从一个表中提取小时值并在另一个表中填充一小时增量的“桶”的函数

【问题标题】：Function that will extract hour values from one table and populate "buckets" of one hour increments in another table从一个表中提取小时值并在另一个表中填充一小时增量的“桶”的函数
【发布时间】：2019-09-03 14:28:47
【问题描述】：

我有以下格式的数据供人们打卡工作时间：

(dat<-data.frame(Date = c("1/1/19", "1/2/19", "1/4/19", "1/2/19"), 
                Person = c("John Doe", "Brian Smith", "Jane Doe", "Alexandra Wakes"), 
                Time_In = c("1:15pm", "1:45am", "11:00pm", "1:00am"), 
                Time_Out = c("2:30pm","3:33pm","3:00am","1:00am")))

    Date          Person Time_In Time_Out
1 1/1/19        John Doe  1:15pm   2:30pm
2 1/2/19     Brian Smith  1:45am   3:33pm
3 1/4/19        Jane Doe  3:00pm   3:00am
4 1/2/19 Alexandra Wakes  1:00am   1:00am

我希望用 R 或 Python 编写一个函数，它将每个人工作的总小时数提取到 24 个不同的桶中，每个桶作为自己的列。它看起来像这样：

所以在第一种情况下，这个人从下午 1:15 到下午 2:30 工作，所以他们从下午 1 点到下午 2 点（13-14 点）工作了 0.75 小时，从下午 2 点到下午 3 点（14-15 点）工作了 0.5 小时）。

我认为可能有用的一些事情是......

  1. 一系列嵌套循环
  2. 一长串 if/then 语句
  3. Tidyverse 或 Pandas 中的一些我还没有想到的功能。

上面的#1 和#2 (?) 的尝试完全失败。不确定工作流程是什么，但非常感谢任何建议。

请注意，结果表中的列不必是数字（可以是第 1 小时、第 2 小时等，或者只是一般的任何因素 - 只要它代表 24 小时的时间段）。

我过去的尝试包括嵌套的 for 循环，如下所示：

for (i in 1:nrow(data)){

  if((int_overlaps(createinterval(data$PunchDate[i],0,1), workinterval[i]))){ `0-1`[i]=1} else{ `0-1`[i]=0}
  if((int_overlaps(createinterval(data$PunchDate[i],1,2), workinterval[i]))){ `1-2`[i]=1} else{ `1-2`[i]=0}
  if((int_overlaps(createinterval(data$PunchDate[i],2,3), workinterval[i]))){ `2-3`[i]=1} else{ `2-3`[i]=0}
  if((int_overlaps(createinterval(data$PunchDate[i],3,4), workinterval[i]))){ `3-4`[i]=1} else{ `3-4`[i]=0}
  if((int_overlaps(createinterval(data$PunchDate[i],4,5), workinterval[i]))){ `4-5`[i]=1} else{ `4-5`[i]=0}
  if((int_overlaps(createinterval(data$PunchDate[i],5,6), workinterval[i]))){ `5-6`[i]=1} else{ `5-6`[i]=0}
  if((int_overlaps(createinterval(data$PunchDate[i],6,7), workinterval[i]))){ `6-7`[i]=1} else{ `6-7`[i]=0}
  if((int_overlaps(createinterval(data$PunchDate[i],7,8), workinterval[i]))){ `7-8`[i]=1} else{ `7-8`[i]=0}
  if((int_overlaps(createinterval(data$PunchDate[i],8,9), workinterval[i]))){ `8-9`[i]=1} else{ `8-9`[i]=0}
  if((int_overlaps(createinterval(data$PunchDate[i],9,10), workinterval[i]))){ `9-10`[i]=1} else{ `9-10`[i]=0}
  if((int_overlaps(createinterval(data$PunchDate[i],10,11), workinterval[i]))){ `10-11`[i]=1} else{ `10-11`[i]=0}
  if((int_overlaps(createinterval(data$PunchDate[i],11,12), workinterval[i]))){ `11-12`[i]=1} else{ `11-12`[i]=0}
  if((int_overlaps(createinterval(data$PunchDate[i],12,13), workinterval[i]))){ `12-13`[i]=1} else{ `12-13`[i]=0}
  if((int_overlaps(createinterval(data$PunchDate[i],13,14), workinterval[i]))){ `13-14`[i]=1} else{ `13-14`[i]=0}
  if((int_overlaps(createinterval(data$PunchDate[i],14,15), workinterval[i]))){ `14-15`[i]=1} else{ `14-15`[i]=0}
  if((int_overlaps(createinterval(data$PunchDate[i],15,16), workinterval[i]))){ `15-16`[i]=1} else{ `15-16`[i]=0}
  if((int_overlaps(createinterval(data$PunchDate[i],16,17), workinterval[i]))){ `16-17`[i]=1} else{ `16-17`[i]=0}
  if((int_overlaps(createinterval(data$PunchDate[i],17,18), workinterval[i]))){ `17-18`[i]=1} else{ `17-18`[i]=0}
  if((int_overlaps(createinterval(data$PunchDate[i],18,19), workinterval[i]))){ `18-19`[i]=1} else{ `18-19`[i]=0}
  if((int_overlaps(createinterval(data$PunchDate[i],19,20), workinterval[i]))){ `19-20`[i]=1} else{ `19-20`[i]=0}
  if((int_overlaps(createinterval(data$PunchDate[i],20,21), workinterval[i]))){ `20-21`[i]=1} else{ `20-21`[i]=0}
  if((int_overlaps(createinterval(data$PunchDate[i],21,22), workinterval[i]))){ `21-22`[i]=1} else{ `21-22`[i]=0}
  if((int_overlaps(createinterval(data$PunchDate[i],22,23), workinterval[i]))){ `22-23`[i]=1} else{ `22-23`[i]=0}
  if((int_overlaps(createinterval(data$PunchDate[i],23,24), workinterval[i]))){ `23-24`[i]=1} else{ `23-24`[i]=0}


