Using loop to calculate bearings over a list - r

Question

My goal is to apply the geosphere::bearing function to a very large data frame,
yet because the data frame concerns multiple individuals, I split it using the purrr package and split function.
I have seen the use of 'lists' and 'forloops' in the past but I have no experience with these.
Below is a fraction of my dataset, I have split the dataframe by ID, into a list with 43 elements. I have attached long and lat in wgs84 to the initial data frame.
ID Date Time Datetime Long Lat x y
10_17 4/18/2017 15:02:00 4/18/2017 15:02 379800.5 5181001 -91.72272 46.35156
10_17 4/20/2017 6:00:00 4/20/2017 6:00 383409 5179885 -91.7044 46.34891
10_17 4/21/2017 21:02:00 4/21/2017 21:02 383191.2 5177960 -91.72297 46.35134
10_24 4/22/2017 10:03:00 4/22/2017 10:03 383448.6 5179918 -91.72298 46.35134
10_17 4/23/2017 12:01:00 4/23/2017 12:01 378582.5 5182110 -91.7242 46.34506
10_24 4/24/2017 1:00:00 4/24/2017 1:00 383647.4 5180009 -91.72515 46.34738
10_24 4/25/2017 16:01:00 4/25/2017 16:01 383407.9 5179872 -91.7184 46.32236
10_17 4/26/2017 18:02:00 4/26/2017 18:02 380691.9 5179353 -91.65361 46.34712
10_36 4/27/2017 20:00:00 4/27/2017 20:00 382521.9 5175266 -91.66127 46.3485
10_36 4/29/2017 11:01:00 4/29/2017 11:01 383443.8 5179909 -91.70303 46.35451
10_36 4/30/2017 0:00:00 4/30/2017 0:00 383060.8 5178361 -91.6685 46.32941
10_40 4/30/2017 13:02:00 4/30/2017 13:02 383426.3 5179873 -91.70263 46.35481
10_40 5/2/2017 17:02:00 5/2/2017 17:02 383393.7 5179883 -91.67099 46.34138
10_40 5/3/2017 6:01:00 5/3/2017 6:01 382875.8 5179376 -91.66324 46.34763
10_88 5/3/2017 19:02:00 5/3/2017 19:02 383264.3 5179948 -91.73075 46.3684
10_88 5/4/2017 8:01:00 5/4/2017 8:01 378554.4 5181966 -91.70413 46.35429
10_88 5/4/2017 21:03:00 5/4/2017 21:03 379830.5 5177232 -91.66452 46.37274
I then try this function
library(geosphere)
library(sf)
library(magrittr)
dis_list <- split(data, data$ID)
answer <- lapply(dis_list, function(df) {
start <- df[-1 , c("x", "y")] %>%
st_as_sf(coords = c('x', 'y'))
end <- df[-nrow(df), c("x", "y")] %>%
st_as_sf(coords = c('x', 'y'))
angles <-geosphere::bearing(start, end)
df$angles <- c(NA, angles)
df
})
answer
which gives the error
Error in .pointsToMatrix(p1) :
'list' object cannot be coerced to type 'double'

