Halyomorpha halys; observation time-series irregular: regularized weekly capture rates

# variable monitoring time
tempo <- as.POSIXct(c("2021-05-29 17:00:00 UTC","2021-06-05 10:52:00
UTC","2021-06-01 17:00:00 UTC","2021-05-16 08:34:00 UTC","2021-06-05
17:00:00 UTC","2021-05-29 05:30:00 UTC","2021-05-23 06:30:00
UTC","2021-05-20 13:00:00 UTC","2021-05-15 12:09:00 UTC","2021-06-06
19:18:00 UTC"))
# variable capture of BMSB
catture <- c(25,92,23,2,5,30,23,3,15,21,15,40,22)

# resulting table
library(data.table)
arvaia_catture <- data.table(tempo,catture)
# order by time
arvaia_catture_order <- arvaia_catture[order(arvaia_catture$tempo)]
# calculate the rate per hour of capture
arvaia_catture_order$difftime_ora <-
c(0,diff(arvaia_catture_order$tempo))
arvaia_catture_order$catture_ora <-
arvaia_catture_order$catture/arvaia_catture_order$difftime_ora

# use of padr to fill the gap
library(padr)
arvaia_catture_ora <- thicken(arvaia_catture_order,interval = "hour")
pad(arvaia_catture_ora,by=arvaia_catture_ora$tempo_hour)

Hi:


On 6/7/21 11:44 AM, Enrico Gabrielli wrote:

Hello
I'm looking at if and how to find support with paid computer
scientists
because it is not possible for an agricultural technician in the
midst
of activities in the field, who does not know how to use R
solve this problem also through a mailing-list.
However it is right to complete the description of the problem,
maybe
you have ideas that I will give to the computer scientists that we
will
pay with the project
so I am not asking here for the solution, but only for ideas.

This is an data example (really)
datetime	capture
2021-05-15 14:09:00	15
2021-05-16 10:34:00	2
2021-05-20 15:00:00	3
2021-05-23 08:30:00	23
2021-05-29 07:30:00	30
2021-05-29 19:00:00	25
2021-06-01 19:00:00	23
2021-06-05 12:52:00	92
2021-06-05 19:00:00	5

This is the results of two possibile solution
week		simple SUM week 	right SUM week
19	17	19,44027778
20	26	52,91388889
21	55	46,4375
22	120	99,20833333

simple is done with a simple sum by aggregate with weeknum
right is done with a it is done with the calculation of the ratio
between catches and time interval

It's still not clear. time interval in hours? or days?


Have a look at this code, using your example data:


library(dplyr)


Hh <- read.table(header = FALSE, text="2021-05-15 14:09:00    15
2021-05-16 10:34:00    2
2021-05-20 15:00:00    3
2021-05-23 08:30:00    23
2021-05-29 07:30:00    30
2021-05-29 19:00:00    25
2021-06-01 19:00:00    23
2021-06-05 12:52:00    92
2021-06-05 19:00:00    5")
colnames(Hh) <- c("Date", "Time", "Captures")

Hh$DateTime <- as.POSIXct(paste(Hh$Date, Hh$Time),
                          format = "%Y-%m-%d %H:%M:%S",
                          tz = "UTC")         # What time zone??

# I assume time interval in days

# The first row must have timediff=0, since there is no previous
DateTime

Hh$Timediff <- c(0, diff(Hh$DateTime)/24)
# Captures per hour
Hh$Capt.per.Day <- ifelse(Hh$Timediff == 0, 0,
Hh$Captures/Hh$Timediff)

# Week number:
Hh$Weeknum <- strftime(Hh$DateTime, "%Y%W")
Hh
         Date     Time Captures            DateTime  Timediff 
Capt.per.Day Weeknum
1 2021-05-15 14:09:00       15 2021-05-15 14:09:00 0.0000000
0.0000000  
202119
2 2021-05-16 10:34:00        2 2021-05-16 10:34:00 0.8506944
2.3510204  
202119
3 2021-05-20 15:00:00        3 2021-05-20 15:00:00 4.1847222
0.7168935  
202120
4 2021-05-23 08:30:00       23 2021-05-23 08:30:00 2.7291667
8.4274809  
202120
5 2021-05-29 07:30:00       30 2021-05-29 07:30:00 5.9583333
5.0349650  
202121
6 2021-05-29 19:00:00       25 2021-05-29 19:00:00 0.4791667
52.1739130  
202121
7 2021-06-01 19:00:00       23 2021-06-01 19:00:00 3.0000000
7.6666667  
202122
8 2021-06-05 12:52:00       92 2021-06-05 12:52:00 3.7444444
24.5697329  
202122
9 2021-06-05 19:00:00        5 2021-06-05 19:00:00 0.2555556
19.5652174  
202122


# Now sum by wekknum

Hh_weekly <- Hh %>%

   group_by(Weeknum) %>%
   summarise(Hourly.Capt.per.Week = sum(Capt.per.Day)) %>%
   ungroup()

Hh_weekly
# A tibble: 4 x 2
   Weeknum Hourly.Capt.per.Week
   <chr>                  <dbl>
1 202119                  2.35
2 202120                  9.14
3 202121                 57.2
4 202122                 51.8



Does that bring you any closer??

but this is done with a spreadsheet, but in order to automate the
generation of an html graph on the project server page, I need to
do
this with R

I tested the xts package, and then I found lubridate and padr
interesting, but I stopped due to lack of time