如何group_by变量并将时间从13:24:00开始精确切换到10s箱,并为group_by变量求平均值

2023年5月29日 146次阅读

我有30个传感器的二氧化碳测量数据,这些传感器并非同时测量,也不是完全同时开始.我想尽可能地调整它们,所以我认为平均10分可能是一个很好的解决方案.

在之前的一个问题中：Group by multiple variables and summarise dplyr我为每个传感器减少了10s的时间,并将每个传感器的平均值读取为10s.听起来不错但是,我已经意识到以下代码会从每个传感器启动的时间开始减少时间,因此它们仍然没有对齐.我该如何对齐它们？

require(tidyverse)
require(lubridate)  
  df %>%
        group_by(Sensor, BinnedTime = cut(DeviceTime, breaks="10 sec")) %>%
            mutate(Concentration = mean(calCO2)) %>%
        ungroup()

    head(df)
# A tibble: 6 x 7
# Groups: BinnedTime [1]

  Sensor Date       Time   calCO2 DeviceTime          cuts   BinnedTime         
  <fctr> <date>     <time>  <dbl> <dttm>              <fctr> <chr>              
1 N1     2019-02-12 13:24     400 2019-02-12 13:24:02 (0,10] 2019-02-12 13:24:02
2 N1     2019-02-12 13:24     400 2019-02-12 13:24:02 (0,10] 2019-02-12 13:24:02
3 N1     2019-02-12 13:24     400 2019-02-12 13:24:03 (0,10] 2019-02-12 13:24:03
4 N2     2019-02-12 13:24     400 2019-02-12 13:24:03 (0,10] 2019-02-12 13:24:02
5 N3     2019-02-12 13:24     400 2019-02-12 13:24:03 (0,10] 2019-02-12 13:24:02
6 N3     2019-02-12 13:24     400 2019-02-12 13:24:05 (0,10] 2019-02-12 13:24:04

编辑

我试过了：

dt<-seq(
  from=as.POSIXct("2019-02-12 13:24:00", tz="GMT"),
  to=as.POSIXct("2019-02-12 14:00:00", tz="GMT"),
  by="10 sec"
) 

cut(df$BinnedTime,dt)

但它给出一个错误,说x必须是数字,所以我将df $BinnedTime和dt $dt转换为数字,这只产生NAs.

cut(as.numeric(as.POSIXct(df$BinnedTime)), as.numeric(dt))

我错过了什么？

编辑2

我有以下内容：

df$DeviceTime <- as.POSIXct(paste(d$Date, d$Time), format="%Y-%m-%d %H:%M:%S")

df<-df%>%
  mutate(BinnedTime=floor_date(ymd_hms(DeviceTime),unit="10 sec"))%>%
  group_by(Sensor)%>%
  group_by(BinnedTime,add=TRUE)%>%
  summarize(calCO2 = mean(na.omit(calCO2)))

我认为现在我正在追求但它并不优雅.

这是onedrive中的数据文件：df.txt until 30th March 19

最佳答案

library(tidyverse)
library(lubridate)  
#> 
#> Attaching package: 'lubridate'
#> The following object is masked from 'package:base':
#> 
#>     date

df <- read_delim("https://gist.githubusercontent.com/ramiromagno/4347eefec2aa36ec94423b75b145fccb/raw/5c1b445686bd014ea3a1f0336433e3b364779766/df.txt", delim = " ", col_types = cols())

df$DeviceTime <- as.POSIXct(paste(df$Date, df$Time), format="%Y-%m-%d %H:%M:%S")

dt <- seq(
  from = as.POSIXct("2019-02-12 13:24:00", tz = "GMT"),
  to = as.POSIXct("2019-02-12 14:00:00", tz = "GMT"),
  by = "10 sec"
)

df %>% 
  mutate(BinnedTime = cut(DeviceTime, breaks=dt)) %>%
  group_by(Sensor)%>%
  group_by(BinnedTime,add=TRUE)%>%
  summarize(calCO2 = mean(na.omit(calCO2))) -> df2

df2
#> # A tibble: 7,557 x 3
#> # Groups:   Sensor [?]
#>    Sensor BinnedTime          calCO2
#>    <chr>  <fct>                <dbl>
#>  1 A1     2019-02-12 13:24:00    400
#>  2 A1     2019-02-12 13:24:10    401
#>  3 A1     2019-02-12 13:24:20    401
#>  4 A1     2019-02-12 13:24:30    401
#>  5 A1     2019-02-12 13:24:40    401
#>  6 A1     2019-02-12 13:24:50    400
#>  7 A1     2019-02-12 13:25:00    400
#>  8 A1     2019-02-12 13:25:10    398
#>  9 A1     2019-02-12 13:25:20    397
#> 10 A1     2019-02-12 13:25:30    394
#> # ... with 7,547 more rows
点赞