How can one work fully generically in data.table in R with column names in variables

Question

First of all: thanks to @MattDowle; data.table is among the best things that ever happened to me since I started using R.

Second: I am aware of many workarounds for various use cases of variable column names in data.table, including:

1. Select / assign to data.table variables which names are stored in a character vector
2. pass column name in data.table using variable in R
3. Referring to data.table columns by names saved in variables
4. passing column names to data.table programmatically
5. Data.table meta-programming
6. How to write a function that calls a function that calls data.table?
7. Using dynamic column names in `data.table`
8. dynamic column names in data.table, R
9. Assign multiple columns using := in data.table, by group
10. Setting column name in "group by" operation with data.table
11. R summarizing multiple columns with data.table

and probably more I haven't referenced.

But: even if I learned all the tricks documented above to the point that I never had to look them up to remind myself how to use them, I still would find that working with column names that are passed as parameters to a function is an extremely tedious task.

What I'm looking for is a "best-practices-approved" alternative to the following workaround / workflow. Consider that I have a bunch of columns of similar data, and would like to perform a sequence of similar operations on these columns or sets of them, where the operations are of arbitrarily high complexity, and the groups of column names passed to each operation specified in a variable.

I realize this issue sounds contrived, but I run into it with surprising frequency. The examples are usually so messy that it is difficult to separate out the features relevant to this question, but I recently stumbled across one that was fairly straightforward to simplify for use as a MWE here:

library(data.table)
library(lubridate)
library(zoo)

the.table <- data.table(year=1991:1996,var1=floor(runif(6,400,1400)))
the.table[,`:=`(var2=var1/floor(runif(6,2,5)),
                var3=var1/floor(runif(6,2,5)))]

# Replicate data across months
new.table <- the.table[, list(asofdate=seq(from=ymd((year)*10^4+101),
                                           length.out=12,
                                           by="1 month")),by=year]

# Do a complicated procedure to each variable in some group.
var.names <- c("var1","var2","var3")

for(varname in var.names) {
    #As suggested in an answer to Link 3 above
    #Convert the column name to a 'quote' object
    quote.convert <- function(x) eval(parse(text=paste0('quote(',x,')')))

    #Do this for every column name I'll need
    varname <- quote.convert(varname)
    anntot <- quote.convert(paste0(varname,".annual.total"))
    monthly <- quote.convert(paste0(varname,".monthly"))
    rolling <- quote.convert(paste0(varname,".rolling"))
    scaled <- quote.convert(paste0(varname,".scaled"))

    #Perform the relevant tasks, using eval()
    #around every variable columnname I may want
    new.table[,eval(anntot):=
               the.table[,rep(eval(varname),each=12)]]
    new.table[,eval(monthly):=
               the.table[,rep(eval(varname)/12,each=12)]]
    new.table[,eval(rolling):=
               rollapply(eval(monthly),mean,width=12,
                         fill=c(head(eval(monthly),1),
                                tail(eval(monthly),1)))]
    new.table[,eval(scaled):=
               eval(anntot)/sum(eval(rolling))*eval(rolling),
              by=year]
}

Of course, the particular effect on the data and variables here is irrelevant, so please do not focus on it or suggest improvements to accomplishing what it accomplishes in this particular case. What I am looking for, rather, is a generic strategy for the workflow of repeatedly applying an arbitrarily complicated procedure of data.table actions to a list of columns or list of lists-of-columns, specified in a variable or passed as an argument to a function, where the procedure must refer programmatically to columns named in the variable/argument, and possibly includes updates, joins, groupings, calls to the data.table special objects .I, .SD, etc.; BUT one which is simpler, more elegant, shorter, or easier to design or implement or understand than the one above or others that require frequent quote-ing and eval-ing.

In particular please note that because the procedures can be fairly complex and involve repeatedly updating the data.table and then referencing the updated columns, the standard lapply(.SD,...), ... .SDcols = ... approach is usually not a workable substitute. Also replacing each call of eval(a.column.name) with DT[[a.column.name]] neither simplifies much nor works completely in general since that doesn't play nice with the other data.table operations, as far as I am aware.

Answer 1

Problem you are describing is not strictly related to data.table.
Complex queries cannot be easily translated to code that machine can parse, thus we are not able to escape complexity in writing the query for complex operations.
Just imagine how to programmatically construct query for the following data.table query

DT[, c(f1(v1, v2, opt=TRUE),
       f2(v3, v4, v5, opt1=FALSE, opt2=TRUE),
       lapply(.SD, f3, opt1=TRUE, opt2=FALSE))
   , by=.(id1, id2)]

using dplyr or SQL - assuming all columns (id1, id2, v1...v5) or even options (opt, opt1, opt2) should be passed as variables.

Because of the above I don't think you could easily accomplish requirement stated in your question:

is simpler, more elegant, shorter, or easier to design or implement or understand than the one above or others that require frequent quote-ing and eval-ing.

Although, comparing to other programming languages, base R provides very useful tools to deal with such problems.

---

You already found suggestions to use get, mget, DT[[col_name]], parse, quote, eval.

