Warning: implicit list embedding of S4 objects is deprecated

Question

From this code:

na_cells <- as.data.frame(extract(r_temp, poly_contour, cellnumbers = TRUE))

Why do I get this warning?

Warning in `[<-`(`*tmp*`, cnt, value = p@polygons[[i]]@Polygons[[j]]) :
  implicit list embedding of S4 objects is deprecated

Here are some details on my raster and shapefile:

> r_temp
class       : RasterLayer 
dimensions  : 4876, 9864, 48096864  (nrow, ncol, ncell)
resolution  : 30, 30  (x, y)
extent      : 188384.5, 484304.5, 4914481, 5060761  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=tmerc +lat_0=0 +lon_0=-73.5 +k=0.9999 +x_0=304800 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs 
data source : C:\Users\Pierre\AppData\Local\Temp\RtmpKWmDgF\raster\r_tmp_2016-05-16_204325_10956_97931.grd 
names       : layer 
values      : 1, 8  (min, max)

> poly_contour
class       : SpatialPolygons 
features    : 1 
extent      : 189644.5, 483014.5, 4914961, 5060761  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=tmerc +lat_0=0 +lon_0=-73.5 +k=0.9999 +x_0=304800 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs 

Recently, I have installed a new version of R (version 3.3.0). Before that, my code has worked. Is it possible that this warning is caused my version of R ?

Answer 1

The output of extract is a list of cell numbers and values stored in matrices, it needs tidying to a data frame. (Data frames are best since they can store different types of data, like integer cell numbers and numeric values - which is essentially what cellnumbers=TRUE is for).

library(raster)
r <- raster(volcano)
## simplify the values
r <- (r %/% 20) * 20

p <- rasterToPolygons(r, dissolve = TRUE)

cells <- extract(r, p, cellnumbers = TRUE)
## note there's a list element for every individual polygon object
class(cells)
#[1] "list"
length(cells)
#[1] 6
nrow(p)
#[1] 6

## each element is a matrix, so standard tidying is easy
library(dplyr)
## (see purrr for more recommended ways to map functions like this)
d <- bind_rows(lapply(cells, as.data.frame), .id = "polygon")

head(d)
#  polygon cell value
#1       1    1   100
#2       1    2   100
#3       1    3   100
#4       1    4   100

Then you can use the dplyr tools in the usual ways, piping or chaining as you like:

summarize(group_by(d, polygon), mnval = mean(value))

Source: local data frame [6 x 2]

  polygon      mnval
    <chr>       <dbl>
1       1         100
2       2         120
3       3         140
4       4         160
5       5         180
6       6          80

The reason you'd work on these cell numbers individually is to make future extractions faster, or converting back to cell geometry etc., if you had changing layers coming in in a loop. (Then you don't redo the math to figure out which cells belong to which polygons).

Something like

d$value <- extract(r2, d$cell)

and so on for whatever summary etc. needed.

You don't have to use dplyr for this kind of work, but I highly recommend it. If you just want raster::extract to do your summary, give it a function rather than ask for cell numbers i.e.

cells <- extract(r, p, fun = mean)

(If the first argument to extract is multi-layered it's already optimized in the cellnumbers way, but that doesn't always scale so more flexibility is good).