diff --git a/R/ggR.R b/R/ggR.R index f7999a3..41a391c 100644 --- a/R/ggR.R +++ b/R/ggR.R @@ -29,10 +29,6 @@ #' When \code{img} contains factor values and \code{annotation=TRUE}, the raster values will automatically be converted #' to numeric in order to proceed with the brightness calculation. #' -#' The raster package provides a class lookup-table for categorical rasters (e.g. what you get if you run superClass in classification mode). If your raster has a lookup-table ggR will automatically treat it as categorical (see \link[terra]{factor}). -#' However, the factor status of Raster objects is easily lost and the values are interpreted as numeric. In such cases you should make use of the \code{forceCat = TRUE} argument, which makes sure -#' that ggplot2 uses a discrete scale, not a continuous one. -#' #' The geom_raster argument switches from the default use of annotation_raster to geom_raster. The difference between the two is that geom_raster performs #' a meaningful mapping from pixel values to fill colour, while annotation_raster is simply adding a picture to your plot. In practice this means that whenever you #' need a legend for your raster you should use \code{geom_raster = TRUE}. This also allows you to specify and modify the fill scale manually. diff --git a/R/rasterCVA.R b/R/rasterCVA.R index 59870d4..9b0838c 100644 --- a/R/rasterCVA.R +++ b/R/rasterCVA.R @@ -21,7 +21,7 @@ #' Returns a SpatRaster with two layers: change vector angle and change vector magnitude #' @export #' @examples -#' \donttest{ +#' \dontrun{ #' library(terra) #' pca <- rasterPCA(lsat)$map #' diff --git a/R/rasterPCA.R b/R/rasterPCA.R index c087a55..1309d94 100644 --- a/R/rasterPCA.R +++ b/R/rasterPCA.R @@ -24,7 +24,7 @@ #' @return Returns a named list containing the PCA model object ($model) and a SpatRaster with the principal component layers ($object). #' @export #' @examples -#' \donttest{ +#' \dontrun{ #' library(ggplot2) #' library(reshape2) #' ggRGB(rlogo, 1,2,3) diff --git a/R/readMeta.R b/R/readMeta.R index baf9145..3f5d270 100644 --- a/R/readMeta.R +++ b/R/readMeta.R @@ -268,7 +268,7 @@ readMeta <- function(file, raw = FALSE){ #' @param na Numeric vector. No-data value per band #' @param vsat Numeric vector. Saturation value per band #' @param scal Numeric vector. Scale factor per band. e.g. if data was scaled to 1000*reflectance for integer conversion. -#' @param dtyp Character vector. Data type per band. See \code{\link[terra]{dataType}} for options. +#' @param dtyp Character vector. Data type per band. #' @param radRes Numeric vector. Radiometric resolution per band. #' @param spatRes Numeric vector. Spatial resolution per band. #' @param calrad data.frame. Calibration coefficients for dn->radiance conversion. Must have columns 'gain' and 'offset'. Rows named according to \code{bands}. diff --git a/man/ImageMetaData.Rd b/man/ImageMetaData.Rd index 88e9611..d30a803 100644 --- a/man/ImageMetaData.Rd +++ b/man/ImageMetaData.Rd @@ -80,7 +80,7 @@ ImageMetaData( \item{scal}{Numeric vector. Scale factor per band. e.g. if data was scaled to 1000*reflectance for integer conversion.} -\item{dtyp}{Character vector. Data type per band. See \code{\link[terra]{dataType}} for options.} +\item{dtyp}{Character vector. Data type per band.} \item{calrad}{data.frame. Calibration coefficients for dn->radiance conversion. Must have columns 'gain' and 'offset'. Rows named according to \code{bands}.