R tidyverse - 按组关联，将多列与单列进行比较并返回单个数据帧

发布于 2025-01-11 17:21:03 字数 997 浏览 0 评论 0原文

我有一个包含计数数据的数据集，其结构如下：

Sample	ID	Expected	Observed_A	Observed_B
A	id1	10	8	10
A	id2	6	8	4
B	id1	15	12	18
B	id2	1	2	4

我试图用 tidyr/ 实现什么dplyr 是每个观察到的计数与预期计数之间的每个样本相关性（即我不担心通过每个观察列之间的相关性）。

样本	数据	集相关性
A	Observed_A	0.99
A	Observed_B	0.93
B	Observed_A	0.89
B	Observed_B	0.91

我可以通过循环来做到这一点，但想知道是否有一种使用 tidyverse 函数的“更清晰”的方法？

任何帮助非常感谢！

原文

I have a dataset which contains count data where the structure looks like:

Sample	ID	Expected	Observed_A	Observed_B
A	id1	10	8	10
A	id2	6	8	4
B	id1	15	12	18
B	id2	1	2	4

What I'm trying to get to with tidyr/dplyr is the per-sample correlation between each of the observed counts and the expected counts (i.e. I'm unfussed by the correlation between each of the observed columns).

Sample	Dataset	Correlation
A	Observed_A	0.99
A	Observed_B	0.93
B	Observed_A	0.89
B	Observed_B	0.91

I can do this by looping, but was wondering whether there is a 'clearer' approach to take using tidyverse functions?

Any help much appreciated!!

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情愿 2025-01-18 17:21:03

df %>% 
  group_by(Sample) %>%
  summarize(across(Observed_A:Observed_B, ~cor(.x, Expected))) %>%
  pivot_longer(!Sample, values_to = "Correlation", names_to = "Dataset")

df %>% 
  group_by(Sample) %>%
  summarize(across(Observed_A:Observed_B, ~cor(.x, Expected))) %>%
  pivot_longer(!Sample, values_to = "Correlation", names_to = "Dataset")

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星星的軌跡 2025-01-18 17:21:03

怎么样：

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(tidyr)
d <- tibble::tribble(~Sample,   ~ID,    ~Expected,  ~Observed_A,    ~Observed_B,
"A",    "id1",  10, 8,  10,
"A",    "id2",  6,  8,  4,
"B",    "id1",  15, 12, 18,
"B",    "id2",  1,  2,  4)

d %>% 
  group_by(Sample) %>%
  summarise(as.data.frame(cor(Expected, cbind(Observed_A, Observed_B)))) %>% 
  pivot_longer(-Sample, names_to = "Dataset", values_to="Correlation")
#> Warning in cor(Expected, cbind(Observed_A, Observed_B)): the standard deviation
#> is zero
#> # A tibble: 4 × 3
#>   Sample Dataset    Correlation
#>   <chr>  <chr>            <dbl>
#> 1 A      Observed_A          NA
#> 2 A      Observed_B           1
#> 3 B      Observed_A           1
#> 4 B      Observed_B           1

^{由 reprex 包（v2.0.1）于 2022 年 3 月 4 日创建< /sup>}

How about this:

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(tidyr)
d <- tibble::tribble(~Sample,   ~ID,    ~Expected,  ~Observed_A,    ~Observed_B,
"A",    "id1",  10, 8,  10,
"A",    "id2",  6,  8,  4,
"B",    "id1",  15, 12, 18,
"B",    "id2",  1,  2,  4)

d %>% 
  group_by(Sample) %>%
  summarise(as.data.frame(cor(Expected, cbind(Observed_A, Observed_B)))) %>% 
  pivot_longer(-Sample, names_to = "Dataset", values_to="Correlation")
#> Warning in cor(Expected, cbind(Observed_A, Observed_B)): the standard deviation
#> is zero
#> # A tibble: 4 × 3
#>   Sample Dataset    Correlation
#>   <chr>  <chr>            <dbl>
#> 1 A      Observed_A          NA
#> 2 A      Observed_B           1
#> 3 B      Observed_A           1
#> 4 B      Observed_B           1

^{Created on 2022-03-04 by the reprex package (v2.0.1)}

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