From 43c45e536954561dcc29ba531c7dcebc5a267f47 Mon Sep 17 00:00:00 2001 From: HannaMeyer Date: Sat, 23 Mar 2024 17:30:06 +0100 Subject: [PATCH] replace reading of dataset --- R/aoa.R | 4 ++-- R/calibrate_aoa.R | 4 ++-- R/geodist.R | 4 ++-- R/global_validation.R | 4 ++-- R/knndm.R | 6 +++--- R/nndm.R | 6 +++--- R/normalize_DI.R | 3 ++- data-raw/create-cookfarm.R | 6 ------ inst/extdata/Cookfarm.RDS | Bin 653140 -> 0 bytes inst/extdata/bioclim_global.tif | Bin 127800 -> 0 bytes man/aoa.Rd | 4 ++-- man/calibrate_aoa.Rd | 4 ++-- man/geodist.Rd | 4 ++-- man/global_validation.Rd | 4 ++-- man/knndm.Rd | 6 +++--- man/nndm.Rd | 6 +++--- man/normalize_DI.Rd | 3 ++- tests/testthat/test-aoa.R | 4 ++-- tests/testthat/test-geodist.R | 8 ++++---- tests/testthat/test-knndm.R | 30 +++++++++++++++--------------- tests/testthat/test-nndm.R | 6 +++--- tests/testthat/test_trainDI.R | 4 ++-- 22 files changed, 58 insertions(+), 62 deletions(-) delete mode 100644 data-raw/create-cookfarm.R delete mode 100644 inst/extdata/Cookfarm.RDS delete mode 100644 inst/extdata/bioclim_global.tif diff --git a/R/aoa.R b/R/aoa.R index 41aac018..c52fc056 100644 --- a/R/aoa.R +++ b/R/aoa.R @@ -61,8 +61,8 @@ #' library(viridis) #' #' # prepare sample data: -#' dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) -#' dat <- aggregate(dat[,c("VW","Easting","Northing")],by=list(as.character(dat$SOURCEID)),mean) +#' data(cookfarm) +#' dat <- aggregate(cookfarm[,c("VW","Easting","Northing")],by=list(as.character(cookfarm$SOURCEID)),mean) #' pts <- st_as_sf(dat,coords=c("Easting","Northing")) #' pts$ID <- 1:nrow(pts) #' set.seed(100) diff --git a/R/calibrate_aoa.R b/R/calibrate_aoa.R index c580206c..55b96c6b 100644 --- a/R/calibrate_aoa.R +++ b/R/calibrate_aoa.R @@ -33,8 +33,8 @@ #' library(latticeExtra) #' #' #' # prepare sample data: -#' dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) -#' dat <- aggregate(dat[,c("VW","Easting","Northing")],by=list(as.character(dat$SOURCEID)),mean) +#' data(cookfarm) +#' dat <- aggregate(cookfarm[,c("VW","Easting","Northing")],by=list(as.character(cookfarm$SOURCEID)),mean) #' pts <- st_as_sf(dat,coords=c("Easting","Northing")) #' pts$ID <- 1:nrow(pts) #' studyArea <- rast(system.file("extdata","predictors_2012-03-25.tif",package="CAST"))[[1:8]] diff --git a/R/geodist.R b/R/geodist.R index 82dc0282..31f4b6e2 100644 --- a/R/geodist.R +++ b/R/geodist.R @@ -76,8 +76,8 @@ #'############Distances in temporal space #' library(lubridate) #' library(ggplot2) -#' dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) -#' dat <- st_as_sf(dat,coords=c("Easting","Northing")) +#' data(cookfarm) +#' dat <- st_as_sf(cookfarm,coords=c("Easting","Northing")) #' st_crs(dat) <- 26911 #' trainDat <- dat[dat$altitude==-0.3&lubridate::year(dat$Date)==2010,] #' predictionDat <- dat[dat$altitude==-0.3&lubridate::year(dat$Date)==2011,] diff --git a/R/global_validation.R b/R/global_validation.R index c024b01d..d3a1cc8a 100644 --- a/R/global_validation.R +++ b/R/global_validation.R @@ -9,8 +9,8 @@ #' @author Hanna Meyer #' @seealso \code{\link{CreateSpacetimeFolds}} #' @examples -#' dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) -#' dat <- dat[sample(1:nrow(dat),500),] +#' data(cookfarm) +#' dat <- cookfarm[sample(1:nrow(cookfarm),500),] #' indices <- CreateSpacetimeFolds(dat,"SOURCEID","Date") #' ctrl <- caret::trainControl(method="cv",index = indices$index,savePredictions="final") #' model <- caret::train(dat[,c("DEM","TWI","BLD")],dat$VW, method="rf", trControl=ctrl, ntree=10) diff --git a/R/knndm.R b/R/knndm.R index 2d125949..0044a106 100644 --- a/R/knndm.R +++ b/R/knndm.R @@ -145,9 +145,9 @@ #' library(ggplot2) #' #' ### prepare sample data: -#' dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) -#' dat <- aggregate(dat[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")], -#' by=list(as.character(dat$SOURCEID)),mean) +#' data(cookfarm) +#' dat <- aggregate(cookfarm[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")], +#' by=list(as.character(cookfarm$SOURCEID)),mean) #' pts <- dat[,-1] #' pts <- st_as_sf(pts,coords=c("Easting","Northing")) #' st_crs(pts) <- 26911 diff --git a/R/nndm.R b/R/nndm.R index c8c24925..d53a5473 100644 --- a/R/nndm.R +++ b/R/nndm.R @@ -107,9 +107,9 @@ #' library(terra) #' #' ### prepare sample data: -#' dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) -#' dat <- aggregate(dat[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")], -#' by=list(as.character(dat$SOURCEID)),mean) +#' data(cookfarm) +#' dat <- aggregate(cookfarm[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")], +#' by=list(as.character(cookfarm$SOURCEID)),mean) #' pts <- dat[,-1] #' pts <- st_as_sf(pts,coords=c("Easting","Northing")) #' st_crs(pts) <- 26911 diff --git a/R/normalize_DI.R b/R/normalize_DI.R index 7afcbb6d..ec1ff585 100644 --- a/R/normalize_DI.R +++ b/R/normalize_DI.R @@ -14,7 +14,8 @@ #' #' # prepare sample data: #' dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) -#' dat <- aggregate(dat[,c("VW","Easting","Northing")],by=list(as.character(dat$SOURCEID)),mean) +#' data(cookfarm) +#' dat <- aggregate(cookfarm[,c("VW","Easting","Northing")],by=list(as.character(cookfarm$SOURCEID)),mean) #' pts <- st_as_sf(dat,coords=c("Easting","Northing")) #' pts$ID <- 1:nrow(pts) #' set.seed(100) diff --git a/data-raw/create-cookfarm.R b/data-raw/create-cookfarm.R deleted file mode 100644 index 2a63d7c0..00000000 --- a/data-raw/create-cookfarm.R +++ /dev/null @@ -1,6 +0,0 @@ -# This script creates the cookfarm dataset -# - - -cookfarm = readRDS("inst/extdata/Cookfarm.RDS") -save(cookfarm, file = "data/cookfarm.rda", compress = "xz") diff --git a/inst/extdata/Cookfarm.RDS b/inst/extdata/Cookfarm.RDS deleted file mode 100644 index 0b8b33924973f8edecf4cc9ae97572e222262181..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 653140 zcmb@t30PCt);E4zE3`#PTdJrGX=|0X7J{NOCvCmTwN|;US3wzqQbk1x7%_xQ)f5pK zQnd&OB(-X(3JFM+F^M6Vs6e71lVpMbM@Yy?$Z#^}i*0Y;-uvF~``-V+dh+D_*4}%q zb@s{LYd9Nt@vDFN^>2aN;Z@Jtb6HkU8weRH={X&Bb<(f_X&8MQK@j-$+Spj-zHjSS zPNcp1?HeyQy>a{Rtsi~n^S7hIce``GedDuN-+gwx>q*j3=x(F_S3c6U%28sdZEC(H$IGJs{P;LXkeC;W&c>r|CQ7Bin ztIst}X|Z$TQ*;zGBxke%E{J65@U^&{qZZ504AgS2t#`4*xl^yV$DkNSpkw_dn@OMI zVA|&O#r70^tc?q70v(_dW_3_NF3V{O^Ua$+Rx(qEuvgh3@p>*-(?OXygM=Jw@AAc@ zgE|l~W)>T(uqBoQ0pKxOZVwj?Vd_dS0@DO-PAhMhqm3Q>jH4C@i83vtRA-2rfl<90 zZ;G*fB__950DC5GQn(s9lc34iXt!Mg#OAxPF5$@%U68M`b5_n;v5j0Nw%;oQZ9K>c zJ)z>vx=fi8;zC?