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20 hours ago •
r-spatial
r-spatial
Sftime
This requires spacetime from https://github.com/edzer/spacetime , currently, to run.
When running demo/sftime.R, I get an error:
plot(locKrig_sft[1], col = sf.colors(), breaks = "equal")
#> Error in image.default(dims[[dimx]], dims[[dimy]], ar, asp = asp, xlab = xlab, : must have one more break than colour
The reason is that locKrig_sft contains as first attribute a ._dummy variable, whereas I assume it should only contain the two last attributes:
locKrig_sft
#> stars object with 3 dimensions and 3 attributes
#> attribute(s):
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> ._dummy 0.0000000 0.0000000 0.00000000 0.00000000 0.0000000 0.0000000
#> var1.pred -1.6971826 -0.3289438 -0.04080341 -0.02610443 0.2841662 1.8389138
#> var1.var 0.2235846 0.3247676 0.35852900 0.36139993 0.3937046 0.5958156
#> dimension(s):
#> from to offset delta refsys point values x/y
#> x 1 20 0 0.05 NA NA NULL [x]
#> y 1 20 1 -0.05 NA NA NULL [y]
#> time 1 10 2022-06-12 14:19:25 CEST 1.25923 days POSIXct NA NULL
I have seen that the ._dummy attribute gets created in krigeST to get the function run with stars objects and removed (as I would expect) in the end, but only if nmax = Inf and fullCovariance = FALSE. In the example nmax = 20 and I assume this is why the ._dummy attribute doesn't get removed.
Is this an error from my side?
Here's the full reprex:
## FNN local prediction
########################
library(sp)
library(spacetime)
library(gstat)
library(lattice)
# create n space-time points over [0,1] x [0,1] x [Now, Now+some days]
t0 = Sys.time() # now
n = 1000
set.seed(13131) # fix outcomes
x = runif(n)
y = runif(n)
t = t0 + 1e6 * runif(n)
z = rnorm(n)
stidf = STIDF(SpatialPoints(cbind(x,y)), sort(t), data.frame(z=z))
stplot(stidf, number=21, main="random spatio-temporal noise")
library(sftime)
#> Loading required package: sf
#> Linking to GEOS 3.9.1, GDAL 3.3.2, PROJ 7.2.1; sf_use_s2() is TRUE
sft = st_as_sftime(stidf)
# create a regular 20 x 20 x 10 grid of prediction locations:
grd = as(SpatialGrid(GridTopology(c(0.025,0.025), c(.05, .05), c(20,20))), "SpatialPixels")
tgrd = seq(min(t)+10000, max(t)-10000, length.out = 10)
stf = STF(grd, tgrd)
#stf = STFDF(grd, tgrd, data.frame(x=rep(0,400*10)))
library(stars)
#> Loading required package: abind
st = st_as_stars(stf)
# define a variogram model
sumMetricModel <- vgmST("sumMetric",
space=vgm(1/6, "Sph", 0.25, 1/60),
time =vgm(2/6, "Exp", 1e5, 1/60),
joint=vgm(0.4, "Exp", 0.3, 0.1),
stAni=1/1e6)
attr(sumMetricModel, "temporal unit") <- "secs"
dg <- data.frame(spacelag=rep(c(0.001,1:10)/10,6),
timelag=rep(0:5*50e3, each=11))
#wireframe(model~spacelag+timelag,
# variogramSurface(sumMetricModel, dist_grid = dg),
# scales=list(arrows=F),
# drape=T, col.regions=bpy.colors(),
# zlim=c(0,1.2),
# main="imposed sum-metric model")
locKrig_sft <- krigeST(z~1, sft, st, sumMetricModel, nmax=20, computeVar = T)
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locKrig <- krigeST(z~1, stidf, stf, sumMetricModel, nmax=20, computeVar = T)
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stplot(locKrig[,,"var1.pred"], col.regions=bpy.colors(), scales=list(draw=T))
plot(locKrig_sft[1], col = sf.colors(), breaks = "equal")
#> Error in image.default(dims[[dimx]], dims[[dimy]], ar, asp = asp, xlab = xlab, : must have one more break than colour
stplot(locKrig[,,"var1.var"], col.regions=bpy.colors(), scales=list(draw=T))
