Filter by ranges supplied by two vectors, without a join operation
7
I wish to do exactly this: Take dates from one dataframe and filter data in another dataframe - R
except without joining, as I am afraid that after I join my data the result will be too big to fit in memory, prior to the filter.
Here is sample data:
tmp_df <- data.frame(a = 1:10)
I wish to do an operation that looks like this:
lower_bound <- c(2, 4)
upper_bound <- c(2, 5)
tmp_df %>%
filter(a >= lower_bound & a <= upper_bound) # does not work as <= is vectorised inappropriately
and my desired result is:
> tmp_df[(tmp_df$a <= 2 & tmp_df$a >= 2) | (tmp_df$a <= 5 & tmp_df$a >= 4), , drop = F]
# one way to get indices to subset data frame, impractical for a long range vector
a
2 2
4 4
5 5
My problem with memory requirements (with respect to the join solution linked) is when tmp_df has many more rows and the lower_bound and upper_bound vectors have many more entries. A dplyr solution, or a solution that can be part of pipe is preferred.
r data.table dplyr subset
edited Nov 22 at 18:13
Henrik
40.7k992107
asked Jun 19 '17 at 3:06
Alex
6,87554180
|
show 2 more comments
7
I wish to do exactly this: Take dates from one dataframe and filter data in another dataframe - R
except without joining, as I am afraid that after I join my data the result will be too big to fit in memory, prior to the filter.
Here is sample data:
tmp_df <- data.frame(a = 1:10)
I wish to do an operation that looks like this:
lower_bound <- c(2, 4)
upper_bound <- c(2, 5)
tmp_df %>%
filter(a >= lower_bound & a <= upper_bound) # does not work as <= is vectorised inappropriately
and my desired result is:
> tmp_df[(tmp_df$a <= 2 & tmp_df$a >= 2) | (tmp_df$a <= 5 & tmp_df$a >= 4), , drop = F]
# one way to get indices to subset data frame, impractical for a long range vector
a
2 2
4 4
5 5
My problem with memory requirements (with respect to the join solution linked) is when tmp_df has many more rows and the lower_bound and upper_bound vectors have many more entries. A dplyr solution, or a solution that can be part of pipe is preferred.
r data.table dplyr subset
edited Nov 22 at 18:13
Henrik
40.7k992107
asked Jun 19 '17 at 3:06
Alex
6,87554180
1
Related post: stackoverflow.com/questions/36454565/…
– mt1022
Jun 19 '17 at 4:02
You could simply dotmp_df %>% filter(a == 2 | between(a, 4, 5))or combine a fewbetweenstatements, or you could simply use the same syntax as in base R such astmp_df %>% filter(a == 2 | (a <= 5 & a >= 4))or eventmp_df %>% filter(a %in% c(2, 4:5)). I really fail to understand what's the question's about even.
– David Arenburg
Jun 19 '17 at 8:06
The question is about how you do this when the vectors supplying the ranges have lots of elements, say 100 each.
– Alex
Jun 19 '17 at 8:08
As always, you are answering comments without using @ (so I won't see your further comments), but I suggest you clarify that in your question and show a real use case rather just a small example which could be easily solved in many simple ways.
– David Arenburg
Jun 19 '17 at 8:11
I have reworded the question which hopefully addresses your comments regarding the clarity. I do not believe in real use cases when simple examples are easier to read and understand.
– Alex
Jun 19 '17 at 8:26
|
show 2 more comments
7
7
7
1
I wish to do exactly this: Take dates from one dataframe and filter data in another dataframe - R
except without joining, as I am afraid that after I join my data the result will be too big to fit in memory, prior to the filter.
Here is sample data:
tmp_df <- data.frame(a = 1:10)
I wish to do an operation that looks like this:
lower_bound <- c(2, 4)
upper_bound <- c(2, 5)
tmp_df %>%
filter(a >= lower_bound & a <= upper_bound) # does not work as <= is vectorised inappropriately
and my desired result is:
> tmp_df[(tmp_df$a <= 2 & tmp_df$a >= 2) | (tmp_df$a <= 5 & tmp_df$a >= 4), , drop = F]
# one way to get indices to subset data frame, impractical for a long range vector
a
2 2
4 4
5 5
My problem with memory requirements (with respect to the join solution linked) is when tmp_df has many more rows and the lower_bound and upper_bound vectors have many more entries. A dplyr solution, or a solution that can be part of pipe is preferred.
r data.table dplyr subset
edited Nov 22 at 18:13
Henrik
40.7k992107
asked Jun 19 '17 at 3:06
Alex
6,87554180
I wish to do exactly this: Take dates from one dataframe and filter data in another dataframe - R
except without joining, as I am afraid that after I join my data the result will be too big to fit in memory, prior to the filter.
