使用R中的dplyr循环删除数据框中的条件组合
Looping to delete conditional combination in dataframe using dplyr in R
这是我的数据
datex <- c(rep("2021-01-18", 61), rep("2021-01-19", 139))
hourx <- c(0,1,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13,14,14,15,16,10,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13,14,14,15,11,0,0,0,0,0,0,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,3,3,3,3,3,3,3,4,4,4,4,4,4,4,5,5,5,5,5,5,5,6,6,6,6,6,6,6,6,7,7,7,7,7,7,7,7,8,8,8,8,8,8,8,8,9,9,9,9,9,9,9,9,10,10,10,10,10,10,10,10,11,11,11,11,11,11,11,11,11,11,12,12,12,12,12,12,12,12,13,13,13,13,13,13,13,13,14,14,14,14,14,14,14,14,14,15,15,15,15,16,16,16,16,0,0,0,0,1,2,3,4,5,6,7,8,9,10)
seller <- c("dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp3","dombsdpapp3","dombsdpapp3","dombsdpapp4","dombsdpapp4","dombsdpapp4","dombsdpapp4","dombsdpapp4","dombsdpapp4","dombsdpapp4","dombsdpapp4","dombsdpapp4","dombsdpapp4","dombsdpapp4")
product <- c("00021460","00021460","00021460","00021459","00021460","00021459","00021460","00021459","00021460","00021459","00021460","00021459","00021460","00021460","00021459","00021460","00021459","00021460","00021459","00021460","00021459","00021460","00021459","00021459","00021460","00021459","00021460","00021460","00021460","00021459","00021459","00021460","00021459","00021459","00021460","00021460","00021459","00021459","00021460","00021459","00021460","00021459","00021460","00021459","00021460","00021460","00021459","00021459","00021460","00021460","00021459","00021459","00021460","00021460","00021459","00021460","00021459","00021460","00021459","00021460","00021459","00021459","00021460","00021459","00021459","00021459","00021460","00021459","00021459","00021460","00021460","00021459","00021459","00021460","00021460","00021459","00021460","00021460","00021460","00021459","00021459","00021460","00021459","00021459","00021460","00021459","00021460","00021460","00021459","00021460","00021459","00021460","00021459","00021459","00021460","00021460","00021460","00021460","00021459","00021459","00021460","00021459","00021459","00021460","00021460","00021459","00021459","00021459","00021460","00021460","00021459","00021460","00021459","00021460","00021459","00021459","00021459","00021460","00021460","00021460","00021460","00021459","00021459","00021459","00021459","00021460","00021460","00021459","00021459","00021460","00021460","00021459","00021459","00021460","00021460","00021459","00021460","00021459","00021460","00021460","00021459","00021460","00021459","00021460","00021460","00021459","00021460","00021459","00021460","00021459","00021459","00021460","00021460","00021459","00021459","00021460","00021460","00021460","00021459","00021460","00021459","00021459","00021459","00021460","00021460","00021459","00021459","00021460","00021460","00021460","00021459","00021459","00021460","00021459","00021459","00021459","00021460","00021460","00021460","00021460","00021460","00021460","00021460","00021460","00021460","00021460","21459","21459","21459","21459","21459","21459","21459","21459","21459","21459","21459","21459","21459","21459")