plot(locKrig_sft[2], col = sf.colors(), breaks = "equal")
Created on 2022-06-12 by the reprex package (v2.0.1)
Session info
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.2.0 (2022-04-22 ucrt)
#> os Windows 10 x64 (build 22000)
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#> ui RTerm
#> language (EN)
#> collate German_Germany.utf8
#> ctype German_Germany.utf8
#> tz Europe/Berlin
#> date 2022-06-12
#> pandoc 2.17.1.1 @ C:/Program Files/RStudio/bin/quarto/bin/ (via rmarkdown)
#>
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#> package * version date (UTC) lib source
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#> fansi 1.0.3 2022-03-24 [1] CRAN (R 4.2.0)
#> fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.2.0)
#> FNN 1.1.3 2019-02-15 [1] CRAN (R 4.2.0)
#> fs 1.5.2 2021-12-08 [1] CRAN (R 4.2.0)
#> generics 0.1.2 2022-01-31 [1] CRAN (R 4.2.0)
#> glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.0)
#> gstat * 2.1-0 2022-06-12 [1] Github (r-spatial/gstat@7ba6941)
#> highr 0.9 2021-04-16 [1] CRAN (R 4.2.0)
#> htmltools 0.5.2 2021-08-25 [1] CRAN (R 4.2.0)
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I have some points/questions regarding krigeST.R:
-
The variable
to_sftimeis currently not used: https://github.com/r-spatial/gstat/blob/a00b20f46f9829eb6ab8aba0078669ad8f26969c/R/krigeST.R#L86 Will this be added later? In any case, it would not work currently since ifinherits(data, "sftime"),datawill already be converted toSTIin l. 70. -
For the same reason, l. 88 will never be executed and l. 87 to 88 can be dropped as far as I can see.
-
I think the documentation is currently not updated and therefore does not mention
sftimeorstarsobjects. -
In the code block from l. 57 to 83, it is checked whether the
starsandsfpackage are installed ifinherits(data, c("stars")), but not ifinherits(data, "sftime"). Wouldn't the latter case also require thesfandstarspackage to be installed? -
krigeSTdoes not work whendatais anSTIDFobject andnewdatais astarsorsftimeobject. This is a somewhat awkward use case, but I mention it in case you think it would be good to describe this in the documentation or throw some more informative error message:
## FNN local prediction
########################
library(sp)
library(spacetime)
library(gstat)
library(lattice)
# create n space-time points over [0,1] x [0,1] x [Now, Now+some days]
t0 = Sys.time() # now
n = 1000
set.seed(13131) # fix outcomes
x = runif(n)
y = runif(n)
t = t0 + 1e6 * runif(n)
z = rnorm(n)
stidf = STIDF(SpatialPoints(cbind(x,y)), sort(t), data.frame(z=z))
stplot(stidf, number=21, main="random spatio-temporal noise")
library(sftime)
#> Loading required package: sf
#> Linking to GEOS 3.9.1, GDAL 3.3.2, PROJ 7.2.1; sf_use_s2() is TRUE
sft = st_as_sftime(stidf)
# create a regular 20 x 20 x 10 grid of prediction locations:
grd = as(SpatialGrid(GridTopology(c(0.025,0.025), c(.05, .05), c(20,20))), "SpatialPixels")
tgrd = seq(min(t)+10000, max(t)-10000, length.out = 10)
stf = STF(grd, tgrd)
#stf = STFDF(grd, tgrd, data.frame(x=rep(0,400*10)))
library(stars)
#> Loading required package: abind
st = st_as_stars(stf)
# define a variogram model
sumMetricModel <- vgmST("sumMetric",
space=vgm(1/6, "Sph", 0.25, 1/60),
time =vgm(2/6, "Exp", 1e5, 1/60),
joint=vgm(0.4, "Exp", 0.3, 0.1),
stAni=1/1e6)
attr(sumMetricModel, "temporal unit") <- "secs"
locKrig_sft <- krigeST(z~1, stidf, st, sumMetricModel, nmax=20, computeVar = T)
#> Error in krigeST(z ~ 1, stidf, st, sumMetricModel, nmax = 20, computeVar = T): trying to get slot "sp" from an object (class "stars") that is not an S4 object