}
cbind(data, `0-1`, `1-2`, `2-3`, `3-4`, `4-5`, `5-6`,
            `6-7`, `7-8`, `8-9`, `9-10`, `10-11`, `11-12`,
            `12-13`, `13-14`, `14-15`, `15-16`, `16-17`, `17-18`, `18-19`,
            `19-20`, `20-21`, `21-22`, `22-23`, `23-24`
      )

【问题讨论】：

标签： python r pandas dataframe


【解决方案1】：

我从@Jon Spring 的数据框开始，其中字符串被转换为日期时间。我用所有时间组合完成了数据框，但如果有足够多的人，你可能会跳过它。 

library(tidyverse)
library(lubridate)

dat<-tibble(ID = 1:4,
            Date = c("1/1/19", "1/2/19", "1/4/19", "1/2/19"), 
             Person = c("John Doe", "Brian Smith", "Jane Doe", "Alexandra Wakes"), 
             Time_In = c("1:15pm", "1:45am", "11:00pm", "1:00am"), 
             Time_Out = c("2:30pm","3:33pm","3:00am","1:00am"))

# Stolen from Jon Spring
# https://stackoverflow.com/a/55698472/11355066
dat2<- dat%>%
  mutate(Shift_Start = mdy_hm(paste(Date, Time_In)),
         Shift_End = mdy_hm(paste(Date, Time_Out)),
         Shift_End = Shift_End + if_else(Shift_End <= Shift_Start, ddays(1), 0)) 

# Different solution
dat2%>%
  group_by(ID, Person, Shift_Start, Shift_End)%>%
  do(
    tibble(hours_worked = seq.POSIXt(from = floor_date(.$Shift_Start, 'hour'), to = ceiling_date(.$Shift_End - dhours(), 'hour'), by = 'hour')
           ,hours_values = na.omit(
             c(if_else(minute(.$Shift_Start) == 0, NA_integer_, as.integer(60 - minute(.$Shift_Start)))
               ,rep(60L, length(seq.POSIXt(from = ceiling_date(.$Shift_Start, 'hour'), to = floor_date(.$Shift_End, 'hour'), by = 'hour'))-1)
               ,if_else(minute(.$Shift_End) == 0, NA_integer_, as.integer(minute(.$Shift_End))))
             )/60
           )
    )%>%
  complete(hours_worked = seq.POSIXt(from = min(floor_date(Shift_Start, 'day')), to = max(ceiling_date(Shift_End, 'day')), by = 'hour'))%>%
  mutate(hour_minutes = format(hours_worked, "%H:%M"))%>%
  select(-hours_worked)%>%
  na.omit()%>%
  # ID only grouped to match order of the poster
  # group_by(ID, Person, hour_minutes)%>%summarize(hours_values = sum(hours_values))%>%
  spread(hour_minutes, hours_values, fill = 0)

