使用R将行保留到第一次出现

使用R将行保留到第一次出现,r,R,我在R'HLS'中有一个数据框,基本上是访问者访问网站时的页面细节。每一行代表从第3页到最多第10页的每一次访问,如果他移动到第10页,则第10页由页数表示,如下所示 ID page_count purchase_flag prob hl_flag V1 3 1 0.76 1 V1 4 1 0.65 1 V1 5

我在R'HLS'中有一个数据框,基本上是访问者访问网站时的页面细节。每一行代表从第3页到最多第10页的每一次访问,如果他移动到第10页,则第10页由页数表示,如下所示

ID   page_count   purchase_flag   prob     hl_flag
V1      3              1          0.76       1
V1      4              1          0.65       1
V1      5              1          0.04       0
V1      6              1          0.86       1
V1      7              1          0.04       0
V1      8              1          0.65       1
V1      9              1          0.01       0
V1      10             1          0.00       0
V2      3              0          0.03       0
V2      4              0          0.01       0
V2      5              0          0.02       0
V2      6              0          0.00       0
V3      3              1          0.02       0
V3      4              1          0.001      0
V3      5              1          0.76       1
V3      6              1          0.03       0
V4      3              0          0.04       0
V4      4              0          0.65       1
V4      5              0          0.03       0 
我想创建一个表,该表在第一次出现hl_标志=1之前,如果该情况为真,那么该表将接收行;如果hl_标志=0,则该表将接收任何ID的所有行。输出需要如下所示

ID     page_count     purchase_flag    prob     hl_flag
V1         3                1          0.76      1
V2         3                0          0.03      0
V2         4                0          0.01      0
V2         5                0          0.02      0
V2         6                0          0.00      0
V3         3                1          0.02      0
V3         4                1          0.001     0
V3         5                1          0.76      1
V4         3                0          0.04      0
V4         4                0          0.65      1
提前谢谢你的帮助

