Last updated on 2021-11-09 10:50:16 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.1.4 | 80.09 | 472.03 | 552.12 | OK | |
| r-devel-linux-x86_64-debian-gcc | 0.1.4 | OK | ||||
| r-devel-linux-x86_64-fedora-clang | 0.1.4 | 692.15 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 0.1.4 | 668.55 | OK | |||
| r-devel-windows-x86_64 | 0.1.4 | 112.00 | 411.00 | 523.00 | ERROR | |
| r-devel-windows-x86_64-gcc10-UCRT | 0.1.4 | OK | ||||
| r-patched-linux-x86_64 | 0.1.4 | 77.13 | 487.15 | 564.28 | OK | |
| r-patched-solaris-x86 | 0.1.4 | 884.90 | OK | |||
| r-release-linux-x86_64 | 0.1.4 | 73.09 | 485.31 | 558.40 | OK | |
| r-release-macos-arm64 | 0.1.4 | OK | ||||
| r-release-macos-x86_64 | 0.1.4 | OK | ||||
| r-release-windows-ix86+x86_64 | 0.1.4 | 140.00 | 613.00 | 753.00 | OK | |
| r-oldrel-macos-x86_64 | 0.1.4 | OK | ||||
| r-oldrel-windows-ix86+x86_64 | 0.1.4 | 136.00 | 580.00 | 716.00 | ERROR |
Version: 0.1.4
Check: examples
Result: ERROR
Running examples in 'amt-Ex.R' failed
The error most likely occurred in:
> ### Name: extract_covariates
> ### Title: Extract covariate values
> ### Aliases: extract_covariates extract_covariates.track_xy
> ### extract_covariates.random_points extract_covariates.steps_xy
> ### extract_covariates_along extract_covariates_along.steps_xy
> ### extract_covariates_var_time extract_covariates_var_time.track_xyt
> ### extract_covariates_var_time.steps_xyt
>
> ### ** Examples
>
> data(deer)
> data(sh_forest)
> deer %>% extract_covariates(sh_forest)
# A tibble: 826 x 5
x_ y_ t_ burst_ sh.forest
<dbl> <dbl> <dttm> <dbl> <dbl>
1 4314068. 3445807. 2008-03-30 00:01:47.000000 1 2
2 4314053. 3445768. 2008-03-30 06:00:54.000000 1 2
3 4314105. 3445859. 2008-03-30 12:01:47.000000 1 2
4 4314044. 3445785. 2008-03-30 18:01:24.000000 1 2
5 4313015. 3445858. 2008-03-31 00:01:23.000000 1 1
6 4312860. 3445857. 2008-03-31 06:01:45.000000 1 1
7 4312854. 3445856. 2008-03-31 12:01:11.000000 1 1
8 4312858. 3445858. 2008-03-31 18:01:55.000000 1 1
9 4312745. 3445862. 2008-04-01 00:01:24.000000 1 1
10 4312651. 3446024. 2008-04-01 06:00:54.000000 1 1
# ... with 816 more rows
> deer %>% steps %>% extract_covariates(sh_forest)
Warning in steps.track_xyt(.) :
burst's are ignored, use steps_by_burst instead.
# A tibble: 825 x 11
x1_ x2_ y1_ y2_ sl_ direction_p ta_
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 4314068. 4314053. 3445807. 3445768. 41.8 NA NA
2 4314053. 4314105. 3445768. 3445859. 104. -1.95 3.00
3 4314105. 4314044. 3445859. 3445785. 95.3 1.05 2.97
4 4314044. 4313015. 3445785. 3445858. 1032. -2.27 -0.945
5 4313015. 4312860. 3445858. 3445857. 155. 3.07 0.0756
6 4312860. 4312854. 3445857. 3445856. 6.08 -3.14 0.146
7 4312854. 4312858. 3445856. 3445858. 4.47 -2.99 -2.84
8 4312858. 4312745. 3445858. 3445862. 113. 0.449 2.66
9 4312745. 4312651. 3445862. 3446024. 187. 3.11 -1.02
10 4312651. 4312649. 3446024. 3446030. 6.32 2.09 -0.216
# ... with 815 more rows, and 4 more variables: t1_ <dttm>, t2_ <dttm>,
# dt_ <drtn>, sh.forest <dbl>
> deer %>% steps %>% extract_covariates(sh_forest, where = "start")
Warning in steps.track_xyt(.) :
burst's are ignored, use steps_by_burst instead.
# A tibble: 825 x 11
x1_ x2_ y1_ y2_ sl_ direction_p ta_
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 4314068. 4314053. 3445807. 3445768. 41.8 NA NA
2 4314053. 4314105. 3445768. 3445859. 104. -1.95 3.00
3 4314105. 4314044. 3445859. 3445785. 95.3 1.05 2.97
4 4314044. 4313015. 3445785. 3445858. 1032. -2.27 -0.945
5 4313015. 4312860. 3445858. 3445857. 155. 3.07 0.0756
6 4312860. 4312854. 3445857. 3445856. 6.08 -3.14 0.146
7 4312854. 4312858. 3445856. 3445858. 4.47 -2.99 -2.84
8 4312858. 4312745. 3445858. 3445862. 113. 0.449 2.66
9 4312745. 4312651. 3445862. 3446024. 187. 3.11 -1.02
10 4312651. 4312649. 3446024. 3446030. 6.32 2.09 -0.216
# ... with 815 more rows, and 4 more variables: t1_ <dttm>, t2_ <dttm>,
# dt_ <drtn>, sh.forest <dbl>
> ## Not run:
> ##D data(deer) # relocation
> ##D data("sh_forest") # env covar
> ##D
> ##D p1 <- deer %>% steps() %>% random_steps() %>%
> ##D extract_covariates(sh_forest) %>% # extract at the endpoint
> ##D mutate(for_path = extract_covariates_along(., sh_forest)) %>%
> ##D # 1 = forest, lets calc the fraction of forest along the path
> ##D mutate(for_per = purrr::map_dbl(for_path, ~ mean(. == 1)))
> ## End(Not run)
>
> # Simulate some dummy data
> # Hourly data for 10 days: 24 * 10
> set.seed(123)
> path <- data.frame(x = cumsum(rnorm(240)),
+ y = cumsum(rnorm(240)),
+ t = lubridate::ymd("2018-01-01") + hours(0:239))
> trk <- make_track(path, x, y, t)
.t found, creating `track_xyt`.