• As you mentioned DT[[col_name]] might not play well with data.table optimizations, thus is not that useful here.
• parse is probably the easiest way to construct complex queries as you can just operate on strings, but it doesn't provide basic language syntax validation. So you can ended up trying to parse a string that R parser does not accept. Additionally there is a security concern as presented in 2655#issuecomment-376781159.
• get/mget are the ones most commonly suggested to deal with such problems. get and mget are internally catch by [.data.table and translated to expected columns. So you are assuming your arbitrary complex query will be able to be decomposed by [.data.table and expected columns properly inputted.
• Since you asked this question few years back, the new feature - dot-dot prefix - is being rolled out in recently. You prefix variable name using dot-dot to refer to a variable outside of the scope of current data.table. Similarly as you refer parent directory in file system. Internals behind dot-dot will be quite similar to get, variables having prefix will be de-referenced inside of [.data.table. . In future releases dot-dot prefix may allow calls like:

col1="a"; col2="b"; col3="g"; col4="x"; col5="y"
DT[..col4==..col5, .(s1=sum(..col1), s2=sum(..col2)), by=..col3]

• Personally I prefer quote and eval instead. quote and eval is interpreted almost as written by hand from scratch. This method does not rely on data.table abilities to manage references to columns. We can expect all optimisations to work the same way as if you would write those queries by hand. I found it also easier to debug as at any point you can just print quoted expression to see what is actually passed to data.table query. Additionally there is a less space for bugs to occur. Constructing complex queries using R language object is sometimes tricky, it is easy to wrap the procedure into function so it can be applied in different use cases and easily re-used. Important to note that this method is independent from data.table. It uses R language constructs. You can find more information about that in official R Language Definition in Computing on the language chapter.
• What else? I submitted proposal of a new concept called macro in #1579. In short it is a wrapper on DT[eval(qi), eval(qj), eval(qby)] so you still have to operate on R language objects. You are welcome to put your comment there.

---

Going to the example. I will wrap all logic into do_vars function. Calling do_vars(donot=TRUE) will print expressions to be computed on data.table instead of eval them. Below code should be run just after the OP code.

expected = copy(new.table)
new.table = the.table[, list(asofdate=seq(from=ymd((year)*10^4+101), length.out=12, by="1 month")), by=year]

do_vars = function(x, y, vars, donot=FALSE) {
  name.suffix = function(x, suffix) as.name(paste(x, suffix, sep="."))
  do_var = function(var, x, y) {
    substitute({
      x[, .anntot := y[, rep(.var, each=12)]]
      x[, .monthly := y[, rep(.var/12, each=12)]]
      x[, .rolling := rollapply(.monthly, mean, width=12, fill=c(head(.monthly,1), tail(.monthly,1)))]
      x[, .scaled := .anntot/sum(.rolling)*.rolling, by=year]
    }, list(
      .var=as.name(var),
      .anntot=name.suffix(var, "annual.total"),
      .monthly=name.suffix(var, "monthly"),
      .rolling=name.suffix(var, "rolling"),
      .scaled=name.suffix(var, "scaled")
    ))
  }
  ql = lapply(setNames(nm=vars), do_var, x, y)
  if (donot) return(ql)
  lapply(ql, eval.parent)
  invisible(x)
}
do_vars(new.table, the.table, c("var1","var2","var3"))
all.equal(expected, new.table)
#[1] TRUE

do_vars(new.table, the.table, c("var1","var2","var3"), donot=TRUE)
#$var1
#{
#    x[, `:=`(var1.annual.total, y[, rep(var1, each = 12)])]
#    x[, `:=`(var1.monthly, y[, rep(var1/12, each = 12)])]
#    x[, `:=`(var1.rolling, rollapply(var1.monthly, mean, width = 12, 
#        fill = c(head(var1.monthly, 1), tail(var1.monthly, 1))))]
#    x[, `:=`(var1.scaled, var1.annual.total/sum(var1.rolling) * 
#        var1.rolling), by = year]
#}
#
#$var2
#{
#    x[, `:=`(var2.annual.total, y[, rep(var2, each = 12)])]
#    x[, `:=`(var2.monthly, y[, rep(var2/12, each = 12)])]
#    x[, `:=`(var2.rolling, rollapply(var2.monthly, mean, width = 12, 
#        fill = c(head(var2.monthly, 1), tail(var2.monthly, 1))))]
#    x[, `:=`(var2.scaled, var2.annual.total/sum(var2.rolling) * 
#        var2.rolling), by = year]
#}
#
#$var3
#{
#    x[, `:=`(var3.annual.total, y[, rep(var3, each = 12)])]
#    x[, `:=`(var3.monthly, y[, rep(var3/12, each = 12)])]
#    x[, `:=`(var3.rolling, rollapply(var3.monthly, mean, width = 12, 
#        fill = c(head(var3.monthly, 1), tail(var3.monthly, 1))))]
#    x[, `:=`(var3.scaled, var3.annual.total/sum(var3.rolling) * 
#        var3.rolling), by = year]
#}
#

Answer 2

I tried to do this in data.table thinking "this isn't so bad"... but after an embarrassing length of time, I gave up. Matt says something like 'do in pieces then join', but I couldn't figure out elegant ways to do these pieces, especially because the last one depends on previous steps.

