I am attempting to filter one dataframe 'Blond_GSE' e.g. (bird tracking data which contains lots of variables including a timestamp) by the timestamps from a separate dataframe 'Blond_Prey' (variables including a timestamp of when a bird bought food to a nest) . I would like to filter, so I have a new data frame with all tracking data (Blond_GSE) 30 minutes prior to the timestamps from the 'Blond_Prey.
Here is a look at each separate data frame.
head(Blond_GSE)
tag_id sensor_type_id acceleration_raw_x acceleration_raw_y
1 977476871 653 30 -942
2 977476871 653 32 -949
3 977476871 653 34 -949
4 977476871 653 40 -944
5 977476871 653 36 -943
6 977476871 653 36 -944
acceleration_raw_z barometric_height battery_charge_percent
1 454 0 100
2 445 0 100
3 450 0 100
4 446 0 100
5 451 0 100
6 455 0 100
battery_charging_current external_temperature flt_switch gps_hdop
1 0 33 NA 0.9
2 0 33 NA 1.0
3 0 33 NA 1.0
4 0 34 NA 0.9
5 0 33 NA 1.0
6 0 33 NA 0.8
gps_maximum_signal_strength gps_satellite_count gps_time_to_fix
1 NA 7 21.46
2 NA 6 12.48
3 NA 7 14.48
4 NA 8 26.41
5 NA 7 7.95
6 NA 9 8.98
ground_speed gsm_mcc_mnc heading height_above_ellipsoid
1 0 NA 86 NA
2 0 NA 296 NA
3 0 NA 331 NA
4 0 NA 44 NA
5 0 NA 213 NA
6 0 NA 225 NA
height_above_msl import_marked_outlier light_level
1 152 false 0
2 152 false 0
3 152 false 0
4 152 false 0
5 152 false 0
6 152 false 0
location_error_numerical location_lat location_long
1 NA 51.86663 27.59045
2 NA 51.86654 27.59053
3 NA 51.86645 27.59056
4 NA 51.86644 27.59071
5 NA 51.86636 27.59047
6 NA 51.86646 27.59067
magnetic_field_raw_x magnetic_field_raw_y magnetic_field_raw_z
1 0.067 -0.354 -0.024
2 0.065 -0.360 -0.013
3 0.067 -0.352 -0.019
4 0.061 -0.360 -0.012
5 0.061 -0.356 -0.014
6 0.073 -0.350 -0.019
ornitela_transmission_protocol tag_voltage timestamp
1 GPRS 4155 2019-04-26 01:42:00
2 GPRS 4150 2019-04-26 01:46:51
3 GPRS 4150 2019-04-26 01:51:53
4 GPRS 4150 2019-04-26 01:57:05
5 GPRS 4147 2019-04-26 02:01:46
6 GPRS 4147 2019-04-26 02:06:47
transmission_timestamp update_ts
1 2019-10-07 09:46:52.104
2 2019-10-07 09:46:52.104
3 2019-10-07 09:46:52.104
4 2019-10-07 09:46:52.104
5 2019-10-07 09:46:52.104
6 2019-10-07 09:46:52.104
vertical_error_numerical visible deployment_id event_id
1 NA true 1003456347 12506913411
2 NA true 1003456347 12506913412
3 NA true 1003456347 12506913413
4 NA true 1003456347 12506913414
5 NA true 1003456347 12506913415
6 NA true 1003456347 12506913416
sensor_type tag_local_identifier location_long.1 location_lat.1
1 GPS 171035 27.59045 51.86663
2 GPS 171035 27.59053 51.86654
3 GPS 171035 27.59056 51.86645
4 GPS 171035 27.59071 51.86644
5 GPS 171035 27.59047 51.86636
6 GPS 171035 27.59067 51.86646
optional sensor timestamps trackId comments
1 TRUE GPS 2019-04-26 01:42:00 Blond NA
2 TRUE GPS 2019-04-26 01:46:51 Blond NA
3 TRUE GPS 2019-04-26 01:51:53 Blond NA
4 TRUE GPS 2019-04-26 01:57:05 Blond NA
5 TRUE GPS 2019-04-26 02:01:46 Blond NA
6 TRUE GPS 2019-04-26 02:06:47 Blond NA
death_comments earliest_date_born exact_date_of_birth