>
> # dummy env data
> rs <- raster::raster(xmn = -50, xmx = 50, ymn = -50, ymx = 50, res = 1)
>
> # create dummy covars for each day
> rs <- raster::stack(lapply(1:10, function(i)
+ raster::setValues(rs, runif(1e4, i - 1, i))))
>
> # Env covariates are always taken at noon
> rs <- raster::setZ(rs, lubridate::ymd_hm("2018-01-01 12:00") + days(0:9))
>
> # Allow up to 2 hours after
> trk %>% extract_covariates_var_time(rs, max_time = hours(2), when = "after") %>%
+ print(n = 25)
# A tibble: 240 x 4
x_ y_ t_ time_var_covar
<dbl> <dbl> <dttm> <dbl>
1 -0.560 -0.789 2018-01-01 00:00:00.000000 NA
2 -0.791 -1.29 2018-01-01 01:00:00.000000 NA
3 0.768 0.205 2018-01-01 02:00:00.000000 NA
4 0.839 -0.932 2018-01-01 03:00:00.000000 NA
5 0.968 -1.11 2018-01-01 04:00:00.000000 NA
6 2.68 0.791 2018-01-01 05:00:00.000000 NA
7 3.14 0.690 2018-01-01 06:00:00.000000 NA
8 1.88 -0.670 2018-01-01 07:00:00.000000 NA
9 1.19 -1.33 2018-01-01 08:00:00.000000 NA
10 0.746 -0.849 2018-01-01 09:00:00.000000 NA
11 1.97 -1.22 2018-01-01 10:00:00.000000 NA
12 2.33 -1.79 2018-01-01 11:00:00.000000 NA
13 2.73 -2.13 2018-01-01 12:00:00.000000 0.415
14 2.84 -2.04 2018-01-01 13:00:00.000000 0.415
15 2.29 -0.441 2018-01-01 14:00:00.000000 0.820
16 4.07 -0.530 2018-01-01 15:00:00.000000 NA
17 4.57 0.551 2018-01-01 16:00:00.000000 NA
18 2.60 1.18 2018-01-01 17:00:00.000000 NA
19 3.31 1.07 2018-01-01 18:00:00.000000 NA
20 2.83 -0.465 2018-01-01 19:00:00.000000 NA
21 1.76 -0.986 2018-01-01 20:00:00.000000 NA
22 1.55 -1.48 2018-01-01 21:00:00.000000 NA
23 0.521 -1.43 2018-01-01 22:00:00.000000 NA
24 -0.208 -0.128 2018-01-01 23:00:00.000000 NA
25 -0.833 2.16 2018-01-02 00:00:00.000000 NA
# ... with 215 more rows
> trk %>% extract_covariates_var_time(rs, max_time = hours(2), when = "before") %>%
+ print(n = 25)
# A tibble: 240 x 4
x_ y_ t_ time_var_covar
<dbl> <dbl> <dttm> <dbl>
1 -0.560 -0.789 2018-01-01 00:00:00.000000 NA
2 -0.791 -1.29 2018-01-01 01:00:00.000000 NA
3 0.768 0.205 2018-01-01 02:00:00.000000 NA
4 0.839 -0.932 2018-01-01 03:00:00.000000 NA
5 0.968 -1.11 2018-01-01 04:00:00.000000 NA
6 2.68 0.791 2018-01-01 05:00:00.000000 NA
7 3.14 0.690 2018-01-01 06:00:00.000000 NA
8 1.88 -0.670 2018-01-01 07:00:00.000000 NA
9 1.19 -1.33 2018-01-01 08:00:00.000000 NA
10 0.746 -0.849 2018-01-01 09:00:00.000000 NA
11 1.97 -1.22 2018-01-01 10:00:00.000000 0.632
12 2.33 -1.79 2018-01-01 11:00:00.000000 0.749
13 2.73 -2.13 2018-01-01 12:00:00.000000 0.415
14 2.84 -2.04 2018-01-01 13:00:00.000000 NA
15 2.29 -0.441 2018-01-01 14:00:00.000000 NA
16 4.07 -0.530 2018-01-01 15:00:00.000000 NA
17 4.57 0.551 2018-01-01 16:00:00.000000 NA
18 2.60 1.18 2018-01-01 17:00:00.000000 NA
19 3.31 1.07 2018-01-01 18:00:00.000000 NA
20 2.83 -0.465 2018-01-01 19:00:00.000000 NA
21 1.76 -0.986 2018-01-01 20:00:00.000000 NA
22 1.55 -1.48 2018-01-01 21:00:00.000000 NA
23 0.521 -1.43 2018-01-01 22:00:00.000000 NA
24 -0.208 -0.128 2018-01-01 23:00:00.000000 NA
25 -0.833 2.16 2018-01-02 00:00:00.000000 NA
# ... with 215 more rows
> trk %>% extract_covariates_var_time(rs, max_time = hours(2), when = "any") %>%
+ print(n = 25)
# A tibble: 240 x 4
x_ y_ t_ time_var_covar
<dbl> <dbl> <dttm> <dbl>
1 -0.560 -0.789 2018-01-01 00:00:00.000000 NA
2 -0.791 -1.29 2018-01-01 01:00:00.000000 NA
3 0.768 0.205 2018-01-01 02:00:00.000000 NA
4 0.839 -0.932 2018-01-01 03:00:00.000000 NA
5 0.968 -1.11 2018-01-01 04:00:00.000000 NA
6 2.68 0.791 2018-01-01 05:00:00.000000 NA
7 3.14 0.690 2018-01-01 06:00:00.000000 NA
8 1.88 -0.670 2018-01-01 07:00:00.000000 NA
9 1.19 -1.33 2018-01-01 08:00:00.000000 NA
10 0.746 -0.849 2018-01-01 09:00:00.000000 NA
11 1.97 -1.22 2018-01-01 10:00:00.000000 0.632
12 2.33 -1.79 2018-01-01 11:00:00.000000 0.749
13 2.73 -2.13 2018-01-01 12:00:00.000000 0.415
14 2.84 -2.04 2018-01-01 13:00:00.000000 0.415
15 2.29 -0.441 2018-01-01 14:00:00.000000 0.820
16 4.07 -0.530 2018-01-01 15:00:00.000000 NA
17 4.57 0.551 2018-01-01 16:00:00.000000 NA
18 2.60 1.18 2018-01-01 17:00:00.000000 NA
19 3.31 1.07 2018-01-01 18:00:00.000000 NA
20 2.83 -0.465 2018-01-01 19:00:00.000000 NA
21 1.76 -0.986 2018-01-01 20:00:00.000000 NA