locKrig_sft <- krigeST(z~1, stidf, sft, sumMetricModel, nmax=20, computeVar = T)
#> Error in krigeST(z ~ 1, stidf, sft, sumMetricModel, nmax = 20, computeVar = T): trying to get slot "sp" from an object (class "sftime") that is not an S4 object
Created on 2022-06-12 by the reprex package (v2.0.1)
Session info
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.2.0 (2022-04-22 ucrt)
#> os Windows 10 x64 (build 22000)
#> system x86_64, mingw32
#> ui RTerm
#> language (EN)
#> collate German_Germany.utf8
#> ctype German_Germany.utf8
#> tz Europe/Berlin
#> date 2022-06-12
#> pandoc 2.17.1.1 @ C:/Program Files/RStudio/bin/quarto/bin/ (via rmarkdown)
#>
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#> purrr 0.3.4 2020-04-17 [1] CRAN (R 4.2.0)
#> R.cache 0.15.0 2021-04-30 [1] CRAN (R 4.2.0)
#> R.methodsS3 1.8.1 2020-08-26 [1] CRAN (R 4.2.0)
#> R.oo 1.24.0 2020-08-26 [1] CRAN (R 4.2.0)
#> R.utils 2.11.0 2021-09-26 [1] CRAN (R 4.2.0)
#> R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0)
#> Rcpp 1.0.8.3 2022-03-17 [1] CRAN (R 4.2.0)
#> reprex 2.0.1 2021-08-05 [1] CRAN (R 4.2.0)
#> rlang 1.0.2 2022-03-04 [1] CRAN (R 4.2.0)
#> rmarkdown 2.14 2022-04-25 [1] CRAN (R 4.2.0)
#> rstudioapi 0.13 2020-11-12 [1] CRAN (R 4.2.0)
#> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.0)
#> sf * 1.0-8 2022-06-12 [1] Github (r-spatial/sf@2299f4a)
#> sftime * 0.2.0.9000 2022-06-12 [1] Github (r-spatial/sftime@b85255b)
#> sp * 1.5-0 2022-06-12 [1] Github (edzer/sp@cfe9cdb)
#> spacetime * 1.2-7 2022-06-12 [1] Github (edzer/spacetime@9cc9e6c)
#> stars * 0.5-6 2022-06-12 [1] Github (r-spatial/stars@7588258)
#> stringi 1.7.6 2021-11-29 [1] CRAN (R 4.2.0)
#> stringr 1.4.0 2019-02-10 [1] CRAN (R 4.2.0)
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#> tibble 3.1.7 2022-05-03 [1] CRAN (R 4.2.0)
#> tidyselect 1.1.2 2022-02-21 [1] CRAN (R 4.2.0)
#> units 0.8-0 2022-02-05 [1] CRAN (R 4.2.0)
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#>
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#>
#> ──────────────────────────────────────────────────────────────────────────────
The other functions and files look fine for me!
Thanks, @henningte ! I think this commit addresses all of them. The to_sftime was indeed an unfinished bit, and had to be set on depending on the class of newdata. I added now an example to the sftime.R demo that takes an sftime as newdata and returns an sftime in that case. I also mentioned in the docs that old and new style objects shouldn't be mixed.
I'm not sure whether the changes address all points I raised. I think the following are still open (or at least, it is unanswered what you think of them):
- I think the documentation is currently not updated and therefore does not mention
sftimeorstarsobjects (i.e. it does not mention forkrigeST()thatdataandnewdatacan bestarsorsftimeobjects). - In the code block from l. 57 to 83, it is checked whether the
starsandsfpackage are installed ifinherits(data, c("stars")), but not ifinherits(data, "sftime"). Wouldn't the latter case also require thesfandstarspackage to be installed? - (a new point): It is currently not checked whether the
sftimepackage is installed, even though thesftimepackage is needed wheninherits(newdata, "sftime").
I also had a closer look at krigeST(). I have the feeling that the function structure is unnecessarily complex and that there are some redundancies which make the function hard to understand and perhaps also maintain.