更新: 添加dput的输出,如下所示

structure(list(ung_id = c("00000f23-1019-4aff-8199-35bd0d032356/1", 
"00000f23-1019-4aff-8199-35bd0d032356/1", "00000f23-1019-4aff-8199-35bd0d032356/1", 
"00000f23-1019-4aff-8199-35bd0d032356/1", "00002b20-82d4-497b-a137-34e3bb4eaf74/1", 
"00002b20-82d4-497b-a137-34e3bb4eaf74/1", "00002b20-82d4-497b-a137-34e3bb4eaf74/1", 
"0000aeff-2d8b-4daa-a084-fb2980f1feed/1", "0000aeff-2d8b-4daa-a084-fb2980f1feed/1", 
"0000b96e-566f-4b6e-925a-b7dcfd4a7208/1", "0000b96e-566f-4b6e-925a-b7dcfd4a7208/1", 
"0000b96e-566f-4b6e-925a-b7dcfd4a7208/1", "0000b96e-566f-4b6e-925a-b7dcfd4a7208/1", 
"0000b96e-566f-4b6e-925a-b7dcfd4a7208/1", "0000b96e-566f-4b6e-925a-b7dcfd4a7208/1", 
"0000b96e-566f-4b6e-925a-b7dcfd4a7208/1", "0000b96e-566f-4b6e-925a-b7dcfd4a7208/1", 
"0000d089-edda-4c8b-8b17-d9def3cae7cf/1", "0000d089-edda-4c8b-8b17-d9def3cae7cf/1", 
"0000d089-edda-4c8b-8b17-d9def3cae7cf/1"), nop_count = c(3L, 
4L, 5L, 6L, 3L, 4L, 5L, 3L, 4L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
3L, 4L, 5L), purchase_flag = c(1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), prob = c(0.0777615841278747, 
0.0738346887497272, 0.0741130887754292, 0.0785370078084892, 0.0619573259953132, 
0.0516201527986966, 0.0562025814090338, 0.0837301511694211, 0.0579033581198143, 
0.0364358545936557, 0.0329682922619259, 0.0420157964561273, 0.049855260762479, 
0.0500481302257314, 0.0463893143028813, 0.049855260762479, 0.0391886960037603, 
0.0683568422952682, 0.0570168506417919, 0.0661965354597502), 
decile = structure(c(8L, 8L, 8L, 8L, 6L, 4L, 5L, 8L, 5L, 
1L, 1L, 2L, 4L, 4L, 3L, 4L, 2L, 7L, 5L, 7L), .Label = c("(0.0257,0.0364]", 
"(0.0364,0.0428]", "(0.0428,0.0482]", "(0.0482,0.0531]", 
"(0.0531,0.0583]", "(0.0583,0.0645]", "(0.0645,0.0722]", 
"(0.0722,0.0842]"), class = "factor"), hl_Flag = c(1L, 1L, 
1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
1L, 1L, 1L)), .Names = c("ung_id", "nop_count", "purchase_flag", 
"prob", "decile", "hl_Flag"), row.names = c(NA, -20L), .internal.selfref = <pointer: 0x00000000002b0788>, class = c("data.table", 
"data.frame"))
结构(列表(ung_id=c(“00000f23-1019-4aff-8199-35bd0d032356/1”), “00000f23-1019-4aff-8199-35bd0d032356/1”、“00000f23-1019-4aff-8199-35bd0d032356/1”, “00000f23-1019-4aff-8199-35bd0d032356/1”、“00002b20-82d4-497b-a137-34e3bb4eaf74/1”, “00002b20-82d4-497b-a137-34e3bb4eaf74/1”、“00002b20-82d4-497b-a137-34e3bb4eaf74/1”, “0000aeff-2d8b-4daa-a084-fb2980f1feed/1”、“0000aeff-2d8b-4daa-a084-fb2980f1feed/1”, “0000b96e-566f-4b6e-925a-b7dcfd4a7208/1”、“0000b96e-566f-4b6e-925a-b7dcfd4a7208/1”, “0000b96e-566f-4b6e-925a-b7dcfd4a7208/1”、“0000b96e-566f-4b6e-925a-b7dcfd4a7208/1”, “0000b96e-566f-4b6e-925a-b7dcfd4a7208/1”、“0000b96e-566f-4b6e-925a-b7dcfd4a7208/1”, “0000b96e-566f-4b6e-925a-b7dcfd4a7208/1”、“0000b96e-566f-4b6e-925a-b7dcfd4a7208/1”, “0000d089-edda-4c8b-8b17-d9def3cae7cf/1”、“0000d089-edda-4c8b-8b17-d9def3cae7cf/1”, “0000d089-edda-4c8b-8b17-d9def3cae7cf/1”,nop_计数=c(3L, 4L,5L,6L,3L,4L,5L,3L,4L,3L,4L,5L,6L,7L,8L,9L,10L, 3L,4L,5L),采购标志=c(1L,1L,1L,0L,0L,0L, 0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L),概率=c(0.0777615841278747, 0.0738346887497272, 0.0741130887754292, 0.0785370078084892, 0.0619573259953132, 0.0516201527986966, 0.0562025814090338, 0.0837301511694211, 0.0579033581198143, 0.0364358545936557, 0.0329682922619259, 0.0420157964561273, 0.049855260762479, 0.0500481302257314, 0.0463893143028813, 0.049855260762479, 0.0391886960037603, 0.0683568422952682, 0.0570168506417919, 0.0661965354597502), 十分位数=结构(c)(8L,8L,8L,8L,6L,4L,5L,8L,5L, 1L,1L,2L,4L,4L,3L,4L,2L,7L,5L,7L),标签=c(“(0.0257,0.0364)”, "(0.0364,0.0428]", "(0.0428,0.0482]", "(0.0482,0.0531]", "(0.0531,0.0583]", "(0.0583,0.0645]", "(0.0645,0.0722]", “(0.0722,0.0842]”,class=“factor”),hl_标志=c(1L,1L, 1L,1L,1L,0L,1L,1L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L, 1L,1L,1L)),.Names=c(“ung\u id”,“nop\u count”,“purchase\u flag”, “prob”,“decile”,“hl_标志”),row.names=c(NA,-20L),.internal.selfref=,class=c(“data.table”, “data.frame”))
一个选项将使用
data.table
。我们将“data.frame”转换为“data.table”(
setDT(HLS)
),按“ID”分组,我们检查是否有
任何
为1的“hl_标志”值。在这种情况下,我们使用
which.max
获得hl_标志中第一次出现的1的索引,获得序列(
1:(which.max..
),查找行索引(
.I
)或
else
仅返回行索引(
.I
),提取具有行索引(
$V1
)的列,并使用该列对行进行子集化

