R 润滑油不喜欢子集吗？

这个问题与我提出的一个问题有关。 我花了一些时间思考如何更清楚地表达我的问题，并为冗长的问题道歉。非常感谢您的建议

下面是我正在处理的数据集的一个大量子集的百行代码片段

    SPD_2015 <- structure(list(summarized.offense.description = c("ASSAULT", 
"THREATS", "CAR PROWL", "SHOPLIFTING", "MAIL THEFT", "THREATS", 
"DISTURBANCE", "STOLEN PROPERTY", "TRESPASS", "VEHICLE THEFT", 
"CAR PROWL", "THREATS", "STOLEN PROPERTY", "VEHICLE THEFT", "BURGLARY-SECURE PARKING-RES", 
"CAR PROWL", "THREATS", "BIKE THEFT", "BURGLARY", "ASSAULT", 
"STOLEN PROPERTY", "DISTURBANCE", "VEHICLE THEFT", "CAR PROWL", 
"OTHER PROPERTY", "ASSAULT", "PROPERTY DAMAGE", "BURGLARY-SECURE PARKING-RES", 
"ANIMAL COMPLAINT", "OTHER PROPERTY", "BURGLARY", "BURGLARY", 
"CAR PROWL", "SHOPLIFTING", "BURGLARY", "PROPERTY DAMAGE", "DISTURBANCE", 
"PROPERTY DAMAGE", "STOLEN PROPERTY", "OTHER PROPERTY", "MAIL THEFT", 
"PROPERTY DAMAGE", "VEHICLE THEFT", "OTHER PROPERTY", "ROBBERY", 
"CAR PROWL", "NARCOTICS", "OTHER PROPERTY", "BURGLARY", "DISTURBANCE", 
"ASSAULT", "BURGLARY-SECURE PARKING-RES", "OTHER PROPERTY", "FRAUD", 
"SHOPLIFTING", "OTHER PROPERTY", "OTHER PROPERTY", "DISTURBANCE", 
"CAR PROWL", "STOLEN PROPERTY", "OTHER PROPERTY", "OTHER PROPERTY", 
"VIOLATION OF COURT ORDER", "DISTURBANCE", "NARCOTICS", "ASSAULT", 
"DISTURBANCE", "TRESPASS", "NARCOTICS", "CAR PROWL", "NARCOTICS", 
"OTHER PROPERTY", "CAR PROWL", "CAR PROWL", "ASSAULT", "TRAFFIC", 
"OTHER PROPERTY", "CAR PROWL", "PROSTITUTION", "OTHER PROPERTY", 
"OTHER PROPERTY", "ASSAULT", "BURGLARY", "DISTURBANCE", "PROPERTY DAMAGE", 
"PROPERTY DAMAGE", "BURGLARY", "VEHICLE THEFT", "FRAUD", "VEHICLE THEFT", 
"FRAUD", "CAR PROWL", "BIKE THEFT", "CAR PROWL", "WARRANT ARREST", 
"STOLEN PROPERTY", "CAR PROWL", "PROPERTY DAMAGE", "VEHICLE THEFT", 
"BIKE THEFT"), occurred.date.or.date.range.start = c("04/17/2015 01:10:00 AM", 
"11/15/2015 12:04:00 PM", "05/29/2015 08:00:00 PM", "12/15/2015 02:25:00 PM", 
"07/28/2015 12:00:00 AM", "02/24/2015 06:01:00 PM", "05/24/2015 04:20:00 PM", 
"03/13/2015 02:04:00 PM", "06/14/2015 08:00:00 AM", "05/19/2015 03:18:00 PM", 
"07/18/2015 06:00:00 AM", "05/11/2015 05:16:00 PM", "01/08/2015 12:52:00 PM", 
"06/17/2015 05:00:00 PM", "07/04/2015 12:00:00 AM", "10/26/2015 12:12:00 AM", 
"05/01/2015 12:00:00 PM", "07/02/2015 10:00:00 PM", "01/10/2015 07:30:00 PM", 
"02/17/2015 01:29:00 PM", "12/17/2015 02:26:00 AM", "08/04/2015 10:49:00 PM", 
"10/27/2015 12:29:00 AM", "07/29/2015 03:00:00 PM", "10/24/2015 06:30:00 PM", 
"02/20/2015 03:07:00 AM", "11/11/2015 09:00:00 AM", "03/24/2015 10:00:00 PM", 
"11/03/2015 08:47:00 PM", "04/15/2015 02:00:00 PM", "07/15/2015 03:00:00 PM", 
"11/17/2015 08:30:00 AM", "09/22/2015 05:00:00 PM", "02/09/2015 09:19:00 AM", 
"01/07/2015 08:30:00 AM", "05/01/2015 07:30:00 AM", "04/26/2015 03:30:00 AM", 
"04/18/2015 03:00:00 AM", "10/01/2015 08:00:00 PM", "05/07/2015 01:00:00 AM", 
"02/05/2015 03:15:00 PM", "01/18/2015 05:00:00 PM", "10/17/2015 11:00:00 PM", 
"03/23/2015 05:35:00 PM", "02/16/2015 07:25:00 PM", "07/30/2015 08:00:00 PM", 
"11/10/2015 02:28:00 PM", "03/14/2015 10:10:00 AM", "12/10/2015 08:26:00 PM", 
"10/05/2015 01:45:00 AM", "02/16/2015 01:56:00 PM", "10/19/2015 06:27:00 PM", 
"12/01/2015 07:30:00 AM", "01/28/2015 08:40:00 PM", "05/01/2015 01:40:00 PM", 
"10/30/2015 03:15:00 AM", "09/04/2015 03:34:00 PM", "06/06/2015 04:53:00 PM", 
"07/22/2015 06:20:00 AM", "12/11/2015 01:41:00 PM", "05/20/2015 01:09:00 PM", 
"09/18/2015 12:00:00 PM", "07/08/2015 11:05:00 PM", "02/22/2015 01:38:00 AM", 
"07/22/2015 01:12:00 PM", "09/07/2015 10:43:00 AM", "08/11/2015 04:00:00 PM", 
"10/13/2015 06:33:00 AM", "10/10/2015 05:32:00 PM", "11/15/2015 07:09:00 PM", 
"11/19/2015 03:05:00 PM", "04/08/2015 04:33:00 PM", "05/11/2015 12:01:00 AM", 
"04/21/2015 06:15:00 PM", "06/13/2015 10:29:00 AM", "06/22/2015 06:41:00 PM", 
"09/03/2015 08:00:00 AM", "04/08/2015 06:00:00 PM", "07/17/2015 08:00:00 PM", 
"08/29/2015 09:00:00 AM", "04/28/2015 01:46:00 PM", "09/07/2015 07:00:00 PM", 
"12/30/2015 06:30:00 AM", "08/29/2015 11:37:00 PM", "08/24/2015 10:00:00 PM", 
"06/17/2015 07:02:00 AM", "02/14/2015 10:21:00 PM", "03/29/2015 07:00:00 PM", 
"10/01/2015 07:15:00 AM", "06/14/2015 03:00:00 PM", "12/16/2014 09:00:00 AM", 
"02/14/2015 07:54:00 PM", "10/02/2015 08:17:00 AM", "05/14/2015 08:30:00 AM", 
"07/07/2015 10:15:00 AM", "04/07/2015 01:48:00 AM", "11/02/2015 11:00:00 PM", 
"04/16/2015 03:00:00 PM", "08/22/2015 08:09:00 AM", "10/24/2015 05:00:00 PM"
)), .Names = c("summarized.offense.description", "occurred.date.or.date.range.start"
), row.names = c(NA, -100L), class = c("tbl_df", "tbl", "data.frame"
))

