Exercises Week 4

Author

Stefanie Guyer

Input

library("readr")
library("sf")

Warning: Paket 'sf' wurde unter R Version 4.4.3 erstellt

Linking to GEOS 3.13.0, GDAL 3.10.1, PROJ 9.5.1; sf_use_s2() is TRUE

library("dplyr")

Warning: Paket 'dplyr' wurde unter R Version 4.4.3 erstellt


Attache Paket: 'dplyr'

Die folgenden Objekte sind maskiert von 'package:stats':

    filter, lag

Die folgenden Objekte sind maskiert von 'package:base':

    intersect, setdiff, setequal, union

library("tidyverse")

Warning: Paket 'tidyverse' wurde unter R Version 4.4.3 erstellt

Warning: Paket 'ggplot2' wurde unter R Version 4.4.3 erstellt

Warning: Paket 'tidyr' wurde unter R Version 4.4.2 erstellt

Warning: Paket 'purrr' wurde unter R Version 4.4.3 erstellt

Warning: Paket 'lubridate' wurde unter R Version 4.4.3 erstellt

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   4.0.1     ✔ tibble    3.2.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.2.0

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

wildschwein <- read_delim("data/wildschwein_BE_2056 (1).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.

sabi <- wildschwein |> 
  st_as_sf(coords = c("E", "N"), crs = 2056, remove = FALSE) |> 
  filter(
    TierName == "Sabi", 
    DatetimeUTC >= "2015-07-01",
    DatetimeUTC < "2015-07-03"
)

sabi

Simple feature collection with 192 features and 6 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 2569724 ymin: 1204916 xmax: 2570927 ymax: 1205957
Projected CRS: CH1903+ / LV95
# A tibble: 192 × 7
   TierID TierName CollarID DatetimeUTC                E        N
 * <chr>  <chr>       <dbl> <dttm>                 <dbl>    <dbl>
 1 002A   Sabi        12275 2015-06-30 22:00:13 2569972. 1205366.
 2 002A   Sabi        12275 2015-06-30 22:16:06 2569975. 1205637.
 3 002A   Sabi        12275 2015-06-30 22:30:19 2570266. 1205857.
 4 002A   Sabi        12275 2015-06-30 22:45:13 2570208. 1205913.
 5 002A   Sabi        12275 2015-06-30 23:00:10 2570247. 1205731.
 6 002A   Sabi        12275 2015-06-30 23:15:17 2570512. 1205279.
 7 002A   Sabi        12275 2015-06-30 23:30:38 2570684. 1205103.
 8 002A   Sabi        12275 2015-06-30 23:45:16 2570526. 1205051.
 9 002A   Sabi        12275 2015-07-01 00:00:10 2570532. 1205044.
10 002A   Sabi        12275 2015-07-01 00:15:14 2570530. 1205059.
# ℹ 182 more rows
# ℹ 1 more variable: geometry <POINT [m]>

Step a) Specifiy a temporal window

Sampling interval of wildboar dataset = 15’ Temporal window of 60’ includes 4 datapoints.

pos[n-2] to pos[n] pos[n-1] to pos[n] pos[n] to pos[n+1] pos[n] to pos[n+2]

Step b) Measure the distance to every point within v (= temporal window = 60’)

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

sabi <- sabi |>
    mutate(
        nMinus2 = distance_by_element(lag(geometry, 2), geometry),  # distance to pos -30 minutes
        nMinus1 = distance_by_element(lag(geometry, 1), geometry),  # distance to pos -15 minutes
        nPlus1  = distance_by_element(geometry, lead(geometry, 1)), # distance to pos +15 mintues
        nPlus2  = distance_by_element(geometry, lead(geometry, 2))  # distance to pos +30 minutes
    )

sabi

Simple feature collection with 192 features and 10 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 2569724 ymin: 1204916 xmax: 2570927 ymax: 1205957
Projected CRS: CH1903+ / LV95
# A tibble: 192 × 11
   TierID TierName CollarID DatetimeUTC                E        N
 * <chr>  <chr>       <dbl> <dttm>                 <dbl>    <dbl>
 1 002A   Sabi        12275 2015-06-30 22:00:13 2569972. 1205366.
 2 002A   Sabi        12275 2015-06-30 22:16:06 2569975. 1205637.
 3 002A   Sabi        12275 2015-06-30 22:30:19 2570266. 1205857.
 4 002A   Sabi        12275 2015-06-30 22:45:13 2570208. 1205913.
 5 002A   Sabi        12275 2015-06-30 23:00:10 2570247. 1205731.
 6 002A   Sabi        12275 2015-06-30 23:15:17 2570512. 1205279.
 7 002A   Sabi        12275 2015-06-30 23:30:38 2570684. 1205103.
 8 002A   Sabi        12275 2015-06-30 23:45:16 2570526. 1205051.
 9 002A   Sabi        12275 2015-07-01 00:00:10 2570532. 1205044.
10 002A   Sabi        12275 2015-07-01 00:15:14 2570530. 1205059.
# ℹ 182 more rows
# ℹ 5 more variables: geometry <POINT [m]>, nMinus2 <dbl>, nMinus1 <dbl>,
#   nPlus1 <dbl>, nPlus2 <dbl>

sabi <- sabi |>
    rowwise() |>
    mutate(
        stepMean = mean(c(nMinus2, nMinus1, nPlus1, nPlus2))
    ) |>
    ungroup()

sabi

Simple feature collection with 192 features and 11 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 2569724 ymin: 1204916 xmax: 2570927 ymax: 1205957
Projected CRS: CH1903+ / LV95
# A tibble: 192 × 12
   TierID TierName CollarID DatetimeUTC                E        N
   <chr>  <chr>       <dbl> <dttm>                 <dbl>    <dbl>
 1 002A   Sabi        12275 2015-06-30 22:00:13 2569972. 1205366.
 2 002A   Sabi        12275 2015-06-30 22:16:06 2569975. 1205637.
 3 002A   Sabi        12275 2015-06-30 22:30:19 2570266. 1205857.
 4 002A   Sabi        12275 2015-06-30 22:45:13 2570208. 1205913.
 5 002A   Sabi        12275 2015-06-30 23:00:10 2570247. 1205731.
 6 002A   Sabi        12275 2015-06-30 23:15:17 2570512. 1205279.
 7 002A   Sabi        12275 2015-06-30 23:30:38 2570684. 1205103.
 8 002A   Sabi        12275 2015-06-30 23:45:16 2570526. 1205051.
 9 002A   Sabi        12275 2015-07-01 00:00:10 2570532. 1205044.
10 002A   Sabi        12275 2015-07-01 00:15:14 2570530. 1205059.
# ℹ 182 more rows
# ℹ 6 more variables: geometry <POINT [m]>, nMinus2 <dbl>, nMinus1 <dbl>,
#   nPlus1 <dbl>, nPlus2 <dbl>, stepMean <dbl>

sabi <- sabi |>
    mutate(static = stepMean < mean(stepMean, na.rm = TRUE))

sabi_filter <- sabi |>
    filter(!static)

sabi_filter |>
    ggplot(aes(E, N)) +
    geom_point(data = sabi, col = "red") +
    geom_path() +
    geom_point() +
    coord_fixed() +
    theme(legend.position = "bottom")

Exercise A

unique(wildschwein$TierName) # > Auswahl für Aufgabe mit Wildschwein "Rosa"

[1] "Sabi" "Rosa" "Ruth"

# Nun wähle ich ein temporal window von 2h (Input task: 1h)
#