Here is sample data:
tmp_df <- data.frame(a = 1:10)
I wish to do an operation that looks like this:
lower_bound <- c(2, 4)
upper_bound <- c(2, 5)
tmp_df %>%
filter(a >= lower_bound & a <= upper_bound) # does not work as <= is vectorised inappropriately
and my desired result is:
> tmp_df[(tmp_df$a <= 2 & tmp_df$a >= 2) | (tmp_df$a <= 5 & tmp_df$a >= 4), , drop = F]
# one way to get indices to subset data frame, impractical for a long range vector
a
2 2
4 4
5 5
My problem with memory requirements (with respect to the join solution linked) is when tmp_df has many more rows and the lower_bound and upper_bound vectors have many more entries. A dplyr solution, or a solution that can be part of pipe is preferred.
r data.table dplyr subset
r data.table dplyr subset
edited Nov 22 at 18:13
Henrik
40.7k992107
asked Jun 19 '17 at 3:06
Alex
6,87554180
edited Nov 22 at 18:13
Henrik
40.7k992107
asked Jun 19 '17 at 3:06
Alex
6,87554180
edited Nov 22 at 18:13
Henrik
40.7k992107
edited Nov 22 at 18:13
Henrik
40.7k992107
edited Nov 22 at 18:13
Henrik
40.7k992107
40.7k992107
asked Jun 19 '17 at 3:06
Alex
6,87554180
asked Jun 19 '17 at 3:06
Alex
6,87554180
asked Jun 19 '17 at 3:06
Alex
6,87554180
6,87554180
1
Related post: stackoverflow.com/questions/36454565/…
– mt1022
Jun 19 '17 at 4:02
You could simply dotmp_df %>% filter(a == 2 | between(a, 4, 5))or combine a fewbetweenstatements, or you could simply use the same syntax as in base R such astmp_df %>% filter(a == 2 | (a <= 5 & a >= 4))or eventmp_df %>% filter(a %in% c(2, 4:5)). I really fail to understand what's the question's about even.
– David Arenburg
Jun 19 '17 at 8:06
The question is about how you do this when the vectors supplying the ranges have lots of elements, say 100 each.
– Alex
Jun 19 '17 at 8:08
As always, you are answering comments without using @ (so I won't see your further comments), but I suggest you clarify that in your question and show a real use case rather just a small example which could be easily solved in many simple ways.
– David Arenburg
Jun 19 '17 at 8:11
I have reworded the question which hopefully addresses your comments regarding the clarity. I do not believe in real use cases when simple examples are easier to read and understand.
– Alex
Jun 19 '17 at 8:26
|
show 2 more comments
1
Related post: stackoverflow.com/questions/36454565/…
– mt1022
Jun 19 '17 at 4:02
You could simply dotmp_df %>% filter(a == 2 | between(a, 4, 5))or combine a fewbetweenstatements, or you could simply use the same syntax as in base R such astmp_df %>% filter(a == 2 | (a <= 5 & a >= 4))or eventmp_df %>% filter(a %in% c(2, 4:5)). I really fail to understand what's the question's about even.
– David Arenburg
Jun 19 '17 at 8:06
The question is about how you do this when the vectors supplying the ranges have lots of elements, say 100 each.
– Alex
Jun 19 '17 at 8:08
As always, you are answering comments without using @ (so I won't see your further comments), but I suggest you clarify that in your question and show a real use case rather just a small example which could be easily solved in many simple ways.
– David Arenburg
Jun 19 '17 at 8:11
I have reworded the question which hopefully addresses your comments regarding the clarity. I do not believe in real use cases when simple examples are easier to read and understand.