我会得到错误：“错误：美学必须是长度1或与数据（14）：x相同”。这是有道理的，我理解

> dim(car.prowl)
[1] 14  4

但是car.prowl的长度是14，当我输入以下代码时：

> length(hour(car.prowl$occurred.time))
[1] 100

它显示原始数据集的长度，而不是14的子集长度

有人能提出解决方案或解决办法吗？

---

谢谢

有趣的问题。让我们首先获取绘图所需的输出。我们可以使用

mdy_hms

将字符转换为日期时间。通过使用

sub\u str

，它可能比您原来的方法更健壮。之后，

hour

可以根据日期时间提取小时

library(tidyverse)
library(lubridate)
library(stringr)

SPD_2015_updated <- SPD_2015 %>%
  mutate(occurred.time = mdy_hms(occurred.date.or.date.range.start)) %>%
  mutate(occurred.time.hour = hour(occurred.time))

car.prowl_updated <- SPD_2015_updated %>%
  filter(summarized.offense.description == "CAR PROWL")

库（tidyverse）
图书馆（lubridate）
图书馆（stringr）
SPD_2015_更新%
突变（accurrent.time=mdy_hms（accurrent.date.or.date.range.start））%>%
突变（发生。时间。小时=小时（发生。时间））
car.prowl\u更新%
过滤器（summated.offence.description==“CAR PROWL”）

键入

scape（SPD\U 2015\U更新）

和

scape（car.prowl\U更新）

。您可以看到每个记录都是匹配的<代码>已发生。时间在日期时间类中，而

已发生。时间.hour

在整数类中。我认为这些数据帧将为您的绘图做好准备

---

至于你最初的方法出了什么问题，我不完全理解。但是如果您键入

scape（car.prowl）

，您可以看到

发生了。时间
在
S4:Period
中。这可能是
dplyr:：filter
无法工作的关键原因。如果有时间，我将进一步调查为什么
dplyr:：filter
无法对原始数据帧进行子集划分。
感谢您的回复！您提取日期/时间的方法肯定更有效，我能够描绘出我的原始问题。我仍然有点困惑我的方法出了什么问题，但我想这就是R的本质，直到你理解它的所有小怪癖。
> dim(car.prowl)
[1] 14  4

> length(hour(car.prowl$occurred.time))
[1] 100

library(tidyverse)
library(lubridate)
library(stringr)

SPD_2015_updated <- SPD_2015 %>%
  mutate(occurred.time = mdy_hms(occurred.date.or.date.range.start)) %>%
  mutate(occurred.time.hour = hour(occurred.time))

car.prowl_updated <- SPD_2015_updated %>%
  filter(summarized.offense.description == "CAR PROWL")