detail <- c("E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","notEnoughBalance","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","notEnoughBalance","E99","success","success","success","E99","success","success","E99","success","E99","success","E99","E99","success","E99","E99","success","E99","success","E99","success","E99","success","E99","success","success","E99","E99","E99","success","success","E99","success","E99","success","E99","success","success","E99","E99","E99","success","E99","success","success","E99","E99","success","E99","success","E99","success","success","E99","E99","success","success","E99","E99","success","E99","success","success","E99","success","E99","success","E99","E99","success","success","E99","E99","success","E99","success","success","E99","E99","E99","success","success","notEnoughBalance","E99","success","success","E99","success","E99","success","notEnoughBalance","E99","success","E99","E99","success","E99","success","success","E99","success","E99","E99","success","E99","success","success","E99","success","success","E99","E99","success","notEnoughBalance","E99","E99","success","E99","success","success","E99","E99","success","success","E99","success","success","success","success","success","success","success","success","success","success","success","success","success","success")
status <- c("FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","OK01","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","OK01","FI04","OK00","OK00","OK00","FI04","OK00","OK00","FI04","OK00","FI04","OK00","FI04","FI04","OK00","FI04","FI04","OK00","FI04","OK00","FI04","OK00","FI04","OK00","FI04","OK00","OK00","FI04","FI04","FI04","OK00","OK00","FI04","OK00","FI04","OK00","FI04","OK00","OK00","FI04","FI04","FI04","OK00","FI04","OK00","OK00","FI04","FI04","OK00","FI04","OK00","FI04","OK00","OK00","FI04","FI04","OK00","OK00","FI04","FI04","OK00","FI04","OK00","OK00","FI04","OK00","FI04","OK00","FI04","FI04","OK00","OK00","FI04","FI04","OK00","FI04","OK00","OK00","FI04","FI04","FI04","OK00","OK00","OK01","FI04","OK00","OK00","FI04","OK00","FI04","OK00","OK01","FI04","OK00","FI04","FI04","OK00","FI04","OK00","OK00","FI04","OK00","FI04","FI04","OK00","FI04","OK00","OK00","FI04","OK00","OK00","FI04","FI04","OK00","OK01","FI04","FI04","OK00","FI04","OK00","OK00","FI04","FI04","OK00","OK00","FI04","OK00","OK00","OK00","OK00","OK00","OK00","OK00","OK00","OK00","OK00","OK00","OK00","OK00","OK00")
channel <- c("f2","f2","f2","f3","f2","f3","f2","f3","f2","f3","f2","f3","f2","f2","f3","f2","f3","f2","f3","f2","f3","f2","f3","f3","f2","f3","f2","f2","f2","f3","f3","f2","f3","f3","f2","f2","f3","f3","f2","f3","f2","f3","f2","f3","f2","f2","f3","f3","f2","f2","f3","f3","f2","f2","f3","f2","f3","f2","f3","f2","f3","f3","f2","f3","f3","f3","f2","f3","f3","f2","f2","f3","f3","f2","f2","f3","f2","f2","f2","f3","f3","f2","f3","f3","f2","f3","f2","f2","f3","f2","f3","f2","f3","f3","f2","f2","f2","f2","f3","f3","f2","f3","f3","f2","f2","f3","f3","f3","f2","f2","f3","f2","f3","f2","f3","f3","f3","f2","f2","f2","f2","f3","f3","f3","f3","f2","f2","f3","f3","f2","f2","f3","f3","f2","f2","f3","f2","f3","f2","f2","f3","f2","f3","f2","f2","f3","f2","f3","f2","f3","f3","f2","f2","f3","f3","f2","f2","f2","f3","f2","f3","f3","f3","f2","f2","f3","f3","f2","f2","f2","f3","f3","f2","f3","f3","f3","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2")
