#
################################################################################
Sys.setenv(TZ='UTC')
#library("activitytools")
library(devtools)
load_all("./")
data(activity_data)
data(gps_data)
# create parameter that is used for state classification
activity_data$act_xy <- activity_data$act_x + activity_data$act_y
# define all parameter that are used for state detection
pars <- list(act.act = "act_xy",
act.reg_minutes = 5,
act.width_ma = 2,
thresh.n_runs = 1,
thresh.window_width_around_day = 3,
thresh.n_thresholds = c(25:35),
thresh.resting_range_limit = 20,
thresh.threshold_range_limit = 80,
states.min_duration_active = 10,
pta.pos = NULL,
pta.dayshift = "dawn",
pta.dawn_degree = 12,
pta.period = "day",
pta.max_na = 30)
# create activity object
## parameter can also be defined later
deer <- activity(activity_data = activity_data, gps_data = gps_data, parameters = pars)
# activity data are often characterized by data gaps or duplicated data points
# deer$activity_data[,.(min = min(diff(ts)),
# max = max(diff(ts)),
# mean = mean(diff(ts))),
# by = animal_tag]
# --> create a data set with equal time intervals between the activity data (regularize)
# the regular time interal is defined by the parameter 'act.reg_minutes'
deer <- regularize_activity(deer)
# deer$activity_data[,.(min = min(diff(ts)),
# max = max(diff(ts)),
# mean = mean(diff(ts))),
# by = animal_tag]
# Smooth the activity axis with a moving average
deer <- smooth_activity(deer)
# Identify gaps in the activity data
deer <- identify_activity_gaps(deer)
(deer$activity_gaps)
# Remove gaps in the activity data
deer <- remove_activity_gaps(deer)
# Calculate thresholds (this might take some time !!!!!)
deer <- calculate_thresholds(deer, plot_summary = TRUE)
# Calculate states, the proportion of time in state active and assign each
# GPS-position as active or resting (using parameter list defined above)
deer <- calculate_states(deer)
# plotting options
# Thresholds
plot(deer, select = "thresholds")
# Activity data
plot(deer, animal_id = 1)
plot(deer, select = "activity", animal_id = 1) # same
# Active states
plot(deer, select="states", threshold = "a")
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