CRAN Package Check Results for Package amt

Last updated on 2021-10-25 23:52:38 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.1.4 81.47 472.05 553.52 OK
r-devel-linux-x86_64-debian-gcc 0.1.4 58.86 349.73 408.59 OK
r-devel-linux-x86_64-fedora-clang 0.1.4 716.82 OK
r-devel-linux-x86_64-fedora-gcc 0.1.4 719.90 OK
r-devel-windows-x86_64 0.1.4 113.00 612.00 725.00 OK
r-devel-windows-x86_64-gcc10-UCRT 0.1.4 ERROR
r-patched-linux-x86_64 0.1.4 71.93 484.99 556.92 OK
r-patched-solaris-x86 0.1.4 735.90 ERROR
r-release-linux-x86_64 0.1.4 70.81 488.57 559.38 OK
r-release-macos-arm64 0.1.4 OK
r-release-macos-x86_64 0.1.4 OK
r-release-windows-ix86+x86_64 0.1.4 145.00 819.00 964.00 ERROR
r-oldrel-macos-x86_64 0.1.4 OK
r-oldrel-windows-ix86+x86_64 0.1.4 171.00 648.00 819.00 OK

Check Details

Version: 0.1.4
Check: examples
Result: ERROR
    Running examples in 'amt-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: time_of_day
    > ### Title: Time of the day when a fix was taken
    > ### Aliases: time_of_day time_of_day.track_xyt time_of_day.steps_xyt
    >
    > ### ** Examples
    >
    > data(deer)
    > deer %>% time_of_day()
    Warning in sp::spTransform(as_sp(x, end = end), sp::CRS("+init=epsg:4326")) :
     NULL source CRS comment, falling back to PROJ string
    Warning in sp::spTransform(as_sp(x, end = end), sp::CRS("+init=epsg:4326")) :
     +init dropped in PROJ string
    Warning in wkt(obj) : CRS object has no comment
    Warning in wkt(obj) : CRS object has no comment
    Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj = prefer_proj) :
     Discarded datum Unknown based on GRS80 ellipsoid in Proj4 definition,
     but +towgs84= values preserved
    # A tibble: 826 × 5
     x_ y_ t_ burst_ tod_
     <dbl> <dbl> <dttm> <dbl> <fct>
     1 4314068. 3445807. 2008-03-30 00:01:47.000000 1 night
     2 4314053. 3445768. 2008-03-30 06:00:54.000000 1 day
     3 4314105. 3445859. 2008-03-30 12:01:47.000000 1 day
     4 4314044. 3445785. 2008-03-30 18:01:24.000000 1 night
     5 4313015. 3445858. 2008-03-31 00:01:23.000000 1 night
     6 4312860. 3445857. 2008-03-31 06:01:45.000000 1 day
     7 4312854. 3445856. 2008-03-31 12:01:11.000000 1 day
     8 4312858. 3445858. 2008-03-31 18:01:55.000000 1 night
     9 4312745. 3445862. 2008-04-01 00:01:24.000000 1 night
    10 4312651. 3446024. 2008-04-01 06:00:54.000000 1 day
    # … with 816 more rows
    > deer %>% steps_by_burst %>% time_of_day()
Flavor: r-devel-windows-x86_64-gcc10-UCRT

Version: 0.1.4
Check: examples
Result: ERROR
    Running examples in ‘amt-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: fit_ctmm
    > ### Title: Fit a continuous time movement model with 'ctmm'
    > ### Aliases: fit_ctmm
    >
    > ### ** Examples
    >
    > data(deer)
    > m1 <- fit_ctmm(deer, "iid")
    
     *** caught segfault ***
    address 1, cause 'memory not mapped'
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    Traceback:
     1: isFALSE(simplify)
     2: sapply(1:nrow(M), function(i) { all(M[i, ] == 0)})
     3: PDsolve(sRes[i, , ])
     4: FUN(X[[i]], ...)
     5: vapply(1:n, function(i) { PDsolve(sRes[i, , ])}, diag(OBS * DIM))
     6: kalman(z, u, dt = dt, CTMM = CTMM, error = error, DIM = DIM)
     7: loglike.fn(data, CTMM = CTMM, REML = FALSE, profile = FALSE)
     8: ctmm::ctmm.fit(dat, ctmm::ctmm(tau = NULL, error = use_error, ...))
     9: fit_ctmm(deer, "iid")
    An irrecoverable exception occurred. R is aborting now ...
Flavor: r-patched-solaris-x86

Version: 0.1.4
Check: running examples for arch ‘i386’
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-release-windows-ix86+x86_64