基于 R 中 2 个单独数据帧的值创建新 dataframe 的有效方法

Question

I would like help finding an efficient way to create a new df, based on the values of 2 separate dfs: the first contains peoples scores on a set of items and the second contains weights for each item. I want to create a new df that multiplies the values in df1 with their respective weights in df2 for scoring purposes. A small example of what I mean. Data frames 1 and 2:

it1<-c(0,1,2,0,1,2)
it2<-c(1,1,2,1,2,1)
it3<-c(0,2,1,1,1,0)
df1<-cbind(it1,it2,it3)

it<-c("it1","it2","it3")
val<-c(2,4,6)
df2<-cbind.data.frame(it,val)

I would like my new df to multiply the values from df1 by the appropriate weights identified in df2 (so item1 is multiplied by 2, item 2 is multiplied by 4, and item 3 is multiplied by 6). I would like the new df to look like this:

    it1  it2   it3
     0     4     0
     2     4    12
     4     8     6
     0     4     6
     2     8     6
     4     4     0

my actual data has more items and weights than this example

Answer 1

If the columns are in the same order

df1 * df2$val[col(df1)]

Or as @markus mentioned

df1[,df2$it] * as.list(df2$val)

---

There are multiple ways to tackle this. One roundabout option is to convert it to long format and then do a join and reshape it back to 'wide' format

library(dplyr)
library(tidyr)
df1 %>%
   mutate(rn = row_number()) %>%
   pivot_longer(cols = -rn) %>%
   left_join(df2, c('name' = 'it')) %>% 
   mutate(value = val* value) %>% 
   select(-val) %>% 
   pivot_wider(names_from = name, values_from = value) %>% 
   select(-rn)

NOTE: Here we assume the datasets are all data.frame