transaction <- c(1,120,50,5,1,2,1,9,6,12,5,25,14,6,22,9,10,14,15,12,220,12,12,14,9,11,100,90,110,12,13,4,3,1,2,3,3,5,7,5,5,6,9,16,8,13,10,20,15,18,10,19,15,5,13,12,10,12,26,14,0,4,0,0,0,2,0,0,2,0,4,0,6,8,0,2,3,0,2,0,1,0,1,0,2,0,0,2,1,1,0,0,3,0,1,0,3,0,0,6,5,2,0,8,0,0,12,11,0,2,0,11,0,0,14,21,0,0,13,7,0,17,0,0,18,0,7,0,4,4,0,0,7,12,0,13,0,0,130,160,9,0,0,0,16,0,0,16,0,14,0,0,9,0,11,8,0,8,0,0,8,0,10,5,0,15,0,0,3,0,0,8,8,0,0,6,5,0,8,0,0,5,1,0,0,95,11,15,20,100,100,100,100,100,100,100,100,100,100,100)
mydatax <- data.frame(datex, hourx, seller, product, detail, status, channel, transaction)
我有两个任务，我的第一个任务是我需要删除行少于 5 行的组合。示例组合：
- 卖家=“dombsdpapp3”
- 产品=“21459”
- 详细信息=“成功”
- 状态=“OK00”
- 频道=“f2”
我需要从我的数据中删除这种类型组合。
mycomb1 <- subset(mydatax, seller == "dombsdpapp3" & product == "21459" &
detail == "success" & status == "OK00" &
channel == "f2")
mycomb1
# datex hourx seller product detail status channel transaction
#187 2021-01-19 0 dombsdpapp3 21459 success OK00 f2 11
#188 2021-01-19 0 dombsdpapp3 21459 success OK00 f2 15
#189 2021-01-19 0 dombsdpapp3 21459 success OK00 f2 20
我的第二个任务是我需要删除具有相同行的组合。示例组合：
- 卖家=“dombsdpapp4”
- 产品=“21459”
- 详细信息=“成功”
- 状态=“OK00”
- 频道=“f2”
我需要从我的数据中删除这种类型组合。
mycomb2 <- subset(mydatax, seller == "dombsdpapp4" & product == "21459" &
detail == "success" & status == "OK00" &
channel == "f2")
mycomb2
# datex hourx seller product detail status channel transaction
#190 2021-01-19 0 dombsdpapp4 21459 success OK00 f2 100
#191 2021-01-19 1 dombsdpapp4 21459 success OK00 f2 100
#192 2021-01-19 2 dombsdpapp4 21459 success OK00 f2 100
#193 2021-01-19 3 dombsdpapp4 21459 success OK00 f2 100
#194 2021-01-19 4 dombsdpapp4 21459 success OK00 f2 100
#195 2021-01-19 5 dombsdpapp4 21459 success OK00 f2 100
#196 2021-01-19 6 dombsdpapp4 21459 success OK00 f2 100
#197 2021-01-19 7 dombsdpapp4 21459 success OK00 f2 100
#198 2021-01-19 8 dombsdpapp4 21459 success OK00 f2 100
#199 2021-01-19 9 dombsdpapp4 21459 success OK00 f2 100
#200 2021-01-19 10 dombsdpapp4 21459 success OK00 f2 100
我希望得到的结果是：
# Result
datex <- c(rep("2021-01-18", 59), rep("2021-01-19", 61))
hourx <- c(0,1,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13,14,14,15,16,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13,14,14,15,0,0,1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,5,5,5,5,6,6,6,6,7,7,7,7,8,8,8,8,9,9,9,9,10,10,10,10,11,11,11,11,12,12,12,12,13,13,13,13,14,14,14,14,15,15,16,16)
seller <- c("dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp2","dombsdpapp1","dombsdpapp2")
product <- c(21460,21460,21460,21459,21460,21459,21460,21459,21460,21459,21460,21459,21460,21460,21459,21460,21459,21460,21459,21460,21459,21460,21459,21459,21460,21459,21460,21460,21460,21459,21460,21459,21459,21460,21460,21459,21459,21460,21459,21460,21459,21460,21459,21460,21460,21459,21459,21460,21460,21459,21459,21460,21460,21459,21460,21459,21460,21459,21460,21459,21459,21459,21460,21459,21460,21459,21460,21460,21459,21459,21460,21460,21459,21460,21459,21460,21460,21459,21460,21459,21460,21459,21459,21460,21460,21459,21459,21460,21460,21459,21459,21460,21459,21460,21459,21460,21459,21460,21460,21459,21460,21459,21459,21460,21459,21460,21460,21459,21459,21460,21459,21460,21459,21460,21459,21460,21460,21460,21460,21460)
