x)
c("No", "Yes")[(anyDuplicated(x[!is.na(x) & x != "" ])!=0) +1])
df1%slice(-n())#删除最后一个非NA
d22%聚集(规则、值,以“R”开头)
res这相当棘手,因为n个客户的购买必须与一组m规则进行比较。除此之外,还有两点增加了复杂性:
df2
中最后一个非NARULE
列在语义上与其他列不同。不幸的是,给定的数据结构没有反映这一点。因此,df2
缺少一个明确建议的列
最后,必须确定合作伙伴是否已经购买了推荐的商品
出于性能原因,以下方法依赖于melt()
、dcast()
和data.table
包的联接操作。但是,为了避免创建n*m行的笛卡尔叉积,使用了循环
EDIT已将dcast()
移出lappy()
函数
为n:m联接准备数据
一些带有dput
的示例数据会很有帮助。@dash2刚开始提供了数据结构,谢谢!我不明白为什么去掉了11行的“lift”,因为D、C、M不都在df1行中。逻辑是,如果在df1中找到最后一个非na值之前df2中的所有内容,那么它就是一个匹配项。在本例中,最后一个非na值是K。这就是我在上一篇文章中思考的原因,我需要删除最后一个非na值,进行匹配,然后稍后使用与其关联的索引id将其cbind回来@akrunI在这里也问了一个更简单的问题:
df1<-cbind(df1_id=1:nrow(df1),df1)
df2 <- cbind(df2_id=1:nrow(df2),df2)
d11 <- df1 %>% gather(Col, Value,starts_with("C")) #Long
d11 <- d11 %>% na.omit() %>%group_by(df1_id) %>% slice(-n()) #remove last non NA
d22 <- df2 %>% gather(Rule, Value,starts_with("R"))
res <- inner_join(d11,d22)
rm(d22)
rm(d11)
final<-cbind(df1[res$df1_id,],df2[res$df2_id,])
final$Exist <- apply(final, 1, FUN = function(x)
c("No", "Yes")[(anyDuplicated(x[!is.na(x) & x != "" ])!=0) +1])
library(data.table)
# convert to data.table and add row numbers
# here, a copy is used insteasd of setDT() in order to rename the data.tables
purchases <- as.data.table(df1)[, rnp := seq_len(.N)]
rules <- as.data.table(df2)[, rnr := seq_len(.N)]
# prepare purchases for joins
lp <- melt(purchases, id.vars = c("rnp", "Partner"), na.rm = TRUE)
wp <- dcast(lp, rnp ~ value, drop = FALSE)
wp
# rnp A B C D F K M
#1: 1 A B C D NA NA NA
#2: 2 NA NA C D F NA NA
#3: 3 NA NA NA NA NA K M
# prepare rules
lr <- melt(rules, id.vars = c("rnr", "lift"), na.rm = TRUE)
# identify last column of each rule which becomes the recommendation
rn_of_last_col <- lr[, last(.I), by = rnr][, V1]
# reshape from long to wide without recommendation
wr <- dcast(lr[-rn_of_last_col], rnr ~ value)
# add column with recommendations (kind of cbind, no join)
wr[, recommended := lr[rn_of_last_col, value]]
wr
# rnr A B C D K M recommended
#1: 1 A B NA NA NA NA G
#2: 2 A B NA NA NA NA D
#3: 3 NA NA C D NA M K
#4: 4 A B C NA NA NA D
#5: 5 A NA C NA NA NA M
#6: 6 NA NA NA NA K M E
#7: 7 NA NA NA NA NA M T
#8: 8 NA NA NA NA K NA M
combi <- rbindlist(
# implied loop over rules to find matching purchases for each rule
lapply(seq_len(nrow(rules)), function(i) {
# get col names except last col which is the recommendation
cols <- lr[rnr == i, value[-.N]]
# join single rule with all partners on relevant cols for this rule
wp[wr[i, .SD, .SDcols = c(cols, "rnr", "recommended")], on = cols, nomatch = 0]
})
)
# check if recommendation was purchased already
combi[, already_purchased := Reduce(`|`, lapply(.SD, function(x) x == recommended)),
.SDcols = -c("rnp", "rnr", "recommended")]
# clean up already purchased
combi[is.na(already_purchased), already_purchased := FALSE
][, already_purchased := ifelse(already_purchased, "Yes", "No")]
combi
# rnp A B C D F K M rnr recommended already_purchased
#1: 1 A B C D NA NA NA 1 G No
#2: 1 A B C D NA NA NA 2 D Yes
#3: 1 A B C D NA NA NA 4 D Yes
#4: 1 A B C D NA NA NA 5 M No
#5: 3 NA NA NA NA NA K M 6 E No
#6: 3 NA NA NA NA NA K M 7 T No
#7: 3 NA NA NA NA NA K M 8 M Yes
tmp_rules <- rules[combi[, .(rnp, rnr, recommended, already_purchased)], on = "rnr"]
tmp_purch <- purchases[combi[, .(rnp, rnr)], on = "rnp"]
result <- tmp_purch[tmp_rules, on = c("rnp", "rnr")]
result[, (c("rnp", "rnr")) := NULL]
result
# Partner COL1 COL2 COL3 COL4 lift RULE1 RULE2 RULE3 RULE4 recommend already_purchased
#1: Alpha A B C D 9 B A G NA G No
#2: Alpha A B C D 10 B A D NA D Yes
#3: Alpha A B C D 12 A B C D D Yes
#4: Alpha A B C D 12 C A M NA M No
#5: Zeta M K NA NA 23 K M E NA E No
#6: Zeta M K NA NA 12 M T NA NA T No
#7: Zeta M K NA NA 24 K M NA NA M Yes