根据R中的另一个数据框对一个数据框进行子集

Question

df:

  y x
  F T
  F F
  T T
  T F

df1:

 y z probs.x x probs.y   new
 F F    0.08 T     0.4 0.032
 F F    0.24 F     0.4 0.096
 F T    0.12 T     0.6 0.072
 F T    0.36 F     0.6 0.216
 T F    0.40 T     0.5 0.200
 T F    0.20 F     0.5 0.100
 T T    0.40 T     0.5 0.200
 T T    0.20 F     0.5 0.100

df and df1 are the two data frames. And for each row of df, I want to select the matching rows in df1, add the values in column “new”, and store output in a new data frame like this.

df_res:

  y x new
  F T .104
  F F .312
  T T .4
  T F .2

Kindly help me out! I have been toiling over this for a long time now. The table headers will change according to the variables, so please do do not hard code the table headers.

Thanks.

Answer 1

I don't know how long is your data but this can be one approach.

df<- read.table(text="y x
F T
F F
T T
T F",header=T,sep="")

df1 <- read.table(text="y z probs.x x probs.y   new
 F F    0.08 T     0.4 0.032
 F F    0.24 F     0.4 0.096
 F T    0.12 T     0.6 0.072
 F T    0.36 F     0.6 0.216
 T F    0.40 T     0.5 0.200
 T F    0.20 F     0.5 0.100
 T T    0.40 T     0.5 0.200
 T T    0.20 F     0.5 0.100", header=T, sep="")

df$yx <- paste0(df$y,df$x)
df1$yx <- paste0(df1$y, df1$x)

# Update automatically using the for loop

for (i in 1:4){
  new[i] <- sum(df1[which(df1[,7]==df[i,3]),6])
}

df$new <- new
df
      y     x         yx   new
1 FALSE  TRUE  FALSETRUE 0.104
2 FALSE FALSE FALSEFALSE 0.312
3  TRUE  TRUE   TRUETRUE 0.400
4  TRUE FALSE  TRUEFALSE 0.200

Using sapply

new <- sapply(1:4, function(x) sum(df1[which(df1[,7]==df[x,3]),6]))

Answer 2

it seems like if all you want is F,T combination. this works. otherwise you have to write more clearly.

text=" y z probs.x x probs.y   new
 F F    0.08 T     0.4 0.032
 F F    0.24 F     0.4 0.096
 F T    0.12 T     0.6 0.072
 F T    0.36 F     0.6 0.216
 T F    0.40 T     0.5 0.200
 T F    0.20 F     0.5 0.100
 T T    0.40 T     0.5 0.200
 T T    0.20 F     0.5 0.100"

df<-read.table(text=text, header=T)
df_res<-aggregate(data=df, new~interaction(y,x),sum)

  interaction(y, x)   new
1       FALSE.FALSE 0.312
2        TRUE.FALSE 0.200
3        FALSE.TRUE 0.104
4         TRUE.TRUE 0.400

Answer 3

Here's an answer using merge and plyr .

Read in your example data.frame :

df1 <- read.table(text="y z probs.x x probs.y   new
 F F    0.08 T     0.4 0.032
 F F    0.24 F     0.4 0.096
 F T    0.12 T     0.6 0.072
 F T    0.36 F     0.6 0.216
 T F    0.40 T     0.5 0.200
 T F    0.20 F     0.5 0.100
 T T    0.40 T     0.5 0.200
 T T    0.20 F     0.5 0.100", header=T, sep="")

If I understand, there are 2 steps to what your asking. First is to select rows in df1 that match patterns in df. That can be done with merge . The df you gave has all combinations of True and False for x and y. Let's leave one out so we can see the effect:

df <- read.table(text="y x
F T
T T
T F",header=T,sep="")

df_merged <- merge(df, df1, all.y=F)

The results are a new data.frame the omits the rows where both x and y are F. This is equivalent to a left join in a SQL database.

      y     x     z probs.x probs.y   new
1 FALSE  TRUE FALSE    0.08     0.4 0.032
2 FALSE  TRUE  TRUE    0.12     0.6 0.072
3  TRUE FALSE FALSE    0.20     0.5 0.100
4  TRUE FALSE  TRUE    0.20     0.5 0.100
5  TRUE  TRUE FALSE    0.40     0.5 0.200
6  TRUE  TRUE  TRUE    0.40     0.5 0.200

The second part of the question is to group the data and apply a sum to the groups. Plyr is a great tool for this kind of data manipulation:

library(plyr)
ddply(df_merged, .(y,x), function(df) c(new=sum(df$new)))

The dd means we are giving a data.frame and want a data.frame as a result. The next argument .(y,x) is a quoted expression and names the variables we're grouping by. The result is this:

      y     x   new
1 FALSE  TRUE 0.104
2  TRUE FALSE 0.200
3  TRUE  TRUE 0.400