22 1.55 -1.48 2018-01-01 21:00:00.000000 NA
23 0.521 -1.43 2018-01-01 22:00:00.000000 NA
24 -0.208 -0.128 2018-01-01 23:00:00.000000 NA
25 -0.833 2.16 2018-01-02 00:00:00.000000 NA
# ... with 215 more rows
>
> # We can use different time scales
> trk %>%
+ extract_covariates_var_time(
+ rs, max_time = hours(2), when = "any", name_covar = "env_2h") %>%
+ extract_covariates_var_time(
+ rs, max_time = hours(4), when = "any", name_covar = "env_4h") %>%
+ extract_covariates_var_time(
+ rs, max_time = hours(6), when = "any", name_covar = "env_6h") %>%
+ print(n = 25)
# A tibble: 240 x 6
x_ y_ t_ env_2h env_4h env_6h
<dbl> <dbl> <dttm> <dbl> <dbl> <dbl>
1 -0.560 -0.789 2018-01-01 00:00:00.000000 NA NA NA
2 -0.791 -1.29 2018-01-01 01:00:00.000000 NA NA NA
3 0.768 0.205 2018-01-01 02:00:00.000000 NA NA NA
4 0.839 -0.932 2018-01-01 03:00:00.000000 NA NA NA
5 0.968 -1.11 2018-01-01 04:00:00.000000 NA NA NA
6 2.68 0.791 2018-01-01 05:00:00.000000 NA NA NA
7 3.14 0.690 2018-01-01 06:00:00.000000 NA NA 0.410
8 1.88 -0.670 2018-01-01 07:00:00.000000 NA NA 0.684
9 1.19 -1.33 2018-01-01 08:00:00.000000 NA 0.632 0.632
10 0.746 -0.849 2018-01-01 09:00:00.000000 NA 0.485 0.485
11 1.97 -1.22 2018-01-01 10:00:00.000000 0.632 0.632 0.632
12 2.33 -1.79 2018-01-01 11:00:00.000000 0.749 0.749 0.749
13 2.73 -2.13 2018-01-01 12:00:00.000000 0.415 0.415 0.415
14 2.84 -2.04 2018-01-01 13:00:00.000000 0.415 0.415 0.415
15 2.29 -0.441 2018-01-01 14:00:00.000000 0.820 0.820 0.820
16 4.07 -0.530 2018-01-01 15:00:00.000000 NA 0.427 0.427
17 4.57 0.551 2018-01-01 16:00:00.000000 NA 0.208 0.208
18 2.60 1.18 2018-01-01 17:00:00.000000 NA NA 0.944
19 3.31 1.07 2018-01-01 18:00:00.000000 NA NA 0.0925
20 2.83 -0.465 2018-01-01 19:00:00.000000 NA NA NA
21 1.76 -0.986 2018-01-01 20:00:00.000000 NA NA NA
22 1.55 -1.48 2018-01-01 21:00:00.000000 NA NA NA
23 0.521 -1.43 2018-01-01 22:00:00.000000 NA NA NA
24 -0.208 -0.128 2018-01-01 23:00:00.000000 NA NA NA
25 -0.833 2.16 2018-01-02 00:00:00.000000 NA NA NA
# ... with 215 more rows
>
> # We can use different time scales: after
> trk %>%
+ extract_covariates_var_time(
+ rs, max_time = hours(2), when = "after", name_covar = "env_2h") %>%
+ extract_covariates_var_time(
+ rs, max_time = hours(4), when = "after", name_covar = "env_4h") %>%
+ extract_covariates_var_time(
+ rs, max_time = hours(6), when = "after", name_covar = "env_6h") %>%
+ print(n = 25)
# A tibble: 240 x 6
x_ y_ t_ env_2h env_4h env_6h
<dbl> <dbl> <dttm> <dbl> <dbl> <dbl>
1 -0.560 -0.789 2018-01-01 00:00:00.000000 NA NA NA
2 -0.791 -1.29 2018-01-01 01:00:00.000000 NA NA NA
3 0.768 0.205 2018-01-01 02:00:00.000000 NA NA NA
4 0.839 -0.932 2018-01-01 03:00:00.000000 NA NA NA
5 0.968 -1.11 2018-01-01 04:00:00.000000 NA NA NA
6 2.68 0.791 2018-01-01 05:00:00.000000 NA NA NA
7 3.14 0.690 2018-01-01 06:00:00.000000 NA NA NA
8 1.88 -0.670 2018-01-01 07:00:00.000000 NA NA NA
9 1.19 -1.33 2018-01-01 08:00:00.000000 NA NA NA
10 0.746 -0.849 2018-01-01 09:00:00.000000 NA NA NA
11 1.97 -1.22 2018-01-01 10:00:00.000000 NA NA NA
12 2.33 -1.79 2018-01-01 11:00:00.000000 NA NA NA
13 2.73 -2.13 2018-01-01 12:00:00.000000 0.415 0.415 0.415
14 2.84 -2.04 2018-01-01 13:00:00.000000 0.415 0.415 0.415
15 2.29 -0.441 2018-01-01 14:00:00.000000 0.820 0.820 0.820
16 4.07 -0.530 2018-01-01 15:00:00.000000 NA 0.427 0.427
17 4.57 0.551 2018-01-01 16:00:00.000000 NA 0.208 0.208
18 2.60 1.18 2018-01-01 17:00:00.000000 NA NA 0.944
19 3.31 1.07 2018-01-01 18:00:00.000000 NA NA 0.0925
20 2.83 -0.465 2018-01-01 19:00:00.000000 NA NA NA
21 1.76 -0.986 2018-01-01 20:00:00.000000 NA NA NA
22 1.55 -1.48 2018-01-01 21:00:00.000000 NA NA NA
23 0.521 -1.43 2018-01-01 22:00:00.000000 NA NA NA
24 -0.208 -0.128 2018-01-01 23:00:00.000000 NA NA NA
25 -0.833 2.16 2018-01-02 00:00:00.000000 NA NA NA
# ... with 215 more rows
>
> # We can use different time scales: before
> trk %>%
+ extract_covariates_var_time(
+ rs, max_time = hours(2), when = "before", name_covar = "env_2h") %>%
+ extract_covariates_var_time(
+ rs, max_time = hours(4), when = "before", name_covar = "env_4h") %>%
+ extract_covariates_var_time(
