Classic Case of 'Sum' Returning Na Because It Doesn't Sum Nas

Classic case of `sum` returning NA because it doesn't sum NAs

Following Joshua Ulrich's comment, before saying that you have some overflow problem, you should answer these questions:

How many elements are you summing? R can handle a BIG number of entries
How big are the values in your vectors? Again, R can handle quite big numbers
Are you summing integers or floats? If you are summing floating-point numbers, you can't have an integer overflow (floats are not integers)
Do you have NAs in your data? If you sum anything with NAs present, the result will be NA, unless you handle it properly.

That said, some solutions:

Use sum(..., na.rm=T) to ignore NAs from your object (this is the simple solution)
Sum only non NA entries: sum(yourVector[!is.na(yourVector)] (the not so simple one)
If you are summing a column from a data frame, subset the data frame before summing: sum(subset(yourDataFrame, !is.na(columnToSum))[columnToSum]) (this is like using a cannon to kill a mosquito)

Issue of SUM for column in DataFrame

We could use sum_ from hablar which automatically removes the NA

library(hablar)
sum_(df$invoice_amount)

sum non NA elements only, but if all NA then return NA

Following the suggestions from other users, I will post the answer to my question. The solution was provided by @sandipan in the comments above:

As noted in the question, if you need to sum the values of one column which contains NAs,there are two good approaches:

1) using ifelse:

A[, (ifelse(all(is.na(col2)), col2[NA_integer_], sum(col2, na.rm = T))), 
  by = .(col1)]

2) define a function as suggested by @Frank:

suma = function(x) if (all(is.na(x))) x[NA_integer_] else sum(x, na.rm = TRUE)

A[, suma(col2), by = .(col1)]

Note that I added NA_integer_ as @Frank pointed out because I kept getting errors about the types.

Sum 2 columns, ignore NA, except when both are NA

I used the following. It gives sums even when there are NAs, but returns NA when all sumed elements are NA.

rowSums(df, na.rm = TRUE) * NA ^ (rowSums(!is.na(df)) == 0)

using grepl * to return NA values in R dplyr

I ended up creating a custom function to do this:

greplna <- function(data, reg="*", var="Discount code"){
  if(reg == "*"){
    tmp <- grepl("*", as.list(data[var])[[1]]) | is.na(as.list(data[var])[[1]])
  }else{
    tmp <- grepl(reg, as.list(data[var])[[1]])
  }
  return(tmp)
}

You can then use this in a dplyr statement:

df %>% filter(greplna(., search, "Discount code"))

but don't use it after a group, as the . gets the whole dataset, not the grouped datasets

Find sequences of NA values in data.frame rows

You can create a function anlagous to complete.cases that computes consecutive missings values using rle:

cons.missings <- 
function(dat,n)
apply(is.na(dat),1,function(x){
  yy <- rle(x)
  any(yy$lengths[yy$values]>n)
})

Then to keep only good rows:

dat[!cons.missings(dat,20),]

Example with 4 connectives missings values:

dat <- as.matrix(t(data.frame(a= c(1,rep(NA,4),5),
           b= c(2,rep(NA,2),1,rep(NA,2)))))

 [,1] [,2] [,3] [,4] [,5] [,6]
a    1   NA   NA   NA   NA    5
b    2   NA   NA    1   NA   NA

dat[!cons.missings(dat,3),]
[1]  2 NA NA  1 NA NA

R - Combinations of a dataframe with constraints

A brute-force way to tackle this, (which is also beautifully parallelizable and guarantees you all possible combinations) is to calculate all 11-player permutations and then filter out the combinations that don't conform to your limits in a stepwise manner.

To make a program like this fit into your computer's memory, give each player a unique integer ID and create vectors of IDs as team sets. When you then implement your filters your functions can refer to the player info by that ID in a single dataframe.