– Alex
Jun 19 '17 at 8:26
1
1
Related post: stackoverflow.com/questions/36454565/…
– mt1022
Jun 19 '17 at 4:02
Related post: stackoverflow.com/questions/36454565/…
– mt1022
Jun 19 '17 at 4:02
You could simply do
tmp_df %>% filter(a == 2 | between(a, 4, 5)) or combine a few between statements, or you could simply use the same syntax as in base R such as tmp_df %>% filter(a == 2 | (a <= 5 & a >= 4)) or even tmp_df %>% filter(a %in% c(2, 4:5)). I really fail to understand what's the question's about even.– David Arenburg
Jun 19 '17 at 8:06
You could simply do
tmp_df %>% filter(a == 2 | between(a, 4, 5)) or combine a few between statements, or you could simply use the same syntax as in base R such as tmp_df %>% filter(a == 2 | (a <= 5 & a >= 4)) or even tmp_df %>% filter(a %in% c(2, 4:5)). I really fail to understand what's the question's about even.– David Arenburg
Jun 19 '17 at 8:06
The question is about how you do this when the vectors supplying the ranges have lots of elements, say 100 each.
– Alex
Jun 19 '17 at 8:08
The question is about how you do this when the vectors supplying the ranges have lots of elements, say 100 each.
– Alex
Jun 19 '17 at 8:08
As always, you are answering comments without using @ (so I won't see your further comments), but I suggest you clarify that in your question and show a real use case rather just a small example which could be easily solved in many simple ways.
– David Arenburg
Jun 19 '17 at 8:11
As always, you are answering comments without using @ (so I won't see your further comments), but I suggest you clarify that in your question and show a real use case rather just a small example which could be easily solved in many simple ways.
– David Arenburg
Jun 19 '17 at 8:11
I have reworded the question which hopefully addresses your comments regarding the clarity. I do not believe in real use cases when simple examples are easier to read and understand.
– Alex
Jun 19 '17 at 8:26
I have reworded the question which hopefully addresses your comments regarding the clarity. I do not believe in real use cases when simple examples are easier to read and understand.
– Alex
Jun 19 '17 at 8:26
|
show 2 more comments
2 Answers
2
active
oldest
votes
7
Maybe you could borrow the inrange function from data.table, which
checks whether each value in x is in between any of the
intervals provided in lower,upper.
Usage:
inrange(x, lower, upper, incbounds=TRUE)
library(dplyr); library(data.table)
tmp_df %>% filter(inrange(a, c(2,4), c(2,5)))
# a
#1 2
#2 4
#3 5
answered Jun 19 '17 at 3:19
Psidom
122k1281125
2
Great solution ````
– W-B
Jun 19 '17 at 3:20
2
@Wen Thanks for the comment.
– Psidom
Jun 19 '17 at 3:20
2
Very neat, although I note that in the helpfile it saysinrange makes use of this functionality and performs a range join. I have to check that this does not cause memory requirements to blow up.
– Alex
Jun 19 '17 at 3:53
2
@Alex, it was precisely implemented by having memory requirements in mind. I'd love to know how it turned up on your original dataset.
– Arun
Jun 19 '17 at 19:52
2
@arun I am doing a 4 million row by 30 million row range join (sinceinrangeuses non equi-joins I decided to rewrite everything usingdata.tablesyntax) and it is easily staying in memory.
– Alex
Nov 17 '17 at 9:55
|
show 1 more comment
3
If you'd like to stick with dplyr it has similar functionality provided through the between function.
# ranges I want to check between
my_ranges <- list(c(2,2), c(4,5), c(6,7))
tmp_df <- data.frame(a=1:10)
tmp_df %>%
filter(apply(bind_rows(lapply(my_ranges,
FUN=function(x, a){
data.frame(t(between(a, x[1], x[2])))
}, a)
), 2, any))
a
1 2
2 4
3 5
4 6
5 7
Just be aware that the argument boundaries are included by default and that cannot be changed as with inrange
answered Jun 19 '17 at 3:30
Steven M. Mortimer
844618
thanks for this, the problem is that I do not know how to, and is possible inefficient, to expand each vector into individual OR statements to give the required filtration
– Alex
Jun 19 '17 at 3:47
Have you considered keeping your ranges in a list, then using other functions that do the expansion for you? I've edited my answer to be more like this case, although, it makes the code less readable. I'm sure there's a cleaner way to write the code, it's just not coming to me right now.
– Steven M. Mortimer
Jun 19 '17 at 4:01
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
7
Maybe you could borrow the inrange function from data.table, which
checks whether each value in x is in between any of the
intervals provided in lower,upper.