+ rs, max_time = hours(6), when = "before", name_covar = "env_6h") %>%
+ print(n = 25)
# A tibble: 240 x 6
x_ y_ t_ env_2h env_4h env_6h
<dbl> <dbl> <dttm> <dbl> <dbl> <dbl>
1 -0.560 -0.789 2018-01-01 00:00:00.000000 NA NA NA
2 -0.791 -1.29 2018-01-01 01:00:00.000000 NA NA NA
3 0.768 0.205 2018-01-01 02:00:00.000000 NA NA NA
4 0.839 -0.932 2018-01-01 03:00:00.000000 NA NA NA
5 0.968 -1.11 2018-01-01 04:00:00.000000 NA NA NA
6 2.68 0.791 2018-01-01 05:00:00.000000 NA NA NA
7 3.14 0.690 2018-01-01 06:00:00.000000 NA NA 0.410
8 1.88 -0.670 2018-01-01 07:00:00.000000 NA NA 0.684
9 1.19 -1.33 2018-01-01 08:00:00.000000 NA 0.632 0.632
10 0.746 -0.849 2018-01-01 09:00:00.000000 NA 0.485 0.485
11 1.97 -1.22 2018-01-01 10:00:00.000000 0.632 0.632 0.632
12 2.33 -1.79 2018-01-01 11:00:00.000000 0.749 0.749 0.749
13 2.73 -2.13 2018-01-01 12:00:00.000000 0.415 0.415 0.415
14 2.84 -2.04 2018-01-01 13:00:00.000000 NA NA NA
15 2.29 -0.441 2018-01-01 14:00:00.000000 NA NA NA
16 4.07 -0.530 2018-01-01 15:00:00.000000 NA NA NA
17 4.57 0.551 2018-01-01 16:00:00.000000 NA NA NA
18 2.60 1.18 2018-01-01 17:00:00.000000 NA NA NA
19 3.31 1.07 2018-01-01 18:00:00.000000 NA NA NA
20 2.83 -0.465 2018-01-01 19:00:00.000000 NA NA NA
21 1.76 -0.986 2018-01-01 20:00:00.000000 NA NA NA
22 1.55 -1.48 2018-01-01 21:00:00.000000 NA NA NA
23 0.521 -1.43 2018-01-01 22:00:00.000000 NA NA NA
24 -0.208 -0.128 2018-01-01 23:00:00.000000 NA NA NA
25 -0.833 2.16 2018-01-02 00:00:00.000000 NA NA NA
# ... with 215 more rows
>
> # The same works also for steps
> trk %>%
+ steps() %>%
+ extract_covariates_var_time(
+ rs, max_time = hours(2), when = "before", name_covar = "env_2h") %>%
+ print(n = 25)
# A tibble: 239 x 11
x1_ x2_ y1_ y2_ sl_ direction_p ta_
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 -0.560 -0.791 -0.789 -1.29 0.552 NA NA
2 -0.791 0.768 -1.29 0.205 2.16 -2.00 2.77
3 0.768 0.839 0.205 -0.932 1.14 0.765 -2.27
4 0.839 0.968 -0.932 -1.11 0.221 -1.51 0.563
5 0.968 2.68 -1.11 0.791 2.56 -0.945 1.78
6 2.68 3.14 0.791 0.690 0.472 0.837 -1.05
7 3.14 1.88 0.690 -0.670 1.86 -0.216 -2.10
8 1.88 1.19 -0.670 -1.33 0.956 -2.32 -0.0524
9 1.19 0.746 -1.33 -0.849 0.659 -2.37 -1.60
10 0.746 1.97 -0.849 -1.22 1.28 2.31 -2.61
11 1.97 2.33 -1.22 -1.79 0.667 -0.298 -0.703
12 2.33 2.73 -1.79 -2.13 0.528 -1.00 0.292
13 2.73 2.84 -2.13 -2.04 0.143 -0.709 1.39
14 2.84 2.29 -2.04 -0.441 1.69 0.685 1.22
15 2.29 4.07 -0.441 -0.530 1.79 1.91 -1.95
16 4.07 4.57 -0.530 0.551 1.19 -0.0495 1.19
17 4.57 2.60 0.551 1.18 2.07 1.14 1.69
18 2.60 3.31 1.18 1.07 0.711 2.83 -2.99
19 3.31 2.83 1.07 -0.465 1.60 -0.161 -1.71
20 2.83 1.76 -0.465 -0.986 1.19 -1.87 -0.818
21 1.76 1.55 -0.986 -1.48 0.536 -2.69 0.698
22 1.55 0.521 -1.48 -1.43 1.03 -1.99 -1.20
23 0.521 -0.208 -1.43 -0.128 1.49 3.10 -1.01
24 -0.208 -0.833 -0.128 2.16 2.38 2.08 -0.245
25 -0.833 -2.52 2.16 3.71 2.29 1.84 0.562
# ... with 214 more rows, and 4 more variables: t1_ <dttm>, t2_ <dttm>,
# dt_ <drtn>, env_2h <dbl>
>
> # also with start and end
> trk %>%
+ steps() %>%
+ extract_covariates_var_time(
+ rs, max_time = hours(2), when = "before", name_covar = "env_2h",
+ where = "both") %>%
+ print(n = 25)
# A tibble: 239 x 12
x1_ x2_ y1_ y2_ sl_ direction_p ta_
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 -0.560 -0.791 -0.789 -1.29 0.552 NA NA
2 -0.791 0.768 -1.29 0.205 2.16 -2.00 2.77
3 0.768 0.839 0.205 -0.932 1.14 0.765 -2.27
4 0.839 0.968 -0.932 -1.11 0.221 -1.51 0.563
5 0.968 2.68 -1.11 0.791 2.56 -0.945 1.78
6 2.68 3.14 0.791 0.690 0.472 0.837 -1.05
7 3.14 1.88 0.690 -0.670 1.86 -0.216 -2.10
8 1.88 1.19 -0.670 -1.33 0.956 -2.32 -0.0524
9 1.19 0.746 -1.33 -0.849 0.659 -2.37 -1.60
10 0.746 1.97 -0.849 -1.22 1.28 2.31 -2.61
11 1.97 2.33 -1.22 -1.79 0.667 -0.298 -0.703
12 2.33 2.73 -1.79 -2.13 0.528 -1.00 0.292
13 2.73 2.84 -2.13 -2.04 0.143 -0.709 1.39
14 2.84 2.29 -2.04 -0.441 1.69 0.685 1.22
15 2.29 4.07 -0.441 -0.530 1.79 1.91 -1.95
16 4.07 4.57 -0.530 0.551 1.19 -0.0495 1.19
17 4.57 2.60 0.551 1.18 2.07 1.14 1.69
18 2.60 3.31 1.18 1.07 0.711 2.83 -2.99
19 3.31 2.83 1.07 -0.465 1.60 -0.161 -1.71
20 2.83 1.76 -0.465 -0.986 1.19 -1.87 -0.818
