R: Data Frame Manipulations - r

Question

I have two data frames:
>df1
type id1 id2 id3 count1 count2 count3
a x1 y1 z1 10 20 0
b x2 y2 z2 20 0 30
c x3 y3 z3 10 10 10
>df2
id prop
x1 10
x2 5
x3 100
y1 0
y2 50
y3 80
z1 10
z2 20
z3 30
count* are like weights. So, finally I want to join the table such that TotalProp is weighted sum of prop and counts
For e.g. for the first row in df1 TotalProp = 10(prop for x1) * 10(count1) + 0(Prop for y1) * 20(count2) + 10(Prop for z1) * 0(count3) = 100
Hence my final table looks like this:
>result
type id1 id2 id3 TotalProp
a x1 y1 z1 100
b x2 y2 z2 700
c x3 y3 z3 2100
Any idea how can I do this?
Thanks.

Answer 1

One line solution first and then explanation using multiple steps
df1
## type id1 id2 id3 count1 count2 count3
## 1 a x1 y1 z1 10 20 0
## 2 b x2 y2 z2 20 0 30
## 3 c x3 y3 z3 10 10 10
df2
## id prop
## x1 x1 10
## x2 x2 5
## x3 x3 100
## y1 y1 0
## y2 y2 50
## y3 y3 80
## z1 z1 10
## z2 z2 20
## z3 z3 30
rownames(df2) <- df2$id
result <- data.frame(type = df1$type, TotalProp = rowSums(matrix(df2[unlist(df1[, c("id1", "id2", "id3")]), "prop"], nrow = nrow(df1)) * as.matrix(df1[,
c("count1", "count2", "count3")])))
result
## type TotalProp
## 1 a 100
## 2 b 700
## 3 c 2100
Stepwise explanation
First we get all the id values in a vector for which we want to fetch corresponding prop values from df2
Step 1
unlist(df1[, c("id1", "id2", "id3")])
## id11 id12 id13 id21 id22 id23 id31 id32 id33
## "x1" "x2" "x3" "y1" "y2" "y3" "z1" "z2" "z3"
Step 2
We name the rows of df2 with df2$id.
rownames(df2) <- df2$id
Step 3
Then using result from step 1, we get corresponding prop values
df2[unlist(df1[, c("id1", "id2", "id3")]), "prop"]
## [1] 10 5 100 0 50 80 10 20 30
Step 4
Convert the vector from step 3 back to 2 dimensional form
matrix(df2[unlist(df1[, c("id1", "id2", "id3")]), "prop"], nrow = nrow(df1))
## [,1] [,2] [,3]
## [1,] 10 0 10
## [2,] 5 50 20
## [3,] 100 80 30
Step 5
Multiply result of Step 4 with counts from df1
as.matrix(df1[, c("count1", "count2", "count3")])
## count1 count2 count3
## [1,] 10 20 0
## [2,] 20 0 30
## [3,] 10 10 10
matrix(df2[unlist(df1[, c("id1", "id2", "id3")]), "prop"], nrow = nrow(df1)) *
as.matrix(df1[, c("count1", "count2", "count3")])
## count1 count2 count3
## [1,] 100 0 0
## [2,] 100 0 600
## [3,] 1000 800 300
Step 6
Apply rowSums to result from step 5 to get desired TotalProp values
rowSums(matrix(df2[unlist(df1[,c('id1','id2','id3')]),'prop'], nrow=nrow(df1)) * as.matrix(df1[,c('count1', 'count2', 'count3')]))
## [1] 100 700 2100

Answer 2

My solution relies on the data structure, so it is not universal, but short.
m1 <- matrix(df[, tail(names(df1), 3)])
m2 <- matrix(df2$prop, 3)
rowSums(m1 * m2)
[1] 100 700 2100
It does not use ids whatsoever, so be careful!

Answer 3

And another way...
TotalProp <- apply(df1,1,function(x) {
sapply(x[2:4],function(x)df2[df2$id==x,]$prop) %*% as.numeric(x[5:7])
})
result <- cbind(df1[1:4],TotalProp)
%*% is the inner product operator, which is like rowsum, so this is somewhat like #ChinmayPatil's answer. So the steps are:
For each row in df1, extract the elements of df2 which have id = cols 2:4 of df1
Form the inner product of the vector from 1 with the vector formed from cols 5:7 of df1
Repeat for each row of df1 [apply(df1,1, ...)]