detail <- c("E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99")
status <- c("FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04")
channel <- c("f2","f2","f2","f3","f2","f3","f2","f3","f2","f3","f2","f3","f2","f2","f3","f2","f3","f2","f3","f2","f3","f2","f3","f3","f2","f3","f2","f2","f2","f3","f2","f3","f3","f2","f2","f3","f3","f2","f3","f2","f3","f2","f3","f2","f2","f3","f3","f2","f2","f3","f3","f2","f2","f3","f2","f3","f2","f3","f2","f3","f3","f3","f2","f3","f2","f3","f2","f2","f3","f3","f2","f2","f3","f2","f3","f2","f2","f3","f2","f3","f2","f3","f3","f2","f2","f3","f3","f2","f2","f3","f3","f2","f3","f2","f3","f2","f3","f2","f2","f3","f2","f3","f3","f2","f3","f2","f2","f3","f3","f2","f3","f2","f3","f2","f3","f2","f2","f2","f2","f2")
transaction <- c(1,120,50,5,1,2,1,9,6,12,5,25,14,6,22,9,10,14,15,12,220,12,12,14,9,11,100,90,110,13,4,3,1,2,3,3,5,7,5,5,6,9,16,8,13,10,20,15,18,10,19,15,5,13,12,10,12,26,14,4,2,2,4,6,8,2,3,2,1,1,2,2,1,1,3,1,3,6,5,2,8,12,11,2,11,14,21,13,7,17,18,7,4,4,7,12,13,130,160,9,16,16,14,9,11,8,8,8,10,5,15,3,8,8,6,5,8,5,1,95)
myresult <- data.frame(datex, hourx, seller, product, detail, status, channel, transaction)
痛苦的一点是我如何使用 dplyr 循环所有过程以获得所有结果？因为我有 100K 组合。谢谢。
也许，像这样？
library(dplyr)
mydatax %>%
group_by(seller, product, detail, status, channel) %>%
filter(n() >= 5 && n_distinct(transaction) > 1) %>%
ungroup
# A tibble: 120 x 8
# datex hourx seller product detail status channel transaction
# <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <dbl>
# 1 2021-01-18 0 dombsdpapp1 00021460 E99 FI04 f2 1
# 2 2021-01-18 1 dombsdpapp1 00021460 E99 FI04 f2 120
# 3 2021-01-18 2 dombsdpapp1 00021460 E99 FI04 f2 50
# 4 2021-01-18 3 dombsdpapp1 00021459 E99 FI04 f3 5
# 5 2021-01-18 3 dombsdpapp1 00021460 E99 FI04 f2 1
# 6 2021-01-18 4 dombsdpapp1 00021459 E99 FI04 f3 2
# 7 2021-01-18 4 dombsdpapp1 00021460 E99 FI04 f2 1
# 8 2021-01-18 5 dombsdpapp1 00021459 E99 FI04 f3 9
# 9 2021-01-18 5 dombsdpapp1 00021460 E99 FI04 f2 6
#10 2021-01-18 6 dombsdpapp1 00021459 E99 FI04 f3 12
# … with 110 more rows
filter(n() >= 5) 仅保留具有 >= 5 行且 transaction 的唯一值大于 1 的组。
如果我理解了，这会起作用：
mydatax %>%
###########
#First task
###########
#Create a variable with the count of the following variables
add_count(seller,product,detail,status,channel,name = "n1") %>%
#Filter those with less than 5
filter(n1 < 5) %>%
###########
#Second task
###########
#Create a variable with the count of the following variables
add_count(seller,product,detail,status,channel,transaction,name = "n2") %>%
#Filter those with less than 2
filter(n2 < 2)
使用plyr::count、
plyr::count((mydatax[,c("seller", "product", "detail", "status", "channel")])) %>%
filter(freq > 5) %>%
select(-freq)
seller product detail status channel
1 dombsdpapp1 00021459 E99 FI04 f3
2 dombsdpapp1 00021459 success OK00 f3
3 dombsdpapp1 00021460 E99 FI04 f2
4 dombsdpapp1 00021460 success OK00 f2
5 dombsdpapp2 00021459 E99 FI04 f3
6 dombsdpapp2 00021459 success OK00 f3
7 dombsdpapp2 00021460 E99 FI04 f2
8 dombsdpapp2 00021460 success OK00 f2
9 dombsdpapp4 21459 success OK00 f2