Say df is your data frame with all player data.

df$id <- 1:nrow(df)

Get all combinations of ids:

# This will take a long time or run out of memory!
# In my 2.8Gz laptop took 466 seconds just for your 31 players
teams <- combn(df$id, 11)

Of course, if your dataframe is big (like hundreds of players) this implementation could take impossibly long to finish. You probably would be better off just sampling 11-sets from your player set without replacement and construct teams in an "on demand" fashion.

A more clever way is to partition your dataset according to player position into - one for goalkeepers, one for defence, etc. And then use the above approach to create permutations of different players from each position and combine the end results. It would take ridiculously less amount of time, it would still be parallelizable and exhaustive (give you all possible combinations).

data.table vs dplyr: can one do something well the other can't or does poorly?

We need to cover at least these aspects to provide a comprehensive answer/comparison (in no particular order of importance): Speed, Memory usage, Syntax and Features.

My intent is to cover each one of these as clearly as possible from data.table perspective.

Note: unless explicitly mentioned otherwise, by referring to dplyr, we refer to dplyr's data.frame interface whose internals are in C++ using Rcpp.

The data.table syntax is consistent in its form - DT[i, j, by]. To keep i, j and by together is by design. By keeping related operations together, it allows to easily optimise operations for speed and more importantly memory usage, and also provide some powerful features, all while maintaining the consistency in syntax.

1. Speed

Quite a few benchmarks (though mostly on grouping operations) have been added to the question already showing data.table gets faster than dplyr as the number of groups and/or rows to group by increase, including benchmarks by Matt on grouping from 10 million to 2 billion rows (100GB in RAM) on 100 - 10 million groups and varying grouping columns, which also compares pandas. See also updated benchmarks, which include Spark and pydatatable as well.

On benchmarks, it would be great to cover these remaining aspects as well:

Grouping operations involving a subset of rows - i.e., DT[x > val, sum(y), by = z] type operations.
Benchmark other operations such as update and joins.
Also benchmark memory footprint for each operation in addition to runtime.

2. Memory usage

Operations involving filter() or slice() in dplyr can be memory inefficient (on both data.frames and data.tables). See this post.
Note that Hadley's comment talks about speed (that dplyr is plentiful fast for him), whereas the major concern here is memory.
data.table interface at the moment allows one to modify/update columns by reference (note that we don't need to re-assign the result back to a variable).
```
 # sub-assign by reference, updates 'y' in-place
 DT[x >= 1L, y := NA]
```
But dplyr will never update by reference. The dplyr equivalent would be (note that the result needs to be re-assigned):
```
 # copies the entire 'y' column
 ans <- DF %>% mutate(y = replace(y, which(x >= 1L), NA))
```
A concern for this is referential transparency. Updating a data.table object by reference, especially within a function may not be always desirable. But this is an incredibly useful feature: see this and this posts for interesting cases. And we want to keep it.