Usage:
inrange(x, lower, upper, incbounds=TRUE)
library(dplyr); library(data.table)
tmp_df %>% filter(inrange(a, c(2,4), c(2,5)))
# a
#1 2
#2 4
#3 5
answered Jun 19 '17 at 3:19
Psidom
122k1281125
2
Great solution ````
– W-B
Jun 19 '17 at 3:20
2
@Wen Thanks for the comment.
– Psidom
Jun 19 '17 at 3:20
2
Very neat, although I note that in the helpfile it saysinrange makes use of this functionality and performs a range join. I have to check that this does not cause memory requirements to blow up.
– Alex
Jun 19 '17 at 3:53
2
@Alex, it was precisely implemented by having memory requirements in mind. I'd love to know how it turned up on your original dataset.
– Arun
Jun 19 '17 at 19:52
2
@arun I am doing a 4 million row by 30 million row range join (sinceinrangeuses non equi-joins I decided to rewrite everything usingdata.tablesyntax) and it is easily staying in memory.
– Alex
Nov 17 '17 at 9:55
|
show 1 more comment
7
Maybe you could borrow the inrange function from data.table, which
checks whether each value in x is in between any of the
intervals provided in lower,upper.
Usage:
inrange(x, lower, upper, incbounds=TRUE)
library(dplyr); library(data.table)
tmp_df %>% filter(inrange(a, c(2,4), c(2,5)))
# a
#1 2
#2 4
#3 5
answered Jun 19 '17 at 3:19
Psidom
122k1281125
2
Great solution ````
– W-B
Jun 19 '17 at 3:20
2
@Wen Thanks for the comment.
– Psidom
Jun 19 '17 at 3:20
2
Very neat, although I note that in the helpfile it saysinrange makes use of this functionality and performs a range join. I have to check that this does not cause memory requirements to blow up.
– Alex
Jun 19 '17 at 3:53
2
@Alex, it was precisely implemented by having memory requirements in mind. I'd love to know how it turned up on your original dataset.
– Arun
Jun 19 '17 at 19:52
2
@arun I am doing a 4 million row by 30 million row range join (sinceinrangeuses non equi-joins I decided to rewrite everything usingdata.tablesyntax) and it is easily staying in memory.
– Alex
Nov 17 '17 at 9:55
|
show 1 more comment
7
7
7
Maybe you could borrow the inrange function from data.table, which
checks whether each value in x is in between any of the
intervals provided in lower,upper.
Usage:
inrange(x, lower, upper, incbounds=TRUE)
library(dplyr); library(data.table)
tmp_df %>% filter(inrange(a, c(2,4), c(2,5)))
# a
#1 2
#2 4
#3 5
answered Jun 19 '17 at 3:19
Psidom
122k1281125
Maybe you could borrow the inrange function from data.table, which
checks whether each value in x is in between any of the
intervals provided in lower,upper.
Usage:
inrange(x, lower, upper, incbounds=TRUE)
library(dplyr); library(data.table)
tmp_df %>% filter(inrange(a, c(2,4), c(2,5)))
# a
#1 2
#2 4
#3 5
answered Jun 19 '17 at 3:19
Psidom
122k1281125
edited Jun 19 '17 at 3:22
answered Jun 19 '17 at 3:19
Psidom
122k1281125
answered Jun 19 '17 at 3:19
Psidom
122k1281125
answered Jun 19 '17 at 3:19
Psidom
122k1281125
122k1281125
2
Great solution ````
– W-B
Jun 19 '17 at 3:20
2
@Wen Thanks for the comment.
– Psidom
Jun 19 '17 at 3:20
2
Very neat, although I note that in the helpfile it saysinrange makes use of this functionality and performs a range join. I have to check that this does not cause memory requirements to blow up.
– Alex
Jun 19 '17 at 3:53
2
@Alex, it was precisely implemented by having memory requirements in mind. I'd love to know how it turned up on your original dataset.
– Arun
Jun 19 '17 at 19:52
2
@arun I am doing a 4 million row by 30 million row range join (sinceinrangeuses non equi-joins I decided to rewrite everything usingdata.tablesyntax) and it is easily staying in memory.
– Alex
Nov 17 '17 at 9:55
|
show 1 more comment
2
Great solution ````
– W-B
Jun 19 '17 at 3:20
2
@Wen Thanks for the comment.
