英文:
How to replace an entire row between two rows based on a column
问题
我理解你想要的翻译是代码部分，以下是你提供的R代码的翻译：
# 导入必要的库library(tidyverse)# 使用 group_by 和 mutate 进行替换操作test_replace <- test_df %>%group_by(gene_id) %>%mutate(start = replace(start, row_number() > which(exon_idnetity == "Upstream") & row_number() < which(exon_idnetity == "Downstream"), start[exon_idnetity == "Event"]),end = replace(end, row_number() > which(exon_idnetity == "Upstream") & row_number() < which(exon_idnetity == "Downstream"), end[exon_idnetity == "Event"]),exon_idnetity = replace(exon_idnetity, row_number() > which(exon_idnetity == "Upstream") & row_number() < which(exon_idnetity == "Downstream"), "Event"))
希望这可以帮助你完成所需的操作。如果有任何其他问题，请随时提问。
英文:
I am dealing a with a very large mRNA splicing dataset. Here is a toy dataset to exemplify the problem:
test_df <- data.frame(start = c(2, 9, 13, 19, 13, 20, 25, 35, 39),end = c(8, 12, 18, 24, 16, 24, 30, 38, 45),gene_id = c("A", "A", "A", "A", "A", "B", "B", "B", "B"),exon_identity = c(NA, "Upstream", NA, "Downstream", "Event", NA, "Upstream", "Downstream", NA))> test_dfstart end gene_id exon_identity1 2 8 A <NA>2 9 12 A Upstream3 13 18 A <NA>4 19 24 A Downstream5 13 16 A Event6 20 24 B <NA>7 25 30 B Upstream8 35 38 B Downstream9 39 45 B <NA>
For every unique value in gene_id column, I would like to replace an entire row if it is present between "Upstream" and "Downstream" values in the exon_identity column i.e. replace row 3 with row 5. What makes it difficult for me is that there are certain genes in the gene_id column which do not have a row that needs to be replaced, e.g. "B" in the gene_id column.
This question goes in the direction of previously asked questions here and here.
Based on those and other resources, I have tried:
library(tidyverse)test_replace <- test_df %>%group_by(gene_id) %>%mutate(start = replace(start, row_number() > which(exon_idnetity == "Upstream") & row_number() < which(exon_idnetity == "Downstream"), start[exon_idnetity == "Event"]),end = replace(end, row_number() > which(exon_idnetity == "Upstream") & row_number() < which(exon_idnetity == "Downstream"), end[exon_idnetity == "Event"]),exon_idnetity = replace(exon_idnetity, row_number() > which(exon_idnetity == "Upstream") & row_number() < which(exon_idnetity == "Downstream"), "Event"))Warning message:There were 2 warnings in `mutate()`.The first warning was:ℹ In argument: `start = replace(...)`.ℹ In group 1: `gene_id = "A"`.Caused by warning in `x[list] <- values`:! number of items to replace is not a multiple of replacement lengthℹ Run dplyr::last_dplyr_warnings() to see the 1 remaining warning.>> test_replace# A tibble: 9 × 4# Groups: gene_id [2]start end gene_id exon_idnetity<dbl> <dbl> <chr> <chr>1 2 8 A NA2 9 12 A Upstream3 NA NA A Event4 19 24 A Downstream5 13 16 A Event6 20 24 B NA7 25 30 B Upstream8 35 38 B Downstream9 39 45 B NA
Desired output:
> desired_outcomestart end gene_id exon_idnetity1 2 8 A <NA>2 9 12 A Upstream3 13 16 A Event4 19 24 A Downstream5 20 24 B <NA>6 25 30 B Upstream7 35 38 B Downstream8 39 45 B <NA>
A solution, preferably using tidyverse package would be highly appreciated.
Thank you!
答案1
得分: 2
在玩具示例中，重新排序你的数据集几乎可以满足你的要求。这在真实数据集中是否有效呢？例如。
library(tidyverse)test_df |>mutate(sandwich = lag(exon_identity == 'Upstream') & lead(exon_identity == 'Downstream')) |>replace_na(list(sandwich = FALSE)) |>group_by(gene_id) |>arrange(start) |>ungroup() |>filter(!sandwich) |>select(-sandwich)
（在玩具示例中，group_by 和 ungroup 不 需要。我添加它们以防在真实数据集中需要/有用。）
英文:
In the toy example, reordering your data set gives you almost all of what you want. Will that work in the real data set? E.g.
library(tidyverse)test_df |>mutate(sandwich = lag(exon_identity == 'Upstream') & lead(exon_identity == 'Downstream')) |>replace_na(list(sandwich = FALSE)) |>group_by(gene_id) |>arrange(start) |>ungroup() |>filter(!sandwich) |>select(-sandwich)
(In the toy example, group_by and ungroup are not needed. I added them in case it was needed/useful in the real data set.)
答案2
得分: 0
如果@MelissaKey关于您实际数据的结构是正确的，他们的解决方案将非常有效。否则，以下是一个执行此任务的函数，以及group_modify()：
library(dplyr)library(tidyr)replace_rows <- function(x, ...) {is_bad <- replace_na(lag(x$exon_identity) == "Upstream" & lead(x$exon_identity) == "Downstream",FALSE)if (any(is_bad)) {is_event <- replace_na(x$exon_identity == "Event", FALSE)x <- x %>%filter(!is_bad, !is_event) %>%add_row(filter(x, is_event),.before = which(is_bad))}x}test_df %>%group_by(gene_id) %>%group_modify(replace_rows) %>%ungroup()
# A tibble: 8 × 4gene_id start end exon_identity1 A 2 8 <NA>2 A 9 12 Upstream3 A 13 16 Event4 A 19 24 Downstream5 B 20 24 <NA>6 B 25 30 Upstream7 B 35 38 Downstream8 B 39 45 <NA>
英文:
If @MelissaKey is right about the structure of your actual data, their solution will work nicely. Otherwise, here’s a function that does the job along with group_modify():
library(dplyr)library(tidyr)replace_rows <- function(x, ...) {is_bad <- replace_na(lag(x$exon_identity) == "Upstream" & lead(x$exon_identity) == "Downstream",FALSE)if (any(is_bad)) {is_event <- replace_na(x$exon_identity == "Event", FALSE)x <- x %>%filter(!is_bad, !is_event) %>%add_row(filter(x, is_event),.before = which(is_bad))}x}test_df %>%group_by(gene_id) %>%group_modify(replace_rows) %>%ungroup()
# A tibble: 8 × 4gene_id start end exon_identity<chr> <dbl> <dbl> <chr>1 A 2 8 <NA>2 A 9 12 Upstream3 A 13 16 Event4 A 19 24 Downstream5 B 20 24 <NA>6 B 25 30 Upstream7 B 35 38 Downstream8 B 39 45 <NA>
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