21 1.76 1.55 -0.986 -1.48 0.536 -2.69 0.698
22 1.55 0.521 -1.48 -1.43 1.03 -1.99 -1.20
23 0.521 -0.208 -1.43 -0.128 1.49 3.10 -1.01
24 -0.208 -0.833 -0.128 2.16 2.38 2.08 -0.245
25 -0.833 -2.52 2.16 3.71 2.29 1.84 0.562
# ... with 214 more rows, and 5 more variables: t1_ <dttm>, t2_ <dttm>,
# dt_ <drtn>, env_2h_start <dbl>, env_2h_end <dbl>
>
>
>
>
> cleanEx()
Flavor: r-devel-windows-x86_64
Version: 0.1.4
Check: running examples for arch ‘x64’
Result: ERROR
Running examples in 'amt-Ex.R' failed
The error most likely occurred in:
> ### Name: extract_covariates
> ### Title: Extract covariate values
> ### Aliases: extract_covariates extract_covariates.track_xy
> ### extract_covariates.random_points extract_covariates.steps_xy
> ### extract_covariates_along extract_covariates_along.steps_xy
> ### extract_covariates_var_time extract_covariates_var_time.track_xyt
> ### extract_covariates_var_time.steps_xyt
>
> ### ** Examples
>
> data(deer)
> data(sh_forest)
> deer %>% extract_covariates(sh_forest)
# A tibble: 826 x 5
x_ y_ t_ burst_ sh.forest
<dbl> <dbl> <dttm> <dbl> <dbl>
1 4314068. 3445807. 2008-03-30 00:01:47.000000 1 2
2 4314053. 3445768. 2008-03-30 06:00:54.000000 1 2
3 4314105. 3445859. 2008-03-30 12:01:47.000000 1 2
4 4314044. 3445785. 2008-03-30 18:01:24.000000 1 2
5 4313015. 3445858. 2008-03-31 00:01:23.000000 1 1
6 4312860. 3445857. 2008-03-31 06:01:45.000000 1 1
7 4312854. 3445856. 2008-03-31 12:01:11.000000 1 1
8 4312858. 3445858. 2008-03-31 18:01:55.000000 1 1
9 4312745. 3445862. 2008-04-01 00:01:24.000000 1 1
10 4312651. 3446024. 2008-04-01 06:00:54.000000 1 1
# ... with 816 more rows
> deer %>% steps %>% extract_covariates(sh_forest)
Warning in steps.track_xyt(.) :
burst's are ignored, use steps_by_burst instead.
# A tibble: 825 x 11
x1_ x2_ y1_ y2_ sl_ direction_p ta_
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 4314068. 4314053. 3445807. 3445768. 41.8 NA NA
2 4314053. 4314105. 3445768. 3445859. 104. -1.95 3.00
3 4314105. 4314044. 3445859. 3445785. 95.3 1.05 2.97
4 4314044. 4313015. 3445785. 3445858. 1032. -2.27 -0.945
5 4313015. 4312860. 3445858. 3445857. 155. 3.07 0.0756
6 4312860. 4312854. 3445857. 3445856. 6.08 -3.14 0.146
7 4312854. 4312858. 3445856. 3445858. 4.47 -2.99 -2.84
8 4312858. 4312745. 3445858. 3445862. 113. 0.449 2.66
9 4312745. 4312651. 3445862. 3446024. 187. 3.11 -1.02
10 4312651. 4312649. 3446024. 3446030. 6.32 2.09 -0.216
# ... with 815 more rows, and 4 more variables: t1_ <dttm>, t2_ <dttm>,
# dt_ <drtn>, sh.forest <dbl>
> deer %>% steps %>% extract_covariates(sh_forest, where = "start")
Warning in steps.track_xyt(.) :
burst's are ignored, use steps_by_burst instead.
# A tibble: 825 x 11
x1_ x2_ y1_ y2_ sl_ direction_p ta_
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 4314068. 4314053. 3445807. 3445768. 41.8 NA NA
2 4314053. 4314105. 3445768. 3445859. 104. -1.95 3.00
3 4314105. 4314044. 3445859. 3445785. 95.3 1.05 2.97
4 4314044. 4313015. 3445785. 3445858. 1032. -2.27 -0.945
5 4313015. 4312860. 3445858. 3445857. 155. 3.07 0.0756
6 4312860. 4312854. 3445857. 3445856. 6.08 -3.14 0.146
7 4312854. 4312858. 3445856. 3445858. 4.47 -2.99 -2.84
8 4312858. 4312745. 3445858. 3445862. 113. 0.449 2.66
9 4312745. 4312651. 3445862. 3446024. 187. 3.11 -1.02
10 4312651. 4312649. 3446024. 3446030. 6.32 2.09 -0.216
# ... with 815 more rows, and 4 more variables: t1_ <dttm>, t2_ <dttm>,
# dt_ <drtn>, sh.forest <dbl>
> ## Not run:
> ##D data(deer) # relocation
> ##D data("sh_forest") # env covar
> ##D
> ##D p1 <- deer %>% steps() %>% random_steps() %>%
> ##D extract_covariates(sh_forest) %>% # extract at the endpoint
> ##D mutate(for_path = extract_covariates_along(., sh_forest)) %>%
> ##D # 1 = forest, lets calc the fraction of forest along the path
> ##D mutate(for_per = purrr::map_dbl(for_path, ~ mean(. == 1)))
> ## End(Not run)
>
> # Simulate some dummy data
> # Hourly data for 10 days: 24 * 10
> set.seed(123)
> path <- data.frame(x = cumsum(rnorm(240)),
+ y = cumsum(rnorm(240)),
+ t = lubridate::ymd("2018-01-01") + hours(0:239))
> trk <- make_track(path, x, y, t)
.t found, creating `track_xyt`.
