Extracting items from an R data frame using criter

I would like to extract items from a column in a data frame based on criteria pertaining to values in other columns. These criteria are given in the form of a list associating column names with values. The ultimate goal is to use those items to select columns by name in another data structure.

Here is an example data frame:

> experimental_plan
  lib genotype treatment replicate
1   A       WT    normal         1
2   B       WT       hot         1
3   C      mut    normal         1
4   D      mut       hot         1
5   E       WT    normal         2
6   F       WT       hot         2
7   G      mut    normal         2
8   H      mut       hot         2

And my selection criteria are encoded as the following list:

> ref_condition = list(genotype="WT", treatment="normal")

I want to extract the items in the "lib" column where the line matches ref_condition, that is "A" and "E".

1) I can get the columns to use for selection using names on my list of selection criteria:

> experimental_plan[, names(ref_condition)]
  genotype treatment
1       WT    normal
2       WT       hot
3      mut    normal
4      mut       hot
5       WT    normal
6       WT       hot
7      mut    normal
8      mut       hot

2) I can test whether the resulting lines match my selection criteria:

> experimental_plan[, names(ref_condition)] == ref_condition
     genotype treatment
[1,]     TRUE      TRUE
[2,]     TRUE     FALSE
[3,]    FALSE      TRUE
[4,]    FALSE     FALSE
[5,]     TRUE      TRUE
[6,]     TRUE     FALSE
[7,]    FALSE      TRUE
[8,]    FALSE     FALSE
> selection_vector <- apply(experimental_plan[, names(ref_condition)] == ref_condition, 1, all)
> selection_vector
[1]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE

(I think this step, with the apply is not particularly elegant. There must be a better way.)

3) This boolean vector can be used to select the relevant lines:

> selected_lines <- experimental_plan[selection_vector,]
> selected_lines
  lib genotype treatment replicate
1   A       WT    normal         1
5   E       WT    normal         2

4) From this point on, I know how to use dplyr to select items I'm interested in:

> lib1 <- filter(selected_lines, replicate=="1") %>% select(lib) %>% unlist()
> lib2 <- filter(selected_lines, replicate=="2") %>% select(lib) %>% unlist()
> lib1
lib 
  A 
Levels: A B C D E F G H
> lib2
lib 
  E 
Levels: A B C D E F G H

Can dplyr (or other clever techniques) be used in earlier steps?

5) These items happen to correspond to column names in another data structure (named counts_data here). I use them to extract the corresponding columns and put them in a list, associated with replicate numbers as names:

> counts_1 <- counts_data[, lib1]
> counts_2 <- counts_data[, lib2]
> list_of_counts <- list("1" <- counts_1, "2" <- counts_2)

(Ideally, I would like to generalize the code so that I do not need to know (I mean, "hard-code them") what different values exist in the "replicate" column: there could be any number of replicates for a given combination of "genotype" and "treatment" characteristics, and I want my final list to contain the data from the counts_data pertaining to the corresponding "lib" items.)

Is there a way to do the whole process more elegantly / efficiently?

I think you can use data.table for this with a key

library(data.table)
test <- data.table(lib = LETTERS[1:8],
           genotype = rep(c("WT","WT","mut","mut"),2),
           treatment = rep(c("normal","hot"),4),
           replicate = c(rep(1,4),rep(2,4)))
setkeyv(test,c("genotype","treatment"))
ref_condition = list(genotype="WT", treatment="normal")
test[ref_condition,lib]

This gives