根据标准对数据进行汇总和计数

时间：2020-10-11 09:37:39

标签： r tidyverse

我有一个由三列组成的数据框：x，ID和date_time。 “ x”列是每五分钟出现的变量x的记录，ID指示要记录的内容，而date_time指示何时记录。请参见下面的数据框。

我要从此数据帧中计算出一个新的数据帧，该数据帧具有七列：“测量”，“ ID”和“日期”，“ x_4_5_night_15min_yes / no”，“ x_4_5_night_time_15min”，“ x_4_5_night_events_15min”，“ x_ <4_night_15min”

  1. “测量”。该列应说明给定ID的数字量度。测量从23:00:00开始，直到第二天22:59:59。但是，测量是在随机时间开始的，因此第一次测量的持续时间不是24小时。最后一次测量也不是24小时。
  2. “ ID”。指示给定度量的ID。
  3. 日期”。此列应以yyyy.mm.dd格式显示给定测量中最后一次记录的日期。
  4. “ x_4_5_night_events_15min”。 ”。度量标准分为一天（7：00：00-22：59：59）和一夜（23：00：00-6：59：59）。此列应给出x介于4到5（均包括在内）之间的夜间发作次数。但是，仅应计算持续至少15分钟的时间。例如。不应该包括连续两次在4到5之间的记录，然后是在5之上的记录，因为x只会在4到5之间保持10分钟。
  5. “ x_4_5_night_15min_是/否。如果x在给定的夜晚至少15分钟内在4到5（包括两者）之间，则此列应以是（1）或否（0）表示。理解15分钟的标准与第4章所述相同。
  6. “ x_4_5_night_time_15min”。该列应给出给定的夜晚x在4到5（均包括在内）之间的时间。但是，仅应包括持续至少15分钟的时间段。理解15分钟的标准与第4章所述相同。
  7. “ x_ <4_night_events _15min”。如果x在给定的夜晚至少持续15分钟低于4，则此列应以是（1）或否（0）表示。理解15分钟的标准与第4章所述相同。

每个唯一的测量都应该有一行。到目前为止，我有一个代码可以正确返回以下列：“测量”，“ ID”和“日期”：

df1$mydate = as.Date(df1$date_time, format = "%Y.%m.%d %H:%M:%S")
df1$tm <- as.numeric(df1$date_time)
df1$dts <- 86400*as.numeric(df1$mydate)
df2 <- df1 %>% 
group_by(ID,mydate) %>% 
transform(date = case_when(((dts-3600)<tm & tm<(dts+82800)) ~paste0(mydate), ((dts+82800)<=tm) ~paste0(mydate+1) )) %>% 
select(ID,date) %>%   
unique() %>% 
group_by(ID) %>% 
mutate(measurement = row_number())

但是我不知道怎么做最后一个。

这是预期的输出：

expected_output <- read.table(header=TRUE, text ="
                     ID Date          x_4_5_night_events_15min  x_4_5_night_15min_yes/no    x_4_5_night_time_15min  x_<4_night_events_15min
                     12 2020.03.02  0                           0                           0                        0
                     12 2020.03.03  1                           1                           20                      1
                     13 2020.05.09  0                           0                           0                       0
                     14 2020.03.03  2                           1                           40                      0
")

这是数据：

structure(list(date_time = c("2020.03.02 22:00:17", "2020.03.02 22:05:17", 
"2020.03.02 22:10:17", "2020.03.02 22:35:17", "2020.03.02 22:40:17", 
"2020.03.02 22:45:17", "2020.03.02 22:50:17", "2020.03.02 22:55:17", 
"2020.03.02 23:00:17", "2020.03.02 23:05:17", "2020.03.02 23:10:17", 
"2020.03.02 23:15:17", "2020.03.02 23:20:17", "2020.03.02 23:25:17", 
"2020.03.02 23:30:17", "2020.03.02 23:35:17", "2020.03.02 23:40:17", 
"2020.03.02 23:45:17", "2020.03.02 23:50:17", "2020.03.02 23:55:17", 
"2020.03.03 00:00:17", "2020.03.03 00:55:17", "2020.03.03 01:00:17", 
"2020.03.03 01:05:17", "2020.03.03 01:10:17", "2020.03.03 01:15:17", 
"2020.03.03 01:20:17", "2020.03.03 01:25:17", "2020.05.09 08:39:32", 
"2020.05.09 08:44:32", "2020.05.09 08:49:33", "2020.05.09 08:54:33", 
"2020.05.09 08:59:34", "2020.05.09 09:04:34", "2020.05.09 09:09:35", 
"2020.05.09 09:14:35", "2020.05.09 09:19:36", "2020.05.09 09:24:36", 
"2020.05.09 09:29:37", "2020.05.09 09:34:37", "2020.05.09 09:39:38", 
"2020.05.09 09:44:38", "2020.05.09 09:49:39", "2020.05.09 09:54:39", 
"2020.05.09 09:59:40", "2020.03.02 22:40:17", "2020.03.02 22:45:17", 
"2020.03.02 22:50:17", "2020.03.02 22:55:17", "2020.03.02 23:00:17", 
"2020.03.02 23:05:17", "2020.03.02 23:10:17", "2020.03.02 23:15:17", 
"2020.03.02 23:20:17", "2020.03.02 23:25:17", "2020.03.02 23:30:17", 
"2020.03.02 23:35:17", "2020.03.02 23:40:17", "2020.03.02 23:45:17", 
"2020.03.02 23:50:17", "2020.03.02 23:55:17", "2020.03.03 00:00:17", 
"2020.03.03 00:55:17", "2020.03.03 01:00:17", "2020.03.03 01:05:17", 
"2020.03.03 01:10:17"), id = c(12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L), x = c("7.55", "4.55", 
"4.55", "12", "12", "10", "10", "4.3", "", "", "4.3", "4.3", 
"4.3", "", "4.3", "12", "12", "12", "2", "12", "12", "", "8", 
"3", "3", "2", "2", "", "12", "10", "10", "4.3", "4.3", "4.3", 
"4.3", "4.3", "4.3", "4.3", "4.3", "12", "12", "12", "12", "12", 
"12", "2", "12", "12", "", "8", "3", "3", "2", "2", "", "12", 
"10", "10", "4.3", "4.3", "4.3", "4.3", "4.3", "4.3", "4.3", 
"4.3")), row.names = c(NA, 66L), class = "data.frame")

