R subsetting a data frame into multiple data frames based on multiple column values

Question

I am trying to subset a data frame, where I get multiple data frames based on multiple column values. Here is my example ``` >df v1 v2 v3 v4 v5 A Z 1 10 12 D Y 10 12 8 E X 2 12 15 A Z 1 10 12 E X 2 14 16 ``` The expected output is something like this where I am splitting this data frame into multiple data frames based on column `v1` and `v2` ``` >df1 v3 v4 v5 1 10 12 1 10 12 >df2 v3 v4 v5 10 12 8 >df3 v3 v4 v5 2 12 15 2 14 16 ``` I have written a code which is working right now but don't think that's the best way to do it. There must be a better way to do it. Assuming `tab` is the data.frame having the initial data. Here is my code: ``` v1Factors<-levels(factor(tab$v1)) v2Factors<-levels(factor(tab$v2)) for(i in 1:length(v1Factors)){ for(j in 1:length(v2Factors)){ subsetTab<-subset(tab, v1==v1Factors[i] & v2==v2Factors[j], select=c("v3", "v4", "v5")) print(subsetTab) } } ``` Can someone suggest a better method to do the above?

Accepted Answer

You are looking for `split`

```
split(df, with(df, interaction(v1,v2)), drop = TRUE)
$E.X
  v1 v2 v3 v4 v5
3  E  X  2 12 15
5  E  X  2 14 16

$D.Y
  v1 v2 v3 v4 v5
2  D  Y 10 12  8

$A.Z
  v1 v2 v3 v4 v5
1  A  Z  1 10 12
```

As noted in the comments

any of the following would work

```
library(microbenchmark)
microbenchmark(
                split(df, list(df$v1,df$v2), drop = TRUE), 
               split(df, interaction(df$v1,df$v2), drop = TRUE),
               split(df, with(df, interaction(v1,v2)), drop = TRUE))

Unit: microseconds
                                                  expr      min        lq    median       uq      max neval
            split(df, list(df$v1, df$v2), drop = TRUE) 1119.845 1129.3750 1145.8815 1182.119 3910.249   100
     split(df, interaction(df$v1, df$v2), drop = TRUE)  893.749  900.5720  909.8035  936.414 3617.038   100
 split(df, with(df, interaction(v1, v2)), drop = TRUE)  895.150  902.5705  909.8505  927.128 1399.284   100
```

It appears `interaction` is slightly faster (probably due the fact that the `f = list(...)` are just converted to an interaction within the function)

Edit

If you just want use the subset data.frames then I would suggest using data.table for ease of coding

```
library(data.table)

dt <- data.table(df)
dt[, plot(v4, v5), by = list(v1, v2)]
```

R subsetting a data frame into multiple data frames based on multiple column values

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