I have to say, this is a pretty brilliantly constructed question, and I too encounter similar issues frequently. I love data.table, but I still struggle sometimes. I don't know if I'm struggling with data.table or the complexity of the problem.

Here is the incomplete approach I've taken.

Realistically I can imagine that in a normal process you would have more intermediate variables stored that would be useful for calculating these values.

library(data.table)
library(zoo)

## Example yearly data
set.seed(27)
DT <- data.table(year=1991:1996,
                 var1=floor(runif(6,400,1400)))
DT[ , var2 := var1 / floor(runif(6,2,5))]
DT[ , var3 := var1 / floor(runif(6,2,5))]
setkeyv(DT,colnames(DT)[1])
DT

## Convenience function
nonkey <- function(dt){colnames(dt)[!colnames(dt)%in%key(dt)]}

## Annual data expressed monthly
NewDT <- DT[, j=list(asofdate=as.IDate(paste(year, 1:12, 1, sep="-"))), by=year]
setkeyv(NewDT, colnames(NewDT)[1:2])

## Create annual data
NewDT_Annual <- NewDT[DT]
setnames(NewDT_Annual, 
         nonkey(NewDT_Annual), 
         paste0(nonkey(NewDT_Annual), ".annual.total"))

## Compute monthly data
NewDT_Monthly <- NewDT[DT[ , .SD / 12, keyby=list(year)]]
setnames(NewDT_Monthly, 
         nonkey(NewDT_Monthly), 
         paste0(nonkey(NewDT_Monthly), ".monthly"))

## Compute rolling stats
NewDT_roll <- NewDT_Monthly[j = lapply(.SD, rollapply, mean, width=12, 
                                       fill=c(.SD[1],tail(.SD, 1))),
                            .SDcols=nonkey(NewDT_Monthly)]
NewDT_roll <- cbind(NewDT_Monthly[,1:2,with=F], NewDT_roll)
setkeyv(NewDT_roll, colnames(NewDT_roll)[1:2])
setnames(NewDT_roll, 
         nonkey(NewDT_roll), 
         gsub(".monthly$",".rolling",nonkey(NewDT_roll)))

## Compute normalized values

## Compute "adjustment" table which is 
## total of each variable, by year for rolling
## divided by
## original annual totals

## merge "adjustment values" in with monthly data, and then 
## make a modified data.table which is each varaible * annual adjustment factor

## Merge everything
NewDT_Combined <- NewDT_Annual[NewDT_roll][NewDT_Monthly]

Answer 3

Thanks for the question. Your original approach goes a long way towards solving most of the issues.

Here I've tweaked the quoting function slightly, and changed the approach to parse and evaluate the entire RHS expression as a string instead of the individual variables.

The reasoning being:

• You probably don't want to be repeating yourself by declaring every variable you need to use at the start of the loop.
• Strings will scale better since they can be generated programmatically. I've added an example below that calculates row-wise percentages to illustrate this.

---

library(data.table)
library(lubridate)
library(zoo)

set.seed(1)
the.table <- data.table(year=1991:1996,var1=floor(runif(6,400,1400)))
the.table[,`:=`(var2=var1/floor(runif(6,2,5)),
                var3=var1/floor(runif(6,2,5)))]

# Replicate data across months
new.table <- the.table[, list(asofdate=seq(from=ymd((year)*10^4+101),
                                           length.out=12,
                                           by="1 month")),by=year]
# function to paste, parse & evaluate arguments
evalp <- function(..., envir=parent.frame()) {eval(parse(text=paste0(...)), envir=envir)}

# Do a complicated procedure to each variable in some group.
var.names <- c("var1","var2","var3")

for(varname in var.names) {

  # 1. For LHS, use paste0 to generate new column name as string (from @eddi's comment)
  # 2. For RHS, use evalp
  new.table[, paste0(varname, '.annual.total') := evalp(
    'the.table[,rep(', varname, ',each=12)]'
  )]

  new.table[, paste0(varname, '.monthly') := evalp(
    'the.table[,rep(', varname, '/12,each=12)]'
  )]

  # Need to add envir=.SD when working within the table
  new.table[, paste0(varname, '.rolling') := evalp(
    'rollapply(',varname, '.monthly,mean,width=12, 
        fill=c(head(', varname, '.monthly,1), tail(', varname, '.monthly,1)))'
    , envir=.SD
  )]

  new.table[,paste0(varname, '.scaled'):= evalp(
      varname, '.annual.total / sum(', varname, '.rolling) * ', varname, '.rolling'
      , envir=.SD
    )
    ,by=year
  ]

  # Since we're working with strings, more freedom 
  # to work programmatically
  new.table[, paste0(varname, '.row.percent') := evalp(
    'the.table[,rep(', varname, '/ (', paste(var.names, collapse='+'), '), each=12)]'
  )]
}