有条件地过滤，如果 A 列中的某个值被超过，则超出相应时间戳（B 列）的进一步观察将被删除
[英]Filter conditionally, where if a value is exceeded in column A, further observations beyond the respective timestamp (column B) are dropped
问题描述
I am working with fish telemetry data in R, with a simplified dataset provided below:
我正在 R 中处理鱼类遥测数据，下面提供了一个简化的数据集：
df <- structure(list(DATE.TIME = structure(c(1560900051, 1560900101,
1560927373, 1560927504, 1560927533, 1560927585, 1560927689, 1560899962,
1560900026, 1560900026, 1560900076, 1560927328, 1560927498, 1560927529,
1560927558, 1560907660, 1560907720, 1560908037, 1560925131, 1560925260,
1560931034, 1560907630, 1560907695, 1560907746, 1560907804, 1560908189,
1560908268, 1560925097, 1560925300, 1560925426), class = c("POSIXct",
"POSIXt"), tzone = "Canada/Atlantic"), TAG = c(1310230L, 1310230L,
1310230L, 1310230L, 1310230L, 1310230L, 1310230L, 1310230L, 1310230L,
1310230L, 1310230L, 1310230L, 1310230L, 1310230L, 1310230L, 1311038L,
1311038L, 1311038L, 1311038L, 1311038L, 1311038L, 1311038L, 1311038L,
1311038L, 1311038L, 1311038L, 1311038L, 1311038L, 1311038L, 1311038L
), SENSOR.ID = c(5665L, 5665L, 5665L, 5665L, 5665L, 5665L, 5665L,
5666L, 5666L, 5666L, 5666L, 5666L, 5666L, 5666L, 5666L, 5821L,
5821L, 5821L, 5821L, 5821L, 5821L, 5822L, 5822L, 5822L, 5822L,
5822L, 5822L, 5822L, 5822L, 5822L), SENSOR.VALUE = c(8.1796,
8.1796, 35.0095, 35.0095, 35.0095, 35.0095, 35.0095, 0.9024,
0, 0, 0, 34.2986, 0.9024, 18.9544, 18.9544, 8.4934, 8.4934, 8.4934,
35.0095, 35.0095, 35.0095, 0, 0, 0, 0, 0, 0, 13.5388, 1.805,
1.805), SENSOR = c("temp", "temp", "temp", "temp", "temp", "temp",
"temp", "depth", "depth", "depth", "depth", "depth", "depth",
"depth", "depth", "temp", "temp", "temp", "temp", "temp", "temp",
"depth", "depth", "depth", "depth", "depth", "depth", "depth",
"depth", "depth")), row.names = c(435151L, 435152L, 435203L,
435204L, 435205L, 435206L, 435207L, 435614L, 435615L, 435616L,
435617L, 435664L, 435665L, 435666L, 435667L, 455286L, 455287L,
455288L, 455295L, 455296L, 455297L, 455553L, 455554L, 455555L,
455556L, 455557L, 455558L, 455568L, 455569L, 455570L), class = "data.frame")
The data are structured, as follows:数据结构如下：
- DATE.TIME=timestamp of the fish detection DATE.TIME=鱼检测的时间戳
- TAG=unique ID for the acoustic tag implanted in a fish TAG=植入鱼体内的声学标签的唯一ID
- SENSOR.ID=unique ID for each sensor (temp and depth), 2 sensor IDs per SENSOR.ID=每个传感器（温度和深度）的唯一 ID，每个传感器 2 个传感器 ID
- TAG SENSOR.VALUE=recorded temp (C) or depth (m) TAG SENSOR.VALUE=记录的温度 (C) 或深度 (m)
- SENSOR=categorical variable to indicate sensor type (temp or depth) SENSOR=指示传感器类型（温度或深度）的分类变量
What I would like to do is subset/filter this data, such that when the temperature increased above 30C (indicative of predation), any subsequent detections would be removed from both the temperature and depth sensor.我想要做的是对这些数据进行子集/过滤，这样当温度升高到 30C 以上（表明捕食）时，任何后续检测都将从温度和深度传感器中删除。 Each TAG implanted in a fish alternates between transmitting its temperature or depth SENSOR.ID and SENSOR.VALUE.植入鱼中的每个 TAG 在传输其温度或深度 SENSOR.ID 和 SENSOR.VALUE 之间交替。 I can do this filter for the temperature sensor data:我可以为温度传感器数据做这个过滤器：
dfsub <- subset(df, SENSOR=="temp" & SENSOR.VALUE<30)
But this would still allow for the inclusion of detections after the predation event on the depth sensor, which would now reflect the movement of a predator in this instance.但这仍然允许在深度传感器上包含捕食事件之后的检测，这将反映在这种情况下捕食者的运动。 Ideally, a filter would recognize the time stamp of the first instance where temperature increased above 30C, and remove all observations beyond that time for each individual fish (ie, TAG).理想情况下，过滤器将识别温度升高到 30C 以上的第一个实例的时间戳，并删除超过该时间的每条鱼（即 TAG）的所有观察结果。 I was looking to resolve a dataset, as below, once passing through the filter.一旦通过过滤器，我就想解析一个数据集，如下所示。