【讨论】：

    【解决方案2】：

    这涉及到日期和时间的一些摆弄，但使用dcast 似乎可以工作。

    library(lubridate)
library(data.table)

# Data
dat<-data.frame(Date = c("1/1/19", "1/2/19", "1/4/19", "1/2/19"), 
                Person = c("John Doe", "Brian Smith", "Jane Doe", "Alexandra Wakes"), 
                Time_In = c("1:15pm", "1:45am", "11:00pm", "1:00am"), 
                Time_Out = c("2:30pm","3:33pm","3:00am","1:00am"))

# Create Date Out field, if out the next day then need to add extra day to the Date in
dat$Date <- as.Date(dat$Date, format = "%m/%d/%y")
dat$Date_out <- as.Date(ifelse(grepl("am", dat$Time_Out), dat$Date + days(1), dat$Date), origin = "1970-01-01")

# Create date time in and out variable in format yyyy-dd-dd hh:mm:ss
dat$time_in1 <- strptime(paste(dat$Date, " ", dat$Time_In, sep = ""), format = "%Y-%m-%d %I:%M%p")
dat$time_out1 <- strptime(paste(dat$Date_out, " ", dat$Time_Out, sep = ""), format = "%Y-%m-%d %I:%M%p")

# Fiddling with dates and time
# This will be used to duplicate data frame x times for dcast below
dat$diff_time <- ceiling(as.numeric(difftime(dat$time_out1, dat$time_in1, units = "hours")))

dat$time_in_min <- format(dat$time_in1, format = "%M")
dat$time_out_min <- format(dat$time_out1, format = "%M")
dat$diff_time <- ifelse(dat$time_out_min < dat$time_in_min, dat$diff_time + 1, dat$diff_time)

# For Time in add extra hour to minus time in, i.e. if time in is 2:35pm then time in will show 3:00pm to calculate 25 minutes
dat$time_in2 <- strptime(dat$time_in1 + hours(1), format = "%Y-%m-%d %H")
dat$time_out2 <- strptime(dat$time_out1, format = "%Y-%m-%d %H")

# Calculate fraction of hours for the Time in/Out
dat$diff_in <- as.numeric(difftime(dat$time_in2, dat$time_in1, units = "hours"))
dat$diff_out <- as.numeric(difftime(dat$time_out1, dat$time_out2, units = "hours"))

# For the 24 hour bucket for each person
dat$start_hr <- format(dat$time_in1, format = "%H")

# Append Data multiple times based on number of hours in between Out and In
dt <- dat[rep(seq_len(nrow(dat)), dat$diff_time), c("Date", "Person", "Time_In", "Time_Out", "start_hr", "diff_in", "diff_out", "diff_time")]
dt <- data.table(dt)

# For the 24 hour bucket for each person
dt[, rank := 1:.N, by = c("Person", "Date", "Time_In", "Time_Out")]
dt[, start_hr2 := as.numeric(start_hr) + rank]

# Combine with Time in and Out to allow fraction of hour start and end
dt[, rank2 := 1]
dt[rank == 1, rank2:= diff_in]
dt[diff_time == rank & diff_out > 0, rank2 := diff_out]

# 24 hours in a day
dt[start_hr2 > 24, start_hr2 := start_hr2 - 24]

# For the data provided it works without this line because Alexander worked 24 hours
# Need this line to include all 24 hour bucket
dt$start_hr2 <- factor(dt$start_hr2, levels = 1:24)

