R/logbook_anapo_control.R
logbook_anapo_control.RdThe purpose of the logbook_anapo_control function is to provide a table of data that contains an inconsistency between activity position and VMS position
logbook_anapo_control(
dataframe1,
dataframe2,
dataframe3,
activity_crs = 4326,
harbour_crs = 4326,
vms_crs = 4326,
output,
threshold_number_vms = 20,
threshold_geographical = 10,
threshold_time = 7200000,
threshold_score = 0.5,
buffer_harbour = 11100
)data.frame expected. Csv or output of the function data_extraction, which must be done before using the logbook_anapo_control () function.
data.frame expected. Csv or output of the function data_extraction, which must be done before using the logbook_anapo_control () function.
data.frame expected. Csv or output of the function data_extraction, which must be done before using the logbook_anapo_control () function.
numeric expected. Default values: 4326. Coordinate Reference Systems for the position activity
numeric expected. Default values: 4326. Coordinate Reference Systems for the position harbour
numeric expected. Default values: 4326. Coordinate Reference Systems for the position VMS
character expected. Kind of expected output. You can choose between "message", "report" or "logical".
numeric expected. Default values: 20. Minimum number of VMS positions required per day.
numeric expected. Default values: 10. Maximum valid distance threshold (Nautical miles) between position and nearest VMS point.
numeric expected. Default values: 7200000. Maximum valid distance threshold (milliseconds) between position and VMS point.
numeric expected. Default values: 0.5. Minimum valid score between position and VMS point.
numeric expected. Default values: 11100. Buffer to be used for harbour, in meter
The function returns a character with output is "message", two data.frame with output is "report" (the first without geographical location and the second with geographical location), a logical with output is "logical"
The input dataframe must contain all these columns for the function to work :
activity_id
activity_date
activity_time
activity_position
trip_id
trip_id
vessel_code
harbour_position_departure
harbour_position_landing
vms_date
vms_time
vms_position
vessel_code
#Activity 1, 2 and 3 are ok,
#Activity 4 has a number of VMS below the threshold (threshold_number_vms),
#Activity 5 has no position,
#Activity 6 has has a geographical distance above the threshold (threshold_geographical) and
# a score below the threshold (threshold_score)
dataframe1 <- data.frame(activity_id = c("1", "2", "3", "4", "5", "6"),
activity_date = as.Date(c("2020/01/01", "2020/01/12", "2020/01/12",
"2020/01/13", "2020/01/12", "2020/01/12")),
activity_time = c("05:26:01", "10:41:15", "16:41:15", "03:12:34",
"05:56:12", "23:26:47"),
activity_position = c("POINT (1 1)", "POINT (0 0)", "POINT (3 0)",
"POINT (4 4)", NA, "POINT (3 0.6)"),
trip_id = c("1", "2", "2", "2", "2", "2"))
dataframe2 <- data.frame(trip_id = c("1", "2"),
vessel_code = c("1", "1"),
harbour_position_departure = c("POINT (1 1.1)", "POINT (3 3)"),
harbour_position_landing = c("POINT (3 3)", "POINT (3 3)"))
dataframe3 <- data.frame(vms_date = as.Date(c("2020/01/01", "2020/01/12", "2020/01/12")),
vms_time = c("15:26:01", "10:55:15", "22:32:17"),
vms_position = c("POINT (4 4)", "POINT (0 0.1)", "POINT (3 0.3)"),
vessel_code = c("1", "1", "1"))
logbook_anapo_control(dataframe1,dataframe2, dataframe3, output = "report",threshold_number_vms = 1)
#> [[1]]
#> activity_id logical nb_vms min_distance max_score
#> 1 1 TRUE 1 NA [NM] NA
#> 2 2 TRUE 2 6.004055 [NM] NA
#> 3 3 TRUE 2 18.012165 [NM] 2.1795967
#> 4 4 FALSE NA 230.106884 [NM] 0.0000000
#> 5 5 FALSE 2 NA [NM] NA
#> 6 6 FALSE 2 18.012165 [NM] 0.2094411
#>
#> [[2]]
#> activity_id activity_date activity_time activity_position vms_date
#> 1 3 2020-01-12 16:41:15 POINT (3 0) 2020-01-12
#> 2 3 2020-01-12 16:41:15 POINT (3 0) 2020-01-12
#> 3 4 2020-01-13 03:12:34 POINT (4 4) 2020-01-12
#> 4 4 2020-01-13 03:12:34 POINT (4 4) 2020-01-12
#> 5 6 2020-01-12 23:26:47 POINT (3 0.6) 2020-01-12
#> 6 6 2020-01-12 23:26:47 POINT (3 0.6) 2020-01-12
#> 7 1 2020-01-01 05:26:01 POINT (1 1) 2020-01-01
#> 8 2 2020-01-12 10:41:15 POINT (0 0) 2020-01-12
#> 9 2 2020-01-12 10:41:15 POINT (0 0) 2020-01-12
#> 10 5 2020-01-12 05:56:12 <NA> 2020-01-12
#> 11 5 2020-01-12 05:56:12 <NA> 2020-01-12
#> vms_time vms_position distance duration score vms_crs
#> 1 10:55:15 POINT (0 0.1) 180.221603 [NM] 20760000 [ms] 0.0000000 4326
#> 2 22:32:17 POINT (3 0.3) 18.012165 [NM] -21062000 [ms] 2.1795967 4326
#> 3 10:55:15 POINT (0 0.1) 335.278629 [NM] -27761000 [ms] 0.0000000 4326
#> 4 22:32:17 POINT (3 0.3) 230.106884 [NM] -69583000 [ms] 0.0000000 4326
#> 5 10:55:15 POINT (0 0.1) 182.602329 [NM] 45092000 [ms] 0.0000000 4326
#> 6 22:32:17 POINT (3 0.3) 18.012165 [NM] 3270000 [ms] 0.2094411 4326
#> 7 15:26:01 POINT (4 4) NA [NM] NA [ms] NA 4326
#> 8 10:55:15 POINT (0 0.1) 6.004055 [NM] NA [ms] NA 4326
#> 9 22:32:17 POINT (3 0.3) 181.019203 [NM] NA [ms] NA 4326
#> 10 10:55:15 POINT (0 0.1) NA [NM] NA [ms] NA 4326
#> 11 22:32:17 POINT (3 0.3) NA [NM] NA [ms] NA 4326
#> activity_crs
#> 1 4326
#> 2 4326
#> 3 4326
#> 4 4326
#> 5 4326
#> 6 4326
#> 7 4326
#> 8 4326
#> 9 4326
#> 10 4326
#> 11 4326
#>