这是我的数据
datex <- c(rep("2021-01-18", 61), rep("2021-01-19", 139))
hourx <- c(0,1,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13,14,14,15,16,10,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13,14,14,15,11,0,0,0,0,0,0,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,3,3,3,3,3,3,3,4,4,4,4,4,4,4,5,5,5,5,5,5,5,6,6,6,6,6,6,6,6,7,7,7,7,7,7,7,7,8,8,8,8,8,8,8,8,9,9,9,9,9,9,9,9,10,10,10,10,10,10,10,10,11,11,11,11,11,11,11,11,11,11,12,12,12,12,12,12,12,12,13,13,13,13,13,13,13,13,14,14,14,14,14,14,14,14,14,15,15,15,15,16,16,16,16,0,0,0,0,1,2,3,4,5,6,7,8,9,10)
seller <- c("dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp3","dombsdpapp3","dombsdpapp3","dombsdpapp4","dombsdpapp4","dombsdpapp4","dombsdpapp4","dombsdpapp4","dombsdpapp4","dombsdpapp4","dombsdpapp4","dombsdpapp4","dombsdpapp4","dombsdpapp4")
product <- c("00021460","00021460","00021460","00021459","00021460","00021459","00021460","00021459","00021460","00021459","00021460","00021459","00021460","00021460","00021459","00021460","00021459","00021460","00021459","00021460","00021459","00021460","00021459","00021459","00021460","00021459","00021460","00021460","00021460","00021459","00021459","00021460","00021459","00021459","00021460","00021460","00021459","00021459","00021460","00021459","00021460","00021459","00021460","00021459","00021460","00021460","00021459","00021459","00021460","00021460","00021459","00021459","00021460","00021460","00021459","00021460","00021459","00021460","00021459","00021460","00021459","00021459","00021460","00021459","00021459","00021459","00021460","00021459","00021459","00021460","00021460","00021459","00021459","00021460","00021460","00021459","00021460","00021460","00021460","00021459","00021459","00021460","00021459","00021459","00021460","00021459","00021460","00021460","00021459","00021460","00021459","00021460","00021459","00021459","00021460","00021460","00021460","00021460","00021459","00021459","00021460","00021459","00021459","00021460","00021460","00021459","00021459","00021459","00021460","00021460","00021459","00021460","00021459","00021460","00021459","00021459","00021459","00021460","00021460","00021460","00021460","00021459","00021459","00021459","00021459","00021460","00021460","00021459","00021459","00021460","00021460","00021459","00021459","00021460","00021460","00021459","00021460","00021459","00021460","00021460","00021459","00021460","00021459","00021460","00021460","00021459","00021460","00021459","00021460","00021459","00021459","00021460","00021460","00021459","00021459","00021460","00021460","00021460","00021459","00021460","00021459","00021459","00021459","00021460","00021460","00021459","00021459","00021460","00021460","00021460","00021459","00021459","00021460","00021459","00021459","00021459","00021460","00021460","00021460","00021460","00021460","00021460","00021460","00021460","00021460","00021460","21459","21459","21459","21459","21459","21459","21459","21459","21459","21459","21459","21459","21459","21459")