– Psidom
Jun 19 '17 at 3:20
2
Very neat, although I note that in the helpfile it saysinrange makes use of this functionality and performs a range join. I have to check that this does not cause memory requirements to blow up.
– Alex
Jun 19 '17 at 3:53
2
@Alex, it was precisely implemented by having memory requirements in mind. I'd love to know how it turned up on your original dataset.
– Arun
Jun 19 '17 at 19:52
2
@arun I am doing a 4 million row by 30 million row range join (sinceinrangeuses non equi-joins I decided to rewrite everything usingdata.tablesyntax) and it is easily staying in memory.
– Alex
Nov 17 '17 at 9:55
2
2
Great solution ````
– W-B
Jun 19 '17 at 3:20
Great solution ````
– W-B
Jun 19 '17 at 3:20
2
2
@Wen Thanks for the comment.
– Psidom
Jun 19 '17 at 3:20
@Wen Thanks for the comment.
– Psidom
Jun 19 '17 at 3:20
2
2
Very neat, although I note that in the helpfile it says
inrange makes use of this functionality and performs a range join. I have to check that this does not cause memory requirements to blow up.– Alex
Jun 19 '17 at 3:53
Very neat, although I note that in the helpfile it says
inrange makes use of this functionality and performs a range join. I have to check that this does not cause memory requirements to blow up.– Alex
Jun 19 '17 at 3:53
2
2
@Alex, it was precisely implemented by having memory requirements in mind. I'd love to know how it turned up on your original dataset.
– Arun
Jun 19 '17 at 19:52
@Alex, it was precisely implemented by having memory requirements in mind. I'd love to know how it turned up on your original dataset.
– Arun
Jun 19 '17 at 19:52
2
2
@arun I am doing a 4 million row by 30 million row range join (since
inrange uses non equi-joins I decided to rewrite everything using data.table syntax) and it is easily staying in memory.– Alex
Nov 17 '17 at 9:55
@arun I am doing a 4 million row by 30 million row range join (since
inrange uses non equi-joins I decided to rewrite everything using data.table syntax) and it is easily staying in memory.– Alex
Nov 17 '17 at 9:55
|
show 1 more comment
3
If you'd like to stick with dplyr it has similar functionality provided through the between function.
# ranges I want to check between
my_ranges <- list(c(2,2), c(4,5), c(6,7))
tmp_df <- data.frame(a=1:10)
tmp_df %>%
filter(apply(bind_rows(lapply(my_ranges,
FUN=function(x, a){
data.frame(t(between(a, x[1], x[2])))
}, a)
), 2, any))
a
1 2
2 4
3 5
4 6
5 7
Just be aware that the argument boundaries are included by default and that cannot be changed as with inrange
answered Jun 19 '17 at 3:30
Steven M. Mortimer
844618
thanks for this, the problem is that I do not know how to, and is possible inefficient, to expand each vector into individual OR statements to give the required filtration
– Alex
Jun 19 '17 at 3:47
Have you considered keeping your ranges in a list, then using other functions that do the expansion for you? I've edited my answer to be more like this case, although, it makes the code less readable. I'm sure there's a cleaner way to write the code, it's just not coming to me right now.
– Steven M. Mortimer
Jun 19 '17 at 4:01
add a comment |
3
If you'd like to stick with dplyr it has similar functionality provided through the between function.
# ranges I want to check between
my_ranges <- list(c(2,2), c(4,5), c(6,7))
tmp_df <- data.frame(a=1:10)
tmp_df %>%
filter(apply(bind_rows(lapply(my_ranges,
FUN=function(x, a){
data.frame(t(between(a, x[1], x[2])))
}, a)
), 2, any))
a
1 2
2 4
3 5
4 6
5 7
Just be aware that the argument boundaries are included by default and that cannot be changed as with inrange
answered Jun 19 '17 at 3:30
Steven M. Mortimer
844618
thanks for this, the problem is that I do not know how to, and is possible inefficient, to expand each vector into individual OR statements to give the required filtration
– Alex
Jun 19 '17 at 3:47
Have you considered keeping your ranges in a list, then using other functions that do the expansion for you? I've edited my answer to be more like this case, although, it makes the code less readable. I'm sure there's a cleaner way to write the code, it's just not coming to me right now.
– Steven M. Mortimer
Jun 19 '17 at 4:01
add a comment |
3
3
3
If you'd like to stick with dplyr it has similar functionality provided through the between function.
