我正在使用大型数据集，并且由于我正在学习R，所以我希望使其更易于管理。
我有一系列4分钟的时间戳记间隔，分别测量了平均行进距离meters。这是数据示例：
> head(datanet)
Date & Time [Local] meters
1: 19/06/2018 00:08 2.448087
2: 19/06/2018 00:12 58.595811
3: 19/06/2018 00:16 49.057392
4: 19/06/2018 00:20 1.716189
5: 19/06/2018 00:24 2.166232
6: 19/06/2018 00:28 33.752809
我只是想将这4分钟间隔汇总为小时间隔，同时计算一天中每个小时（从0到23的平均行驶距离）。我希望输出表看起来像这样：
> head(data_travel)
hour avg_meters sd_meters
1 0 7.32 12.5
2 1 6.58 11.3
3 2 7.66 17.0
4 3 6.13 14.0
5 4 4.26 10.2
6 5 3.84 7.54
要获取data_travel，我使用了以下代码：
data_travel<-datanet %>%
mutate(date = dmy_hm(`Date & Time [Local]`),
hour = hour(date)) %>%
group_by(hour) %>%
summarise(
avg_meters = mean(meters),
sd_meters = sd(meters))
但是，此代码通过计算每小时中包括的所有4分钟间隔的平均行进距离datanet$meters来得出每小时平均值。如上所述，我想知道如何修改代码，以便将所有4分钟间隔datanet$meters的值求和成小时间隔，然后对datanet中的每一天进行小时平均值。
希望我已经足够清楚了，但是欢迎您询问其他信息。我将在下面附加一个dput(datanet)示例以提高可复制性：
> dput(datanet[1:600,])
structure(list(`Date & Time [Local]` = c("19/06/2018 00:08",
"19/06/2018 00:12", "19/06/2018 00:16", "19/06/2018 00:20", "19/06/2018 00:24",
"19/06/2018 00:28", "19/06/2018 00:32", "19/06/2018 00:36", "19/06/2018 00:40",
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meters = c(2.44808743796531, 58.5958105549192, 49.0573915168004,
1.71618923544311, 2.16623234264144, 33.7528092143734, 1.26876292628825,
4.09301793236524, 6.47380341213986, 2.02287444044928, 6.15764791418056,
4.88198337685391, 6.1518534564327, 19.9293981357374, 31.2807443706163,
11.9760177553429, 7.80034538189425, 2.09856685581426, 15.5559610933875,
5.54425190776513, 4.31727752887657, 1.69695203818687, 3.13701726469391,
4.17079079435503, 0, 0.253518261190509, 0.768515387271088,
5.75312128614323, 1.10161787301914, 0, 1.63627263077218,
1.94331762790558, 0.984128914771002, 0, 1.891376142142, 1.88169045315321,
1.14968339087591, 1.94662436122789, 2.53667294419711, 2.22463784687921,
1.14716037069784, 1.72269599615416, 0.253518262766866, 0.557086177521366,
0.834805798848609, 0.834805798848609, 0.834805798848609,
0.32373515360376, 0.250851604274291, 1.13037794205011, 1.01730179938836,
1.08245009766703, 1.29008139493895, 0.592050658495912, 0.984128914593752,
1.14716037069784, 0.246921421310107, 1.08086752437222, 0,
0.253518261190127, 0.834146432905571, 0.834146432905571,
1.32088856328962, 0, 1.01730179951988, 0.557086174016414,
0, 0, 0.981930302577223, 1.01730179952143, 0.801467385970428,
1.891376142142, 0.984128914771002, 0.984128914771002, 1.891376142142,
1.31556194378487, 1.70331684297699, 11.4746462151774, 5.19876037652309,
18.1991232643532, 5.45048803468078, 3.50474735760544, 0.834439563061093,
0, 1.29008140210492, 0.984760642562075, 1.01730179907391,
32.657190675824, 39.7593130805261, 34.2547425122791, 12.1116764053771,
18.526009117261, 3.86538989203226, 7.07005984938157, 1.72929805654892,
6.73320732100181, 23.0796824899771, 66.8539968083065, 101.031538229766,
142.563193250685, 117.93396678646, 30.4231296530894, 43.3196594211314,
14.3288406144688, 6.01740959634099, 7.35496556381327, 7.0780957149326,
1.06782836828317, 10.3553242863905, 59.205976673877, 34.5440414717095,
90.5195981896788, 32.8711449150829, 3.40710213072646, 6.60140695190154,
20.751091499863, 1.89137226358219, 2.83321448749677, 26.5437774761593,
4.78947844059108, 15.9299548924479, 3.49392462916022, 0.834803361386949,
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答案 0 :(得分：1)
我认为您需要首先收集每天的所有小时总计，然后将这些天平均。可以通过按天和小时进行分组来完成。第一次summarise时，它将获得每个小时一小时组合的总计，并保留按小时分组的数据，以便您可以将所有小时1与其他小时1进行比较。然后，第二个summarise将创建这些小时总计的平均值和SD。
data_travel <- datanet %>%
mutate(date = dmy_hm(`Date & Time [Local]`),
day = as_date(date),
hour = hour(date)) %>%
group_by(hour, day) %>%
summarise(hr_meters = sum(meters)) %>%
summarise(avg_meters = mean(hr_meters),
sd_meters = sd(hr_meters))