detail <- c("E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","notEnoughBalance","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","notEnoughBalance","E99","success","success","success","E99","success","success","E99","success","E99","success","E99","E99","success","E99","E99","success","E99","success","E99","success","E99","success","E99","success","success","E99","E99","E99","success","success","E99","success","E99","success","E99","success","success","E99","E99","E99","success","E99","success","success","E99","E99","success","E99","success","E99","success","success","E99","E99","success","success","E99","E99","success","E99","success","success","E99","success","E99","success","E99","E99","success","success","E99","E99","success","E99","success","success","E99","E99","E99","success","success","notEnoughBalance","E99","success","success","E99","success","E99","success","notEnoughBalance","E99","success","E99","E99","success","E99","success","success","E99","success","E99","E99","success","E99","success","success","E99","success","success","E99","E99","success","notEnoughBalance","E99","E99","success","E99","success","success","E99","E99","success","success","E99","success","success","success","success","success","success","success","success","success","success","success","success","success","success")
status <- c("FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","OK01","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","OK01","FI04","OK00","OK00","OK00","FI04","OK00","OK00","FI04","OK00","FI04","OK00","FI04","FI04","OK00","FI04","FI04","OK00","FI04","OK00","FI04","OK00","FI04","OK00","FI04","OK00","OK00","FI04","FI04","FI04","OK00","OK00","FI04","OK00","FI04","OK00","FI04","OK00","OK00","FI04","FI04","FI04","OK00","FI04","OK00","OK00","FI04","FI04","OK00","FI04","OK00","FI04","OK00","OK00","FI04","FI04","OK00","OK00","FI04","FI04","OK00","FI04","OK00","OK00","FI04","OK00","FI04","OK00","FI04","FI04","OK00","OK00","FI04","FI04","OK00","FI04","OK00","OK00","FI04","FI04","FI04","OK00","OK00","OK01","FI04","OK00","OK00","FI04","OK00","FI04","OK00","OK01","FI04","OK00","FI04","FI04","OK00","FI04","OK00","OK00","FI04","OK00","FI04","FI04","OK00","FI04","OK00","OK00","FI04","OK00","OK00","FI04","FI04","OK00","OK01","FI04","FI04","OK00","FI04","OK00","OK00","FI04","FI04","OK00","OK00","FI04","OK00","OK00","OK00","OK00","OK00","OK00","OK00","OK00","OK00","OK00","OK00","OK00","OK00","OK00")
channel <- c("f2","f2","f2","f3","f2","f3","f2","f3","f2","f3","f2","f3","f2","f2","f3","f2","f3","f2","f3","f2","f3","f2","f3","f3","f2","f3","f2","f2","f2","f3","f3","f2","f3","f3","f2","f2","f3","f3","f2","f3","f2","f3","f2","f3","f2","f2","f3","f3","f2","f2","f3","f3","f2","f2","f3","f2","f3","f2","f3","f2","f3","f3","f2","f3","f3","f3","f2","f3","f3","f2","f2","f3","f3","f2","f2","f3","f2","f2","f2","f3","f3","f2","f3","f3","f2","f3","f2","f2","f3","f2","f3","f2","f3","f3","f2","f2","f2","f2","f3","f3","f2","f3","f3","f2","f2","f3","f3","f3","f2","f2","f3","f2","f3","f2","f3","f3","f3","f2","f2","f2","f2","f3","f3","f3","f3","f2","f2","f3","f3","f2","f2","f3","f3","f2","f2","f3","f2","f3","f2","f2","f3","f2","f3","f2","f2","f3","f2","f3","f2","f3","f3","f2","f2","f3","f3","f2","f2","f2","f3","f2","f3","f3","f3","f2","f2","f3","f3","f2","f2","f2","f3","f3","f2","f3","f3","f3","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2","f2")