# ranges I want to check between
my_ranges <- list(c(2,2), c(4,5), c(6,7))
tmp_df <- data.frame(a=1:10)
tmp_df %>%
filter(apply(bind_rows(lapply(my_ranges,
FUN=function(x, a){
data.frame(t(between(a, x[1], x[2])))
}, a)
), 2, any))
a
1 2
2 4
3 5
4 6
5 7
Just be aware that the argument boundaries are included by default and that cannot be changed as with inrange
answered Jun 19 '17 at 3:30
Steven M. Mortimer
844618
If you'd like to stick with dplyr it has similar functionality provided through the between function.
# ranges I want to check between
my_ranges <- list(c(2,2), c(4,5), c(6,7))
tmp_df <- data.frame(a=1:10)
tmp_df %>%
filter(apply(bind_rows(lapply(my_ranges,
FUN=function(x, a){
data.frame(t(between(a, x[1], x[2])))
}, a)
), 2, any))
a
1 2
2 4
3 5
4 6
5 7
Just be aware that the argument boundaries are included by default and that cannot be changed as with inrange
answered Jun 19 '17 at 3:30
Steven M. Mortimer
844618
edited Jun 19 '17 at 4:07
answered Jun 19 '17 at 3:30
Steven M. Mortimer
844618
answered Jun 19 '17 at 3:30
Steven M. Mortimer
844618
answered Jun 19 '17 at 3:30
Steven M. Mortimer
844618
844618
thanks for this, the problem is that I do not know how to, and is possible inefficient, to expand each vector into individual OR statements to give the required filtration
– Alex
Jun 19 '17 at 3:47
Have you considered keeping your ranges in a list, then using other functions that do the expansion for you? I've edited my answer to be more like this case, although, it makes the code less readable. I'm sure there's a cleaner way to write the code, it's just not coming to me right now.
– Steven M. Mortimer
Jun 19 '17 at 4:01
add a comment |
thanks for this, the problem is that I do not know how to, and is possible inefficient, to expand each vector into individual OR statements to give the required filtration
– Alex
Jun 19 '17 at 3:47
Have you considered keeping your ranges in a list, then using other functions that do the expansion for you? I've edited my answer to be more like this case, although, it makes the code less readable. I'm sure there's a cleaner way to write the code, it's just not coming to me right now.
– Steven M. Mortimer
Jun 19 '17 at 4:01
thanks for this, the problem is that I do not know how to, and is possible inefficient, to expand each vector into individual OR statements to give the required filtration
– Alex
Jun 19 '17 at 3:47
thanks for this, the problem is that I do not know how to, and is possible inefficient, to expand each vector into individual OR statements to give the required filtration
– Alex
Jun 19 '17 at 3:47
Have you considered keeping your ranges in a list, then using other functions that do the expansion for you? I've edited my answer to be more like this case, although, it makes the code less readable. I'm sure there's a cleaner way to write the code, it's just not coming to me right now.
– Steven M. Mortimer
Jun 19 '17 at 4:01
Have you considered keeping your ranges in a list, then using other functions that do the expansion for you? I've edited my answer to be more like this case, although, it makes the code less readable. I'm sure there's a cleaner way to write the code, it's just not coming to me right now.
– Steven M. Mortimer
Jun 19 '17 at 4:01
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Related post: stackoverflow.com/questions/36454565/…
– mt1022
Jun 19 '17 at 4:02
You could simply do
tmp_df %>% filter(a == 2 | between(a, 4, 5))or combine a fewbetweenstatements, or you could simply use the same syntax as in base R such astmp_df %>% filter(a == 2 | (a <= 5 & a >= 4))or eventmp_df %>% filter(a %in% c(2, 4:5)). I really fail to understand what's the question's about even.– David Arenburg
Jun 19 '17 at 8:06
The question is about how you do this when the vectors supplying the ranges have lots of elements, say 100 each.
– Alex
Jun 19 '17 at 8:08
As always, you are answering comments without using @ (so I won't see your further comments), but I suggest you clarify that in your question and show a real use case rather just a small example which could be easily solved in many simple ways.
– David Arenburg
Jun 19 '17 at 8:11
I have reworded the question which hopefully addresses your comments regarding the clarity. I do not believe in real use cases when simple examples are easier to read and understand.
– Alex
Jun 19 '17 at 8:26