>>jZ|9$JcwiHo7#zaQiW+SLNI&v@M83+HT@FDt)dL3(nRw2ps9d zF;KhZQ3g|Hx$ANw25`h{RV}wf1*n#B0=*1KUt2*;DEp9010R8wCkKz-0#$-xwyH@E zHHA@t`uoy7#qiO_KomhsN;o`_tNa?-k&_BZrI7OhbuKfvcZB9}Ht3H_YcK&e*!oPo zIx5vjDpbZE2xuWgk-$VD&8~@6a-LSU)Q?{u0xx9ZZsc}!U+&BQCk-|Ql7ToS0eMG{ zL0$&}^fg$!^g3e$cI_XOBW?C3-JhwB8uHjnLTfBZ$1V=G-W`b|$a4<>HA>sT*cT)U z6JVM5BZtQOLEIy|Sn?am>)NTdk&KSL8+kVv3@89LuzHi zqvkDjCAqRWXXhBLb{xYb2nQj`Az=pgV_=!B*vvth6_%Q!!Der94}lA`gAphFdk0{F zx|UnGi0No=(wiydo+I%Q5QMR48sr@Zdh53W6>WwXNw>et92~1m&U}btU}F)HK`oI$ zGqafq9ZN~z=xlfI!n5c)Le}#XIuh{lO;u@@3z?DFyHKo%jbw~y>vuA+KFeASR>p19 ztz7QI4GbgwKM>h6WLrICy`UoA*uW_=*4~K)pJnM9daWX>3@|!w5IBNcI!1aet@bKA zlVq;BMQ_>=l4F)82`OqTbLb@4p0ynONc5ysU!%{K;>6GTp0UqnhO0LIN+)OAufr$a z%ES$ZiJHiTa)QXA>Bru-I`@e6Imd_Q$@Np&`ulsuw)RT|M?@{0(nC-irzBgF4;TaC zho+B+&9vHEDq>U;eS?ZZH_DX*(UvW&$Z8W%D&VwwLU zZx7E30dM<0#aAlIVS?hPAz~h?6YgMy@zT;lLzgXEw8+C_0q(U0%}xm8$e0_>r0i57 zGk+tpxdw|=w&SYq(EkH*EO#{YFqLD!`w`R|uMb-;WD;lGGxmZ=kwd0_RsM|)?C$*3 z{NRj$e-Z{4V3Eil>jiVM1Zkp~&TpC|9J6q$fEQF<1vFr5kPmMOe(9gO#mQuC0WBi`u_L0E5|y@N zx1KD8bSZ`834X45gbF$4)TWo4Wxr+*4}1OA;+p|P*ut) zgJxRQ)mt3bS1;zd)|iY^ULeL!4vt4J!hpgk^zl;6eiEYTZbjnOn3-4_grAB2&W#*%Y4Ny!T$G3O9nLK|E zXqYeGts<)RJ5p>r>201~N%ZC^_TBwTpNz}Gjx*)Fhl;PW*F{vZGa8S-R(~`y+B?+) zL9O8RC=O`XUCbf`+9xiijNmx^7vP% z@w=3@BCe(23Qw%a(1w|p+ZT~NP*leM4{)^O(jRj0cx~9oa&?Jm5t*4k?bd{?(7j-E z{yef=vM9?VoLbH6>nW|arLBaW^oSNBF^yp(ktRH~wxg()*QYqRKEuseu3qGBq}K98 zY5!j!uwI?Kp^jR`>rFct8dkZychNzQu>UbsR}KGP2ToUJNaK26JkLUL$Km?UpCj6~ zmMlVgz^OEzP?4@(YhFhD-$CX5ZsGPc|hrf`#AL!vlE#P&fjo-vA|9H{T-_gFt z(8Oi`&*2g;OJGG8Z+n`e5|np1n@!E8ibPv=BD9TVxjy`2vzMrdgDWV8a~6QWvWocN z-Uj6^xxP7}MtEO$)F^m#1oUF<2Eo5|PZ1c^Q+F_FE zCOF5z$_~3}<~V2=r-A}T>y(tN=H12FqnJHR${-f29(Fi#L5|T9zLpY(Lx?4PozmXU0Ktys(OBaQ)shxs zmW~6cH(e*qqy)#vhR?3N6?$z{)iCM5Ww0BKOt<}6lb5zn+xGmbg`(rW_m-+WHc&g5|?_vuR-2+S%@vk89F#L{rw$_lW-d;M`f&a7ym}Z?Z{?Nsbj-sW1GWhIpsObdP3`tay(xmZv80rFsBEF%H_#>PILcgSZwkj;Cu+&mgPc_&?r!V#Oy$SrXV|D zskbmYYC))nsu!K67MX}7V21qR||Dizt6O}OO=+}7?l`M z@4ege7Iko-vHd`*2a7t)Qz>$_`72UiFnP0o^og#gFQ|*LX{9Kq!m&clI7Dw+NH9m3 zHGI%9$-|+I?BRxSSgKAM>E?g}a*;cLcEavMaB{eu(A$dLj<`dw*xR#>rK}(SC-t

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zT^q(R-`9B86l{l(E&o1aJEMA(g={;goIRqwH?0~qqB{=rQfn+(c-yHD?`ibfkyhFF z!v$WC_fkWPhzGGcWtIu5vv%1DrlvwhfdgO(jFlE;@GFm`%dija+ oEJU5LoVR)OFTXFNPcKtR@zRB(6mQXXt0?Koiy4s<9=}HZ2l0seQ~&?~ diff --git a/man/aoa.Rd b/man/aoa.Rd index 31e6f2e2..9e4fbae2 100644 --- a/man/aoa.Rd +++ b/man/aoa.Rd @@ -98,8 +98,8 @@ library(caret) library(viridis) # prepare sample data: -dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) -dat <- aggregate(dat[,c("VW","Easting","Northing")],by=list(as.character(dat$SOURCEID)),mean) +data(cookfarm) +dat <- aggregate(cookfarm[,c("VW","Easting","Northing")],by=list(as.character(cookfarm$SOURCEID)),mean) pts <- st_as_sf(dat,coords=c("Easting","Northing")) pts$ID <- 1:nrow(pts) set.seed(100) diff --git a/man/calibrate_aoa.Rd b/man/calibrate_aoa.Rd index bc3bb0d9..f99cae87 100644 --- a/man/calibrate_aoa.Rd +++ b/man/calibrate_aoa.Rd @@ -66,8 +66,8 @@ library(viridis) library(latticeExtra) #' # prepare sample data: -dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) -dat <- aggregate(dat[,c("VW","Easting","Northing")],by=list(as.character(dat$SOURCEID)),mean) +data(cookfarm) +dat <- aggregate(cookfarm[,c("VW","Easting","Northing")],by=list(as.character(cookfarm$SOURCEID)),mean) pts <- st_as_sf(dat,coords=c("Easting","Northing")) pts$ID <- 1:nrow(pts) studyArea <- rast(system.file("extdata","predictors_2012-03-25.tif",package="CAST"))[[1:8]] diff --git a/man/geodist.Rd b/man/geodist.Rd index ec499015..58b0242e 100644 --- a/man/geodist.Rd +++ b/man/geodist.Rd @@ -113,8 +113,8 @@ plot(dist) ############Distances in temporal space library(lubridate) library(ggplot2) -dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) -dat <- st_as_sf(dat,coords=c("Easting","Northing")) +data(cookfarm) +dat <- st_as_sf(cookfarm,coords=c("Easting","Northing")) st_crs(dat) <- 26911 trainDat <- dat[dat$altitude==-0.3&lubridate::year(dat$Date)==2010,] predictionDat <- dat[dat$altitude==-0.3&lubridate::year(dat$Date)==2011,] diff --git a/man/global_validation.Rd b/man/global_validation.Rd index 7496c206..ae9226af 100644 --- a/man/global_validation.Rd +++ b/man/global_validation.Rd @@ -22,8 +22,8 @@ the model to explain the entire gradient of the response. Comparable to LOOCV, predictions from all held back folds are used here together to calculate validation statistics. } \examples{ -dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) -dat <- dat[sample(1:nrow(dat),500),] +data(cookfarm) +dat <- cookfarm[sample(1:nrow(cookfarm),500),] indices <- CreateSpacetimeFolds(dat,"SOURCEID","Date") ctrl <- caret::trainControl(method="cv",index = indices$index,savePredictions="final") model <- caret::train(dat[,c("DEM","TWI","BLD")],dat$VW, method="rf", trControl=ctrl, ntree=10) diff --git a/man/knndm.Rd b/man/knndm.Rd index ff7098f9..4b5f8a58 100644 --- a/man/knndm.Rd +++ b/man/knndm.Rd @@ -167,9 +167,9 @@ library(terra) library(ggplot2) ### prepare sample data: -dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) -dat <- aggregate(dat[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")], - by=list(as.character(dat$SOURCEID)),mean) +data(cookfarm) +dat <- aggregate(cookfarm[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")], + by=list(as.character(cookfarm$SOURCEID)),mean) pts <- dat[,-1] pts <- st_as_sf(pts,coords=c("Easting","Northing")) st_crs(pts) <- 26911 diff --git a/man/nndm.Rd b/man/nndm.Rd index d6296966..b368fbd7 100644 --- a/man/nndm.Rd +++ b/man/nndm.Rd @@ -128,9 +128,9 @@ library(sf) library(terra) ### prepare sample data: -dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) -dat <- aggregate(dat[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")], - by=list(as.character(dat$SOURCEID)),mean) +data(cookfarm) +dat <- aggregate(cookfarm[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")], + by=list(as.character(cookfarm$SOURCEID)),mean) pts <- dat[,-1] pts <- st_as_sf(pts,coords=c("Easting","Northing")) st_crs(pts) <- 26911 diff --git a/man/normalize_DI.Rd b/man/normalize_DI.Rd index 53e035e5..d19e6688 100644 --- a/man/normalize_DI.Rd +++ b/man/normalize_DI.Rd @@ -25,7 +25,8 @@ library(caret) # prepare sample data: dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) -dat <- aggregate(dat[,c("VW","Easting","Northing")],by=list(as.character(dat$SOURCEID)),mean) +data(cookfarm) +dat <- aggregate(cookfarm[,c("VW","Easting","Northing")],by=list(as.character(cookfarm$SOURCEID)),mean) pts <- st_as_sf(dat,coords=c("Easting","Northing")) pts$ID <- 1:nrow(pts) set.seed(100) diff --git a/tests/testthat/test-aoa.R b/tests/testthat/test-aoa.R index 9b39d8e9..8e299441 100644 --- a/tests/testthat/test-aoa.R +++ b/tests/testthat/test-aoa.R @@ -1,7 +1,7 @@ loaddata <- function() { # prepare sample data: - dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) - dat <- aggregate(dat[,c("VW","Easting","Northing")],by=list(as.character(dat$SOURCEID)),mean) + data(cookfarm) + dat <- aggregate(cookfarm[,c("VW","Easting","Northing")],by=list(as.character(cookfarm$SOURCEID)),mean) pts <- sf::st_as_sf(dat,coords=c("Easting","Northing")) pts$ID <- 1:nrow(pts) set.seed(100) diff --git a/tests/testthat/test-geodist.R b/tests/testthat/test-geodist.R index a06fce18..2b7eafd0 100644 --- a/tests/testthat/test-geodist.R +++ b/tests/testthat/test-geodist.R @@ -262,8 +262,8 @@ test_that("geodist works with categorical variables in feature space", { test_that("geodist works in temporal space", { -dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) -dat <- sf::st_as_sf(dat,coords=c("Easting","Northing")) +data(cookfarm) +dat <- sf::st_as_sf(cookfarm,coords=c("Easting","Northing")) sf::st_crs(dat) <- 26911 trainDat <- dat[dat$altitude==-0.3&lubridate::year(dat$Date)==2010,] predictionDat <- dat[dat$altitude==-0.3&lubridate::year(dat$Date)==2011,] @@ -282,8 +282,8 @@ expect_equal(mean_prediction_to_sample, 4674.37) }) test_that("geodist works in temporal space and with CV", { - dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) - dat <- sf::st_as_sf(dat,coords=c("Easting","Northing")) + data(cookfarm) + dat <- sf::st_as_sf(cookfarm,coords=c("Easting","Northing")) sf::st_crs(dat) <- 26911 trainDat <- dat[dat$altitude==-0.3&lubridate::year(dat$Date)==2010,] predictionDat <- dat[dat$altitude==-0.3&lubridate::year(dat$Date)==2011,] diff --git a/tests/testthat/test-knndm.R b/tests/testthat/test-knndm.R index aa858c2a..aee706d1 100644 --- a/tests/testthat/test-knndm.R +++ b/tests/testthat/test-knndm.R @@ -195,9 +195,9 @@ test_that("kNNDM yields the expected results with SpatRast modeldomain", { set.seed(1234) # prepare sample data - dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) - dat <- terra::aggregate(dat[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")], - by=list(as.character(dat$SOURCEID)),mean) + data(cookfarm) + dat <- terra::aggregate(cookfarm[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")], + by=list(as.character(cookfarm$SOURCEID)),mean) pts <- dat[,-1] pts <- sf::st_as_sf(pts,coords=c("Easting","Northing")) sf::st_crs(pts) <- 26911 @@ -214,9 +214,9 @@ test_that("kNNDM works in feature space with kmeans clustering and raster as mod set.seed(1234) # prepare sample data - dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) - dat <- terra::aggregate(dat[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")], - by=list(as.character(dat$SOURCEID)),mean) + data(cookfarm) + dat <- terra::aggregate(cookfarm[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")], + by=list(as.character(cookfarm$SOURCEID)),mean) pts <- dat[,-1] pts <- sf::st_as_sf(pts,coords=c("Easting","Northing")) sf::st_crs(pts) <- 26911 @@ -238,9 +238,9 @@ test_that("kNNDM works in feature space with hierarchical clustering and raster set.seed(1234) # prepare sample data - dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) - dat <- terra::aggregate(dat[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")], - by=list(as.character(dat$SOURCEID)),mean) + data(cookfarm) + dat <- terra::aggregate(cookfarm[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")], + by=list(as.character(cookfarm$SOURCEID)),mean) pts <- dat[,-1] pts <- sf::st_as_sf(pts,coords=c("Easting","Northing")) sf::st_crs(pts) <- 26911 @@ -284,9 +284,9 @@ test_that("kNNDM works in feature space with categorical variables and predpoint set.seed(1234) # prepare sample data - dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) - dat <- terra::aggregate(dat[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")], - by=list(as.character(dat$SOURCEID)),mean) + data(cookfarm) + dat <- terra::aggregate(cookfarm[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")], + by=list(as.character(cookfarm$SOURCEID)),mean) pts <- dat[,-1] pts <- sf::st_as_sf(pts,coords=c("Easting","Northing")) sf::st_crs(pts) <- 26911 @@ -365,9 +365,9 @@ test_that("kNNDM works in feature space with Mahalanobis distance without cluste set.seed(1234) # prepare sample data - dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) - dat <- terra::aggregate(dat[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")], - by=list(as.character(dat$SOURCEID)),mean) + data(cookfarm) + dat <- terra::aggregate(cookfarm[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")], + by=list(as.character(cookfarm$SOURCEID)),mean) pts <- dat[,-1] pts <- sf::st_as_sf(pts,coords=c("Easting","Northing")) sf::st_crs(pts) <- 26911 diff --git a/tests/testthat/test-nndm.R b/tests/testthat/test-nndm.R index 3913133d..916c45b9 100644 --- a/tests/testthat/test-nndm.R +++ b/tests/testthat/test-nndm.R @@ -116,9 +116,9 @@ test_that("NNDM yields the expected results with SpatRast modeldomain", { set.seed(1234) # prepare sample data - dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) - dat <- terra::aggregate(dat[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")], - by=list(as.character(dat$SOURCEID)),mean) + data(cookfarm) + dat <- terra::aggregate(cookfarm[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")], + by=list(as.character(cookfarm$SOURCEID)),mean) pts <- dat[,-1] pts <- sf::st_as_sf(pts,coords=c("Easting","Northing")) sf::st_crs(pts) <- 26911 diff --git a/tests/testthat/test_trainDI.R b/tests/testthat/test_trainDI.R index 014c579a..fac49d18 100644 --- a/tests/testthat/test_trainDI.R +++ b/tests/testthat/test_trainDI.R @@ -1,7 +1,7 @@ loaddata <- function() { # prepare sample data: - dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST")) - dat <- aggregate(dat[,c("VW","Easting","Northing")],by=list(as.character(dat$SOURCEID)),mean) + data("cookfarm") + dat <- aggregate(cookfarm[,c("VW","Easting","Northing")],by=list(as.character(cookfarm$SOURCEID)),mean) pts <- sf::st_as_sf(dat,coords=c("Easting","Northing")) pts$ID <- 1:nrow(pts) set.seed(100)