>
> # dummy env data
> rs <- raster::raster(xmn = -50, xmx = 50, ymn = -50, ymx = 50, res = 1)
>
> # create dummy covars for each day
> rs <- raster::stack(lapply(1:10, function(i)
+ raster::setValues(rs, runif(1e4, i - 1, i))))
>
> # Env covariates are always taken at noon
> rs <- raster::setZ(rs, lubridate::ymd_hm("2018-01-01 12:00") + days(0:9))
>
> # Allow up to 2 hours after
> trk %>% extract_covariates_var_time(rs, max_time = hours(2), when = "after") %>%
+ print(n = 25)
# A tibble: 240 x 4
x_ y_ t_ time_var_covar
<dbl> <dbl> <dttm> <dbl>
1 -0.560 -0.789 2018-01-01 00:00:00.000000 NA
2 -0.791 -1.29 2018-01-01 01:00:00.000000 NA
3 0.768 0.205 2018-01-01 02:00:00.000000 NA
4 0.839 -0.932 2018-01-01 03:00:00.000000 NA
5 0.968 -1.11 2018-01-01 04:00:00.000000 NA
6 2.68 0.791 2018-01-01 05:00:00.000000 NA
7 3.14 0.690 2018-01-01 06:00:00.000000 NA
8 1.88 -0.670 2018-01-01 07:00:00.000000 NA
9 1.19 -1.33 2018-01-01 08:00:00.000000 NA
10 0.746 -0.849 2018-01-01 09:00:00.000000 NA
11 1.97 -1.22 2018-01-01 10:00:00.000000 NA
12 2.33 -1.79 2018-01-01 11:00:00.000000 NA
13 2.73 -2.13 2018-01-01 12:00:00.000000 0.415
14 2.84 -2.04 2018-01-01 13:00:00.000000 0.415
15 2.29 -0.441 2018-01-01 14:00:00.000000 0.820
16 4.07 -0.530 2018-01-01 15:00:00.000000 NA
17 4.57 0.551 2018-01-01 16:00:00.000000 NA
18 2.60 1.18 2018-01-01 17:00:00.000000 NA
19 3.31 1.07 2018-01-01 18:00:00.000000 NA
20 2.83 -0.465 2018-01-01 19:00:00.000000 NA
21 1.76 -0.986 2018-01-01 20:00:00.000000 NA
22 1.55 -1.48 2018-01-01 21:00:00.000000 NA
23 0.521 -1.43 2018-01-01 22:00:00.000000 NA
24 -0.208 -0.128 2018-01-01 23:00:00.000000 NA
25 -0.833 2.16 2018-01-02 00:00:00.000000 NA
# ... with 215 more rows
> trk %>% extract_covariates_var_time(rs, max_time = hours(2), when = "before") %>%
+ print(n = 25)
# A tibble: 240 x 4
x_ y_ t_ time_var_covar
<dbl> <dbl> <dttm> <dbl>
1 -0.560 -0.789 2018-01-01 00:00:00.000000 NA
2 -0.791 -1.29 2018-01-01 01:00:00.000000 NA
3 0.768 0.205 2018-01-01 02:00:00.000000 NA
4 0.839 -0.932 2018-01-01 03:00:00.000000 NA
5 0.968 -1.11 2018-01-01 04:00:00.000000 NA
6 2.68 0.791 2018-01-01 05:00:00.000000 NA
7 3.14 0.690 2018-01-01 06:00:00.000000 NA
8 1.88 -0.670 2018-01-01 07:00:00.000000 NA
9 1.19 -1.33 2018-01-01 08:00:00.000000 NA
10 0.746 -0.849 2018-01-01 09:00:00.000000 NA
11 1.97 -1.22 2018-01-01 10:00:00.000000 0.632
12 2.33 -1.79 2018-01-01 11:00:00.000000 0.749
13 2.73 -2.13 2018-01-01 12:00:00.000000 0.415
14 2.84 -2.04 2018-01-01 13:00:00.000000 NA
15 2.29 -0.441 2018-01-01 14:00:00.000000 NA
16 4.07 -0.530 2018-01-01 15:00:00.000000 NA
17 4.57 0.551 2018-01-01 16:00:00.000000 NA
18 2.60 1.18 2018-01-01 17:00:00.000000 NA
19 3.31 1.07 2018-01-01 18:00:00.000000 NA
20 2.83 -0.465 2018-01-01 19:00:00.000000 NA
21 1.76 -0.986 2018-01-01 20:00:00.000000 NA
22 1.55 -1.48 2018-01-01 21:00:00.000000 NA
23 0.521 -1.43 2018-01-01 22:00:00.000000 NA
24 -0.208 -0.128 2018-01-01 23:00:00.000000 NA
25 -0.833 2.16 2018-01-02 00:00:00.000000 NA
# ... with 215 more rows
> trk %>% extract_covariates_var_time(rs, max_time = hours(2), when = "any") %>%
+ print(n = 25)
# A tibble: 240 x 4
x_ y_ t_ time_var_covar
<dbl> <dbl> <dttm> <dbl>
1 -0.560 -0.789 2018-01-01 00:00:00.000000 NA
2 -0.791 -1.29 2018-01-01 01:00:00.000000 NA
3 0.768 0.205 2018-01-01 02:00:00.000000 NA
4 0.839 -0.932 2018-01-01 03:00:00.000000 NA
5 0.968 -1.11 2018-01-01 04:00:00.000000 NA
6 2.68 0.791 2018-01-01 05:00:00.000000 NA
7 3.14 0.690 2018-01-01 06:00:00.000000 NA
8 1.88 -0.670 2018-01-01 07:00:00.000000 NA
9 1.19 -1.33 2018-01-01 08:00:00.000000 NA
10 0.746 -0.849 2018-01-01 09:00:00.000000 NA
11 1.97 -1.22 2018-01-01 10:00:00.000000 0.632
12 2.33 -1.79 2018-01-01 11:00:00.000000 0.749
13 2.73 -2.13 2018-01-01 12:00:00.000000 0.415
14 2.84 -2.04 2018-01-01 13:00:00.000000 0.415
15 2.29 -0.441 2018-01-01 14:00:00.000000 0.820
16 4.07 -0.530 2018-01-01 15:00:00.000000 NA
17 4.57 0.551 2018-01-01 16:00:00.000000 NA
18 2.60 1.18 2018-01-01 17:00:00.000000 NA
19 3.31 1.07 2018-01-01 18:00:00.000000 NA
20 2.83 -0.465 2018-01-01 19:00:00.000000 NA
21 1.76 -0.986 2018-01-01 20:00:00.000000 NA
22 1.55 -1.48 2018-01-01 21:00:00.000000 NA
23 0.521 -1.43 2018-01-01 22:00:00.000000 NA
24 -0.208 -0.128 2018-01-01 23:00:00.000000 NA
