Reputation: 2254
I have a data frame like
> x = data.frame(A=c(1,2,3),B=c(2,3,4))
> x
A B
1 1 2
2 2 3
3 3 4
and subsetting conditions in a data frame like
> cond = data.frame(condition=c('A>1','B>2 & B<4'))
> cond
condition
1 A>1
2 B>2 & B<4
which I then apply dynamically
> eval(parse(text=paste0("subset(x,",cond[1,'condition'],")")))
A B
2 2 3
3 3 4
> eval(parse(text=paste0("subset(x,",cond[2,'condition'],")")))
A B
2 2 3
Now, instead of subsetting, I would like to add the subsetting conditions as variables into the data. The end result would look like
A B condition1 condition2
1 1 2 0 0
2 2 3 1 1
3 3 4 1 0
How could I derive the above table using the dynamic conditions?
Upvotes: 1
Views: 78
Reputation: 887291
Here is an option using tidyverse
library(tidyverse)
x %>%
mutate(!!! rlang::parse_exprs(str_c(cond$condition, collapse=";"))) %>%
rename_at(3:4, ~ paste0("condition", 1:2))
# A B condition1 condition2
#1 1 2 FALSE FALSE
#2 2 3 TRUE TRUE
#3 3 4 TRUE FALSE
If needed, the logical
columns can be easily converted to binary with as.integer
Upvotes: 0
Reputation: 389055
Before using eval
parse
, I hope you have gone through some readings like
What specifically are the dangers of eval(parse(…))?
and many others which are available.
However, to answer your question, we can continue your flow and use eval
parse
in sapply
+(sapply(seq_len(nrow(cond)), function(i)
eval(parse(text=paste0("with(x,",cond[i,'condition'],")")))))
# [,1] [,2]
#[1,] 0 0
#[2,] 1 1
#[3,] 1 0
To add it to the dataframe,
x[paste0("condition", 1:nrow(cond))] <-
+(sapply(seq_len(nrow(cond)), function(i)
eval(parse(text=paste0("with(x,",cond[i,'condition'],")")))))
x
# A B condition1 condition2
#1 1 2 0 0
#2 2 3 1 1
#3 3 4 1 0
Simplifying it a bit (using @jogo's comment)
+(sapply(cond$condition, function(i) with(x, eval(parse(text=as.character(i))))))
# [,1] [,2]
#[1,] 0 0
#[2,] 1 1
#[3,] 1 0
Upvotes: 2