Answer 1

A google search on "pass sf points to geosphere bearings" brings up this SE::GIS answer that seems to address the issue which I would characterize as "how to extract numeric vectors from items that are sf-classed POINTS": https://gis.stackexchange.com/questions/416316/compute-east-west-or-north-south-orientation-of-polylines-sf-linestring-in-r
I needed to work with a single section first and then apply the lessons from #Spacedman to this task:
> st_coordinates( st_as_sf(dis_list[[1]], coords = c('x', 'y')) )
X Y
1 -91.72272 46.35156
2 -91.70440 46.34891
3 -91.72297 46.35134
4 -91.72420 46.34506
5 -91.65361 46.34712
So st_coordinates wilL extract the POINTS classed values into a two column matrix that can THEN get passed to geosphere::bearings
dis_list <- split(dat, dat$ID)
answer <- lapply(dis_list, function(df) {
start <- df[-1 , c("x", "y")] %>%
st_as_sf(coords = c('x', 'y')) %>% st_coordinates
end1 <- df[-nrow(df), c("x", "y")] %>%
st_as_sf(coords = c('x', 'y')) %>% st_coordinates
angles <-geosphere::bearing(start, end1)
df$angles <- c(NA, angles)
df
})
answer
#------------------------
$`10_17`
ID Date Time date time Long Lat x y
1 10_17 4/18/2017 15:02:00 4/18/2017 15:02 379800.5 5181001 -91.72272 46.35156
2 10_17 4/20/2017 6:00:00 4/20/2017 6:00 383409.0 5179885 -91.70440 46.34891
3 10_17 4/21/2017 21:02:00 4/21/2017 21:02 383191.2 5177960 -91.72297 46.35134
5 10_17 4/23/2017 12:01:00 4/23/2017 12:01 378582.5 5182110 -91.72420 46.34506
8 10_17 4/26/2017 18:02:00 4/26/2017 18:02 380691.9 5179353 -91.65361 46.34712
Datetime angles
1 4/18/2017 15:02 NA
2 4/20/2017 6:00 -78.194383
3 4/21/2017 21:02 100.694352
5 4/23/2017 12:01 7.723513
8 4/26/2017 18:02 -92.387473
$`10_24`
ID Date Time date time Long Lat x y
4 10_24 4/22/2017 10:03:00 4/22/2017 10:03 383448.6 5179918 -91.72298 46.35134
6 10_24 4/24/2017 1:00:00 4/24/2017 1:00 383647.4 5180009 -91.72515 46.34738
7 10_24 4/25/2017 16:01:00 4/25/2017 16:01 383407.9 5179872 -91.71840 46.32236
Datetime angles
4 4/22/2017 10:03 NA
6 4/24/2017 1:00 20.77910
7 4/25/2017 16:01 -10.58228
$`10_36`
ID Date Time date time Long Lat x y
9 10_36 4/27/2017 20:00:00 4/27/2017 20:00 382521.9 5175266 -91.66127 46.34850
10 10_36 4/29/2017 11:01:00 4/29/2017 11:01 383443.8 5179909 -91.70303 46.35451
11 10_36 4/30/2017 0:00:00 4/30/2017 0:00 383060.8 5178361 -91.66850 46.32941
Datetime angles
9 4/27/2017 20:00 NA
10 4/29/2017 11:01 101.72602
11 4/30/2017 0:00 -43.60192
$`10_40`
ID Date Time date time Long Lat x y
12 10_40 4/30/2017 13:02:00 4/30/2017 13:02 383426.3 5179873 -91.70263 46.35481
13 10_40 5/2/2017 17:02:00 5/2/2017 17:02 383393.7 5179883 -91.67099 46.34138
14 10_40 5/3/2017 6:01:00 5/3/2017 6:01 382875.8 5179376 -91.66324 46.34763
Datetime angles
12 4/30/2017 13:02 NA
13 5/2/2017 17:02 -58.48235
14 5/3/2017 6:01 -139.34297
$`10_88`
ID Date Time date time Long Lat x y
15 10_88 5/3/2017 19:02:00 5/3/2017 19:02 383264.3 5179948 -91.73075 46.36840
16 10_88 5/4/2017 8:01:00 5/4/2017 8:01 378554.4 5181966 -91.70413 46.35429
17 10_88 5/4/2017 21:03:00 5/4/2017 21:03 379830.5 5177232 -91.66452 46.37274
Datetime angles
15 5/3/2017 19:02 NA
16 5/4/2017 8:01 -52.55217
17 5/4/2017 21:03 -123.91920
The help page for st_coordinates characterizes its function as "retrieve coordinates in matrix form".

Answer 2

Given the data is all ready in longitude and latitude form.
Then just using bearing(data[, c("Long", "Lat")]) and distGeo(data[, c("Long", "Lat")]) from geosphere on the split data frames will work. No need to create a start and end points.
library(geosphere)
dfs <- split(data, data$ID)
library(geosphere)
answer <- lapply(dfs, function(df) {
df$distances <-c(distGeo(df[,c("Long", "Lat")]))
df$bearings <- c(bearing(df[,c("Long", "Lat")]))
df
})
answer
The sf package is useful for converting between coordinate systems, but with the data set above, that step can be skipped. I find the geosphere package more straight forward and simpler to use.