EX6

pacman::p_load("sf", "dplyr", "ggplot2", "spatstat.geom", "spatstat.explore",
  "gstat", "tidyr", "terra", "tmap","readr")

wildschwein_BE <- read_delim("../data/wildschwein_BE_2056.csv", delim=",") |>     st_as_sf(coords = c("E", "N"), crs = 2056, remove = FALSE)

Rows: 51246 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (2): TierID, TierName
dbl  (3): CollarID, E, N
dttm (1): DatetimeUTC

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Task 1: Import and visualize spatial data

feldaufnahmen <- read_sf("../data/Feldaufnahmen_Fanel.gpkg")

st_layers("../data/Feldaufnahmen_Fanel.gpkg") #One Layer

Driver: GPKG 
Available layers:
           layer_name geometry_type features fields       crs_name
1 Feldaufnahmen_Fanel       Polygon      975      2 CH1903+ / LV95

st_crs(feldaufnahmen) #CH1903+ / LV95

Coordinate Reference System:
  User input: CH1903+ / LV95 
  wkt:
PROJCRS["CH1903+ / LV95",
    BASEGEOGCRS["CH1903+",
        DATUM["CH1903+",
            ELLIPSOID["Bessel 1841",6377397.155,299.1528128,
                LENGTHUNIT["metre",1]]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["degree",0.0174532925199433]],
        ID["EPSG",4150]],
    CONVERSION["Swiss Oblique Mercator 1995",
        METHOD["Hotine Oblique Mercator (variant B)",
            ID["EPSG",9815]],
        PARAMETER["Latitude of projection centre",46.9524055555556,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8811]],
        PARAMETER["Longitude of projection centre",7.43958333333333,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8812]],
        PARAMETER["Azimuth of initial line",90,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8813]],
        PARAMETER["Angle from Rectified to Skew Grid",90,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8814]],
        PARAMETER["Scale factor on initial line",1,
            SCALEUNIT["unity",1],
            ID["EPSG",8815]],
        PARAMETER["Easting at projection centre",2600000,
            LENGTHUNIT["metre",1],
            ID["EPSG",8816]],
        PARAMETER["Northing at projection centre",1200000,
            LENGTHUNIT["metre",1],
            ID["EPSG",8817]]],
    CS[Cartesian,2],
        AXIS["(E)",east,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["(N)",north,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    USAGE[
        SCOPE["Cadastre, engineering survey, topographic mapping (large and medium scale)."],
        AREA["Liechtenstein; Switzerland."],
        BBOX[45.82,5.96,47.81,10.49]],
    ID["EPSG",2056]]

Task 2: Annotate Trajectories from vector data

wildschwein_join <- st_join(wildschwein_BE,feldaufnahmen)

Task 3: Explore annotated trajectories

library("lubridate")

Attaching package: 'lubridate'

The following objects are masked from 'package:terra':

    intersect, union

The following objects are masked from 'package:base':

    date, intersect, setdiff, union

library("forcats")
wildschwein_smry<- wildschwein_join |> 
  st_drop_geometry() |> 
  mutate(
    hour=hour(round_date(DatetimeUTC,"hour")),
    Frucht=fct_lump(Frucht,3)) |> 
  group_by(TierName,hour,Frucht) |> 
  summarise(n=n()) |> 
  group_by(TierName,hour) |> 
  mutate(perc = n/sum(n)*100)

`summarise()` has grouped output by 'TierName', 'hour'. You can override using
the `.groups` argument.

wildschwein_smry |> 
  ggplot(aes(hour, perc, fill=Frucht))+
  geom_col()+
  facet_wrap(~TierName)+
  scale_y_continuous(
    labels = scales::label_number(suffix = "%"))+
  labs(
    x = "Time (rounded to the nearest hour)",
    y = "Percentage",
    title = "Percentages of samples in a given crop per hour",
    subtitle = "(Only showing the most common categories)"
  )

Task 4: Import and visualize vegetationindex (raster data)

veg <- rast("../data/vegetationshoehe_LFI.tif")
plot(veg)

Task 5: Annotate Trajectories from raster data

wildboar_veg<- extract(veg, vect(wildschwein_BE))

Warning: [extract] transforming vector data to the CRS of the raster

nrow(wildschwein_BE) #gleiche Anzahl Spalten

[1] 51246

wildschwein_BE$veg <- wildboar_veg$vegetationshoehe_LFI

library(dplyr)

wildschwein_smry_veg <- wildschwein_BE |>
  st_drop_geometry() |>
  mutate(
    hour = hour(round_date(DatetimeUTC, "hour"))
  ) |>
  group_by(TierName, hour) |>
  summarise(
    veg_avg = mean(veg, na.rm = TRUE),
    .groups = "drop"
  )

Visualize:

library(ggplot2)

wildschwein_smry_veg |>
  ggplot(aes(x = hour, y = veg_avg, fill = TierName)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~TierName) +
  labs(
    x = "Time (rounded to the nearest hour)",
    y = "Mean vegetation height [m]",
    title = "Vegetation height per animal and hour"
  )