英文:
The usage of the key word 'within' and 'overlaps' in join_by
问题
样本1：within
by <- join_by(chromosome, within(x$start, x$end, y$start, y$end))inner_join(segments, reference, by)
样本2：overlaps
by <- join_by(chromosome, overlaps(x$start, x$end, y$start, y$end))full_join(segments, reference, by)
英文:
In R/dplyr help file , there are have below code as attached which have within and overlaps, how to understand this two key words ? Thanks!
library(dplyr)segments <- tibble(segment_id = 1:4,chromosome = c("chr1", "chr2", "chr2", "chr1"),start = c(140, 210, 380, 230),end = c(150, 240, 415, 280))reference <- tibble(reference_id = 1:4,chromosome = c("chr1", "chr1", "chr2", "chr2"),start = c(100, 200, 300, 415),end = c(150, 250, 399, 450))
sample 1: within
by <- join_by(chromosome, within(x$start, x$end, y$start, y$end))inner_join(segments, reference, by)
sample 2: overlaps
by <- join_by(chromosome, overlaps(x$start, x$end, y$start, y$end))full_join(segments, reference, by)
答案1
得分: 3
within 只会捕捉在 x 范围完全包含在 y 范围内的行。
overlaps 会捕捉 x 范围与 y 范围存在任何类型的重叠的行。但是，它不会捕捉完全包含在内的行，即如果 x_lower >= y_lower 且 x_upper <= y_upper。
可能更容易理解的方式如下（请注意这使用了 overlap 的默认边界："[]"）
示例：
x_lower = c(1, 10, 5, 10)x_upper = c(4, 25, 6, 15)y_lower = c(0, 15, 10, 3)y_upper = c(10, 16, 20, 30)df <- data.frame(x_lower, x_upper, y_lower, y_upper)transform(df,is_within = x_lower >= y_lower & x_upper <= y_upper,is_overlap = x_lower <= y_lower & x_upper >= y_upper)# x_lower x_upper y_lower y_upper is_within is_overlap# 1 1 4 0 10 TRUE FALSE# 2 10 25 15 16 FALSE TRUE# 3 5 6 10 20 FALSE FALSE# 4 10 15 3 30 TRUE FALSE
从文档中可以看到：
within(x_lower, x_upper, y_lower, y_upper)对于 [x_lower, x_upper] 中的每个范围，这会找到该范围完全位于 [y_lower, y_upper] 中的所有位置。等同于 x_lower >= y_lower, x_upper <= y_upper。
以及
overlaps(x_lower, x_upper, y_lower, y_upper, ..., bounds = "[]")对于 [x_lower, x_upper] 中的每个范围，这会找到该范围在任何容量上与 [y_lower, y_upper] 重叠的所有位置。默认情况下等同于 x_lower <= y_upper, x_upper >= y_lower。
英文:
within only captures rows if the range in x is entirely in the range of y.
overlaps capture rows if there is any type of overlap between the range of x and y. BUT it does not capture rows that are entirely within, i.e. if x_lower > y_lower & x_upper < y_upper.
It might be easier to understand like this (note this uses overlap's default bound: "[]")
Example:
x_lower = c(1, 10, 5, 10)x_upper = c(4, 25, 6, 15)y_lower = c(0, 15, 10, 3)y_upper = c(10, 16, 20, 30)df <- data.frame(x_lower, x_upper, y_lower, y_upper)transform(df,is_within = x_lower >= y_lower & x_upper <= y_upper,is_overlap = x_lower <= y_lower & x_upper >= y_upper)# x_lower x_upper y_lower y_upper is_within is_overlap# 1 1 4 0 10 TRUE FALSE# 2 10 25 15 16 FALSE TRUE# 3 5 6 10 20 FALSE FALSE# 4 10 15 3 30 TRUE FALSE
From the documentation:
> within(x_lower, x_upper, y_lower, y_upper)
>
> For each range in [x_lower, x_upper], this finds everywhere that
> range falls completely within [y_lower, y_upper]. Equivalent to
> x_lower >= y_lower, x_upper <= y_upper.
And
> overlaps(x_lower, x_upper, y_lower, y_upper, ..., bounds = "[]")
>
> For each range in [x_lower, x_upper], this finds everywhere that
> range overlaps [y_lower, y_upper] in any capacity. Equivalent to
> x_lower <= y_upper, x_upper >= y_lower by default.
