r - 根据另一列中的匹配复制值

Question

In this data frame:

Item <- c("A","B","A","A","A","A","A","B")
Trial <- c("Fam","Fam","Test","Test","Test","Test","Test","Test")
Condition <-c("apple","cherry","Trash","Trash","Trash","Trash","Trash","Trash")
ID <- c(rep("01",8))


df <- data.frame(cbind(Item,Trial,Condition,ID))

I would like to replace the "Trash" value of df$condition at df$Trial == "Test" . The new value of df$condition should be a copy df$condition at df$Trial == "Fam" , based on a match of Fam and Test Trials in df$Item .

So my final data frame should look like this

  Item Trial Condition ID
1    A   Fam     apple 01
2    B   Fam    cherry 01
3    A  Test     apple 01
4    A  Test     apple 01
5    A  Test     apple 01
6    A  Test     apple 01
7    A  Test     apple 01
8    B  Test    cherry 01

Ultimately I would like to do this for unique ID's in my original data frame. So I guess I will have to apply the function within ddply or so later on.

Answer 1

You could do a self binary join on df when Trial != "Test" and update the Condition column by reference using the data.table package, for instance

library(data.table) ## V 1.9.6+
setDT(df)[df[Trial != "Test"], Condition := i.Condition, on = c("Item", "ID")]
df
#    Item Trial Condition ID
# 1:    A   Fam     apple 01
# 2:    B   Fam    cherry 01
# 3:    A  Test     apple 01
# 4:    A  Test     apple 01
# 5:    A  Test     apple 01
# 6:    A  Test     apple 01
# 7:    A  Test     apple 01
# 8:    B  Test    cherry 01

Or (with some modification of @docendos) suggestion, simply

setDT(df)[, Condition := Condition[Trial != "Test"], by = .(Item, ID)]

Answer 2

Here is an option using dplyr

library(dplyr)
distinct(df) %>% 
    filter(Trial=='Fam') %>% 
    left_join(df, ., by = c('Item', 'ID')) %>% 
    mutate(Condition = ifelse(Condition.x=='Trash',
            as.character(Condition.y), as.character(Condition.x))) %>% 
    select(c(1,2,4,7))

Or as suggested by @docendodiscimus

df %>% 
    group_by(ID, Item) %>%
    mutate(Condition = Condition[Condition != "Trash"])

Answer 3

You could also just create a for-loop and loop through all the values that need to be changed. This setup makes it easy to add other items and/or change the type of condition later on.

> for(i in 1:nrow(df)) {
>     
>     if(df[i, 1] == "A") {
>         df2[i, 3] <- "apple"
>     }
>     else if(df[i, 1] == "B") {
>         df2[i, 3] <- "cherry"
>     }
> }