Therefore we are working towards exporting shallow() function in data.table that will provide the user with both possibilities. For example, if it is desirable to not modify the input data.table within a function, one can then do:
```
 foo <- function(DT) {
     DT = shallow(DT)          ## shallow copy DT
     DT[, newcol := 1L]        ## does not affect the original DT 
     DT[x > 2L, newcol := 2L]  ## no need to copy (internally), as this column exists only in shallow copied DT
     DT[x > 2L, x := 3L]       ## have to copy (like base R / dplyr does always); otherwise original DT will 
                               ## also get modified.
 }
```
By not using shallow(), the old functionality is retained:
```
 bar <- function(DT) {
     DT[, newcol := 1L]        ## old behaviour, original DT gets updated by reference
     DT[x > 2L, x := 3L]       ## old behaviour, update column x in original DT.
 }
```
By creating a shallow copy using shallow(), we understand that you don't want to modify the original object. We take care of everything internally to ensure that while also ensuring to copy columns you modify only when it is absolutely necessary. When implemented, this should settle the referential transparency issue altogether while providing the user with both possibilties.
Also, once shallow() is exported dplyr's data.table interface should avoid almost all copies. So those who prefer dplyr's syntax can use it with data.tables.
But it will still lack many features that data.table provides, including (sub)-assignment by reference.
Aggregate while joining:
Suppose you have two data.tables as follows:
```
 DT1 = data.table(x=c(1,1,1,1,2,2,2,2), y=c("a", "a", "b", "b"), z=1:8, key=c("x", "y"))
 #    x y z
 # 1: 1 a 1
 # 2: 1 a 2
 # 3: 1 b 3
 # 4: 1 b 4
 # 5: 2 a 5
 # 6: 2 a 6
 # 7: 2 b 7
 # 8: 2 b 8
 DT2 = data.table(x=1:2, y=c("a", "b"), mul=4:3, key=c("x", "y"))
 #    x y mul
 # 1: 1 a   4
 # 2: 2 b   3
```
And you would like to get sum(z) * mul for each row in DT2 while joining by columns x,y. We can either:
- 1. aggregate DT1 to get sum(z), 2) perform a join and 3) multiply (or)
    data.table way
    DT1[, .(z = sum(z)), keyby = .(x,y)][DT2][, z := z*mul][]
    dplyr equivalent
    DF1 %>% group_by(x, y) %>% summarise(z = sum(z)) %>%
    right_join(DF2) %>% mutate(z = z * mul)
- 1. do it all in one go (using by = .EACHI feature):
    DT1[DT2, list(z=sum(z) * mul), by = .EACHI]
What is the advantage?
- We don't have to allocate memory for the intermediate result.
- We don't have to group/hash twice (one for aggregation and other for joining).
- And more importantly, the operation what we wanted to perform is clear by looking at j in (2).
Check this post for a detailed explanation of by = .EACHI. No intermediate results are materialised, and the join+aggregate is performed all in one go.
Have a look at this, this and this posts for real usage scenarios.
In dplyr you would have to join and aggregate or aggregate first and then join, neither of which are as efficient, in terms of memory (which in turn translates to speed).
Update and joins:
Consider the data.table code shown below:
```
 DT1[DT2, col := i.mul]
```
adds/updates DT1's column col with mul from DT2 on those rows where DT2's key column matches DT1. I don't think there is an exact equivalent of this operation in dplyr, i.e., without avoiding a *_join operation, which would have to copy the entire DT1 just to add a new column to it, which is unnecessary.
Check this post for a real usage scenario.

