p1_getting_started.R
In amt: Animal Movement Tools

## ---- warning=FALSE, message=FALSE--------------------------------------------
library(dplyr)
library(ggplot2)
library(amt)
df1 <- tibble(x = 1:3, y = 1:3)
is.data.frame(df1)
df1

# Now we can create a track
tr1 <- make_track(df1, x, y)
is.data.frame(tr1)
tr1

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class(tr1)

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df1 <- tibble(x = 1:3, y = 1:3, t = lubridate::ymd("2017-01-01") + lubridate::days(0:2))
tr2 <- make_track(df1, x, y, t)
class(tr2)

## -----------------------------------------------------------------------------
df1 <- tibble(x = 1:3, y = 1:3, t = lubridate::ymd("2017-01-01") + lubridate::days(0:2), 
                  id = 1, age = 4)

# first we only create a track_xy
tr3 <- make_track(df1, x, y, id = id, age = age)
tr3

# now lets create a track_xyt
tr4 <- make_track(df1, x, y, t, id = id, age = age)
tr4

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data(sh)
head(sh)

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# check if all observations are complete
all(complete.cases(sh)) # no action required

# parse date and time and create time stamps
sh$ts <- as.POSIXct(lubridate::ymd(sh$day) +
                      lubridate::hms(sh$time))

# check for duplicated time stamps
any(duplicated(sh$ts))

# We have some duplicated time stamps, these need to be removed prior to
# creating a track.
sh <- sh[!duplicated(sh$ts), ]

# create new columns
sh$id <- "Animal 1"
sh$month <- lubridate::month(sh$ts)

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tr1 <- make_track(sh, x_epsg31467, y_epsg31467, ts, id = id, month = month)

## -----------------------------------------------------------------------------
tr1 <- make_track(sh, x_epsg31467, y_epsg31467, ts, id = id, month = month, 
                crs = 31467)

## -----------------------------------------------------------------------------
data(sh)
tr2 <- sh |> filter(complete.cases(sh)) |> 
  mutate(
    ts = as.POSIXct(lubridate::ymd(day) + lubridate::hms(time)), 
    id = "Animal 1", 
    month = lubridate::month(ts)
  ) |> 
  filter(!duplicated(ts)) |> 
  make_track(x_epsg31467, y_epsg31467, ts, id = id, month = month, 
           crs = 31467)
tr2

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tr3 <- tr2 |> filter(month == 5)

# we are left with a track
class(tr3)

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transform_coords(tr2, 4326)

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tr2 <- tr2 |> mutate(sl_ = step_lengths(tr2))

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summary(tr2$sl_)

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summarize_sampling_rate(tr2)

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tr3 <- tr2 |> track_resample(rate = hours(6), tolerance = minutes(20))
tr3

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data("amt_fisher")
trk <- amt_fisher |> make_track(x_, y_, t_, id = id)

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trk1 <- trk |> nest(data = -"id")
trk1

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# get the data for the first animal
x <- trk1$data[[1]]

# apply the data analysis
x |> track_resample(rate = minutes(30), tolerance = minutes(5)) |>
  steps_by_burst()

## -----------------------------------------------------------------------------
trk2 <- trk1 |> 
  mutate(steps = map(data, function(x) 
    x |> track_resample(rate = minutes(30), tolerance = minutes(5)) |> steps_by_burst()))

trk2

## -----------------------------------------------------------------------------
trk2 |> select(id, steps) |> unnest(cols = steps) |> 
  ggplot(aes(sl_, fill = factor(id))) + geom_density(alpha = 0.4)

## -----------------------------------------------------------------------------
sessioninfo::session_info()