25 -0.833 2.16 2018-01-02 00:00:00.000000 NA
# ... with 215 more rows
>
> # We can use different time scales
> trk %>%
+ extract_covariates_var_time(
+ rs, max_time = hours(2), when = "any", name_covar = "env_2h") %>%
+ extract_covariates_var_time(
+ rs, max_time = hours(4), when = "any", name_covar = "env_4h") %>%
+ extract_covariates_var_time(
+ rs, max_time = hours(6), when = "any", name_covar = "env_6h") %>%
+ print(n = 25)
# A tibble: 240 x 6
x_ y_ t_ env_2h env_4h env_6h
<dbl> <dbl> <dttm> <dbl> <dbl> <dbl>
1 -0.560 -0.789 2018-01-01 00:00:00.000000 NA NA NA
2 -0.791 -1.29 2018-01-01 01:00:00.000000 NA NA NA
3 0.768 0.205 2018-01-01 02:00:00.000000 NA NA NA
4 0.839 -0.932 2018-01-01 03:00:00.000000 NA NA NA
5 0.968 -1.11 2018-01-01 04:00:00.000000 NA NA NA
6 2.68 0.791 2018-01-01 05:00:00.000000 NA NA NA
7 3.14 0.690 2018-01-01 06:00:00.000000 NA NA 0.410
8 1.88 -0.670 2018-01-01 07:00:00.000000 NA NA 0.684
9 1.19 -1.33 2018-01-01 08:00:00.000000 NA 0.632 0.632
10 0.746 -0.849 2018-01-01 09:00:00.000000 NA 0.485 0.485
11 1.97 -1.22 2018-01-01 10:00:00.000000 0.632 0.632 0.632
12 2.33 -1.79 2018-01-01 11:00:00.000000 0.749 0.749 0.749
13 2.73 -2.13 2018-01-01 12:00:00.000000 0.415 0.415 0.415
14 2.84 -2.04 2018-01-01 13:00:00.000000 0.415 0.415 0.415
15 2.29 -0.441 2018-01-01 14:00:00.000000 0.820 0.820 0.820
16 4.07 -0.530 2018-01-01 15:00:00.000000 NA 0.427 0.427
17 4.57 0.551 2018-01-01 16:00:00.000000 NA 0.208 0.208
18 2.60 1.18 2018-01-01 17:00:00.000000 NA NA 0.944
19 3.31 1.07 2018-01-01 18:00:00.000000 NA NA 0.0925
20 2.83 -0.465 2018-01-01 19:00:00.000000 NA NA NA
21 1.76 -0.986 2018-01-01 20:00:00.000000 NA NA NA
22 1.55 -1.48 2018-01-01 21:00:00.000000 NA NA NA
23 0.521 -1.43 2018-01-01 22:00:00.000000 NA NA NA
24 -0.208 -0.128 2018-01-01 23:00:00.000000 NA NA NA
25 -0.833 2.16 2018-01-02 00:00:00.000000 NA NA NA
# ... with 215 more rows
>
> # We can use different time scales: after
> trk %>%
+ extract_covariates_var_time(
+ rs, max_time = hours(2), when = "after", name_covar = "env_2h") %>%
+ extract_covariates_var_time(
+ rs, max_time = hours(4), when = "after", name_covar = "env_4h") %>%
+ extract_covariates_var_time(
+ rs, max_time = hours(6), when = "after", name_covar = "env_6h") %>%
+ print(n = 25)
# A tibble: 240 x 6
x_ y_ t_ env_2h env_4h env_6h
<dbl> <dbl> <dttm> <dbl> <dbl> <dbl>
1 -0.560 -0.789 2018-01-01 00:00:00.000000 NA NA NA
2 -0.791 -1.29 2018-01-01 01:00:00.000000 NA NA NA
3 0.768 0.205 2018-01-01 02:00:00.000000 NA NA NA
4 0.839 -0.932 2018-01-01 03:00:00.000000 NA NA NA
5 0.968 -1.11 2018-01-01 04:00:00.000000 NA NA NA
6 2.68 0.791 2018-01-01 05:00:00.000000 NA NA NA
7 3.14 0.690 2018-01-01 06:00:00.000000 NA NA NA
8 1.88 -0.670 2018-01-01 07:00:00.000000 NA NA NA
9 1.19 -1.33 2018-01-01 08:00:00.000000 NA NA NA
10 0.746 -0.849 2018-01-01 09:00:00.000000 NA NA NA
11 1.97 -1.22 2018-01-01 10:00:00.000000 NA NA NA
12 2.33 -1.79 2018-01-01 11:00:00.000000 NA NA NA
13 2.73 -2.13 2018-01-01 12:00:00.000000 0.415 0.415 0.415
14 2.84 -2.04 2018-01-01 13:00:00.000000 0.415 0.415 0.415
15 2.29 -0.441 2018-01-01 14:00:00.000000 0.820 0.820 0.820
16 4.07 -0.530 2018-01-01 15:00:00.000000 NA 0.427 0.427
17 4.57 0.551 2018-01-01 16:00:00.000000 NA 0.208 0.208
18 2.60 1.18 2018-01-01 17:00:00.000000 NA NA 0.944
19 3.31 1.07 2018-01-01 18:00:00.000000 NA NA 0.0925
20 2.83 -0.465 2018-01-01 19:00:00.000000 NA NA NA
21 1.76 -0.986 2018-01-01 20:00:00.000000 NA NA NA
22 1.55 -1.48 2018-01-01 21:00:00.000000 NA NA NA
23 0.521 -1.43 2018-01-01 22:00:00.000000 NA NA NA
24 -0.208 -0.128 2018-01-01 23:00:00.000000 NA NA NA
25 -0.833 2.16 2018-01-02 00:00:00.000000 NA NA NA
# ... with 215 more rows
>
> # We can use different time scales: before
> trk %>%
+ extract_covariates_var_time(
+ rs, max_time = hours(2), when = "before", name_covar = "env_2h") %>%
+ extract_covariates_var_time(
+ rs, max_time = hours(4), when = "before", name_covar = "env_4h") %>%
+ extract_covariates_var_time(
+ rs, max_time = hours(6), when = "before", name_covar = "env_6h") %>%
+ print(n = 25)
# A tibble: 240 x 6
x_ y_ t_ env_2h env_4h env_6h
<dbl> <dbl> <dttm> <dbl> <dbl> <dbl>
1 -0.560 -0.789 2018-01-01 00:00:00.000000 NA NA NA
2 -0.791 -1.29 2018-01-01 01:00:00.000000 NA NA NA