} diff --git a/man/ggR.Rd b/man/ggR.Rd index 9508846..c49521f 100644 --- a/man/ggR.Rd +++ b/man/ggR.Rd @@ -66,10 +66,6 @@ Plot single layer imagery in grey-scale. Can be used with a SpatRaster. When \code{img} contains factor values and \code{annotation=TRUE}, the raster values will automatically be converted to numeric in order to proceed with the brightness calculation. -The raster package provides a class lookup-table for categorical rasters (e.g. what you get if you run superClass in classification mode). If your raster has a lookup-table ggR will automatically treat it as categorical (see \link[terra]{factor}). -However, the factor status of Raster objects is easily lost and the values are interpreted as numeric. In such cases you should make use of the \code{forceCat = TRUE} argument, which makes sure -that ggplot2 uses a discrete scale, not a continuous one. - The geom_raster argument switches from the default use of annotation_raster to geom_raster. The difference between the two is that geom_raster performs a meaningful mapping from pixel values to fill colour, while annotation_raster is simply adding a picture to your plot. In practice this means that whenever you need a legend for your raster you should use \code{geom_raster = TRUE}. This also allows you to specify and modify the fill scale manually. diff --git a/man/rasterCVA.Rd b/man/rasterCVA.Rd index 5145a67..27d66d5 100644 --- a/man/rasterCVA.Rd +++ b/man/rasterCVA.Rd @@ -33,7 +33,7 @@ For example for a given pixel in image A and B for the red and nir band the chan Angles are returned *in degree* beginning with 0 degrees pointing 'north', i.e. the y-axis, i.e. the second band. } \examples{ -\donttest{ +\dontrun{ library(terra) pca <- rasterPCA(lsat)$map diff --git a/man/rasterPCA.Rd b/man/rasterPCA.Rd index c7ec20a..fe7f20c 100644 --- a/man/rasterPCA.Rd +++ b/man/rasterPCA.Rd @@ -47,7 +47,7 @@ Standardised PCA (SPCA) can be useful if imagery or bands of different dynamic r has the same effect as using normalised bands of unit variance. } \examples{ -\donttest{ +\dontrun{ library(ggplot2) library(reshape2) ggRGB(rlogo, 1,2,3) diff --git a/tests/testthat/test-panSharpen.R b/tests/testthat/test-panSharpen.R index cdb6414..f266bcf 100755 --- a/tests/testthat/test-panSharpen.R +++ b/tests/testthat/test-panSharpen.R @@ -3,6 +3,9 @@ context("panSharpen") library(terra) test_that("panSharpen methods", { + skip_on_ci() + skip_on_cran() + skip_on_covr() suppressWarnings({ agg <- aggregate(lsat, 10) pan <- sum(lsat[[1:3]]) @@ -11,7 +14,7 @@ test_that("panSharpen methods", { nlayers <- c(3,3,7) names(nlayers) <- names(panList) <- meth - for(m in meth) expect_is(panList[[m]], "SpatRaster", info = m) + for(m in meth) expect_is(panList[[m]], "SpatRaster") for(m in meth) expect_equal(names(panList[[m]]), paste0(names(agg)[1:nlayers[m]], "_pan"), info = m) # tests inmplicitly for correct number of layers for(m in meth) expect_equal(res(panList[[m]]), res(pan), info = m) }) diff --git a/tests/testthat/test-rasterPCA.R b/tests/testthat/test-rasterPCA.R index f230e95..6c6d545 100644 --- a/tests/testthat/test-rasterPCA.R +++ b/tests/testthat/test-rasterPCA.R @@ -7,6 +7,9 @@ ld <- as.data.frame(lsat_t) for(spc in c(FALSE, TRUE)) { test_that(paste("stats::princomp(covMat(raster)) == stats::princomp(sample) with spca=",spc), { + skip_on_ci() + skip_on_cran() + skip_on_covr() expect_s3_class(r <- rasterPCA(lsat_t, nSamples = NULL, spca = spc), c("RStoolbox", "rasterPCA")) expect_s3_class(rs <- rasterPCA(lsat_t, nSamples = ncell(lsat_t), spca = spc), c("RStoolbox", "rasterPCA")) expect_equal(abs(unclass(rs$model$loadings)), abs(unclass(r$model$loadings))) @@ -22,6 +25,9 @@ for(i in seq_len(nrow(G))){ spc <- G[i,"spc"] smpl <- if(G[i,"smpl"]) ncell(lsat_t) else NULL test_that(paste("rasterPCA NA handling; spca =",spc, "; nSamples =", deparse(smpl)), { + skip_on_ci() + skip_on_cran() + skip_on_covr() suppressWarnings({ expect_s3_class(r <- rasterPCA(lsat_t, nSamples = smpl, spca = spc), c("RStoolbox", "rasterPCA")) expect_true(all(is.na(r$map[c(100:200,400:500)])))