重塑矩陣的加權變量

Question

我DATAS：

datas=structure(list(Id = 1:4, Product1 = structure(c(3L, 2L, 2L, 1L 
), .Label = c("1,2,5", "1,3", "5"), class = "factor"), Product2 = structure(c(4L, 
3L, 1L, 2L), .Label = c("A", "A,B,E", "B,D", "D"), class = "factor")), .Names = c("Id", 
"Product1", "Product2"), class = "data.frame", row.names = c(NA, 
-4L)) 

這給視覺（人會買兩個產品，每個產品的或幾個原因有關產品1（原因= 1,2,3,4， 5）和產物2（原因= A，B，C，d，e）和其原因可以combinated

Id Product1 Product2 
1 5   D 
2 1,3   B,D 
3 1,3   A 
4 1,2,5  A,B,E 

我想如下

對於每個ID computate重塑它：

Id= 1 

     A B C D E 
    1 0 0 0 0 0 
    2 0 0 0 0 0 
    3 0 0 0 0 0 
    4 0 0 0 0 0 
    5 0 0 0 1 0 

Id= 2 : here 0.25 because we have B1,B3,D1,D3 so 1/4 for each one 

    A B  C D  E 
1 0 0,25 0 0,25 0 
2 0 0  0 0  0 
3 0 0,25 0 0,25 0 
4 0 0  0 0  0 
5 0 0  0 1  0 


on so on Id = 4, we have : a1,a2,a5,b1,b2,b5,e1,e2,e5, so 1/9 for each one. 

    A    B   C  D   E 
1 0,11+0,5 0,11+0,25  0  0,25  0,11 
2 0,11+0  0,11+0   0  0   0,11 
3 0,5   0,25   0  0,25  0 
4 0   0    0  0   0 
5 0,11+0  0,11+0   0  1   0,11 

如何輕鬆地，或者我應該計算每個矩陣，並在每次迭代中求和？

非常感謝！

Answer 1

試試這個：

library(dplyr) 
library(reshape2) 

# split strings 
L <- lapply(datas[,2:3],function(v) strsplit(as.character(v),',')) 

# generate all combinations of products 
d <- mapply(expand.grid,L$Product1, L$Product2,SIMPLIFY = F,stringsAsFactors=F) 

df <- melt(d,id.vars=c('Var1','Var2')) %>% # convert to long format 
    group_by(L1) %>% 
    mutate(weight=1/n()) %>%    # calculate weights 
    group_by(Var1,Var2) %>%    # 
    summarize(sm=sum(weight))    # calculate sums 

dcast(df,Var1~Var2) 
# Note that it ignores column C and row 4 because no data were available for them 
# Var1   A   B D   E 
# 1 1 0.6111111 0.3611111 0.25 0.1111111 
# 2 2 0.1111111 0.1111111 NA 0.1111111 
# 3 3 0.5000000 0.2500000 0.25  NA 
# 4 5 0.1111111 0.1111111 1.00 0.1111111 

Answer 2

datas=structure(list(Id = 1:4, Product1 = structure(c(3L, 2L, 2L, 1L), .Label = c("1,2,5", "1,3", "5"), class = "factor"), 
        Product2 = structure(c(4L, 3L, 1L, 2L), .Label = c("A", "A,B,E", "B,D", "D"), class = "factor")), 
       .Names = c("Id", "Product1", "Product2"), class = "data.frame", row.names = c(NA, -4L)) 


f <- function(p1, p2, lvls = 1:5) { 
    # p1 <- datas$Product1[2]; p2 <- datas$Product2[2] 
    p1 <- strsplit(as.character(p1), ',')[[1]] 
    p2 <- strsplit(as.character(p2), ',')[[1]] 
    t2 <- factor(rep(p2, each = length(p1)), levels = LETTERS[lvls]) 
    t1 <- factor(rep(p1, length(p2)), levels = lvls) 
    tbl <- table(t1, t2) 
    tbl/sum(tbl) 
} 

對於單標識

Map(f, datas$Product1, datas$Product2) 

# [[1]] 
# t2 
# t1 A B C D E 
# 1 0 0 0 0 0 
# 2 0 0 0 0 0 
# 3 0 0 0 0 0 
# 4 0 0 0 0 0 
# 5 0 0 0 1 0 
# 
# [[2]] 
# t2 
# t1  A B C D E 
# 1 0.00 0.25 0.00 0.25 0.00 
# 2 0.00 0.00 0.00 0.00 0.00 
# 3 0.00 0.25 0.00 0.25 0.00 
# 4 0.00 0.00 0.00 0.00 0.00 
# 5 0.00 0.00 0.00 0.00 0.00 
# 
# [[3]] 
# t2 
# t1 A B C D E 
# 1 0.5 0.0 0.0 0.0 0.0 
# 2 0.0 0.0 0.0 0.0 0.0 
# 3 0.5 0.0 0.0 0.0 0.0 
# 4 0.0 0.0 0.0 0.0 0.0 
# 5 0.0 0.0 0.0 0.0 0.0 
# 
# [[4]] 
# t2 
# t1   A   B   C   D   E 
# 1 0.1111111 0.1111111 0.0000000 0.0000000 0.1111111 
# 2 0.1111111 0.1111111 0.0000000 0.0000000 0.1111111 
# 3 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 
# 4 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 
# 5 0.1111111 0.1111111 0.0000000 0.0000000 0.1111111 

積累個個

Reduce(`+`, Map(f, datas$Product1, datas$Product2), accumulate = TRUE) 

# [[1]] 
# t2 
# t1 A B C D E 
# 1 0 0 0 0 0 
# 2 0 0 0 0 0 
# 3 0 0 0 0 0 
# 4 0 0 0 0 0 
# 5 0 0 0 1 0 
# 
# [[2]] 
# t2 
# t1  A B C D E 
# 1 0.00 0.25 0.00 0.25 0.00 
# 2 0.00 0.00 0.00 0.00 0.00 
# 3 0.00 0.25 0.00 0.25 0.00 
# 4 0.00 0.00 0.00 0.00 0.00 
# 5 0.00 0.00 0.00 1.00 0.00 
# 
# [[3]] 
# t2 
# t1  A B C D E 
# 1 0.50 0.25 0.00 0.25 0.00 
# 2 0.00 0.00 0.00 0.00 0.00 
# 3 0.50 0.25 0.00 0.25 0.00 
# 4 0.00 0.00 0.00 0.00 0.00 
# 5 0.00 0.00 0.00 1.00 0.00 
# 
# [[4]] 
# t2 
# t1   A   B   C   D   E 
# 1 0.6111111 0.3611111 0.0000000 0.2500000 0.1111111 
# 2 0.1111111 0.1111111 0.0000000 0.0000000 0.1111111 
# 3 0.5000000 0.2500000 0.0000000 0.2500000 0.0000000 
# 4 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 
# 5 0.1111111 0.1111111 0.0000000 1.0000000 0.1111111