答案2
得分: 1
以下是翻译好的内容：
对于 within（我加粗的部分）：
> 对于[x_lower, x_upper]中的每个范围，它找到了范围完全位于[y_lower, y_upper]内的所有位置。等价于x_lower >= y_lower, x_upper <= y_upper。
>
> 用于构建 within() 的不等式与提供的范围的包含性无关。
因此，join_by(within()) 会得到：
by <- join_by(chromosome, within(x$start, x$end, y$start, y$end))inner_join(segments, reference, join_by(chromosome, within(x$start, x$end, y$start, y$end)))# A tibble: 1 × 7segment_id chromosome start.x end.x reference_id start.y end.y1 1 chr1 140 150 1 100 150
对于 overlaps（我加粗的部分）：
> 对于[x_lower, x_upper]中的每个范围，它找到了范围在任何情况下与[y_lower, y_upper]重叠的所有位置。默认情况下等价于x_lower <= y_upper, x_upper >= y_lower。
>
> bounds 可以是 "[]"、"[)"、"(]" 或 "()" 中的一个，以改变下限和上限的包含性。 "[]" 使用 <= 和 >=，但其他 3 个选项使用 < 和 > 并生成完全相同的不等式。
因此，join_by(overlaps()) 会得到：
# A tibble: 5 × 7segment_id chromosome start.x end.x reference_id start.y end.y1 1 chr1 140 150 1 100 1502 2 chr2 210 240 NA NA NA3 3 chr2 380 415 3 300 3994 3 chr2 380 415 4 415 4505 4 chr1 230 280 2 200 250
英文:
The documentation of join_by actually covers these two helper functions.
For within (my bold):
>For each range in [x_lower, x_upper], this finds everywhere that range falls completely within [y_lower, y_upper]. Equivalent to x_lower >= y_lower, x_upper <= y_upper.
>
>The inequalities used to build within() are the same regardless of the inclusiveness of the supplied ranges.
library(dplyr)full_join(segments, reference, by = "chromosome")# A tibble: 8 × 7segment_id chromosome start.x end.x reference_id start.y end.y<int> <chr> <dbl> <dbl> <int> <dbl> <dbl>1 1 chr1 140 150 1 100 150 # yes2 1 chr1 140 150 2 200 250 # both x smaller than y3 2 chr2 210 240 3 300 399 # both x smaller than y4 2 chr2 210 240 4 415 450 # both x smaller than y5 3 chr2 380 415 3 300 399 # x$end (415) outside range6 3 chr2 380 415 4 415 450 # x$start (380) outside range7 4 chr1 230 280 1 100 150 # both x greater than y8 4 chr1 230 280 2 200 250 # x$end (280) outside range
Therefore, join_by(within()) gives:
by <- join_by(chromosome, within(x$start, x$end, y$start, y$end))inner_join(segments, reference, join_by(chromosome, within(x$start, x$end, y$start, y$end)))# A tibble: 1 × 7segment_id chromosome start.x end.x reference_id start.y end.y<int> <chr> <dbl> <dbl> <int> <dbl> <dbl>1 1 chr1 140 150 1 100 150
<hr>
For overlaps (my bold):
>For each range in [x_lower, x_upper], this finds everywhere that range overlaps [y_lower, y_upper] in any capacity. Equivalent to x_lower <= y_upper, x_upper >= y_lower by default.
>
>bounds can be one of "[]", "[)", "(]", or "()" to alter the inclusiveness of the lower and upper bounds. "[]" uses <= and >=, but the 3 other options use < and > and generate the exact same inequalities.
# A tibble: 8 × 7segment_id chromosome start.x end.x reference_id start.y end.y<int> <chr> <dbl> <dbl> <int> <dbl> <dbl>1 1 chr1 140 150 1 100 150 # yes2 1 chr1 140 150 2 200 250 # x$end (150) smaller than y$start (200)3 2 chr2 210 240 3 300 399 # x$end (240) smaller than y$start (300)4 2 chr2 210 240 4 415 450 # x$end (240) smaller than y$start (415)5 3 chr2 380 415 3 300 399 # yes6 3 chr2 380 415 4 415 450 # yes7 4 chr1 230 280 1 100 150 # x$start (230) > y$end (150)8 4 chr1 230 280 2 200 250 # yes
Therefore, join_by(overlaps()) gives:
# A tibble: 5 × 7segment_id chromosome start.x end.x reference_id start.y end.y<int> <chr> <dbl> <dbl> <int> <dbl> <dbl>1 1 chr1 140 150 1 100 1502 2 chr2 210 240 NA NA NA3 3 chr2 380 415 3 300 3994 3 chr2 380 415 4 415 4505 4 chr1 230 280 2 200 250
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