任何建议都非常感谢，谢谢！

1 个答案:

答案 0 :(得分：1)

将初始数据帧命名为df11，下面的代码将提供所需的输出。请注意，我已经假设15分钟（在标准5、6、7中）意味着在4和5之间连续/连续3个记录（对于＃7，少于4）与标准4相同。我相信其他人可以写得短得多和更优雅的代码。现在，我已经逐步编写了该文档，以便您可以检查每个步骤。

df11$xn <- as.numeric(df11$x)
df1 <- df11 %>% transform(xmin = ifelse((xn<4 | xn>5 | is.na(xn)),0,5 ),
                          xlt4 = ifelse((xn>=4 | is.na(xn)),0,1),
                          x45 = ifelse((xn<4 | xn>5 | is.na(xn)),0,1)) 

df1$dateTime = as_datetime(df1$date_time, format = "%Y.%m.%d %H:%M:%S")
df1$mydate = as.Date(df1$date_time, format = "%Y.%m.%d %H:%M:%S")

df1$tm <- as.numeric(df1$dateTime)
df1$dts <- 86400*as.numeric(df1$mydate)

df2 <- df1 %>% 
  group_by(ID,mydate) %>% 
  transform(date = case_when(((dts-3600)<tm & tm<(dts+82800)) ~paste0(mydate), ((dts+82800)<=tm) ~paste0(mydate+1) )) %>% 
  transform(dayrnight = ifelse((tm>=(dts+25200) & tm<(dts+82800) ),'day','night' ) ) # %>% 

df3 <- df2 %>% group_by(ID,date,dayrnight) %>% mutate(x45l1 = lag(x45), x45l2=lag(x45,2), xlt4l1 = lag(xlt4))  %>% 
  mutate_if(is.numeric , replace_na, replace = 0) %>% 
  mutate(x45cum = ifelse(row_number()>2, cumsum(x45)*x45*x45l1,cumsum(x45)), 
         xlt4cum= ifelse(row_number()>2, cumsum(xlt4)*xlt4*xlt4l1,cumsum(xlt4)) ) %>% 
  mutate(x45event = ifelse((x45cum %% 3) == 0, 1*(x45cum>0),0),
         xlt4event= ifelse((xlt4cum %% 3) == 0, 1*(xlt4cum>0),0)) %>% 
  mutate(x45event_tot = cumsum(x45event), xlt4event_tot = cumsum(xlt4event)) %>% 
  mutate(x45min = cumsum(xmin)*(x45event_tot>0)) %>% 
  dplyr::summarise(x45mint = max(x45min), x45eventt = max(x45event_tot), xlt4eventt = max(xlt4event_tot)) %>% 
  mutate(x45_15min_yn = ifelse(x45eventt>0,1,0), xlt4_15min_yn = ifelse(xlt4eventt>0,1,0)) %>% 
  pivot_wider(id_cols = c(ID,date), names_from = dayrnight, values_from = c("x45mint", "x45eventt", "x45_15min_yn", "xlt4_15min_yn")) %>% 
  mutate_if(is.numeric , replace_na, replace = 0) %>% 
  select(ID, date, x_4_5_night_events_15min = x45eventt_night, x_4_5_night_15min_yesorno = x45_15min_yn_night, 
         x_4_5_night_time_15min = x45mint_night, x_lt4_night_events_15min = xlt4_15min_yn_night)
  
 
>  df3
# A tibble: 5 x 6
# Groups:   ID, date [5]
     ID date       x_4_5_night_events_15min x_4_5_night_15min_yesorno x_4_5_night_time_15min x_lt4_night_events_15min
  <int> <chr>                         <dbl>                     <dbl>                  <dbl>                    <dbl>
1    12 2020-03-02                        0                         0                      0                        0
2    12 2020-03-03                        1                         1                     20                        1
3    13 2020-05-09                        0                         0                      0                        0
4    14 2020-03-02                        0                         0                      0                        0
5    14 2020-03-03                        2                         1                     40                        1
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