Weighted Average by Year in ragged data frame in R

I have a data frame with eight variables. I would like to calculate the average mean of annual weighted average percent loss. However, not all variables exist for each year in my dataset. What would be the simplest method to do so? Included below is a sample dataset and final desired output.

Thank you!

sample dataset

Fruit.Type  Year    Primary.Wgt Primary.Loss.PCT    Retail.Wgt  Retail.Loss.PCT Cons.Wgt    Cons.Loss.PCT
Oranges.F   1970    16.16   3.0 15.68   11.6    13.86   36.0
Oranges.F   1971    15.73   3.0 15.26   11.6    13.49   36.0
Oranges.F   1972    14.47   3.0 14.04   11.6    12.41   36.0
Oranges.F   1973    14.43   3.0 14.00   11.6    12.38   36.0
Tangerines.F    1971    2.34    5.0 2.22    20.4    1.80    52.0
Tangerines.F    1972    2.06    5.0 1.96    20.4    1.60    52.0
Tangerines.F    1973    2.07    5.0 1.97    20.4    1.60    52.0
Grapefruit.F    1970    8.22    3.0 7.97    12.8    6.90    20.0
Grapefruit.F    1971    8.55    3.0 8.29    12.8    7.20    20.0
Grapefruit.F    1972    8.56    3.0 8.31    12.8    7.20    20.0
Grapefruit.F    1973    8.57    3.0 8.31    12.8    7.20    20.0

desired output (calc'd in excel) Output (weighted average percent loss)

Year    Primary.Loss.PCT    Retail.Loss.PCT Cons.Loss.PCT
1970    3.00    11.82   11.98
1971    3.00    14.95   32.16
1972    3.16    14.66   31.78
1973    3.17    14.68   31.77

Mean    3.08    14.03   26.92
Standard Error  0.048   0.737   4.980

There are many ways. I would prefer via a data.table. First convert your data into a data.table:

require(data.table) #tested in data.table 1.9.4
setDT(mydata)
> mydata
      Fruit.Type Year Primary.Wgt Primary.Loss.PCT Retail.Wgt Retail.Loss.PCT
 1:    Oranges.F 1970       16.16                3      15.68            11.6
 2:    Oranges.F 1971       15.73                3      15.26            11.6
 3:    Oranges.F 1972       14.47                3      14.04            11.6
 4:    Oranges.F 1973       14.43                3      14.00            11.6
 5: Tangerines.F 1971        2.34                5       2.22            20.4
 6: Tangerines.F 1972        2.06                5       1.96            20.4
 7: Tangerines.F 1973        2.07                5       1.97            20.4
 8: Grapefruit.F 1970        8.22                3       7.97            12.8
 9: Grapefruit.F 1971        8.55                3       8.29            12.8
10: Grapefruit.F 1972        8.56                3       8.31            12.8
11: Grapefruit.F 1973        8.57                3       8.31            12.8
    Cons.Wgt Cons.Loss.PCT
 1:    13.86            36
 2:    13.49            36
 3:    12.41            36
 4:    12.38            36
 5:     1.80            52
 6:     1.60            52
 7:     1.60            52
 8:     6.90            20
 9:     7.20            20
10:     7.20            20
11:     7.20            20

Then let's do the group-based aggregation:

mydata2 <- mydata[,list(
  Primary.Loss.PCT=sum(Primary.Wgt*Primary.Loss.PCT)/sum(Primary.Wgt),
  Retail.Loss.PCT=sum(Retail.Wgt*Retail.Loss.PCT)/sum(Retail.Wgt),
  Cons.Loss.PCT=sum(Cons.Wgt*Cons.Loss.PCT)/sum(Cons.Wgt)),
       by=Year]

> mydata2
   Year Primary.Loss.PCT Retail.Loss.PCT Cons.Loss.PCT
1: 1970         3.000000        12.00440      30.68208
2: 1971         3.175808        12.74412      32.15829
3: 1972         3.164209        12.71970      31.77558
4: 1973         3.165138        12.72471      31.76959

Finally, we compute the mean and se:

> colMeans(mydata2[,-1,with=FALSE])
Primary.Loss.PCT  Retail.Loss.PCT    Cons.Loss.PCT
        3.126289        12.548234        31.596386
> require(plotrix); std.error(mydata2[,-1,with=FALSE])
Primary.Loss.PCT  Retail.Loss.PCT    Cons.Loss.PCT
      0.04217833       0.18135513       0.31804132

I hope I had understand the logic of your computation. However, the final output is different from yours. Anyway, you may adjust the code to follow your needs.