dt_dcast <- dcast(dt, Person + Date + Time_In + Time_Out ~ start_hr2, value.var = "rank2", fill = 0, drop = c(TRUE, FALSE))
setnames(dt_dcast, names(dt_dcast), c("Person", "Date", "Time In", "Time Out", paste0(1:24 - 1, "-", 1:24)))
dt_dcast
            Person       Date Time In Time Out 0-1  1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22 22-23 23-24
1: Alexandra Wakes 2019-01-02  1:00am   1:00am   1 1.00   1   1   1   1   1   1   1    1     1     1     1  1.00   1.0  1.00     1     1     1     1     1     1     1     1
2:     Brian Smith 2019-01-02  1:45am   3:33pm   0 0.25   1   1   1   1   1   1   1    1     1     1     1  1.00   1.0  0.55     0     0     0     0     0     0     0     0
3:        Jane Doe 2019-01-04 11:00pm   3:00am   1 1.00   1   0   0   0   0   0   0    0     0     0     0  0.00   0.0  0.00     0     0     0     0     0     0     0     1
4:        John Doe 2019-01-01  1:15pm   2:30pm   0 0.00   0   0   0   0   0   0   0    0     0     0     0  0.75   0.5  0.00     0     0     0     0     0     0     0     0

    使用for循环：

    library(lubridate)
library(data.table)

# Data
dat<-data.frame(Date = c("1/1/19", "1/2/19", "1/4/19", "1/2/19"), 
                Person = c("John Doe", "Brian Smith", "Jane Doe", "Alexandra Wakes"), 
                Time_In = c("1:15pm", "1:45am", "11:00pm", "1:00am"), 
                Time_Out = c("2:30pm","3:33pm","3:00am","1:00am"))

# Create Date Out field, if out the next day then need to add extra day to the Date in
dat$Date <- as.Date(dat$Date, format = "%m/%d/%y")
dat$Date_out <- as.Date(ifelse(grepl("am", dat$Time_Out), dat$Date + days(1), dat$Date), origin = "1970-01-01")

# Create date time in and out variable in format yyyy-dd-dd hh:mm:ss
dat$time_in <- strptime(paste(dat$Date, " ", dat$Time_In, sep = ""), format = "%Y-%m-%d %I:%M%p")
dat$time_out <- strptime(paste(dat$Date_out, " ", dat$Time_Out, sep = ""), format = "%Y-%m-%d %I:%M%p")

# Create 'hour' gap
dat$diff_time <- ceiling(as.numeric(difftime(dat$time_out, dat$time_in, units = "hours")))
dat$time_in_min <- format(dat$time_in, format = "%M")
dat$time_out_min <- format(dat$time_out, format = "%M")
dat$diff_time <- ifelse(dat$time_out_min < dat$time_in_min, dat$diff_time + 1, dat$diff_time)

# For the 24 hour bucket for each person
dat$start_hr <- as.numeric(format(dat$time_in + hours(1), format = "%H"))
dat$start_hr <- ifelse(dat$start_hr == 0, 24, dat$start_hr)

# For Time in add extra hour to minus time in, i.e. if time in is 2:35pm then time in will show 3:00pm to calculate 25 minutes
dat$hour_in <- strptime(dat$time_in + hours(1), format = "%Y-%m-%d %H")
dat$hour_out <- strptime(dat$time_out, format = "%Y-%m-%d %H")

# Calculate fraction of hours for the Time in/Out
dat$diff_in <- as.numeric(difftime(dat$hour_in, dat$time_in, units = "hours"))
dat$diff_out <- as.numeric(difftime(dat$time_out, dat$hour_out, units = "hours"))

hr_bucket <- data.frame(matrix(0, ncol = 24, nrow = nrow(dat)))
names(hr_bucket) <- paste0(1:24-1, "_", 1:24)

stg_data <- dat[, c("start_hr", "diff_time", "diff_in", "diff_out")]
stg_calc <- cbind(stg_data, hr_bucket)

col_index <- ncol(stg_data)
for (i in 1:nrow(stg_calc)) {

  ref_start_hr <- stg_calc[i ,]$start_hr 
  ref_diff_time <- stg_calc[i ,]$diff_time

  ref_diff_in <- stg_calc[i ,]$diff_in
  ref_diff_out <- stg_calc[i ,]$diff_out

  # if a person works till the next morning
  if ((ref_start_hr + ref_diff_time) > 24) {

    offset_col_used <- 24 - ref_start_hr + 1
    offset_col_rem <- ref_diff_time - offset_col_used

    stg_calc[i, (col_index + ref_start_hr):(col_index + 24)] <- 1
    stg_calc[i, (col_index + 1):(col_index + offset_col_rem)] <- 1

  } else {

    stg_calc[i, (col_index + ref_start_hr):(col_index + ref_start_hr + ref_diff_time - 1)] <- 1

