r - data.table row-wise sum, mean, min, max like dplyr?

Question

There are other posts about row-wise operators on datatable. They are either too simple or solves a specific scenario

My question here is more generic. There is a solution using dplyr. I have played around but failed to find a an equivalent solution using data.table syntax. Can you please suggest an elegant data.table solution that reproduce the same results than the dplyr version?

EDIT 1: Summary of benchmarks of the suggested solutions on real dataset (10MB, 73000 rows, stats made on 24 numeric columns). The benchmark results is subjective. However, the elapsed time is consistently reproducible.

| Solution By | Speed compared to dplyr     |
|-------------|-----------------------------|
| Metrics v1  |  4.3 times SLOWER (use .SD) |
| Metrics v2  |  5.6 times FASTER           |
| ExperimenteR| 15   times FASTER           |
| Arun v1     |  3   times FASTER (Map func)|
| Arun v2     |  3   times FASTER (foo func)|
| Ista        |  4.5 times FASTER           |

EDIT 2: I have added NACount column a day after. This is why this column is not found in the solutions suggested by various contributors.

Data Setup

library(data.table)
dt <- data.table(ProductName = c("Lettuce", "Beetroot", "Spinach", "Kale", "Carrot"),
    Country = c("CA", "FR", "FR", "CA", "CA"),
    Q1 = c(NA, 61, 40, 54, NA), Q2 = c(22,  8, NA,  5, NA),
    Q3 = c(51, NA, NA, 16, NA), Q4 = c(79, 10, 49, NA, NA))

#    ProductName Country Q1 Q2 Q3 Q4
# 1:     Lettuce      CA NA 22 51 79
# 2:    Beetroot      FR 61  8 NA 10
# 3:     Spinach      FR 40 NA NA 49
# 4:        Kale      CA 54  5 16 NA
# 5:      Carrot      CA NA NA NA NA

SOLUTION using dplyr + rowwise()

library(dplyr) ; library(magrittr)
dt %>% rowwise() %>% 
    transmute(ProductName, Country, Q1, Q2, Q3, Q4,
     AVG = mean(c(Q1, Q2, Q3, Q4), na.rm=TRUE),
     MIN = min (c(Q1, Q2, Q3, Q4), na.rm=TRUE),
     MAX = max (c(Q1, Q2, Q3, Q4), na.rm=TRUE),
     SUM = sum (c(Q1, Q2, Q3, Q4), na.rm=TRUE),
     NAcnt= sum(is.na(c(Q1, Q2, Q3, Q4))))

#   ProductName Country Q1 Q2 Q3 Q4      AVG MIN  MAX SUM NAcnt
# 1     Lettuce      CA NA 22 51 79 50.66667  22   79 152     1
# 2    Beetroot      FR 61  8 NA 10 26.33333   8   61  79     1
# 3     Spinach      FR 40 NA NA 49 44.50000  40   49  89     2
# 4        Kale      CA 54  5 16 NA 25.00000   5   54  75     1
# 5      Carrot      CA NA NA NA NA      NaN Inf -Inf   0     4

ERROR with data.table (compute entire column instead of per-row)

dt[, .(ProductName, Country, Q1, Q2, Q3, Q4,
    AVG = mean(c(Q1, Q2, Q3, Q4), na.rm=TRUE),
    MIN = min (c(Q1, Q2, Q3, Q4), na.rm=TRUE),
    MAX = max (c(Q1, Q2, Q3, Q4), na.rm=TRUE),
    SUM = sum (c(Q1, Q2, Q3, Q4), na.rm=TRUE),
    NAcnt= sum(is.na(c(Q1, Q2, Q3, Q4))))]

