I am working with an animal movement dataset. I have been trying to code the movement distance as opposed to just using the distance formula in excel, as I want to be able to apply the process to potentially larger datasets. I found this old post (Calculating Daily Scaled Travel Distance for UTM Animal Movement Data in R) which has generally worked. Here is my current progress:
library(sf)
library(readr)
library(dplyr)
## Load in the data and clean it to ensure correct format.
ToadDistCalc <- read_csv("Documents/RProjects/ToadMovement/ToadDistCalc.csv")
ToadDistCalc$DateTime <- as.POSIXct(ToadDistCalc$DateTime,
format = "%Y-%m-%d %H:%M:%S",
tz = "America/Jamaica")
## Make the data a spatial object. Look up what the correct EPSG is.
toad.so <- st_as_sf(
ToadDistCalc,
coords = c('X', 'Y'),
crs = "EPSG+26918")
## Calculate distance. group_by attribute dependent on data.
toad.so <- toad.so %>%
group_by(ID) %>%
mutate(
lead = geometry[row_number() + 1],
dist = st_distance(geometry, lead, by_element = T),)
However, when I inspect the data, the movement distances are incorrectly assigned to the date before they occurred.
If I try to fix this by changing the lead variable to be row_number() - 1, I get this error
Changing the position of the variable names does not affect the error. I would also like to average the movement by days. I can do that easily in excel, but I would also like to know the coding solution.
UPDATE:
Here is a version of the code that allows for time averaging
library(sf)
library(readr)
library(dplyr)
## Load in the data and clean it to ensure correct format.
ToadDistCalc <- read_csv("Documents/RProjects/ToadMovement/ToadDistCalc.csv")
ToadDistCalc$DateTime <- as.POSIXct(ToadDistCalc$DateTime,
format = "%Y-%m-%d %H:%M:%S",
tz = "America/Jamaica")
## Make the data a spatial object. Look up what the correct EPSG is.
toad.so <- st_as_sf(
ToadDistCalc,
coords = c('X', 'Y'),
crs = "EPSG+26918")
## Calculate distance. group_by attribute dependent on data.
succ.dist = function(toad){
c(0,
st_distance(
toad$geometry[-nrow(toad)],
toad$geometry[-1],
by_element=TRUE))
}
step.distances = unlist(lapply(split(toad.so, toad.so$ID), succ.dist))
toad.so$step.distances = step.distances
## Time averaged
succ.date = function(toadDate){
c(0, difftime(
strptime(toadDate$Date[-1], "%m/%d/%Y"),
strptime(toadDate$Date[-nrow(toadDate)], "%m/%d/%Y"),
units="days"))
}
DaysBetween = unlist(lapply(split(toad.so, toad.so$ID), succ.date))
toad.so$DaysBetween = DaysBetween
toad.so$AvgdDist <- step.distances/DaysBetween
It looks like you are trying to calculate the distance between successive geometry entries within ID variables for each toad.
Lets make a sample data set of random points on the UK grid system of 5 toads each of which has 20 points.
> pts = st_as_sf(data.frame(x=runif(100), y=runif(100)), coords=1:2, crs="EPSG:27700")
> pts$ID = rep(1:5, each=20)
To compute the successive distances in a geometry, use st_distance but drop the last element from the first argument and the first element from the second argument. Add a 0 at the start to get the distance travelled at the first point. Then you are comparing point 1 with 2, then 2 with 3, and so on.
succ.dist = function(toad){
c(0,
st_distance(
toad$geometry[-nrow(toad)],
toad$geometry[-1],
by_element=TRUE))
}
That works on a full data frame. But you want to apply this to each ID value to get the distances for each toad. Use a split-apply-combine strategy:
step.distances = unlist(lapply(split(pts, pts$ID), succ.dist))
Now you can add that to your data frame:
> pts$step.distances = step.distances
> head(pts)
Simple feature collection with 6 features and 2 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 0.08351158 ymin: 0.161554 xmax: 0.9587816 ymax: 0.7150356
Projected CRS: OSGB36 / British National Grid
geometry ID step.distances
1 POINT (0.1272845 0.4104881) 1 0.0000000
2 POINT (0.4923749 0.161554) 1 0.4418814
3 POINT (0.08351158 0.70452) 1 0.6796920
4 POINT (0.9587816 0.38298) 1 0.9324621
5 POINT (0.5181507 0.7150356) 1 0.5517396
6 POINT (0.3361486 0.4482891) 1 0.3229218