[英]Calculating averages based on adjustable, non-overlapping averaging period (4, 7, 30, 42 days) while aggregating (grouping) based multiple variables
[英]Calculate duration based on conditons, while grouping but not aggregating
客觀的:
我有一個數據集 df,我想按 ID 對其進行分組,並根據某些條件找到持續時間:Focus == True、Read == True 和 ID != ""。 但是,我不想聚合 ID,因為我希望將它們放在自己單獨的“塊”中
ID Date Focus Read
A 1/2/2020 5:00:00 AM True True
A 1/2/2020 5:00:05 AM True True
1/3/2020 6:00:00 AM True
1/3/2020 6:00:05 AM True
B 1/4/2020 7:00:00 AM True True
B 1/4/2020 7:00:02 AM True True
B 1/4/2020 7:00:10 AM True True
A 1/2/2020 7:30:00 AM True True
A 1/2/2020 7:30:20 AM True True
我想要這個輸出:
ID Duration Date
A 5 sec 1/2/2020
B 10 sec 1/4/2020
A 20 sec 1/2/2020
輸入:
structure(list(ID = structure(c(2L, 2L, 1L, 1L, 3L, 3L, 3L, 2L,
2L), .Label = c("", "A", "B"), class = "factor"), Date = structure(c(1L,
2L, 5L, 6L, 7L, 8L, 9L, 3L, 4L), .Label = c("1/2/2020 5:00:00 AM",
"1/2/2020 5:00:05 AM", "1/2/2020 7:30:00 AM", "1/2/2020 7:30:20 AM",
"1/3/2020 6:00:00 AM", "1/3/2020 6:00:05 AM", "1/4/2020 7:00:00 AM",
"1/4/2020 7:00:02 AM", "1/4/2020 7:00:10 AM"), class = "factor"),
Focus = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "True ", class = "factor"),
Read = structure(c(2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L), .Label = c("",
"True "), class = "factor")), class = "data.frame", row.names = c(NA,
-9L))
這很好用,但不是聚合 ID,我如何將它們分開:
library(dplyr)
library(lubridate)
df %>%
filter(as.logical(trimws(Read)), as.logical(trimws(Focus))) %>%
mutate(Date = mdy_hms(Date)) %>%
group_by(ID) %>%
summarise(Duration = difftime(last(Date), first(Date), units = "secs"))
任何建議表示贊賞。
我們可以為 'ID' 中相鄰的非相等元素創建具有 run-length-encoding-id rleid
的組,然后在轉換為DateTime
后在 'Date' 上應用difftime
library(dplyr)
library(lubridate)
library(data.table)
df %>%
filter(as.logical(trimws(Read)), as.logical(trimws(Focus))) %>%
mutate(Date = mdy_hms(Date)) %>%
group_by(grp = rleid(ID), ID) %>%
summarise(Duration = difftime(last(Date), first(Date), units = "secs"),
Date = as.Date(first(Date))) %>%
ungroup %>%
select(-grp)
# A tibble: 3 x 3
# ID Duration Date
# <fct> <drtn> <date>
#1 A 5 secs 2020-01-02
#2 B 10 secs 2020-01-04
#3 A 20 secs 2020-01-02
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