library(data.table)
setDT(HLS)[HLS[, if(any(hl_flag==1)) .I[1:(which.max(hl_flag))]
               else .I, ID]$V1]
#     ID page_count purchase_flag  prob hl_flag
# 1: V1          3             1 0.760       1
# 2: V2          3             0 0.030       0
# 3: V2          4             0 0.010       0
# 4: V2          5             0 0.020       0
# 5: V2          6             0 0.000       0
# 6: V3          3             1 0.020       0
# 7: V3          4             1 0.001       0
# 8: V3          5             1 0.760       1
# 9: V4          3             0 0.040       0
#10: V4          4             0 0.650       1

或者类似于我为
数据显示的
方法。表
,一个
基本R
选项

do.call(rbind, lapply(split(HLS, HLS$ID), 
           function(x) if(any(x$hl_flag==1)) 
                  x[seq(which.max(x$hl_flag)), ] 
                else x))

或者使用
dplyr

library(dplyr)
HLS %>% 
    group_by(ID) %>% 
    filter(all(!hl_flag)| row_number() %in% seq(which.max(hl_flag)))
 #      ID page_count purchase_flag  prob hl_flag
 #    (chr)      (int)         (int) (dbl)   (int)
 #1     V1          3             1 0.760       1
 #2     V2          3             0 0.030       0
 #3     V2          4             0 0.010       0
 #4     V2          5             0 0.020       0
 #5     V2          6             0 0.000       0
 #6     V3          3             1 0.020       0
 #7     V3          4             1 0.001       0
 #8     V3          5             1 0.760       1
 #9     V4          3             0 0.040       0
 #10    V4          4             0 0.650       1

一个选项是使用
data.table
。我们将'data.frame'转换为'data.table'(
setDT(HLS)
),按'ID'分组,我们检查是否有
任何
的'hl_flag'值为1。在这种情况下,我们使用
which.max
获得hl_flag中第一次出现的1的索引,得到序列(
1:(which.max..
),查找行索引(
.I
)或
else
仅返回行索引(
.I
),提取具有行索引(
$V1
)的列,并使用该列对行进行子集化

library(data.table)
setDT(HLS)[HLS[, if(any(hl_flag==1)) .I[1:(which.max(hl_flag))]
               else .I, ID]$V1]
#     ID page_count purchase_flag  prob hl_flag
# 1: V1          3             1 0.760       1
# 2: V2          3             0 0.030       0
# 3: V2          4             0 0.010       0
# 4: V2          5             0 0.020       0
# 5: V2          6             0 0.000       0
# 6: V3          3             1 0.020       0
# 7: V3          4             1 0.001       0
# 8: V3          5             1 0.760       1
# 9: V4          3             0 0.040       0
#10: V4          4             0 0.650       1

或者类似于我为
数据显示的
方法。表
,一个
基本R
选项

do.call(rbind, lapply(split(HLS, HLS$ID), 
           function(x) if(any(x$hl_flag==1)) 
                  x[seq(which.max(x$hl_flag)), ] 
                else x))

