基于数据框 R 子集的一列中“分类值”的百分比

Question

I have data that consists of three columns (as an example) named data$engine, data$unit, and data$Turn. data$Turn is categorical variable with values 0, 1 and 2. For each unique value of data$engine, there can be several values of data$unit.

I would like to calculate percent of 0, 1, and 2 in data$Turn for unique data$unit and data$engine respectively. I have hundred thousand rows but I am pasting data structure for only two unique values of data$engine... Please note that that each data$unit (for specific data$engine) can have thousands of rows, and thus for calculating %ages, i would like to proceed as:

%age of 0's for data$unit 207 and data$engine 1111 = 
counts of all zeros within data$unit 207 and data$engine 1111 (DIVIDED BY) 
summation of all counts of 0, 1, and 2 for this data$unit and data$engine.*emphasized text*

Similarly for % ages of 1's and 2's for data$unit 207 and data$engine 1111, 
and it continues for all other values of units and engines....

data$engine  data$unit     data$AvailableLeft
    1111       207                1
    1111       207                0
    1111       207                2
    1111       207                0
    1111       207                0
    1111       207                2
    1111       207                0
    1111       207                1
    1111       208                0
    1111       208                1
    1111       208                2
    1111       208                1
    1122       209                2
    1122       209                2
    1122       209                0
    1122       209                0
    1122       209                1

I would like to get my output in this manner ie getting average %age of 0, 1, and 2s for each data$unit and for each data$engine:

data$engine  data$unit     %age of 0s     %age of 1s    %age of 2s
 1111          207              ?              ?            ?
 1111          208              ?              ?            ?    
 1122          209              ?              ?            ?    
   .             .                    .
   .             .                    .
   .             .                    .

Answer 1

you can use data.table :

library(data.table)
setDT(data)[, .(p0=sum(AvailableLeft==0)/.N, 
                p1=sum(AvailableLeft==1)/.N, 
                p2=sum(AvailableLeft==2)/.N), 
             keyby=.(data, engine, unit)]

   engine unit   p0   p1   p2
1:   1111  207 0.50 0.25 0.25
2:   1111  208 0.25 0.50 0.25
3:   1122  209 0.40 0.20 0.40

Answer 2

library(data.table)
dt <- as.data.table(your_data)
dt[,.("p1"=paste(as.character(round(sum(data.AvailableLeft==1)*100/.N,2)),"%")),.(data.engine,data.unit)]

I would leave the % of data.AvailableLeft==0 and % of data.AvailableLeft==2 as figuring out them is trivial from here