Could this suggestion declutter the function a bit? (I don't say this is the best option, but hopefully this shows what I mean when I write "redundancies" and "unnecessarily complex"; this suggestion also addresses the new point 3 from the list above in this comment):
## krigeST
krigeST_new <- function(formula, data, newdata, modelList, beta, y, ...,
nmax=Inf, stAni=NULL,
computeVar = FALSE, fullCovariance = FALSE,
bufferNmax=2, progress=TRUE) {
stopifnot(inherits(modelList, "StVariogramModel") || is.function(modelList))
stopifnot(inherits(data, c("STF", "STS", "STI", "sftime", "stars")) &&
inherits(newdata, c("STF", "STS", "STI", "sftime", "stars")))
class_data =
if(inherits(data, "stars")) {
"stars"
} else if (inherits(data, "sftime")) {
"sftime"
} else {
"spacetime"
}
class_newdata =
if(inherits(newdata, "stars")) {
"stars"
} else if (inherits(newdata, "sftime")) {
"sftime"
} else {
"spacetime"
}
if(class_data %in% c("stars", "sftime") && class_newdata %in% c("stars", "sftime")) {
if (!requireNamespace("sf", quietly = TRUE))
stop("sf required: install that first") # nocov
if ((class_data == "stars" || class_newdata == "stars") && !requireNamespace("stars", quietly = TRUE))
stop("stars required: install that first") # nocov
if ((class_data == "sftime" || class_newdata == "sftime") && !requireNamespace("sftime", quietly = TRUE))
stop("sftime required: install that first") # nocov
if (sf::st_crs(data) != sf::st_crs(newdata))
warning("CRS for data and newdata are not identical; assign CRS or use st_transform to correct")
} else {
if (!identical(data@sp@proj4string@projargs, newdata@sp@proj4string@projargs))
message("please verify that the CRSs of data and newdata are identical, or transform them first to make them identical")
}
# convert classes to spacetime
switch(
class_data,
"stars" = {
data = as(data, "STFDF")
newdata = as(newdata, "STFDF")
},
"sftime" = {
data = as(data, "STIDF")
newdata =
switch(
class_newdata,
"stars" = {
if (length(newdata) == 0)
newdata$._dummy = 0.
as(newdata, "STFDF")
},
"sftime" = {
as(newdata, "STIDF")
},
stop("spacetime and stars/sftime objects cannot be mixed. Change `newdata` or `data` to avoid this.")
)
}
)
stopifnot(class(data@time) == class(newdata@time))
stopifnot(nmax > 0)
tUnitModel <- attr(modelList, "temporal unit")
tUnitData <- units(abs(outer(index(data@time[1]), index(newdata@time[1]), "-")))
if (is.null(tUnitModel)) {
warning("The spatio-temporal variogram model does not carry the strongly recommended attribute 'temporal unit'.\n The unit '", tUnitData,
"' has been assumed. krigeST could not check whether the temporal distances between locations and in the variogram coincide.")
tUnit <- tUnitData
attr(modelList, "temporal unit") <- tUnit
} else {
tUnit <- tUnitModel
gstat:::debug_time_unit(tUnit) #---todo: remove 'gstat:::'
}
if(nmax < Inf) { # local neighbourhood ST kriging:
ret = gstat:::krigeST.local( formula = formula, data = data,
newdata = newdata, modelList = modelList, beta=beta, # y=y, # for later use
nmax = nmax, stAni = stAni,
computeVar = computeVar, fullCovariance = fullCovariance,
bufferNmax = bufferNmax, progress = progress) #---todo: remove 'gstat:::'
} else {
ret = gstat:::krigeST.df(formula = formula, data = data, newdata = newdata,
modelList = modelList, beta = beta, y = y,
...,
nmax=nmax, stAni=stAni,
computeVar = computeVar, fullCovariance = fullCovariance,
bufferNmax = bufferNmax, progress = progress) #---todo: remove 'gstat:::'
# wrapping the predictions in ST*DF again
if (!fullCovariance) {
ret = addAttrToGeom(geometry(newdata), ret)
}
}
switch(
class_newdata,
"stars" = {
ret$._dummy = NULL