或者使用
dplyr

library(dplyr)
HLS %>% 
    group_by(ID) %>% 
    filter(all(!hl_flag)| row_number() %in% seq(which.max(hl_flag)))
 #      ID page_count purchase_flag  prob hl_flag
 #    (chr)      (int)         (int) (dbl)   (int)
 #1     V1          3             1 0.760       1
 #2     V2          3             0 0.030       0
 #3     V2          4             0 0.010       0
 #4     V2          5             0 0.020       0
 #5     V2          6             0 0.000       0
 #6     V3          3             1 0.020       0
 #7     V3          4             1 0.001       0
 #8     V3          5             1 0.760       1
 #9     V4          3             0 0.040       0
 #10    V4          4             0 0.650       1
你可以试试

l <- lapply(split(df, df$ID), function(x) {if(any(x[5] == 1)) x[1:which.max(x[5] == 1),] else x})
要获得预期结果,您可以使用
dplyr
包中的
bind_行

library(dplyr)
bind_rows(l)

#ID page_count purchase_flag  prob hl_flag
#(fctr)      (int)         (int) (dbl)   (int)
#1      V1          3             1 0.760       1
#2      V2          3             0 0.030       0
#3      V2          4             0 0.010       0
#4      V2          5             0 0.020       0
#5      V2          6             0 0.000       0
#6      V3          3             1 0.020       0
#7      V3          4             1 0.001       0
#8      V3          5             1 0.760       1
#9      V4          3             0 0.040       0
#10     V4          4             0 0.650       1
你可以试试

l <- lapply(split(df, df$ID), function(x) {if(any(x[5] == 1)) x[1:which.max(x[5] == 1),] else x})
要获得预期结果,您可以使用
dplyr
包中的
bind_行

library(dplyr)
bind_rows(l)

#ID page_count purchase_flag  prob hl_flag
#(fctr)      (int)         (int) (dbl)   (int)
#1      V1          3             1 0.760       1
#2      V2          3             0 0.030       0
#3      V2          4             0 0.010       0
#4      V2          5             0 0.020       0
#5      V2          6             0 0.000       0
#6      V3          3             1 0.020       0
#7      V3          4             1 0.001       0
#8      V3          5             1 0.760       1
#9      V4          3             0 0.040       0
#10     V4          4             0 0.650       1

感谢akrun的回复。我刚刚澄清了我的id是否为“00000f23-1019-4aff-8199-35bd0d032356/1”而不是V1,因此应该做什么更改?@rahuliggu我们不需要更改任何内容,因为我们只使用“id”进行分组。您在原始数据集上尝试过吗?是的,我在origi上尝试过nal数据集,但它给了我一个错误“错误:中意外的数字常量:”,因此我想知道这是否与代码结尾“else.I,ID]$V1]”部分中使用的ID与V1不同有关。如果我对代码的一些基本理解有误,请原谅。R对我来说是非常新的,因此存在混淆。@rahuliggu您能检查一下吗e
str(HLS)
和“ID”的类别列?ID的数据类型是Factor。是否应该更改为character?感谢您的回复akrun。我刚刚澄清了我的ID是否是格式为“00000f23-1019-4aff-8199-35bd0d032356/1”而不是V1,因此应该做什么更改?@rahuliggu我们不需要更改任何内容,因为我们只使用“ID”进行分组。你在原始数据集上试过这个吗?是的,我在原始数据集上试过,但它给了我一个错误“error:unexpected numeric constant in:”所以我想知道这是否与代码结尾“else.I,ID]$V1]”部分中使用的ID与V1不同有关。如果我理解了一些非常基本的错误代码,请原谅.R对我来说是非常新的,因此会产生混淆。@rahuliggu你能检查一下
str(HLS)
和“ID”列的类别吗?ID的数据类型是Factor。它应该改为character吗?