我正在尝试确定数据帧中每个UserId的提前和滞后时间戳之间的时间差。以下是我正在使用的数据的模拟示例：-
df<-structure(list(OrigTime = structure(c(1622918877.032, 1622990559.267,
1623523623.486, 1622964997.979, 1622906958.74, 1622926722.027,
1622995932.538, 1623007146.343, 1622852334.564, 1623060816.584,
1623088121.297, 1623106707.248, 1623157212.89, 1623193304.839,
1623274436.729, 1623043054.385, 1623538988.387, 1623005857.513,
1622910331.285, 1622896371.372, 1623578741.851, 1623587705.782,
1622962414.979, 1623537531.112, 1622903658.463, 1622919800.373,
1623141876.369, 1622853086.032, 1623160223.889, 1623550454.237,
1622977109.735, 1622897061.318, 1622905574.829, 1622975250.648,
1622926151.326, 1623051278.279, 1623155086.613, 1622903064.758,
1623548739.613, 1622926195.481, 1622909740.446, 1622886108.56,
1623571384.742, 1623605761.052, 1622993030.692, 1622970165.788,
1623011480.065, 1622988209.8, 1623322872.845, 1622920880.023,
1623086821.623, 1623612400.05, 1622999424.629, 1622884467.164,
1622909027.54, 1623357575.019, 1623520403.778, 1623506301.231,
1623500077.499, 1622940184.402, 1623495635.381, 1623546311.154,
1623160176.129, 1623346026.209, 1623536993.306, 1622909817.164,
1623530930.965, 1622877595.896, 1622935176.086, 1622966029.777,
1623000495.258, 1623143927.116, 1623174826.058, 1623251814.229,
1623334538.838, 1623340615.546, 1623583357.417, 1622888019.697,
1622987815.488, 1622985968.726, 1622992132.955, 1623074972.982,
1623163375.997, 1622963144.848, 1623314383.152, 1623338313.831,
1623551534.787, 1623309944.257, 1622976858.145, 1622917035.885,
1623004832.589, 1623503830.715, 1622909067.695, 1623237521.393,
1623560279.459, 1623536321.051, 1623519172.633, 1622904213.48,
1622930692.598, 1623534517.344), class = c("POSIXct", "POSIXt"
), tzone = ""),
LastTime = structure(c(1622920407.314, 1622992017.446,
1623523816.193, 1622967138.074, 1622908479.07, 1622929803.083,
1622998806.578, 1623009559.964, 1622852937.119, 1623060829.114,
1623088500.114, 1623107957.757, 1623160460.245, 1623196568.13,
1623277115.719, 1623043075.018, 1623541056.552, 1623009099.355,
1622912024.838, 1622896604.061, 1623581281.396, 1623587723.349,
1622964263.202, 1623540285.881, 1622904906.681, 1622920279.246,
1623145192.007, 1622853411.776, 1623161274.767, 1623552259.907,
1622979765.947, 1622900302.79, 1622908080.385, 1622977216.257,
1622926569.129, 1623052916.319, 1623157846.969, 1622906004.01,
1623550464.577, 1622926894.527, 1622909784.953, 1622888260.143,
1623574636.082, 1623608199.766, 1622993135.284, 1622972841.446,
1623011795.668, 1622991602.355, 1623325007.005, 1622920971.11,
1623089131.737, 1623615902.665, 1623001797.927, 1622884502.675,
1622909092.535, 1623358953.803, 1623522069.405, 1623506926.633,
1623500257.459, 1622943093.707, 1623496149.811, 1623546362.939,
1623161795.166, 1623346301.719, 1623538246.607, 1622910287.546,
1623531717.106, 1622878786.985, 1622935344.171, 1622967457.633,
1623003649.624, 1623146488.585, 1623175049.705, 1623254364.036,
1623337920.846, 1623343354.911, 1623584490.558, 1622890693.611,
1622990339.013, 1622988379.946, 1622993619.888, 1623077241.267,
1623164370.961, 1622963916.184, 1623314543.741, 1623340169.331,
1623551844.722, 1623309979.964, 1622976999.899, 1622918705.036,
1623007011.831, 1623506533.509, 1622910346.854, 1623237991.188,
1623563695.799, 1623537537.925, 1623520857.21, 1622904770.506,
1622931363.125, 1623535224.247), class = c("POSIXct", "POSIXt"), tzone = ""),
