[datatable-help] Force evaluation of first argument to [

[Arunkumar Srinivasan]

Melanie,
`set` modifies by reference. Yours'll make a copy. 
Arun
From: Bacou, Melanie Bacou, Melanie
Reply: Bacou, Melanie mel at mbacou.com
Date: February 14, 2014 at 12:52:56 PM
To: Matt Dowle mdowle at mdowle.plus.com, John Laing john.laing at gmail.com
Subject:  Re: [datatable-help] Force evaluation of first argument to [  
Hi John, Matt,

In this case, why not simply using the standard data.table approach with .SD?

fbq.cp[, lapply(.SD, function(x) ifelse(is.na(x), FALSE, x)), .SDcols=c("foo", "bar", "qux")]

--Mel.


On 2/12/2014 2:22 PM, Matt Dowle wrote:

Ha.  Yes we certainly don't hold back from making the messages as long and as helpful as possible.  If the code knows, or can know what exactly is wrong, it's a deliberate policy to put that info right there into the message. data.table is written by users; i.e. we wrote it for ourselves doing real jobs. I think that may be the root of that.  If any messages could more helpful,  those suggestions are very welcome.

Matt

On 12/02/14 17:58, John Laing wrote:
Thanks, Matt! With a slight amendment that works great:
for (x in c("foo", "bar", "qux")) set(fbq, which(is.na(fbq[[x]])), x, FALSE)

Which highlights an opportunity to say that I really appreciate the unusually helpful error messages in this package.

-John


On Wed, Feb 12, 2014 at 12:44 PM, Matt Dowle <mdowle at mdowle.plus.com> wrote:

Hi John,

In examples like this I'd use set() and [[,  since it's a bit easier to write but memory efficient too.

for (x in c("foo", "bar", "qux"))   set(fbq, is.na(fbq[[x]]), x, FALSE)           [untested]

A downside here is one repetition of the "fbq" symbol,  but can live with that.  If you have a large number of columns  (and I've been surprised just how many columns some poeple have!) then calling set() many times has lower overhead than DT[, :=],  see ?set.   Note also that [[ is base R, doesn't copy the column and often useful to use with data.table.

Or, use get() in either i or j rather than eval().

HTH, Matt



On 12/02/14 17:24, John Laing wrote:
Let's say I merge together several data.tables such that I wind up
with lots of NAs:

require(data.table)
foo <- data.table(k=1:4, foo=TRUE, key="k")
bar <- data.table(k=3:6, bar=TRUE, key="k")
qux <- data.table(k=5:8, qux=TRUE, key="k")
fbq <- merge(merge(foo, bar, all=TRUE), qux, all=TRUE)
print(fbq)
#    k  foo  bar  qux
# 1: 1 TRUE   NA   NA
# 2: 2 TRUE   NA   NA
# 3: 3 TRUE TRUE   NA
# 4: 4 TRUE TRUE   NA
# 5: 5   NA TRUE TRUE
# 6: 6   NA TRUE TRUE
# 7: 7   NA   NA TRUE
# 8: 8   NA   NA TRUE

I want to go through those columns and turn each NA into FALSE. I can
do this by writing code for each column:

fbq.cp <- copy(fbq)
fbq.cp[is.na(foo), foo:=FALSE]
fbq.cp[is.na(bar), bar:=FALSE]
fbq.cp[is.na(qux), qux:=FALSE]
print(fbq.cp)
#    k   foo   bar   qux
# 1: 1  TRUE FALSE FALSE
# 2: 2  TRUE FALSE FALSE
# 3: 3  TRUE  TRUE FALSE
# 4: 4  TRUE  TRUE FALSE
# 5: 5 FALSE  TRUE  TRUE
# 6: 6 FALSE  TRUE  TRUE
# 7: 7 FALSE FALSE  TRUE
# 8: 8 FALSE FALSE  TRUE

But I can't figure out how to do it in a loop. More precisely, I can't
figure out how to make the [ operator evaluate its first argument in
the context of the data.table. All of these have no effect:
for (x in c("foo", "bar", "qux")) fbq[is.na(x), eval(x):=FALSE]
for (x in c("foo", "bar", "qux")) fbq[is.na(eval(x)), eval(x):=FALSE]
for (x in c("foo", "bar", "qux")) fbq[eval(is.na(x)), eval(x):=FALSE]

I'm running R 3.0.2 on Linux, data.table 1.8.10.

Thanks in advance,
John


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--  
Melanie BACOU
International Food Policy Research Institute
Agricultural Economist, HarvestChoice
Work +1(202)862-5699
E-mail mel at mbacou.com
Visit harvestchoice.org  
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