subsetting data.cube inside custom function - r

Question

I am trying to make a function of my own to subset a data.cube in R, and format the result automatically for some predefined plots I aim to build.
This is my function.
require(data.table)
require(data.cube)
secciona <- function(cubo = NULL,
fecha_valor = list(),
loc_valor = list(),
prod_valor = list(),
drop = FALSE){
cubo[fecha_valor, loc_valor, prod_valor, drop = drop]
## The line above will really be an asignment of type y <- format(cubo[...drop])
## Rest of code which will end up plotting the subset of the function
}
The thing is I keep on getting the error: Error in eval(expr, envir, enclos) : object 'fecha_valor' not found
What is most strange for me, is that on the console everything works fine, but not when defined inside the subsetting function of mine.
In console:
> dc[list(as.Date("2013/01/01"))]
> dc[list(as.Date("2013/01/01")),]
> dc[list(as.Date("2013/01/01")),,]
> dc[list(as.Date("2013/01/01")),list(),list()]
all give as result:
<data.cube>
fact:
5627 rows x 2 dimensions x 1 measures (0.32 MB)
dimensions:
localizacion : 4 entities x 3 levels (0.01 MB)
producto : 153994 entities x 3 levels (21.29 MB)
total size: 21.61 MB
But whenever I try
secciona(dc)
secciona(dc, fecha_valor = list(as.Date("2013/01/01")))
secciona(dc, fecha_valor = list())
I always get the error above mentioned.
Any ideas why this is happening? should I proceed in else way for my approach of editing the subset for plotting?

Answer 1

This is the standard issue that R users will face when dealing with non-standard evaluation. This is a consequence of Computing on the language R language feature.
[.data.cube function expects to be used in interactive way, that extends the flexibility of the arguments passed to it, but gives some restrictions. In that aspect it is similar to [.data.table when passing expressions from wrapper function to [ subset operator. I've added dummy example to make it reproducible.
I see you are already using data.cube-oop branch, so just to clarify for other readers. data.cube-oop branch is 92 commits ahead of master branch, to install use the following.
install.packages("data.cube", repos = paste0("https://", c(
"jangorecki.gitlab.io/data.cube",
"Rdatatable.github.io/data.table",
"cran.rstudio.com"
)))
library(data.cube)
set.seed(1)
ar = array(rnorm(8,10,5), rep(2,3),
dimnames = list(color = c("green","red"),
year = c("2014","2015"),
country = c("IN","UK"))) # sorted
dc = as.data.cube(ar)
f = function(color=list(), year=list(), country=list(), drop=FALSE){
expr = substitute(
dc[color=.color, year=.year, country=.country, drop=.drop],
list(.color=color, .year=year, .country=country, .drop=drop)
)
eval(expr)
}
f(year=list(c("2014","2015")), country="UK")
#<data.cube>
#fact:
# 4 rows x 3 dimensions x 1 measures (0.00 MB)
#dimensions:
# color : 2 entities x 1 levels (0.00 MB)
# year : 2 entities x 1 levels (0.00 MB)
# country : 1 entities x 1 levels (0.00 MB)
#total size: 0.01 MB
You can track the expression just by putting print(expr) before/instead eval(expr).
Read more about non-standard evaluation:
- R Language Definition: Computing on the language
- Advanced R: Non-standard evaluation
- manual of substitute function
And some related SO questions:
- Passing on non-standard evaluation arguments to the subset function
- In R, why is [ better than subset?