  } 

  # To adjust for fraction of hour worked at start and end
  if (stg_calc[i, ]$diff_in %% 1 > 0) stg_calc[i, col_index + ref_start_hr] <- ref_diff_in
  if (stg_calc[i, ]$diff_out %% 1 > 0) stg_calc[i, col_index + ref_start_hr + ref_diff_time - 1] <- ref_diff_out

}

dat2 <- cbind(dat[, c("Person", "Date", "Time_In", "Time_Out")], stg_calc[, names(hr_bucket)])
dat2
           Person       Date Time_In Time_Out 0_1  1_2 2_3 3_4 4_5 5_6 6_7 7_8 8_9 9_10 10_11 11_12 12_13 13_14 14_15 15_16 16_17 17_18 18_19 19_20 20_21 21_22 22_23 23_24
1        John Doe 2019-01-01  1:15pm   2:30pm   0 0.00   0   0   0   0   0   0   0    0     0     0     0  0.75   0.5  0.00     0     0     0     0     0     0     0     0
2     Brian Smith 2019-01-02  1:45am   3:33pm   0 0.25   1   1   1   1   1   1   1    1     1     1     1  1.00   1.0  0.55     0     0     0     0     0     0     0     0
3        Jane Doe 2019-01-04 11:00pm   3:00am   1 1.00   1   0   0   0   0   0   0    0     0     0     0  0.00   0.0  0.00     0     0     0     0     0     0     0     1
4 Alexandra Wakes 2019-01-02  1:00am   1:00am   1 1.00   1   1   1   1   1   1   1    1     1     1     1  1.00   1.0  1.00     1     1     1     1     1     1     1     1

    【讨论】：

      【解决方案3】：

      这似乎可行，尽管它有一些笨拙的步骤。

      library(tidyverse)
library(lubridate)

(dat<-tibble(Date = c("1/1/19", "1/2/19", "1/4/19", "1/2/19"), 
             Person = c("John Doe", "Brian Smith", "Jane Doe", "Alexandra Wakes"), 
             Time_In = c("1:15pm", "1:45am", "11:00pm", "1:00am"), 
             Time_Out = c("2:30pm","3:33pm","3:00am","1:00am")))

dat2 <- dat %>%
  mutate(Time_In2 = mdy_hm(paste(Date, Time_In)),
         Time_Out2 = mdy_hm(paste(Date, Time_Out)),
         Time_Out2 = Time_Out2 + if_else(Time_Out2 <= Time_In2, ddays(1), 0)) %>%
  select(Person, Time_In2, Time_Out2) %>%
  gather(type, time, -Person) %>%

  # Kludge #1: gather seems to have converted POSIXct into numeric, switch back
  mutate(time = as.POSIXct(time, origin="1970-01-01", tz = "UTC")) %>%

  # Kludge #2: add rows for all minutes of day for each person.
  #   Clearly not most efficient method! This might be slowish if you have
  #   many thousands of Person values.
  group_by(Person) %>%
  padr::pad(interval = "min") %>%
  mutate(hour = hour(time)) %>%
  # Exclude ending minute to avoid double-counting
  filter(type != "Time_Out2" | is.na(type)) %>%
  ungroup() %>%

  count(Person, hour) %>%
  mutate(n = n/60) %>%
  spread(hour, n, fill = 0)

> dat2
# A tibble: 4 x 25
  Person            `0`   `1`   `2`   `3`   `4`   `5`   `6`   `7`   `8`   `9`  `10`  `11`  `12`  `13`  `14`  `15`  `16`  `17`  `18`  `19`  `20`  `21`  `22`  `23`
  <chr>           <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Alexandra Wakes     1  1        1     1     1     1     1     1     1     1     1     1     1  1      1    1        1     1     1     1     1     1     1     1
2 Brian Smith         0  0.25     1     1     1     1     1     1     1     1     1     1     1  1      1    0.55     0     0     0     0     0     0     0     0
3 Jane Doe            1  1        1     0     0     0     0     0     0     0     0     0     0  0      0    0        0     0     0     0     0     0     0     1
4 John Doe            0  0        0     0     0     0     0     0     0     0     0     0     0  0.75   0.5  0        0     0     0     0     0     0     0     0

      【讨论】：

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