stars::st_as_stars(as(ret, "STFDF"))
},
"sftime" = {
sftime::st_as_sftime(ret)
},
"spacetime" = ret
)
}
## FNN local prediction
########################
library(sp)
library(spacetime)
library(gstat)
library(lattice)
# create n space-time points over [0,1] x [0,1] x [Now, Now+some days]
t0 = Sys.time() # now
n = 1000
set.seed(13131) # fix outcomes
x = runif(n)
y = runif(n)
t = t0 + 1e6 * runif(n)
z = rnorm(n)
stidf = STIDF(SpatialPoints(cbind(x,y)), sort(t), data.frame(z=z))
stplot(stidf, number=21, main="random spatio-temporal noise")
library(sftime)
#> Loading required package: sf
#> Linking to GEOS 3.9.1, GDAL 3.3.2, PROJ 7.2.1; sf_use_s2() is TRUE
sft = st_as_sftime(stidf)
# create a regular 20 x 20 x 10 grid of prediction locations:
grd = as(SpatialGrid(GridTopology(c(0.025,0.025), c(.05, .05), c(20,20))), "SpatialPixels")
tgrd = seq(min(t)+10000, max(t)-10000, length.out = 10)
stf = STF(grd, tgrd)
#stf = STFDF(grd, tgrd, data.frame(x=rep(0,400*10)))
library(stars)
#> Loading required package: abind
st = st_as_stars(stf)
# define a variogram model
sumMetricModel <- vgmST("sumMetric",
space=vgm(1/6, "Sph", 0.25, 1/60),
time =vgm(2/6, "Exp", 1e5, 1/60),
joint=vgm(0.4, "Exp", 0.3, 0.1),
stAni=1/1e6)
attr(sumMetricModel, "temporal unit") <- "secs"
dg <- data.frame(spacelag=rep(c(0.001,1:10)/10,6),
timelag=rep(0:5*50e3, each=11))
#wireframe(model~spacelag+timelag,
# variogramSurface(sumMetricModel, dist_grid = dg),
# scales=list(arrows=F),
# drape=T, col.regions=bpy.colors(),
# zlim=c(0,1.2),
# main="imposed sum-metric model")
# old
locKrig_sft <- gstat::krigeST(z~1, sft, st, sumMetricModel, nmax=20, computeVar = T)
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locKrig <- gstat::krigeST(z~1, stidf, stf, sumMetricModel, nmax=20, computeVar = T)
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stplot(locKrig[,,"var1.pred"], col.regions=bpy.colors(), scales=list(draw=T))
plot(locKrig_sft[1], col = sf.colors(), breaks = "equal")
stplot(locKrig[,,"var1.var"], col.regions=bpy.colors(), scales=list(draw=T))
plot(locKrig_sft[2], col = sf.colors(), breaks = "equal")
# new
locKrig_sft_new <- krigeST_new(z~1, sft, st, sumMetricModel, nmax=20, computeVar = T)
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locKrig_new <- krigeST_new(z~1, stidf, stf, sumMetricModel, nmax=20, computeVar = T)
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# old
st$foo = 0
st_as_sf(st, long = TRUE) |> st_as_sftime() -> st.sftime
locKrig_sft2 <- gstat::krigeST(z~1, sft, st.sftime, sumMetricModel, nmax=20, computeVar = T)
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plot(locKrig_sft["var1.pred"])
locKrig_sft_nofullcov <- gstat::krigeST(z~1, sft, st, sumMetricModel, nmax=20, computeVar = T, fullCovariance = FALSE)
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# new
locKrig_sft2_new <- krigeST_new(z~1, sft, st.sftime, sumMetricModel, nmax=20, computeVar = T)
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locKrig_sft_nofullcov_new <- krigeST_new(z~1, sft, st, sumMetricModel, nmax=20, computeVar = T, fullCovariance = FALSE)
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# check identity with new proposed function krigeST
identical(locKrig_sft, locKrig_sft_new)
#> [1] TRUE
identical(locKrig, locKrig_new)
#> [1] TRUE
identical(locKrig_sft2, locKrig_sft2_new)
#> [1] TRUE
identical(locKrig_sft_nofullcov, locKrig_sft_nofullcov_new)
#> [1] TRUE
Created on 2022-06-24 by the reprex package (v2.0.1)
Session info
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.2.0 (2022-04-22 ucrt)
#> os Windows 10 x64 (build 22000)
#> system x86_64, mingw32
#> ui RTerm
