View source: R/A_EVENTS_v1_v2_pipeline.r
eva | R Documentation |
Fetches the raw data using a connection to the database based on dbcon, then calculates when an individual was assumably at the nest box. Runs across the databases SNBatWESTERHOLZ and SNBatWESTERHOLZ_v2. Uses parallel computing.
eva(
df,
setTZ = "Etc/GMT-2",
time_threshold_v1 = 2,
max_distance_v1 = 16 * 60 * 60,
tr_threshold_v2 = 5,
cluster_events_threshold_v2 = 2,
max_distance_v2 = 16 * 60 * 60,
cluster_fronts_threshold_v2 = 5,
no_front_v2 = NULL,
N_cores = 50
)
df |
a data.table that contains three columns about which data should be fetched: the box numbers, and the corresponding from and to (column names: box, from, to). See examples. |
time_threshold_v1 |
time_threshold argument for function events_v1 |
max_distance_v1 |
max_distance argument for function events_v1 |
tr_threshold_v2 |
tr_threshold argument for function events_v2 |
cluster_events_threshold_v2 |
cluster_events_threshold argument for function events_v2 |
max_distance_v2 |
max_distance argument for function events_v2 |
cluster_fronts_threshold_v2 |
arguments for function events_v2 |
no_front_v2 |
no_front argument for function events_v2 |
con |
the connection to the database. |
Note that currently you need to have saved your credentials via save_credentials in order to run the function.
LS
df = data.table(box = 1, from = "2019-05-01 00:00:00", to = "2019-06-30 00:00:00")
hooray = eva(df,
time_threshold_v1 = 10, tr_threshold_v2 = 100, N_cores = 2)
hooray
plot(hooray)
plot(subset(hooray, !is.na(transp) & box == 1 & in_ > "2019-05-15" & in_ < "2019-05-25"))
#Note that you can access the colours and counts of individual transponders by using
tr = plot(hooray)
tr
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