Difference between large Rasterlayer and formal class Raster Layer in R?

Question

I currently possess two rasters with the EXACT SAME resolution and extent, and yet they show different number of rows when loaded into the global environment in R.

In addition, before these rasters were made a dataframe in R by using the asctodataframe() function in R, they were initially stacked and the PCA was calculated using the rasterPCA() function, and then, I obtained the rasters in the snapshot shown below.

However, the individual rasters were loaded as a large Rasterlayer and a formal class raster layer. What is the difference between the two? And is this difference causing a change in the number of rows when loaded into R.

Snapshot of Individual rasters:

For more info: please see Same extent and resolution of rasters, but different number of cells

---

library(raster)
library(RStoolbox)

###################################
##Loading the Present day variables and then creating a raster stack, followed by a PCA
##################################

bio3 <- raster("C:\\Users\\rameshv\\4_ForR\\bio3")
bio4 <- raster("C:\\Users\\rameshv\\4_ForR\\bio4")
bio5 <- raster("C:\\Users\\rameshv\\\4_ForR\\bio5")

pres_stack <- stack(bio3, bio4,bio5)

pre_pca <-  rasterPCA(pres_stack, nComp = 2) #Choosing the first two axes

#Note: YOU NEED TO CALL the right element, else it will not writeRaster
writeRaster(pre_pca$map,"C:\\Users\\rameshv\\Downloads\\5_PCAforR\\PC.asc", format="ascii", bylayer=T)

#################################
##Loading the LGM variables and then creating a raster stack, followed by a PCA

lg3 <- raster("C:\\Users\\rameshv\\4_ForR\\cclgmbi3")
lg4 <- raster("C:\\Users\\rameshv\\4_ForR\\cclgmbi4")
lg5 <- raster("C:\\Users\\rameshv\\4_ForR\\cclgmbi5")


lg_stack <- stack(lg3,lg4,lg5)

lg_pca <- rasterPCA(lg_stack, nComp = 2) #Choosing the first two axes

#Note: YOU NEED TO CALL the right element, else it will not writeRaster
writeRaster(lg_pca$map,"C:\\Users\\rameshv\\5_PCAforR\\PC.asc", format="ascii", bylayer=T)

#########Now I have two rasters that are PC1 and PC2 of the variables    chosen and shall run the multivariate code provided by Hamann et al., 2015

library(SDMTools)     # install package to read and write ESRI ASCII grids
library(yaImpute)     # install package for k-nearest neighbour (kNN) search

lg1 <- asc2dataframe("C:\\Users\\rameshv\\5_PCAforR\\PC_1.asc") 
lg2 <- asc2dataframe("C:\\Users\\rameshv\\5_PCAforR\\PC_2.asc")
present1  <- asc2dataframe("C:\\Users\\rameshv\\5_PCAforR\\PC_1.asc")
present2  <- asc2dataframe("C:\\Users\\rameshv\\5_PCAforR\\PC_2.asc")

---

> str(lg1)
 'data.frame':   44352 obs. of  3 variables:
  $ y    : num  2209806 2209806 2209806 2209806 2209806 ...
  $ x    : num  -5265209 -5260209 -5250209 -5245209 -5240209 ...
  $ var.1: num  -260 -252 -214 -198 -187 ...
   - attr(*, "filenames")=List of 2
  ..$      : chr "C:\\Users\\rameshv\\Downloads\\Climate Stability\\Data_LGM_Present\\LGM\\5_PCAforR\\PC_1.asc"
  ..$ names: chr "var.1"

  > str(present1)
   'data.frame':   44340 obs. of  3 variables:
    $ y    : num  2209806 2209806 2209806 2209806 2209806 ...
    $ x    : num  -5265209 -5260209 -5250209 -5245209 -5240209 ...
    $ var.1: num  -38.26 -32.95 -8.26 3.47 9.82 ...
    - attr(*, "filenames")=List of 2
    ..$      : chr "C:\\Users\\rameshv\\Downloads\\Climate Stability\\Data_LGM_Present\\Present\\5_PCAforR\\PC_1.asc"
    ..$ names: chr "var.1"

Answer 1

First, please look at the objects in R and not RStudio, or whatever IDE you are using. There have been continuing issues with spatial objects in RStudio and I do not consider it reliable. It would also be helpful if you provided your code so we could track how each object was created.

I took a look at the source code for the rasterPCA function and the Large RasterLayer object class is not being assigned in the function. I am wondering if the object name is coming from RStudio. I would not worry because I think that these objects are still formally RasterLayer and RasterStack objects.

The difference in number of rows, columns, values (from your previous post) could be attributed to the spatial object information not displaying correctly in the IDE or the function not assigning the values correctly. The rasterPCA function is; 1) taking a sample, 2) predicting to the raster values, 3) assigning the predicted values back to the raster. The code looks sound and when I have used it, have observed no problems. Keep in mind that the function could be producing NA values that also may effect the displayed number of values in the raster. The critical element is that the number of rows, columns and geographic extent do not change.