#> language (EN)
#> collate German_Germany.utf8
#> ctype German_Germany.utf8
#> tz Europe/Berlin
#> date 2022-06-24
#> pandoc 2.17.1.1 @ C:/Program Files/RStudio/bin/quarto/bin/ (via rmarkdown)
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> package * version date (UTC) lib source
#> abind * 1.4-5 2016-07-21 [1] CRAN (R 4.2.0)
#> class 7.3-20 2022-01-16 [2] CRAN (R 4.2.0)
#> classInt 0.4-3 2020-04-07 [1] CRAN (R 4.2.0)
#> cli 3.3.0 2022-04-25 [1] CRAN (R 4.2.0)
#> crayon 1.5.1 2022-03-26 [1] CRAN (R 4.2.0)
#> curl 4.3.2 2021-06-23 [1] CRAN (R 4.2.0)
#> DBI 1.1.2 2021-12-20 [1] CRAN (R 4.2.0)
#> digest 0.6.29 2021-12-01 [1] CRAN (R 4.2.0)
#> dplyr 1.0.9 2022-04-28 [1] CRAN (R 4.2.0)
#> e1071 1.7-9 2021-09-16 [1] CRAN (R 4.2.0)
#> ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.0)
#> evaluate 0.15 2022-02-18 [1] CRAN (R 4.2.0)
#> fansi 1.0.3 2022-03-24 [1] CRAN (R 4.2.0)
#> fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.2.0)
#> FNN 1.1.3 2019-02-15 [1] CRAN (R 4.2.0)
#> fs 1.5.2 2021-12-08 [1] CRAN (R 4.2.0)
#> generics 0.1.2 2022-01-31 [1] CRAN (R 4.2.0)
#> glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.0)
#> gstat * 2.1-0 2022-06-24 [1] Github (r-spatial/gstat@d5ba538)
#> highr 0.9 2021-04-16 [1] CRAN (R 4.2.0)
#> htmltools 0.5.2 2021-08-25 [1] CRAN (R 4.2.0)
#> httr 1.4.3 2022-05-04 [1] CRAN (R 4.2.0)
#> intervals 0.15.2 2020-04-04 [1] CRAN (R 4.2.0)
#> KernSmooth 2.23-20 2021-05-03 [2] CRAN (R 4.2.0)
#> knitr 1.39 2022-04-26 [1] CRAN (R 4.2.0)
#> lattice * 0.20-45 2021-09-22 [1] CRAN (R 4.2.0)
#> lifecycle 1.0.1 2021-09-24 [1] CRAN (R 4.2.0)
#> lwgeom 0.2-8 2021-10-06 [1] CRAN (R 4.2.0)
#> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.0)
#> mime 0.12 2021-09-28 [1] CRAN (R 4.2.0)
#> pillar 1.7.0 2022-02-01 [1] CRAN (R 4.2.0)
#> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.0)
#> proxy 0.4-26 2021-06-07 [1] CRAN (R 4.2.0)
#> purrr 0.3.4 2020-04-17 [1] CRAN (R 4.2.0)
#> R.cache 0.15.0 2021-04-30 [1] CRAN (R 4.2.0)
#> R.methodsS3 1.8.1 2020-08-26 [1] CRAN (R 4.2.0)
#> R.oo 1.24.0 2020-08-26 [1] CRAN (R 4.2.0)
#> R.utils 2.11.0 2021-09-26 [1] CRAN (R 4.2.0)
#> R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0)
#> Rcpp 1.0.8.3 2022-03-17 [1] CRAN (R 4.2.0)
#> reprex 2.0.1 2021-08-05 [1] CRAN (R 4.2.0)
#> rlang 1.0.2 2022-03-04 [1] CRAN (R 4.2.0)
#> rmarkdown 2.14 2022-04-25 [1] CRAN (R 4.2.0)
#> rstudioapi 0.13 2020-11-12 [1] CRAN (R 4.2.0)
#> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.0)
#> sf * 1.0-8 2022-06-12 [1] Github (r-spatial/sf@2299f4a)
#> sftime * 0.2.0.9000 2022-06-12 [1] Github (r-spatial/sftime@b85255b)
#> sp * 1.5-0 2022-06-12 [1] Github (edzer/sp@cfe9cdb)
#> spacetime * 1.2-8 2022-06-24 [1] Github (edzer/spacetime@b10a361)
#> stars * 0.5-6 2022-06-12 [1] Github (r-spatial/stars@7588258)
#> stringi 1.7.6 2021-11-29 [1] CRAN (R 4.2.0)
#> stringr 1.4.0 2019-02-10 [1] CRAN (R 4.2.0)
#> styler 1.7.0 2022-03-13 [1] CRAN (R 4.2.0)
#> tibble 3.1.7 2022-05-03 [1] CRAN (R 4.2.0)
#> tidyselect 1.1.2 2022-02-21 [1] CRAN (R 4.2.0)
#> units 0.8-0 2022-02-05 [1] CRAN (R 4.2.0)
#> utf8 1.2.2 2021-07-24 [1] CRAN (R 4.2.0)
#> vctrs 0.4.1 2022-04-13 [1] CRAN (R 4.2.0)
#> withr 2.5.0 2022-03-03 [1] CRAN (R 4.2.0)
#> xfun 0.30 2022-03-02 [1] CRAN (R 4.2.0)
#> xml2 1.3.3 2021-11-30 [1] CRAN (R 4.2.0)
#> xts 0.12.1 2020-09-09 [1] CRAN (R 4.2.0)
#> yaml 2.3.5 2022-02-21 [1] CRAN (R 4.2.0)
#> zoo 1.8-10 2022-04-15 [1] CRAN (R 4.2.0)
#>
#> [1] C:/Users/henni/AppData/Local/R/win-library/4.2
#> [2] C:/Program Files/R/R-4.2.0/library
#>
#> ──────────────────────────────────────────────────────────────────────────────