age <- function(birthday, output_unit){
difftime(Sys.Date(), birthday, units = output_unit)
}
age("2000-10-02", "days")/365Time difference of 24.4879 daysWeek 5
Demo
Task 1
# 1.
BM1 <- function(Weight, Height){
BMI_output <- Weight/(Height^2)
return(BMI_output)
} #return output is often not needed, but can help in complex cases
BM1(80, 1.87)[1] 22.87741# 2.
Farenheight <- function(Temperature){Temperature*(9/5)+32}
Farenheight(20)[1] 68# 3.
eucledian_distance <- function(x1, y1, x2, y2){
sqrt((x2-x1)^2 + (y2-y1)^2)
}
eucledian_distance(0, 0, 1, 1)[1] 1.414214Task 2
library("readr")
library("sf")Linking to GEOS 3.12.2, GDAL 3.9.3, PROJ 9.4.1; sf_use_s2() is TRUElibrary("dplyr")
Attache Paket: 'dplyr'Die folgenden Objekte sind maskiert von 'package:stats':
filter, lagDie folgenden Objekte sind maskiert von 'package:base':
intersect, setdiff, setequal, unionlibrary("ggplot2")
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.# selecting animal & time span
Sabi <- wildschwein |>
filter(
TierName == "Sabi",
DatetimeUTC >= "2015-04-01",
DatetimeUTC < "2015-04-15")
Rosa <- wildschwein |>
filter(
TierName == "Rosa",
DatetimeUTC >= "2015-04-01",
DatetimeUTC < "2015-04-15")Task 3
#taking a look
head(Sabi)# A tibble: 6 × 6
TierID TierName CollarID DatetimeUTC E N
<chr> <chr> <dbl> <dttm> <dbl> <dbl>
1 002A Sabi 12275 2015-03-31 22:00:28 2570296. 1205283.
2 002A Sabi 12275 2015-03-31 22:15:44 2570259. 1205259.
3 002A Sabi 12275 2015-03-31 22:30:44 2570255. 1205259.
4 002A Sabi 12275 2015-03-31 22:46:04 2570245. 1205268.
5 002A Sabi 12275 2015-03-31 23:00:17 2570364. 1205314.
6 002A Sabi 12275 2015-03-31 23:15:12 2570375. 1205320.head(Rosa)# A tibble: 6 × 6
TierID TierName CollarID DatetimeUTC E N
<chr> <chr> <dbl> <dttm> <dbl> <dbl>
1 016A Rosa 13972 2015-03-31 22:00:18 2570778. 1204985.
2 016A Rosa 13972 2015-03-31 22:15:16 2570795. 1204957.
3 016A Rosa 13972 2015-03-31 22:30:34 2570797. 1204928.
4 016A Rosa 13972 2015-03-31 22:45:13 2570810. 1204914.
5 016A Rosa 13972 2015-03-31 23:00:10 2570850. 1204876.
6 016A Rosa 13972 2015-03-31 23:15:19 2570844. 1204864.library(lubridate)
Attache Paket: 'lubridate'Die folgenden Objekte sind maskiert von 'package:base':
date, intersect, setdiff, union# rounding time
Sabi <- Sabi |>
mutate(Round_time = round_date(DatetimeUTC, "15 mins"))
Rosa <- Rosa |>
mutate(Round_time = round_date(DatetimeUTC, "15 mins"))
# joining the two animals and making coordinates long format
Wildschwein_complete <- full_join(Sabi, Rosa, by = "Round_time",
suffix = c("_x", "_y")) Task 4
# calculation of distance
Wildschwein_complete <- Wildschwein_complete |>
mutate(Eucledian_distance = eucledian_distance(
E_x, N_x, E_y, N_y
))
# checking if they met
Wildschwein_complete <- Wildschwein_complete |>
mutate(meet = if_else(Eucledian_distance <= 100, TRUE, FALSE))Task 5
# select data
Wildschwein_meet <- Wildschwein_complete |>
filter(meet == TRUE)
# visualize, dots with black outlines are meeting points
ggplot(Rosa, aes(E, N)) +
geom_point(aes(color = "Rosa"), alpha = 0.1) +
geom_point(data = Sabi, aes(color = "Sabi"), alpha = 0.1) +
geom_point(data = Wildschwein_meet, aes(E_x, N_x, fill = "Rosa"),
shape = 21, stroke = 1.2, size = 1.2, color = "black") +
geom_point(data = Wildschwein_meet, aes(E_y, N_y, fill = "Sabi"),
shape = 21, stroke = 1.2, size = 1.2, color = "black") +
scale_color_manual(values = c("Rosa" = "red", "Sabi" = "blue")) +
scale_fill_manual(values = c("Rosa" = "red", "Sabi" = "blue")) +
labs(color = "Locations", fill = "Meets") +
theme(legend.position = "right")