df <- structure(list(DATE.TIME = structure(c(1560900051, 1560900101,
1560899962, 1560900026, 1560900026, 1560900076, 1560907660, 1560907720,
1560908037, 1560907630, 1560907695, 1560907746, 1560907804, 1560908189,
1560908268, 1560925097), class = c("POSIXct", "POSIXt"), tzone = "Canada/Atlantic"),
TAG = c(1310230L, 1310230L, 1310230L, 1310230L, 1310230L,
1310230L, 1311038L, 1311038L, 1311038L, 1311038L, 1311038L,
1311038L, 1311038L, 1311038L, 1311038L, 1311038L), SENSOR.ID = c(5665L,
5665L, 5666L, 5666L, 5666L, 5666L, 5821L, 5821L, 5821L, 5822L,
5822L, 5822L, 5822L, 5822L, 5822L, 5822L), SENSOR.VALUE = c(8.1796,
8.1796, 0.9024, 0, 0, 0, 8.4934, 8.4934, 8.4934, 0, 0, 0,
0, 0, 0, 13.5388), SENSOR = c("temp", "temp", "depth", "depth",
"depth", "depth", "temp", "temp", "temp", "depth", "depth",
"depth", "depth", "depth", "depth", "depth")), row.names = c(435151L,
435152L, 435614L, 435615L, 435616L, 435617L, 455286L, 455287L,
455288L, 455553L, 455554L, 455555L, 455556L, 455557L, 455558L,
455568L), class = "data.frame")
Appreciate the insight!欣赏洞察力！
1 个解决方案
解决方案1
0 2021-10-22 16:57:02
Cool dataset!很酷的数据集！ Here is one option using tidyr::fill .这是使用tidyr::fill一种选择。 I edited and slimmed down your data a bit to make a better reprex.我对您的数据进行了一些编辑和精简，以制作更好的 reprex。
Also separated it into steps for pedagogic purposes but in reality, you should do this in a single pipe chain.还出于教学目的将其分为多个步骤，但实际上，您应该在单个管道链中执行此操作。
library(tidyverse)
fishdat <- tibble::tribble(
~DATE.TIME, ~FISH.TAG, ~SENSOR.ID, ~SENSOR.VALUE, ~SENSOR,
"2019-06-18 20:19:41", 1, 65, 9, "temp",
"2019-06-18 20:20:51", 1, 65, 37, "temp",
"2019-06-18 20:19:22", 1, 66, 1, "depth",
"2019-06-18 20:21:16", 1, 66, 0, "depth",
"2019-06-18 22:27:40", 2, 21, 35, "temp",
"2019-06-18 22:33:57", 2, 21, 38, "temp",
"2019-06-18 22:27:10", 2, 22, 0, "depth",
"2019-06-19 3:18:17", 2, 22, 13, "depth"
)
Tag the values indicating predation标记指示捕食的值
fishdat_marked <-
fishdat %>%
mutate(predated = ifelse(SENSOR == "temp" & SENSOR.VALUE > 30,
"predated",
NA_character_))
fishdat_marked
#> # A tibble: 8 × 6
#> DATE.TIME FISH.TAG SENSOR.ID SENSOR.VALUE SENSOR predated
#> <chr> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 2019-06-18 20:19:41 1 65 9 temp <NA>
#> 2 2019-06-18 20:20:51 1 65 37 temp predated
#> 3 2019-06-18 20:19:22 1 66 1 depth <NA>
#> 4 2019-06-18 20:21:16 1 66 0 depth <NA>
#> 5 2019-06-18 22:27:40 2 21 35 temp <NA>
#> 6 2019-06-18 22:33:57 2 21 38 temp predated
#> 7 2019-06-18 22:27:10 2 22 0 depth <NA>
#> 8 2019-06-19 3:18:17 2 22 13 depth <NA>
Cascade down the predation marker级联捕食标记
fishdat_filled <-
fishdat_marked %>%
group_by(FISH.TAG) %>% ## for each fish
arrange(DATE.TIME, .by_group = T) %>%
fill(predated, .direction = "down")
fishdat_filled
#> # A tibble: 8 × 6
#> # Groups: FISH.TAG [2]
#> DATE.TIME FISH.TAG SENSOR.ID SENSOR.VALUE SENSOR predated
#> <chr> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 2019-06-18 20:19:22 1 66 1 depth <NA>
#> 2 2019-06-18 20:19:41 1 65 9 temp <NA>
#> 3 2019-06-18 20:20:51 1 65 37 temp predated
#> 4 2019-06-18 20:21:16 1 66 0 depth predated
#> 5 2019-06-18 22:27:10 2 22 0 depth <NA>
#> 6 2019-06-18 22:27:40 2 21 35 temp <NA>
#> 7 2019-06-18 22:33:57 2 21 38 temp predated
#> 8 2019-06-19 3:18:17 2 22 13 depth predated
Now, filter现在，过滤
fishdat_filled %>%
filter(is.na(predated))
#> # A tibble: 4 × 6
#> # Groups: FISH.TAG [2]
#> DATE.TIME FISH.TAG SENSOR.ID SENSOR.VALUE SENSOR predated
#> <chr> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 2019-06-18 20:19:22 1 66 1 depth <NA>
#> 2 2019-06-18 20:19:41 1 65 9 temp <NA>
#> 3 2019-06-18 22:27:10 2 22 0 depth <NA>
#> 4 2019-06-18 22:27:40 2 21 35 temp <NA>
Created on 2021-10-22 by the reprex package (v2.0.1)由reprex 包(v2.0.1) 于 2021 年 10 月 22 日创建
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