1 NA
2 NA
3 NA
4 NA
5 NA
6 NA
individual_id latest_date_born local_identifier nick_name ring_id
1 1003455374 NA Blond Blond
2 1003455374 NA Blond Blond
3 1003455374 NA Blond Blond
4 1003455374 NA Blond Blond
5 1003455374 NA Blond Blond
6 1003455374 NA Blond Blond
sex taxon_canonical_name timestamp_start
1 Aquila clanga 2018-08-31 00:01:23.000
2 Aquila clanga 2018-08-31 00:01:23.000
3 Aquila clanga 2018-08-31 00:01:23.000
4 Aquila clanga 2018-08-31 00:01:23.000
5 Aquila clanga 2018-08-31 00:01:23.000
6 Aquila clanga 2018-08-31 00:01:23.000
timestamp_end number_of_events number_of_deployments
1 2020-07-16 09:54:12.000 85156 1
2 2020-07-16 09:54:12.000 85156 1
3 2020-07-16 09:54:12.000 85156 1
4 2020-07-16 09:54:12.000 85156 1
5 2020-07-16 09:54:12.000 85156 1
6 2020-07-16 09:54:12.000 85156 1
sensor_type_ids taxon_detail
1 GPS Clanga clanga
2 GPS Clanga clanga
3 GPS Clanga clanga
4 GPS Clanga clanga
5 GPS Clanga clanga
6 GPS Clanga clanga
head(Blond_prey)
Location ID Species Habitat Year Date Activity Gender
1 ?????? Blond BP Fen Mire 2019 2019-04-25 Arrival M
2 ?????? Blond BP Fen Mire 2019 2019-04-27 Arrival M
3 ?????? Blond BP Fen Mire 2019 2019-04-27 Arrival M
4 ?????? Blond BP Fen Mire 2019 2019-05-03 Arrival M
5 ?????? Blond BP Fen Mire 2019 2019-05-12 Arrival M
6 ?????? Blond BP Fen Mire 2019 2019-05-13 Arrival M
Activity_1 Category Prey
1 Prey Delivery ? medium-sized bird or large vole
2 Prey Delivery ? Something Small
3 Prey Delivery Crane-like Spotted Crake
4 Prey Delivery Geese Large Duck
5 Prey Delivery ? medium-sized bird or large vole
6 Prey Delivery Snake Grass Snake
Class Age Condition Weight..g. Notes
1 ? <NA> <NA> 100 Imperfectly Seen
2 ? <NA> <NA> NA <NA>
3 Aves ad <NA> NA <NA>
4 Aves ad duck spine with head NA <NA>
5 ? <NA> <NA> 100 Imperfectly Seen
6 Reptilia <NA> <NA> NA <NA>
New_Time
1 2019-04-25 17:03:00 UTC
2 2019-04-27 04:39:00 UTC
3 2019-04-27 07:33:00 UTC
4 2019-05-03 07:26:00 UTC
5 2019-05-12 06:40:00 UTC
6 2019-05-13 13:19:00 UTC
The columns with the timestamps are called "timestamp" in Blond_GSE and "New_Time in Blond_Prey.
Here are a look at the two timestamps.
head(Blond_GSE$timestamp)
[1] "2019-04-26 01:42:00 UTC" "2019-04-26 01:46:51 UTC"
[3] "2019-04-26 01:51:53 UTC" "2019-04-26 01:57:05 UTC"
[5] "2019-04-26 02:01:46 UTC" "2019-04-26 02:06:47 UTC"
head(Blond_prey$New_Time)
[1] "2019-04-25 17:03:00 UTC" "2019-04-27 04:39:00 UTC"
[3] "2019-04-27 07:33:00 UTC" "2019-05-03 07:26:00 UTC"
[5] "2019-05-12 06:40:00 UTC" "2019-05-13 13:19:00 UTC"
I would like to filter the Blond_GSE data by the timestamp of Blond_prey, so i get all data 30 mins prior to the Blond_Prey timestamps.
Is this possible?
I have tried the code.
Blond.GSE <- Blond_GSE %>% filter_time(timestamp => Blond_prey$New_Time <=(Blond_prey&New_Time - 30))
However that returns an error message:
Error: unexpected '>' in "Blond.GSE <- Blond_GSE %>% filter_time(timestamp =>"
Please can someone help?
question from:https://stackoverf
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