flux_match.md
In fluxible: Ecosystem Gas Fluxes Calculations for Closed Loop Chamber Setup

matching works

Code
  dplyr::distinct(dplyr::select(flux_match(co2_df_short, record_short, datetime,
    start, measurement_length = 180), f_fluxid, f_start, f_end))
Output
  # A tibble: 6 x 3
    f_fluxid f_start             f_end              
    <fct>    <dttm>              <dttm>             
  1 1        2022-07-28 23:43:25 2022-07-28 23:46:25
  2 2        2022-07-28 23:47:12 2022-07-28 23:50:12
  3 3        2022-07-28 23:52:00 2022-07-28 23:55:00
  4 4        2022-07-28 23:59:22 2022-07-29 00:02:22
  5 5        2022-07-29 00:03:00 2022-07-29 00:06:00
  6 6        2022-07-29 00:06:25 2022-07-29 00:09:25

time_diff works

Code
  flux_match(co2_df_short_180, record_short, datetime, start, measurement_length = 220,
    time_diff = 180)
Output
  # A tibble: 1,299 x 11
     datetime            temp_air temp_soil  conc   PAR turfID       type 
     <dttm>                 <dbl>     <dbl> <dbl> <dbl> <chr>        <chr>
   1 2022-07-28 23:43:25    NA         NA    439. NA    156 AN2C 156 ER   
   2 2022-07-28 23:43:26     7.21      10.9  440.  2.29 156 AN2C 156 ER   
   3 2022-07-28 23:43:27    NA         NA    441. NA    156 AN2C 156 ER   
   4 2022-07-28 23:43:28    NA         NA    441. NA    156 AN2C 156 ER   
   5 2022-07-28 23:43:29    NA         NA    442. NA    156 AN2C 156 ER   
   6 2022-07-28 23:43:30    NA         NA    443. NA    156 AN2C 156 ER   
   7 2022-07-28 23:43:31    NA         NA    443. NA    156 AN2C 156 ER   
   8 2022-07-28 23:43:32    NA         NA    444. NA    156 AN2C 156 ER   
   9 2022-07-28 23:43:33    NA         NA    446. NA    156 AN2C 156 ER   
  10 2022-07-28 23:43:34    NA         NA    446. NA    156 AN2C 156 ER   
  # i 1,289 more rows
  # i 4 more variables: start <dttm>, f_start <dttm>, f_fluxid <fct>,
  #   f_end <dttm>

renaming variables works

Code
  flux_match(co2_df_short, record_short, date_time, starting, measurement_length = 220)
Output
  # A tibble: 1,299 x 11
     date_time           temp_air temp_soil CO2_conc   PAR turfID       type 
     <dttm>                 <dbl>     <dbl>    <dbl> <dbl> <chr>        <chr>
   1 2022-07-28 23:43:25    NA         NA       439. NA    156 AN2C 156 ER   
   2 2022-07-28 23:43:26     7.21      10.9     440.  2.29 156 AN2C 156 ER   
   3 2022-07-28 23:43:27    NA         NA       441. NA    156 AN2C 156 ER   
   4 2022-07-28 23:43:28    NA         NA       441. NA    156 AN2C 156 ER   
   5 2022-07-28 23:43:29    NA         NA       442. NA    156 AN2C 156 ER   
   6 2022-07-28 23:43:30    NA         NA       443. NA    156 AN2C 156 ER   
   7 2022-07-28 23:43:31    NA         NA       443. NA    156 AN2C 156 ER   
   8 2022-07-28 23:43:32    NA         NA       444. NA    156 AN2C 156 ER   
   9 2022-07-28 23:43:33    NA         NA       446. NA    156 AN2C 156 ER   
  10 2022-07-28 23:43:34    NA         NA       446. NA    156 AN2C 156 ER   
  # i 1,289 more rows
  # i 4 more variables: starting <dttm>, f_start <dttm>, f_fluxid <fct>,
  #   f_end <dttm>

flags on nb of data

Code
  suppressWarnings(flux_match(co2_df_missing, record_short, datetime, start,
    measurement_length = 220))
Output
  # A tibble: 686 x 11
     datetime            temp_air temp_soil  conc   PAR turfID       type 
     <dttm>                 <dbl>     <dbl> <dbl> <dbl> <chr>        <chr>
   1 2022-07-28 23:43:25       NA        NA   NA     NA 156 AN2C 156 ER   
   2 2022-07-28 23:45:37       NA        NA  514.    NA 156 AN2C 156 ER   
   3 2022-07-28 23:45:38       NA        NA  513.    NA 156 AN2C 156 ER   
   4 2022-07-28 23:45:39       NA        NA  513.    NA 156 AN2C 156 ER   
   5 2022-07-28 23:45:40       NA        NA  514.    NA 156 AN2C 156 ER   
   6 2022-07-28 23:45:41       NA        NA  514.    NA 156 AN2C 156 ER   
   7 2022-07-28 23:45:42       NA        NA  515.    NA 156 AN2C 156 ER   
   8 2022-07-28 23:45:43       NA        NA  515.    NA 156 AN2C 156 ER   
   9 2022-07-28 23:45:44       NA        NA  515.    NA 156 AN2C 156 ER   
  10 2022-07-28 23:45:45       NA        NA  515.    NA 156 AN2C 156 ER   
  # i 676 more rows
  # i 4 more variables: start <dttm>, f_start <dttm>, f_fluxid <fct>,
  #   f_end <dttm>

matching works with end col

Code
  dplyr::distinct(dplyr::select(flux_match(co2_df_short, record_short_end,
    datetime, start, end, fixed_length = FALSE), f_fluxid, f_start, f_end))
Output
  # A tibble: 6 x 3
    f_fluxid f_start             f_end              
    <fct>    <dttm>              <dttm>             
  1 1        2022-07-28 23:43:25 2022-07-28 23:45:25
  2 2        2022-07-28 23:47:12 2022-07-28 23:50:12
  3 3        2022-07-28 23:52:00 2022-07-28 23:54:00
  4 4        2022-07-28 23:59:22 2022-07-29 00:02:22
  5 5        2022-07-29 00:03:00 2022-07-29 00:05:00
  6 6        2022-07-29 00:06:25 2022-07-29 00:09:25