3 0.768 0.205 2018-01-01 02:00:00.000000 NA NA NA
4 0.839 -0.932 2018-01-01 03:00:00.000000 NA NA NA
5 0.968 -1.11 2018-01-01 04:00:00.000000 NA NA NA
6 2.68 0.791 2018-01-01 05:00:00.000000 NA NA NA
7 3.14 0.690 2018-01-01 06:00:00.000000 NA NA 0.410
8 1.88 -0.670 2018-01-01 07:00:00.000000 NA NA 0.684
9 1.19 -1.33 2018-01-01 08:00:00.000000 NA 0.632 0.632
10 0.746 -0.849 2018-01-01 09:00:00.000000 NA 0.485 0.485
11 1.97 -1.22 2018-01-01 10:00:00.000000 0.632 0.632 0.632
12 2.33 -1.79 2018-01-01 11:00:00.000000 0.749 0.749 0.749
13 2.73 -2.13 2018-01-01 12:00:00.000000 0.415 0.415 0.415
14 2.84 -2.04 2018-01-01 13:00:00.000000 NA NA NA
15 2.29 -0.441 2018-01-01 14:00:00.000000 NA NA NA
16 4.07 -0.530 2018-01-01 15:00:00.000000 NA NA NA
17 4.57 0.551 2018-01-01 16:00:00.000000 NA NA NA
18 2.60 1.18 2018-01-01 17:00:00.000000 NA NA NA
19 3.31 1.07 2018-01-01 18:00:00.000000 NA NA NA
20 2.83 -0.465 2018-01-01 19:00:00.000000 NA NA NA
21 1.76 -0.986 2018-01-01 20:00:00.000000 NA NA NA
22 1.55 -1.48 2018-01-01 21:00:00.000000 NA NA NA
23 0.521 -1.43 2018-01-01 22:00:00.000000 NA NA NA
24 -0.208 -0.128 2018-01-01 23:00:00.000000 NA NA NA
25 -0.833 2.16 2018-01-02 00:00:00.000000 NA NA NA
# ... with 215 more rows
>
> # The same works also for steps
> trk %>%
+ steps() %>%
+ extract_covariates_var_time(
+ rs, max_time = hours(2), when = "before", name_covar = "env_2h") %>%
+ print(n = 25)
# A tibble: 239 x 11
x1_ x2_ y1_ y2_ sl_ direction_p ta_
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 -0.560 -0.791 -0.789 -1.29 0.552 NA NA
2 -0.791 0.768 -1.29 0.205 2.16 -2.00 2.77
3 0.768 0.839 0.205 -0.932 1.14 0.765 -2.27
4 0.839 0.968 -0.932 -1.11 0.221 -1.51 0.563
5 0.968 2.68 -1.11 0.791 2.56 -0.945 1.78
6 2.68 3.14 0.791 0.690 0.472 0.837 -1.05
7 3.14 1.88 0.690 -0.670 1.86 -0.216 -2.10
8 1.88 1.19 -0.670 -1.33 0.956 -2.32 -0.0524
9 1.19 0.746 -1.33 -0.849 0.659 -2.37 -1.60
10 0.746 1.97 -0.849 -1.22 1.28 2.31 -2.61
11 1.97 2.33 -1.22 -1.79 0.667 -0.298 -0.703
12 2.33 2.73 -1.79 -2.13 0.528 -1.00 0.292
13 2.73 2.84 -2.13 -2.04 0.143 -0.709 1.39
14 2.84 2.29 -2.04 -0.441 1.69 0.685 1.22
15 2.29 4.07 -0.441 -0.530 1.79 1.91 -1.95
16 4.07 4.57 -0.530 0.551 1.19 -0.0495 1.19
17 4.57 2.60 0.551 1.18 2.07 1.14 1.69
18 2.60 3.31 1.18 1.07 0.711 2.83 -2.99
19 3.31 2.83 1.07 -0.465 1.60 -0.161 -1.71
20 2.83 1.76 -0.465 -0.986 1.19 -1.87 -0.818
21 1.76 1.55 -0.986 -1.48 0.536 -2.69 0.698
22 1.55 0.521 -1.48 -1.43 1.03 -1.99 -1.20
23 0.521 -0.208 -1.43 -0.128 1.49 3.10 -1.01
24 -0.208 -0.833 -0.128 2.16 2.38 2.08 -0.245
25 -0.833 -2.52 2.16 3.71 2.29 1.84 0.562
# ... with 214 more rows, and 4 more variables: t1_ <dttm>, t2_ <dttm>,
# dt_ <drtn>, env_2h <dbl>
>
> # also with start and end
> trk %>%
+ steps() %>%
+ extract_covariates_var_time(
+ rs, max_time = hours(2), when = "before", name_covar = "env_2h",
+ where = "both") %>%
+ print(n = 25)
# A tibble: 239 x 12
x1_ x2_ y1_ y2_ sl_ direction_p ta_
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 -0.560 -0.791 -0.789 -1.29 0.552 NA NA
2 -0.791 0.768 -1.29 0.205 2.16 -2.00 2.77
3 0.768 0.839 0.205 -0.932 1.14 0.765 -2.27
4 0.839 0.968 -0.932 -1.11 0.221 -1.51 0.563
5 0.968 2.68 -1.11 0.791 2.56 -0.945 1.78
6 2.68 3.14 0.791 0.690 0.472 0.837 -1.05
7 3.14 1.88 0.690 -0.670 1.86 -0.216 -2.10
8 1.88 1.19 -0.670 -1.33 0.956 -2.32 -0.0524
9 1.19 0.746 -1.33 -0.849 0.659 -2.37 -1.60
10 0.746 1.97 -0.849 -1.22 1.28 2.31 -2.61
11 1.97 2.33 -1.22 -1.79 0.667 -0.298 -0.703
12 2.33 2.73 -1.79 -2.13 0.528 -1.00 0.292
13 2.73 2.84 -2.13 -2.04 0.143 -0.709 1.39
14 2.84 2.29 -2.04 -0.441 1.69 0.685 1.22
15 2.29 4.07 -0.441 -0.530 1.79 1.91 -1.95
16 4.07 4.57 -0.530 0.551 1.19 -0.0495 1.19
17 4.57 2.60 0.551 1.18 2.07 1.14 1.69
18 2.60 3.31 1.18 1.07 0.711 2.83 -2.99
19 3.31 2.83 1.07 -0.465 1.60 -0.161 -1.71
20 2.83 1.76 -0.465 -0.986 1.19 -1.87 -0.818
21 1.76 1.55 -0.986 -1.48 0.536 -2.69 0.698
22 1.55 0.521 -1.48 -1.43 1.03 -1.99 -1.20
23 0.521 -0.208 -1.43 -0.128 1.49 3.10 -1.01
24 -0.208 -0.833 -0.128 2.16 2.38 2.08 -0.245
25 -0.833 -2.52 2.16 3.71 2.29 1.84 0.562
# ... with 214 more rows, and 5 more variables: t1_ <dttm>, t2_ <dttm>,
# dt_ <drtn>, env_2h_start <dbl>, env_2h_end <dbl>
>
>
>
>
> cleanEx()
Flavor: r-oldrel-windows-ix86+x86_64