data.table 按行求和，平均值，最小值，最大值，如 dplyr

还有其他关于datatable上的行运算符的帖子.他们不是too simple岁就是specific scenario岁

我的问题更一般.有一个使用dplyr的解决方案.我已经try 过了，但没有找到使用数据的等效解决方案.表语法.你能推荐一个优雅的数据吗.与dplyr版本复制相同结果的表解决方案？

EDIT 1:真实数据集(10MB，73000行，24个数字列的统计数据)上建议解决方案的基准概要.基准测试结果是主观的.然而，经过的时间始终是可重复的.

| 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:我在一天后添加了NACount列.这就是为什么在各种贡献者建议的解决方案中找不到本专栏的原因.

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