第一次遇到特定字符串后的子集数据框

Question

我有一个以下格式的数据框，我想提取或子集数据框，这样我在每个项目中只有第一个 funding activity 之前的活动：

 project<- c('A', 'A', 'A', 'B', 'B', 'B','B', 'C', 'C')
 activity<- c('kickoff','funding', 'delivery', 'kickoff','kickoff','funding','kickoff', 'kickoff','delivery')

 df<- data.frame(project,activity)

我期待如下输出：

 project   activity 
 A         kickoff
 B         kickoff
 B         kickoff
 C         kickoff
 C         delivery

有什么建议吗？

Answer 1

您可以尝试cumsum跟踪每个项目是否在资助之前或之后发生了一行：

library(dplyr)

df %>%
  group_by(project) %>%
  mutate(before.funding = cumsum(activity == "funding") == 0) %>%
  ungroup() %>%
  filter(before.funding) %>%
  select(-before.funding)

# A tibble: 5 x 2
  project activity
   <fctr>   <fctr>
1       A  kickoff
2       B  kickoff
3       B  kickoff
4       C  kickoff
5       C delivery

Answer 2

dplyr:

df %>%
    group_by(project) %>%
    dplyr::filter(cummin(activity != "funding") == 1)

产量：

# project activity
# <fctr>   <fctr>
# 1       A  kickoff
# 2       B  kickoff
# 3       B  kickoff
# 4       C  kickoff
# 5       C delivery

base R:

do.call(rbind, lapply(split(dff, dff$project), function(x) {
    x[cummin(x$activity != "funding") == 1, ]
}))

产量：

# project activity
# A       kickoff 
# B       kickoff 
# B       kickoff 
# C       kickoff 
# C       delivery

希望对您有所帮助。

Answer 3

为了完整起见，这里还有一个data.table解决方案：

library(data.table)
setDT(df)[!df[, .I[.I >= first(.I[activity == 'funding'])], by = project]$V1]

   project activity
1:       A  kickoff
2:       B  kickoff
3:       B  kickoff
4:       C  kickoff
5:       C delivery

说明

在每个 project 组中，我们查找 "funding" 在第 activity 列和所有后续行中首次出现的索引：

df[, .I[.I >= first(.I[activity == 'funding'])], by = project]

   project V1
1:       A  2
2:       A  3
3:       B  6
4:       B  7

在data.table中，.I是一个特殊的符号，保存在df中的行位置。第二个子集 .I[.I >= first(.I[activity == 'funding'])] 是必需的，因为 which(.I >= first(.I[activity == 'funding'])) 只会 return 行位置在组内而不是在 df.

现在，我们已经确定了不应不显示的行。因此，我们通过排除这些行号得到最终结果：

df[!df[, .I[.I >= first(.I[activity == 'funding'])], by = project]$V1]

如果有可用的日期信息 - 我敢打赌在处理项目和活动时会有一个 date 专栏 - 我们可以按照@Frank 的建议做一个 anti non -equi join 使用日期列：

# create sample date with date column
project<- c('A', 'A', 'A', 'B', 'B', 'B','B', 'C', 'C')
activity<- c('kickoff','funding', 'delivery', 'kickoff','kickoff','funding','kickoff', 'kickoff','delivery')
date <- (as.Date ("2017-10-02") + c(1,4,7,2,5,8,11,3,6))
df <- data.frame(project,activity, date, stringsAsFactors = FALSE)
df <- df[order(df$date), ]

  project activity       date
1       A  kickoff 2017-10-03
4       B  kickoff 2017-10-04
8       C  kickoff 2017-10-05
2       A  funding 2017-10-06
5       B  kickoff 2017-10-07
9       C delivery 2017-10-08
3       A delivery 2017-10-09
6       B  funding 2017-10-10
7       B  kickoff 2017-10-13

# anti non-equi join
setDT(df)[!df[activity == 'funding', first(date), by = project], on = .(project, date >= V1)]

   project activity       date
1:       A  kickoff 2017-10-03
2:       B  kickoff 2017-10-04
3:       B  kickoff 2017-10-07
4:       C  kickoff 2017-10-05
5:       C delivery 2017-10-08

Answer 4

data.table 包的一些其他替代方案：

1) 与 Reduce:

library(data.table)
setDT(df)[df[, .I[!Reduce('+', activity == 'funding', accumulate = TRUE)], project]$V1]

2) 与 cummax:

library(data.table)
setDT(df)[df[, .I[!cummax(activity == 'funding')], project]$V1]

3) 与 pmax:

library(data.table)
setDT(df)[!df[, pmax(.I, .I[activity == 'funding']), by = project]$V1]

第一次遇到特定字符串后的子集数据框

Subset dataframe after first encounter of a specific string

r

subset

dplyr

data.table

tidyr

说明