Week 5 Excersices

Load all Modules

require(readr)
require(sf)
require(dplyr)
require(ggplot2)
require(tidyr)
require(lubridate)
require(plotly)

Task 1: Write my own functions

BMI <- function(weight, height) {
    weight/(height^2)
}

converter_F_C <- function(celsius) {
  celsius * (9/5) + 35
}

euclidian_distance <- function(x1,y1,x2,y2) {
  sqrt((x1-x2)^2 - (y1-y2)^2)
}

Task 2 Prepare Analysis

wildschwein <- read_delim("data/wildschwein_BE_2056.csv", ",")

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.

data <- wildschwein |>
filter(
    TierName %in% c("Rosa", "Sabi"), 
    DatetimeUTC >= "2015-04-01", 
    DatetimeUTC < "2015-04-15"
)


distance_by_element <- function(later, now) {
  as.numeric(
    st_distance(later, now, by_element = TRUE)
  )
}

Task 3: Create Join Key

data <- data |> 
  mutate(DatetimeRound = round_date(DatetimeUTC, unit = "15 minutes"))

Task 4/5

sabi_data <- data |>
filter(
    TierName == 'Sabi'
)

rosa_data <- data |> 
  filter(
    TierName == 'Rosa'
  )


meets <-  sabi_data|> 
  inner_join(rosa_data, by = "DatetimeRound", suffix = c("_sabi", "_rosa")) |> 
  mutate(Euclidean_Distance = sqrt((E_sabi - E_rosa)^2 + (N_sabi - N_rosa)^2)) |> 
  mutate(Meets = Euclidean_Distance < 100) |> 
  filter(Meets)

ggplot() +
  geom_point(data = sabi_data, aes(x = E, y = N, color = "Sabi"), size = 1, alpha = 0.5) +
  geom_point(data = rosa_data, aes(x = E, y = N, color = "Rosa"), size = 1, alpha = 0.5) +
  geom_point(data = meets, aes(x = E_sabi, y = N_sabi, color = "Meets"), size = 3, alpha = 0.3) + 
  scale_color_manual(values = c("Sabi" = "blue", "Rosa" = "green", "Meets" = "red")) +
  xlim(min(sabi_data$E, rosa_data$E), max(sabi_data$E, rosa_data$E)) +
  ylim(min(sabi_data$N, rosa_data$N), max(sabi_data$N, rosa_data$N)) +
  labs(title = "Räumliche Visualisierung der Treffen",
       x = "E", 
       y = "N",
       color = "Legend") + 
  theme_minimal()

Task 6: with Plotly

plot_ly() %>%
  # Rosa und Sabi Pfade
  add_trace(data = sabi_data, x = ~E, y = ~N, z = ~DatetimeRound, type = 'scatter3d', mode = 'lines', 
            line = list(color = 'blue', width = 3), name = 'Sabi Path') %>%
  add_trace(data = rosa_data, x = ~E, y = ~N, z = ~DatetimeRound, type = 'scatter3d', mode = 'lines', 
            line = list(color = 'green', width = 3), name = 'Rosa Path') %>%
  # Treffen (Meets)
  add_trace(data = meets, x = ~E_sabi, y = ~N_sabi, z = ~DatetimeRound, type = 'scatter3d', mode = 'markers',
            marker = list(color = 'red', size = 5, opacity = 0.7), name = 'Meets') %>%
  layout(title = "Space-Time Cube: Meeting Patterns and Trajectories",
         scene = list(
           xaxis = list(title = "Easting (E)"),
           yaxis = list(title = "Northing (N)"),
           zaxis = list(title = "Time (DatetimeRound)")),
         legend = list(x = 0.8, y = 0.9))