To summarise, it is important to realise that every bit of optimisation matters. As Grace Hopper would say, Mind your nanoseconds!

3. Syntax

Let's now look at syntax. Hadley commented here:

Data tables are extremely fast but I think their concision makes it harder to learn and code that uses it is harder to read after you have written it ...

I find this remark pointless because it is very subjective. What we can perhaps try is to contrast consistency in syntax. We will compare data.table and dplyr syntax side-by-side.

We will work with the dummy data shown below:

DT = data.table(x=1:10, y=11:20, z=rep(1:2, each=5))
DF = as.data.frame(DT)

Basic aggregation/update operations.
```
 # case (a)
 DT[, sum(y), by = z]                       ## data.table syntax
 DF %>% group_by(z) %>% summarise(sum(y)) ## dplyr syntax
 DT[, y := cumsum(y), by = z]
 ans <- DF %>% group_by(z) %>% mutate(y = cumsum(y))

 # case (b)
 DT[x > 2, sum(y), by = z]
 DF %>% filter(x>2) %>% group_by(z) %>% summarise(sum(y))
 DT[x > 2, y := cumsum(y), by = z]
 ans <- DF %>% group_by(z) %>% mutate(y = replace(y, which(x > 2), cumsum(y)))

 # case (c)
 DT[, if(any(x > 5L)) y[1L]-y[2L] else y[2L], by = z]
 DF %>% group_by(z) %>% summarise(if (any(x > 5L)) y[1L] - y[2L] else y[2L])
 DT[, if(any(x > 5L)) y[1L] - y[2L], by = z]
 DF %>% group_by(z) %>% filter(any(x > 5L)) %>% summarise(y[1L] - y[2L])
```
- data.table syntax is compact and dplyr's quite verbose. Things are more or less equivalent in case (a).
- In case (b), we had to use filter() in dplyr while summarising. But while updating, we had to move the logic inside mutate(). In data.table however, we express both operations with the same logic - operate on rows where x > 2, but in first case, get sum(y), whereas in the second case update those rows for y with its cumulative sum.
  This is what we mean when we say the DT[i, j, by] form is consistent.
- Similarly in case (c), when we have if-else condition, we are able to express the logic "as-is" in both data.table and dplyr. However, if we would like to return just those rows where the if condition satisfies and skip otherwise, we cannot use summarise() directly (AFAICT). We have to filter() first and then summarise because summarise() always expects a single value.
  While it returns the same result, using filter() here makes the actual operation less obvious.
  It might very well be possible to use filter() in the first case as well (does not seem obvious to me), but my point is that we should not have to.
Aggregation / update on multiple columns
```
 # case (a)
 DT[, lapply(.SD, sum), by = z]                     ## data.table syntax
 DF %>% group_by(z) %>% summarise_each(funs(sum)) ## dplyr syntax
 DT[, (cols) := lapply(.SD, sum), by = z]
 ans <- DF %>% group_by(z) %>% mutate_each(funs(sum))

 # case (b)
 DT[, c(lapply(.SD, sum), lapply(.SD, mean)), by = z]
 DF %>% group_by(z) %>% summarise_each(funs(sum, mean))

 # case (c)
 DT[, c(.N, lapply(.SD, sum)), by = z]     
 DF %>% group_by(z) %>% summarise_each(funs(n(), mean))
```
- In case (a), the codes are more or less equivalent. data.table uses familiar base function lapply(), whereas dplyr introduces *_each() along with a bunch of functions to funs().
- data.table's := requires column names to be provided, whereas dplyr generates it automatically.
- In case (b), dplyr's syntax is relatively straightforward. Improving aggregations/updates on multiple functions is on data.table's list.
- In case (c) though, dplyr would return n() as many times as many columns, instead of just once. In data.table, all we need to do is to return a list in j. Each element of the list will become a column in the result. So, we can use, once again, the familiar base function c() to concatenate .N to a list which returns a list.
Note: Once again, in data.table, all we need to do is return a list in j. Each element of the list will become a column in result. You can use c(), as.list(), lapply(), list() etc... base functions to accomplish this, without having to learn any new functions.
You will need to learn just the special variables - .N and .SD at least. The equivalent in dplyr are n() and .

Joins

dplyr provides separate functions for each type of join where as data.table allows joins using the same syntax DT[i, j, by] (and with reason). It also provides an equivalent merge.data.table() function as an alternative.

 setkey(DT1, x, y)

 # 1. normal join
 DT1[DT2]            ## data.table syntax
 left_join(DT2, DT1) ## dplyr syntax

 # 2. select columns while join    
 DT1[DT2, .(z, i.mul)]
 left_join(select(DT2, x, y, mul), select(DT1, x, y, z))

 # 3. aggregate while join
 DT1[DT2, .(sum(z) * i.mul), by = .EACHI]
 DF1 %>% group_by(x, y) %>% summarise(z = sum(z)) %>% 
     inner_join(DF2) %>% mutate(z = z*mul) %>% select(-mul)

 # 4. update while join
 DT1[DT2, z := cumsum(z) * i.mul, by = .EACHI]
 ??

 # 5. rolling join
 DT1[DT2, roll = -Inf]
 ??

 # 6. other arguments to control output
 DT1[DT2, mult = "first"]
 ??

Some might find a separate function for each joins much nicer (left, right, inner, anti, semi etc), whereas as others might like data.table's DT[i, j, by], or merge() which is similar to base R.
However dplyr joins do just that. Nothing more. Nothing less.
data.tables can select columns while joining (2), and in dplyr you will need to select() first on both data.frames before to join as shown above. Otherwise you would materialiase the join with unnecessary columns only to remove them later and that is inefficient.
data.tables can aggregate while joining (3) and also update while joining (4), using by = .EACHI feature. Why materialse the entire join result to add/update just a few columns?
data.table is capable of rolling joins (5) - roll forward, LOCF, roll backward, NOCB, nearest.
data.table also has mult = argument which selects first, last or all matches (6).
data.table has allow.cartesian = TRUE argument to protect from accidental invalid joins.

