Meaning of "#" operator in R language? - r

Question

I came across the following and I haven't figured out the purpose of the "#" operator. What's the meaning there? I didn't make heads/tails of the R manual language.
library(lattice)
library(sp)
data(meuse)
coordinates(meuse) <- ~x+y
proj4string(meuse) <- CRS("+init=epsg:28992")
p <- xyplot(copper ~ cadmium, data = meuse#data, col = "grey", pch = 20, cex = 2)
R manuals says
Usage
object#name
object#name <- value
Extract or replace the contents of a slot in a object with a formal (S4) class structure.
These operators support the formal classes of package methods, and are enabled only when package methods is loaded (as per default). See slot for further details, in particular for the differences between slot() and the # operator.
It is checked that object is an S4 object (see isS4), and it is an error to attempt to use # on any other object. (There is an exception for name .Data for internal use only.) The replacement operator checks that the slot already exists on the object (which it should if the object is really from the class it claims to be).
I checked the structure of "meuse" and found no references to a slot named "data".

Answer 1

meuse is an S4 object
isS4(meuse)
[1] TRUE
If you take the structure of of meuse (str_meuse) you'll see some fields are denoted with your # operator, including one called data. These slots can be accessed with # similar to how you might see other slots in other objects accessed using the $ operator. So meuse#data gives you the data portion of the meuse object.
str(meuse)
Formal class 'SpatialPointsDataFrame' [package "sp"] with 5 slots
..# data :'data.frame': 155 obs. of 12 variables:
.. ..$ cadmium: num [1:155] 11.7 8.6 6.5 2.6 2.8 3 3.2 2.8 2.4 1.6 ...
.. ..$ copper : num [1:155] 85 81 68 81 48 61 31 29 37 24 ...
.. ..$ lead : num [1:155] 299 277 199 116 117 137 132 150 133 80 ...
.. ..$ zinc : num [1:155] 1022 1141 640 257 269 ...
.. ..$ elev : num [1:155] 7.91 6.98 7.8 7.66 7.48 ...
.. ..$ dist : num [1:155] 0.00136 0.01222 0.10303 0.19009 0.27709 ...
.. ..$ om : num [1:155] 13.6 14 13 8 8.7 7.8 9.2 9.5 10.6 6.3 ...
.. ..$ ffreq : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ soil : Factor w/ 3 levels "1","2","3": 1 1 1 2 2 2 2 1 1 2 ...
.. ..$ lime : Factor w/ 2 levels "0","1": 2 2 2 1 1 1 1 1 1 1 ...
.. ..$ landuse: Factor w/ 15 levels "Aa","Ab","Ag",..: 4 4 4 11 4 11 4 2 2 15 ...
.. ..$ dist.m : num [1:155] 50 30 150 270 380 470 240 120 240 420 ...
..# coords.nrs : int [1:2] 1 2
..# coords : num [1:155, 1:2] 181072 181025 181165 181298 18130
See how that subsetting is working?
str(meuse#data)
'data.frame': 155 obs. of 12 variables:
$ cadmium: num 11.7 8.6 6.5 2.6 2.8 3 3.2 2.8 2.4 1.6 ...
$ copper : num 85 81 68 81 48 61 31 29 37 24 ...
$ lead : num 299 277 199 116 117 137 132 150 133 80 ...
$ zinc : num 1022 1141 640 257 269 ...
$ elev : num 7.91 6.98 7.8 7.66 7.48 ...
$ dist : num 0.00136 0.01222 0.10303 0.19009 0.27709 ...
$ om : num 13.6 14 13 8 8.7 7.8 9.2 9.5 10.6 6.3 ...
$ ffreq : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
$ soil : Factor w/ 3 levels "1","2","3": 1 1 1 2 2 2 2 1 1 2 ...
$ lime : Factor w/ 2 levels "0","1": 2 2 2 1 1 1 1 1 1 1 ...
$ landuse: Factor w/ 15 levels "Aa","Ab","Ag",..: 4 4 4 11 4 11 4 2 2 15 ...
$ dist.m : num 50 30 150 270 380 470 240 120 240 420 ...