CODEWITHSUNDEEP
rdata.tableapplyna
dayne
dayne

Reputation: 7784

Apply over rows of data.table: find rows where a subset of columns are all NA

I am trying, in my quest to rewrite old (slow) code with the data.table package, to figure out the best way to use apply with a data.table.

I have a data.table with multiple id columns, then multiple columns that have dose-response data in a wide format. I need to generalize the answer because not all data.tables will have the same number of dose-response columns. For simplicity I think the following data.table addresses the issue:

library(data.table)
library(microbenchmark)
set.seed(1234)
DT1 =  data.table(unique_id = paste0('id',1:1e6),
                 dose1 = sample(c(1:9,NA),1e6,replace=TRUE),
                 dose2 = sample(c(1:9,NA),1e6,replace=TRUE)
                 )

> DT1
          unique_id dose1 dose2
       1:       id1     2     2
       2:       id2     7     4
       3:       id3     7     9
       4:       id4     7     4
       5:       id5     9     3
---                      
  999996:  id999996     4     3
  999997:  id999997    NA     3
  999998:  id999998     4     2
  999999:  id999999     8     5
 1000000: id1000000     6     7

So each row has a unique id, some other ids, and I have left out the response columns, because they will be NA where the dose columns are NA. What I need to do is remove rows where all of the dose columns are NA. I came up with the first option, then realized I could trim it down to the second option. 

DT2 <- copy(DT1)
DT3 <- copy(DT1)

len.not.na <- function(x){length(which(!is.na(x)))}

option1 <- function(DT){
  DT[,flag := apply(.SD,1,len.not.na),.SDcols=grep("dose",colnames(DT))]
  DT <- DT[flag != 0]
  DT[ , flag := NULL ]
}

option2 <- function(DT){
  DT[ apply(DT[,grep("dose",colnames(DT)),with=FALSE],1,len.not.na) != 0 ]
}

> microbenchmark(op1 <- option1(DT2), op2 <- option2(DT3),times=25L)
Unit: seconds
                expr      min       lq   median       uq      max neval
 op1 <- option1(DT2) 8.364504 8.863436 9.145341 11.27827 11.50356    25
 op2 <- option2(DT3) 8.291549 8.774746 8.982536 11.15269 11.72199    25

Clearly they two options do about the same thing, with option 1 having a few more steps, but I wanted to test how calling .SD might slow things down as has been suggested by other posts (for example).

Either way both options are still on the slow side. Any suggestions to speeding things up?

EDIT with comment from @AnandaMahto

DT4 <- copy(DT1)
option3 <- function(DT){
  DT[rowSums(DT[,grep("dose",colnames(DT)),with=FALSE]) != 0]
}

> microbenchmark(op2 <- option2(DT3), op3 <- option3(DT4),times=5L)
Unit: milliseconds
               expr        min         lq    median        uq       max neval
op2 <- option2(DT3) 7738.21094 7810.87777 7838.6067 7969.5543 8407.4069     5
op3 <- option3(DT4)   83.78921   92.65472  320.6273  559.8153  783.0742     5

rowSums is definitely faster. I am happy with the solution unless anyone has something faster.

Upvotes: 4

Views: 2142

Answers (3)

A5C1D2H2I1M1N2O1R2T1
A5C1D2H2I1M1N2O1R2T1

Reputation: 193527

My approach would be as follows:

Use rowSums to find the rows you want to keep:

Dose <- grep("dose", colnames(DT1))
# .. menas "up one level
Flag <- rowSums(is.na(DT1[, ..Dose])) != length(Dose)
DT1[Flag]

Upvotes: 6

eddi
eddi

Reputation: 49448

DT1[!is.na(dose1) | !is.na(dose2)]

The Reduce generalization in previous edits was wrong, here's the correct version:

DT1[(!Reduce("*", DT1[, lapply(.SD, is.na), .SDcols = patterns("dose")]))]

Benchmarks

rowsum = function(dt) {
  Dose <- grep("dose", colnames(dt))
  Flag <- rowSums(is.na(dt[, ..Dose])) != length(Dose)
  dt[Flag]
}

reduce = function(dt) {
  dt[(!Reduce("*", dt[, lapply(.SD, is.na), .SDcols = patterns("dose")]))]
}

# original data
microbenchmark(rowsum(copy(DT1)), reduce(copy(DT1)), times = 10)
#Unit: milliseconds
#              expr      min       lq   median       uq      max neval
# rowsum(copy(DT1)) 184.4121 190.9895 238.2935 248.0654 266.5708    10
# reduce(copy(DT1)) 141.2399 172.2020 199.1012 219.4567 424.1526    10

# a lot more columns
for (i in 10:100) DT1[, paste0('dose', i) := sample(c(NA, 1:10), 1e6, T)]

microbenchmark(rowsum(copy(DT1)), reduce(copy(DT1)), times = 10)
#Unit: seconds
#              expr      min       lq   median       uq      max neval
# rowsum(copy(DT1)) 4.160035 4.428527 4.505705 4.754398 4.906849    10
# reduce(copy(DT1)) 3.421675 4.172700 4.507304 4.622355 5.156840    10

So at 100 columns Reduce still does all right.

Upvotes: 4

Troy
Troy

Reputation: 8691

Maybe easier to just select all the rows with no NAs into a new table like this. You can amend the 'which' condition depending on your table:

DT2<-(DT1[which(!is.na(DT1$dose1) & !is.na(DT1$dose2)),])

Upvotes: 0

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