Once again, the syntax is consistent with DT[i, j, by] with additional arguments allowing for controlling the output further.

do()...

dplyr's summarise is specially designed for functions that return a single value. If your function returns multiple/unequal values, you will have to resort to do(). You have to know beforehand about all your functions return value.

 DT[, list(x[1], y[1]), by = z]                 ## data.table syntax
 DF %>% group_by(z) %>% summarise(x[1], y[1]) ## dplyr syntax
 DT[, list(x[1:2], y[1]), by = z]
 DF %>% group_by(z) %>% do(data.frame(.$x[1:2], .$y[1]))

 DT[, quantile(x, 0.25), by = z]
 DF %>% group_by(z) %>% summarise(quantile(x, 0.25))
 DT[, quantile(x, c(0.25, 0.75)), by = z]
 DF %>% group_by(z) %>% do(data.frame(quantile(.$x, c(0.25, 0.75))))

 DT[, as.list(summary(x)), by = z]
 DF %>% group_by(z) %>% do(data.frame(as.list(summary(.$x))))

.SD's equivalent is .
In data.table, you can throw pretty much anything in j - the only thing to remember is for it to return a list so that each element of the list gets converted to a column.
In dplyr, cannot do that. Have to resort to do() depending on how sure you are as to whether your function would always return a single value. And it is quite slow.

Once again, data.table's syntax is consistent with DT[i, j, by]. We can just keep throwing expressions in j without having to worry about these things.

Have a look at this SO question and this one. I wonder if it would be possible to express the answer as straightforward using dplyr's syntax...

To summarise, I have particularly highlighted several instances where dplyr's syntax is either inefficient, limited or fails to make operations straightforward. This is particularly because data.table gets quite a bit of backlash about "harder to read/learn" syntax (like the one pasted/linked above). Most posts that cover dplyr talk about most straightforward operations. And that is great. But it is important to realise its syntax and feature limitations as well, and I am yet to see a post on it.

data.table has its quirks as well (some of which I have pointed out that we are attempting to fix). We are also attempting to improve data.table's joins as I have highlighted here.

But one should also consider the number of features that dplyr lacks in comparison to data.table.

4. Features

I have pointed out most of the features here and also in this post. In addition:

fread - fast file reader has been available for a long time now.
fwrite - a parallelised fast file writer is now available. See this post for a detailed explanation on the implementation and #1664 for keeping track of further developments.
Automatic indexing - another handy feature to optimise base R syntax as is, internally.
Ad-hoc grouping: dplyr automatically sorts the results by grouping variables during summarise(), which may not be always desirable.
Numerous advantages in data.table joins (for speed / memory efficiency and syntax) mentioned above.
Non-equi joins: Allows joins using other operators <=, <, >, >= along with all other advantages of data.table joins.
Overlapping range joins was implemented in data.table recently. Check this post for an overview with benchmarks.
setorder() function in data.table that allows really fast reordering of data.tables by reference.
dplyr provides interface to databases using the same syntax, which data.table does not at the moment.
data.table provides faster equivalents of set operations (written by Jan Gorecki) - fsetdiff, fintersect, funion and fsetequal with additional all argument (as in SQL).
data.table loads cleanly with no masking warnings and has a mechanism described here for [.data.frame compatibility when passed to any R package. dplyr changes base functions filter, lag and [ which can cause problems; e.g. here and here.

Finally:

On databases - there is no reason why data.table cannot provide similar interface, but this is not a priority now. It might get bumped up if users would very much like that feature.. not sure.
On parallelism - Everything is difficult, until someone goes ahead and does it. Of course it will take effort (being thread safe).
- Progress is being made currently (in v1.9.7 devel) towards parallelising known time consuming parts for incremental performance gains using OpenMP.

Classic Case of 'Sum' Returning Na Because It Doesn't Sum Nas