library("readr")
library("sf")
wildschwein_BE <- read_delim("data/wildschwein_BE_2056.csv", ",") |>
st_as_sf(coords = c("E", "N"), crs = 2056, remove = FALSE)Exercises
task 1
What information does the dataset contain? information on different occupation (crops) of fields
What is the geometry type of the dataset (possible types are: Point, Lines and Polygons)? polygons
What are the data types of the other columns? characters (frucht) and numeric (fieldID)
What is the coordinate system of the dataset? CH1903+ / LV95
fanel <- read_sf("data/Feldaufnahmen_Fanel.gpkg")
head(fanel)Simple feature collection with 6 features and 2 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: 2570249 ymin: 1202176 xmax: 2571719 ymax: 1203436
Projected CRS: CH1903+ / LV95
# A tibble: 6 × 3
FieldID Frucht geom
<dbl> <chr> <POLYGON [m]>
1 1 Roggen ((2570914 1202743, 2570917 1202749, 2570985 1202833, 2571294 1…
2 0 <NA> ((2570893 1202758, 2570893 1202758, 2570959 1202845, 2570985 1…
3 0 <NA> ((2570868 1202776, 2570872 1202781, 2570913 1202828, 2570946 1…
4 2 Wiese ((2570882 1203234, 2570641 1202974, 2570630 1202983, 2570606 1…
5 3 Weide ((2570249 1203116, 2570371 1203328, 2570481 1203197, 2570390 1…
6 5 Weide ((2570378 1203320, 2570466 1203436, 2570552 1203289, 2570481 1…task 2
library(lubridate)
library(dplyr)
library(ggplot2)
wildschwein_summer <- wildschwein_BE |>
filter(month(DatetimeUTC) >= 5,
month(DatetimeUTC) <= 6)
ggplot() +
geom_sf(data = fanel, aes(fill = Frucht)) +
geom_sf(data = wildschwein_summer,
color = "black",
size = 0.3) 
wildschwein_fanel <- st_join(wildschwein_summer, fanel)
head(wildschwein_fanel)Simple feature collection with 6 features and 8 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 2570059 ymin: 1205242 xmax: 2570096 ymax: 1205256
Projected CRS: CH1903+ / LV95
# A tibble: 6 × 9
TierID TierName CollarID DatetimeUTC E N
<chr> <chr> <dbl> <dttm> <dbl> <dbl>
1 002A Sabi 12275 2015-05-01 00:00:17 2570093. 1205256.
2 002A Sabi 12275 2015-05-01 00:15:25 2570093. 1205249.
3 002A Sabi 12275 2015-05-01 00:30:15 2570091. 1205253.
4 002A Sabi 12275 2015-05-01 00:45:15 2570059. 1205242.
5 002A Sabi 12275 2015-05-01 01:00:30 2570078. 1205246.
6 002A Sabi 12275 2015-05-01 01:15:43 2570096. 1205256.
# ℹ 3 more variables: geometry <POINT [m]>, FieldID <dbl>, Frucht <chr>task 3
wildschwein_fanel <- wildschwein_fanel |>
mutate(hour = hour(DatetimeUTC))
crop_hour <- wildschwein_fanel |>
filter(!is.na(Frucht)) |>
count(TierName, hour, Frucht) |>
group_by(TierName,hour) |>
mutate(percent = n / sum(n) * 100)
ggplot(crop_hour, aes(x = hour, y = percent, fill = Frucht)) +
geom_col(position = "stack") +
facet_wrap(~ TierName) +
labs(y = "Percent of locations", x = "Hour of day") +
theme_minimal()
task 4
library(terra)
library(tmap)
vhm <- rast("data/vegetationshoehe_LFI.tif")
plot(vhm)
tm_shape(vhm) +
tm_raster(title = "Vegetation height") +
tm_layout(legend.outside = TRUE)
task 5
wild_vect <- vect(wildschwein_summer)
vhm_values <- extract(vhm, wild_vect)
wildschwein_vhm <- cbind(wildschwein_summer, vhm_values)
head(wildschwein_vhm)Simple feature collection with 6 features and 8 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 2570059 ymin: 1205242 xmax: 2570096 ymax: 1205256
Projected CRS: CH1903+ / LV95
TierID TierName CollarID DatetimeUTC E N ID
1 002A Sabi 12275 2015-05-01 00:00:17 2570093 1205256 1
2 002A Sabi 12275 2015-05-01 00:15:25 2570093 1205249 2
3 002A Sabi 12275 2015-05-01 00:30:15 2570091 1205253 3
4 002A Sabi 12275 2015-05-01 00:45:15 2570059 1205242 4
5 002A Sabi 12275 2015-05-01 01:00:30 2570078 1205246 5
6 002A Sabi 12275 2015-05-01 01:15:43 2570096 1205256 6
vegetationshoehe_LFI geometry
1 8.74 POINT (2570093 1205256)
2 20.62 POINT (2570093 1205249)
3 11.06 POINT (2570091 1205253)
4 26.15 POINT (2570059 1205242)
5 17.48 POINT (2570078 1205246)
6 13.17 POINT (2570096 1205256)plot(wildschwein_vhm["vegetationshoehe_LFI"])
wildschwein_vhm <- wildschwein_vhm |>
mutate(veg_class = cut(vegetationshoehe_LFI,
breaks = c(0, 2, 5, 10, 20, Inf),
labels = c("0-2", "2-5", "5-10", "10-20", ">20")))
vhm_percent <- wildschwein_vhm |>
filter(!is.na(veg_class)) |>
count(TierName, veg_class) |>
group_by(TierName) |>
mutate(percent = n / sum(n) * 100)
ggplot(vhm_percent, aes(x = veg_class, y = percent, fill = veg_class)) +
geom_col() +
facet_wrap(~ TierName) +
theme_minimal() +
labs(x = "Vegetation height class",
y = "Percent of locations") +
guides(fill = "none")