#    ProductName Country Q1 Q2 Q3 Q4      AVG MIN MAX SUM NAcnt
# 1:     Lettuce      CA NA 22 51 79 35.90909   5  79 395     9
# 2:    Beetroot      FR 61  8 NA 10 35.90909   5  79 395     9
# 3:     Spinach      FR 40 NA NA 49 35.90909   5  79 395     9
# 4:        Kale      CA 54  5 16 NA 35.90909   5  79 395     9
# 5:      Carrot      CA NA NA NA NA 35.90909   5  79 395     9

ALMOST solution but more complex and missing Q1,Q2,Q3,Q4 output columns

dtmelt <- reshape2::melt(dt, id=c("ProductName", "Country"),
            variable.name="Quarter", value.name="Qty")

dtmelt[, .(AVG = mean(Qty, na.rm=TRUE),
    MIN = min (Qty, na.rm=TRUE),
    MAX = max (Qty, na.rm=TRUE),
    SUM = sum (Qty, na.rm=TRUE),
    NAcnt= sum(is.na(Qty))), by = list(ProductName, Country)]

#    ProductName Country      AVG MIN  MAX SUM NAcnt
# 1:     Lettuce      CA 50.66667  22   79 152     1
# 2:    Beetroot      FR 26.33333   8   61  79     1
# 3:     Spinach      FR 44.50000  40   49  89     2
# 4:        Kale      CA 25.00000   5   54  75     1
# 5:      Carrot      CA      NaN Inf -Inf   0     4

Answer 1

You can use an efficient row-wise functions from matrixStats package.

library(matrixStats)
dt[, `:=`(MIN = rowMins(as.matrix(.SD), na.rm=T),
          MAX = rowMaxs(as.matrix(.SD), na.rm=T),
          AVG = rowMeans(.SD, na.rm=T),
          SUM = rowSums(.SD, na.rm=T)), .SDcols=c(Q1, Q2,Q3,Q4)]

dt
#    ProductName Country Q1 Q2 Q3 Q4 MIN  MAX      AVG SUM
# 1:     Lettuce      CA NA 22 51 79  22   79 50.66667 152
# 2:    Beetroot      FR 61  8 NA 10   8   61 26.33333  79
# 3:     Spinach      FR 40 NA 79 49  40   79 56.00000 168
# 4:        Kale      CA 54  5 16 NA   5   54 25.00000  75
# 5:      Carrot      CA NA NA NA NA Inf -Inf      NaN   0

For dataset with 500000 rows(using the data.table from CRAN)

dt <- rbindlist(lapply(1:100000, function(i)dt))
system.time(dt[, `:=`(MIN = rowMins(as.matrix(.SD), na.rm=T),
                      MAX = rowMaxs(as.matrix(.SD), na.rm=T),
                      AVG = rowMeans(.SD, na.rm=T),
                      SUM = rowSums(.SD, na.rm=T)), .SDcols=c("Q1", "Q2","Q3","Q4")])
#  user  system elapsed 
# 0.089   0.004   0.093

rowwise (or by=1:nrow(dt)) is "euphemism" for for loop, as exemplified by

library(dplyr) ; library(magrittr)
system.time(dt %>% rowwise() %>% 
  transmute(ProductName, Country, Q1, Q2, Q3, Q4,
            MIN = min (c(Q1, Q2, Q3, Q4), na.rm=TRUE),
            MAX = max (c(Q1, Q2, Q3, Q4), na.rm=TRUE),
            AVG = mean(c(Q1, Q2, Q3, Q4), na.rm=TRUE),
            SUM = sum (c(Q1, Q2, Q3, Q4), na.rm=TRUE)))
#   user  system elapsed 
# 80.832   0.111  80.974 

system.time(dt[, `:=`(AVG= mean(as.numeric(.SD),na.rm=TRUE),MIN = min(.SD, na.rm=TRUE),MAX = max(.SD, na.rm=TRUE),SUM = sum(.SD, na.rm=TRUE)),.SDcols=c("Q1", "Q2","Q3","Q4"),by=1:nrow(dt)] )
#    user  system elapsed 
# 141.492   0.196 141.757