transaction <- c(1,120,50,5,1,2,1,9,6,12,5,25,14,6,22,9,10,14,15,12,220,12,12,14,9,11,100,90,110,12,13,4,3,1,2,3,3,5,7,5,5,6,9,16,8,13,10,20,15,18,10,19,15,5,13,12,10,12,26,14,0,4,0,0,0,2,0,0,2,0,4,0,6,8,0,2,3,0,2,0,1,0,1,0,2,0,0,2,1,1,0,0,3,0,1,0,3,0,0,6,5,2,0,8,0,0,12,11,0,2,0,11,0,0,14,21,0,0,13,7,0,17,0,0,18,0,7,0,4,4,0,0,7,12,0,13,0,0,130,160,9,0,0,0,16,0,0,16,0,14,0,0,9,0,11,8,0,8,0,0,8,0,10,5,0,15,0,0,3,0,0,8,8,0,0,6,5,0,8,0,0,5,1,0,0,95,11,15,20,100,100,100,100,100,100,100,100,100,100,100)
mydatax <- data.frame(datex, hourx, seller, product, detail, status, channel, transaction)
我有两个任务，我的第一个任务是我需要删除行少于 5 行的组合。示例组合：
- 卖家=“dombsdpapp3”
- 产品=“21459”
- 详细信息=“成功”
- 状态=“OK00”
- 频道=“f2”
我需要从我的数据中删除这种类型组合。
mycomb1 <- subset(mydatax, seller == "dombsdpapp3" & product == "21459" &
detail == "success" & status == "OK00" &
channel == "f2")
mycomb1
# datex hourx seller product detail status channel transaction
#187 2021-01-19 0 dombsdpapp3 21459 success OK00 f2 11
#188 2021-01-19 0 dombsdpapp3 21459 success OK00 f2 15
#189 2021-01-19 0 dombsdpapp3 21459 success OK00 f2 20
我的第二个任务是我需要删除具有相同行的组合。示例组合：
- 卖家=“dombsdpapp4”
- 产品=“21459”
- 详细信息=“成功”
- 状态=“OK00”
- 频道=“f2”
我需要从我的数据中删除这种类型组合。
mycomb2 <- subset(mydatax, seller == "dombsdpapp4" & product == "21459" &
detail == "success" & status == "OK00" &
channel == "f2")
mycomb2
# datex hourx seller product detail status channel transaction
#190 2021-01-19 0 dombsdpapp4 21459 success OK00 f2 100
#191 2021-01-19 1 dombsdpapp4 21459 success OK00 f2 100
#192 2021-01-19 2 dombsdpapp4 21459 success OK00 f2 100
#193 2021-01-19 3 dombsdpapp4 21459 success OK00 f2 100
#194 2021-01-19 4 dombsdpapp4 21459 success OK00 f2 100
#195 2021-01-19 5 dombsdpapp4 21459 success OK00 f2 100
#196 2021-01-19 6 dombsdpapp4 21459 success OK00 f2 100
#197 2021-01-19 7 dombsdpapp4 21459 success OK00 f2 100
#198 2021-01-19 8 dombsdpapp4 21459 success OK00 f2 100
#199 2021-01-19 9 dombsdpapp4 21459 success OK00 f2 100
#200 2021-01-19 10 dombsdpapp4 21459 success OK00 f2 100
我希望得到的结果是：
# Result
datex <- c(rep("2021-01-18", 59), rep("2021-01-19", 61))
hourx <- c(0,1,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13,14,14,15,16,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13,14,14,15,0,0,1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,5,5,5,5,6,6,6,6,7,7,7,7,8,8,8,8,9,9,9,9,10,10,10,10,11,11,11,11,12,12,12,12,13,13,13,13,14,14,14,14,15,15,16,16)
seller <- c("dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp1","dombsdpapp2","dombsdpapp2","dombsdpapp1","dombsdpapp2","dombsdpapp1","dombsdpapp2")
product <- c(21460,21460,21460,21459,21460,21459,21460,21459,21460,21459,21460,21459,21460,21460,21459,21460,21459,21460,21459,21460,21459,21460,21459,21459,21460,21459,21460,21460,21460,21459,21460,21459,21459,21460,21460,21459,21459,21460,21459,21460,21459,21460,21459,21460,21460,21459,21459,21460,21460,21459,21459,21460,21460,21459,21460,21459,21460,21459,21460,21459,21459,21459,21460,21459,21460,21459,21460,21460,21459,21459,21460,21460,21459,21460,21459,21460,21460,21459,21460,21459,21460,21459,21459,21460,21460,21459,21459,21460,21460,21459,21459,21460,21459,21460,21459,21460,21459,21460,21460,21459,21460,21459,21459,21460,21459,21460,21460,21459,21459,21460,21459,21460,21459,21460,21459,21460,21460,21460,21460,21460)