Count = c(1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 1L, 1L, 3L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 3L, 1L, 4L, 1L, 1L, 1L, 1L, 1L),
Date = structure(c(18783,18784, 18790, 18784, 18783, 18783, 18784, 18784, 18783, 18785,
18785, 18785, 18786, 18787, 18787, 18785, 18791, 18784, 18783,
18783, 18791, 18791, 18784, 18790, 18783, 18783, 18786, 18783,
18786, 18791, 18784, 18783, 18783, 18784, 18783, 18785, 18786,
18783, 18791, 18783, 18783, 18783, 18791, 18791, 18784, 18784,
18784, 18784, 18788, 18783, 18785, 18791, 18784, 18783, 18783,
18788, 18790, 18790, 18790, 18784, 18790, 18791, 18786, 18788,
18790, 18783, 18790, 18783, 18784, 18784, 18784, 18786, 18786,
18787, 18788, 18788, 18791, 18783, 18784, 18784, 18784, 18785,
18786, 18784, 18788, 18788, 18791, 18788, 18784, 18783, 18784,
18790, 18783, 18787, 18791, 18790, 18790, 18783, 18783, 18790
), class = "Date"),
UserId = c("853a97958b", "5697a38454", "c634a20759","ccf82840cd", "c083d8b935", "0410501e6b", "44c7c67c09", "9df7bcac8a",
"83db724191", "ebebd2505c", "9718c1ac40", "70e04f7a77", "8d6640a636",
"5ae2fb243b", "ebebd2505c", "7183f894ba", "83db724191", "0587f9f73b",
"875b5bcf85", "b1e4fc6c16", "5697a38454", "6cd922ee02", "503297a15f",
"def09e5b6a", "c083d8b935", "b7a17366f5", "44c7c67c09", "ee96fb2ea5",
"1053a213ea", "44c7c67c09", "5ae2fb243b", "e34de6863b", "7cf03078e8",
"bdfe5fe4d0", "bdfe5fe4d0", "c4c834065b", "36d0a2a630", "acfef8373f",
"ccf82840cd", "708885c8e0", "a1ce5e9964", "a5d5a264b4", "def09e5b6a",
"779cb9e811", "b1e4fc6c16", "c083d8b935", "a5d5a264b4", "5ae2fb243b",
"8d6a062f0f", "3e7700d63e", "9df7bcac8a", "44c7c67c09", "dfb1730f71",
"4646f53bd2", "c58f3eea85", "70e04f7a77", "de91cac214", "def09e5b6a",
"4646f53bd2", "875b5bcf85", "70e04f7a77", "65a2416cbc", "b3896de6fe",
"efe44e7d92", "44c7c67c09", "ebebd2505c", "0587f9f73b", "0393eacfee",
"8fccf03fc1", "83db724191", "de91cac214", "9718c1ac40", "779cb9e811",
"49f14ca03c", "0410501e6b", "a5d5a264b4", "e2c5bb55c9", "a1ce5e9964",
"3e7700d63e", "875b5bcf85", "70e04f7a77", "dfb1730f71", "46e37e426c",
"708885c8e0", "875b5bcf85", "e2c5bb55c9", "1053a213ea", "7cf03078e8",
"50da214747", "7cf03078e8", "0410501e6b", "779cb9e811", "9df7bcac8a",
"5697a38454", "ddf466ccaa", "c083d8b935", "3e7700d63e", "def09e5b6a",
"f52c20cd44", "5ae2fb243b")), row.names = c(NA, 100L), class = "data.frame")
library(tidyverse)
df<-df%>%
arrange(UserId,OrigTime)
我想要的是能够按UserId对数据进行分组，然后计算一行中的LastTime和下一行中OrigTime之间的时间差(分钟(。以下是我想要的结果类型的示例，以一个UserId为例：-
#5697a38454
#OrigTime LastTime Count Date UserId duration_min
#1 2021-06-06 15:42:39 2021-06-06 16:06:57 1 2021-06-06 5697a38454 NA
#2 2021-06-09 12:18:41 2021-06-09 12:26:31 1 2021-06-09 5697a38454 4091.73
#3 2021-06-13 11:05:41 2021-06-13 11:48:01 3 2021-06-13 5697a38454 6399.17
对于每个UserId的第一个实例，时间差将是NA，因为没有以前的LastTime作为计算的基础。如果一个特定的UserId只有一行/实例，那么duration_min变量也应该是NA。有人能示范一种方法吗？非常感谢：(
您可以使用lag获取LastTime的上一个值，并将其与每个UserId的下一个OrigTime相减。
library(dplyr)
df1 <- df %>%
group_by(UserId) %>%
mutate(duration_min = as.numeric(difftime(OrigTime, lag(LastTime), units = "mins"))) %>%
ungroup
#check ouput
df1 %>% filter(UserId == "5697a38454") %>% data.frame()
# OrigTime LastTime Count Date UserId duration_min
#1 2021-06-06 22:42:39 2021-06-06 23:06:57 1 2021-06-06 5697a38454 NA
#2 2021-06-09 19:18:41 2021-06-09 19:26:31 1 2021-06-09 5697a38454 4091.732
#3 2021-06-13 18:05:41 2021-06-13 18:48:01 3 2021-06-13 5697a38454 5679.178