我有一個資料集，其中包含不同天數（d0-d24）的不同基因（位點）的計數 - dX.2 是給定天/位點組合的第二個等位基因的計數。現在這個資料集是一種“寬”格式，具有基于前綴 (dX) 的成對列：
df<-data.frame(Locus=c("Locus_1","Locus_10","Locus_100","Locus_101","Locus_102","Locus_103")
,d0.1=c(248,20,95,13,0,33),d0.2=c(252,480,405,487,500,467),
d2.1=c(252,24,84,14,0,43),d2.2=c(248,476,416,486,500,457),
d6.1=c(256,30,82,15,0,41),d6.2=c(244,470,418,485,500,459),
d10.1=c(280,21,84,18,0,36),d10.2=c(220,479,416,482,500,464))
df
Locus d0.1 d0.2 d2.1 d2.2 d6.1 d6.2 d10.1 d10.2
1 Locus_1 248 252 252 248 256 244 280 220
2 Locus_10 20 480 24 476 30 470 21 479
3 Locus_100 95 405 84 416 82 418 84 416
4 Locus_101 13 487 14 486 15 485 18 482
5 Locus_102 0 500 0 500 0 500 0 500
6 Locus_103 33 467 43 457 41 459 36 464
Id 想要做的是使用 reshape（或類似的，tidyr）將此資料幀重新格式化為“長”格式，并使用包含日期（例如 d0）和等位基因 ID（.1 或 .2）的變數
IE
long.df<-reshape_code(df...)
long.df
Locus Allele.1 Allele.2 Day
1 Locus_1 248 252 0
2 Locus_10 20 480 0
3 Locus_100 95 405 0
4 Locus_101 13 487 0
5 Locus_102 0 500 0
6 Locus_103 33 467 0
7 Locus_1 252 248 2
8 Locus_10 24 476 2
9 Locus_100 84 416 2
10 Locus_101 14 486 2
11 Locus_102 0 500 2
12 Locus_103 43 457 2
13 Locus_1 256 244 6
14 Locus_10 30 470 6
15 Locus_100 82 418 6
16 Locus_101 15 485 6
17 Locus_102 0 500 6
18 Locus_103 41 459 6
19 Locus_1 280 220 10
20 Locus_10 21 479 10
21 Locus_100 84 416 10
22 Locus_101 18 482 10
23 Locus_102 0 500 10
24 Locus_103 36 464 10
也許使用包nc（命名捕獲）？我不太確定從哪里開始。標準代碼/方法是，reshape(df,id=c("Day","Locus",...)) 但當然還沒有“天”變數。
我也看到了這個類似但可能更簡單的問題的解決方案，使用gather separate spread但我不確定如何通過等位基因 1 和 2 創建集合。感謝您的任何建議！
uj5u.com熱心網友回復：
dplyr
library(dplyr)
library(tidyr)
tidyr::pivot_longer(df, -Locus, names_pattern = "d([0-9] )[.]([0-9] )", names_to = c("Day", ".value")) %>%
rename_with(~ paste0("Allele.", .), .cols = `1`:`2`)
# # A tibble: 24 x 4
# Locus Day Allele.1 Allele.2
# <chr> <chr> <dbl> <dbl>
# 1 Locus_1 0 248 252
# 2 Locus_1 2 252 248
# 3 Locus_1 6 256 244
# 4 Locus_1 10 280 220
# 5 Locus_10 0 20 480
# 6 Locus_10 2 24 476
# 7 Locus_10 6 30 470
# 8 Locus_10 10 21 479
# 9 Locus_100 0 95 405
# 10 Locus_100 2 84 416
# # ... with 14 more rows
資料表
library(data.table)
DT <- as.data.table(df)
tmp <- melt(DT, id.vars = "Locus"
)[, c("Day", "Allele") := strcapture("d([0-9] )[.]([0-9] )", variable, proto = list(Day = "", Allele = ""))
][, variable := NULL]
tmp <- dcast(tmp, Locus Day ~ Allele, value.var = "value")
setnames(tmp, c("1", "2"), paste0("Allele.", c("1", "2")))
tmp
# Locus Day Allele.1 Allele.2
# <char> <char> <num> <num>
# 1: Locus_1 0 248 252
# 2: Locus_1 10 280 220
# 3: Locus_1 2 252 248
# 4: Locus_1 6 256 244
# 5: Locus_10 0 20 480
# 6: Locus_10 10 21 479
# 7: Locus_10 2 24 476
# 8: Locus_10 6 30 470
# 9: Locus_100 0 95 405
# 10: Locus_100 10 84 416
# 11: Locus_100 2 84 416
# 12: Locus_100 6 82 418
# 13: Locus_101 0 13 487
# 14: Locus_101 10 18 482
# 15: Locus_101 2 14 486
# 16: Locus_101 6 15 485
# 17: Locus_102 0 0 500
# 18: Locus_102 10 0 500
# 19: Locus_102 2 0 500
# 20: Locus_102 6 0 500
# 21: Locus_103 0 33 467
# 22: Locus_103 10 36 464
# 23: Locus_103 2 43 457
# 24: Locus_103 6 41 459
# Locus Day Allele.1 Allele.2
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