detail <- c("E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99","E99")
status <- c("FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04","FI04")
channel <- c("f2","f2","f2","f3","f2","f3","f2","f3","f2","f3","f2","f3","f2","f2","f3","f2","f3","f2","f3","f2","f3","f2","f3","f3","f2","f3","f2","f2","f2","f3","f2","f3","f3","f2","f2","f3","f3","f2","f3","f2","f3","f2","f3","f2","f2","f3","f3","f2","f2","f3","f3","f2","f2","f3","f2","f3","f2","f3","f2","f3","f3","f3","f2","f3","f2","f3","f2","f2","f3","f3","f2","f2","f3","f2","f3","f2","f2","f3","f2","f3","f2","f3","f3","f2","f2","f3","f3","f2","f2","f3","f3","f2","f3","f2","f3","f2","f3","f2","f2","f3","f2","f3","f3","f2","f3","f2","f2","f3","f3","f2","f3","f2","f3","f2","f3","f2","f2","f2","f2","f2")
transaction <- c(1,120,50,5,1,2,1,9,6,12,5,25,14,6,22,9,10,14,15,12,220,12,12,14,9,11,100,90,110,13,4,3,1,2,3,3,5,7,5,5,6,9,16,8,13,10,20,15,18,10,19,15,5,13,12,10,12,26,14,4,2,2,4,6,8,2,3,2,1,1,2,2,1,1,3,1,3,6,5,2,8,12,11,2,11,14,21,13,7,17,18,7,4,4,7,12,13,130,160,9,16,16,14,9,11,8,8,8,10,5,15,3,8,8,6,5,8,5,1,95)
myresult <- data.frame(datex, hourx, seller, product, detail, status, channel, transaction)
痛苦的一点是我如何使用 dplyr 循环所有过程以获得所有结果？因为我有 100K 组合。谢谢。
也许，像这样？
library(dplyr)
mydatax %>%
group_by(seller, product, detail, status, channel) %>%
filter(n() >= 5 && n_distinct(transaction) > 1) %>%
ungroup
# A tibble: 120 x 8
# datex hourx seller product detail status channel transaction
# <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <dbl>
# 1 2021-01-18 0 dombsdpapp1 00021460 E99 FI04 f2 1
# 2 2021-01-18 1 dombsdpapp1 00021460 E99 FI04 f2 120
# 3 2021-01-18 2 dombsdpapp1 00021460 E99 FI04 f2 50
# 4 2021-01-18 3 dombsdpapp1 00021459 E99 FI04 f3 5
# 5 2021-01-18 3 dombsdpapp1 00021460 E99 FI04 f2 1
# 6 2021-01-18 4 dombsdpapp1 00021459 E99 FI04 f3 2
# 7 2021-01-18 4 dombsdpapp1 00021460 E99 FI04 f2 1
# 8 2021-01-18 5 dombsdpapp1 00021459 E99 FI04 f3 9
# 9 2021-01-18 5 dombsdpapp1 00021460 E99 FI04 f2 6
#10 2021-01-18 6 dombsdpapp1 00021459 E99 FI04 f3 12
# … with 110 more rows
filter(n() >= 5) 仅保留具有 >= 5 行且 transaction 的唯一值大于 1 的组。
如果我理解了，这会起作用：
mydatax %>%
###########
#First task
###########
#Create a variable with the count of the following variables
add_count(seller,product,detail,status,channel,name = "n1") %>%
#Filter those with less than 5
filter(n1 < 5) %>%
###########
#Second task
###########
#Create a variable with the count of the following variables
add_count(seller,product,detail,status,channel,transaction,name = "n2") %>%
#Filter those with less than 2
filter(n2 < 2)
使用plyr::count、
plyr::count((mydatax[,c("seller", "product", "detail", "status", "channel")])) %>%
filter(freq > 5) %>%
select(-freq)
seller product detail status channel
1 dombsdpapp1 00021459 E99 FI04 f3
2 dombsdpapp1 00021459 success OK00 f3
3 dombsdpapp1 00021460 E99 FI04 f2
4 dombsdpapp1 00021460 success OK00 f2
5 dombsdpapp2 00021459 E99 FI04 f3
6 dombsdpapp2 00021459 success OK00 f3
7 dombsdpapp2 00021460 E99 FI04 f2
8 dombsdpapp2 00021460 success OK00 f2
9 dombsdpapp4 21459 success OK00 f2