在 R 中重新排列数据，拆分列名称
Rearrange Data in R, Breaking Apart Column Names
我得到了一个 table，我需要将其转换成另外两种形式。谁能帮我找到一个系统的 R-way 来转换它而不需要一堆嵌套的 for 循环？
这是 table 的简化版本：
Status <- rep(c(paste0("B",seq(1,4)),"Total"),3)
FID <- c(rep("N123",10),rep("K541",5))
IID <- c(rep(123,5),rep(456,5),rep(789,5))
Value1.G1 <- c(rep(c(888,345,765,875,875,323),2),8039,830,849)
Value2.G1 <- c(rep(c(443,325,761),4),649,975,323)
Value1.G2 <- rep(c(446,345,765,875,323),3)
Value2.G2 <- c(rep(c(540,345,765),4),169,875,431)
dat <-data.frame(FID,IID,Status,Value1.G1,Value2.G1,Value1.G2,Value2.G2)
print(dat)
FID IID Status Value1.G1 Value2.G1 Value1.G2 Value2.G2
1 N123 123 B1 888 443 446 540
2 N123 123 B2 345 325 345 345
3 N123 123 B3 765 761 765 765
4 N123 123 B4 875 443 875 540
5 N123 123 Total 875 325 323 345
6 N123 456 B1 323 761 446 765
7 N123 456 B2 888 443 345 540
8 N123 456 B3 345 325 765 345
9 N123 456 B4 765 761 875 765
10 N123 456 Total 875 443 323 540
11 K541 789 B1 875 325 446 345
12 K541 789 B2 323 761 345 765
13 K541 789 B3 8039 649 765 169
14 K541 789 B4 830 975 875 875
15 K541 789 Total 849 323 323 431
简而言之，除了前三列，每个单元格中的数字都是特定样本的特定值（在本例中为Value1或Value2）（123， 456, 和 789), 在特定凝胶上 (1 和 2), 在特定状态下 (B1,B2,B3、B4 和 Total）。前三列 (FID、IID、Status) 列出有关样本 (FID、IID) 和状态 (Status ).
初一
我需要创建的第一个表格将标题分解成它们的组成部分。 （澄清点："Gel" 列指的是列名称中 "G" 之后的前一个数值。）
FID IID Status Value1 Value2 Gel
1 N123 123 B1 888 443 1
2 N123 456 B1 323 761 1
3 K541 789 B1 875 325 1
4 N123 123 B1 446 540 2
5 N123 456 B1 446 765 2
6 K541 789 B1 446 345 2
7 N123 123 B2 345 325 1
8 N123 456 B2 888 443 1
9 K541 789 B2 323 761 1
10 N123 123 B2 345 345 2
11 N123 456 B2 345 540 2
12 K541 789 B2 345 765 2
13 N123 123 B3 765 761 1
14 N123 456 B3 345 325 1
15 K541 789 B3 8039 649 1
16 N123 123 B3 765 765 2
17 N123 456 B3 765 345 2
18 K541 789 B3 765 169 2
19 N123 123 B4 875 443 1
20 N123 456 B4 765 761 1
21 K541 789 B4 830 975 1
22 N123 123 B4 875 540 2
23 N123 456 B4 875 765 2
24 K541 789 B4 875 875 2
25 N123 123 Total 875 325 1
26 N123 456 Total 875 443 1
27 K541 789 Total 849 323 1
28 N123 123 Total 323 345 2
29 N123 456 Total 323 540 2
30 K541 789 Total 323 431 2
中学
我需要创建的第二种形式要求每个唯一的个人 ID (IID) 有一行包含它的所有值信息。在这种情况下，列名称指示有关值及其特定条件（即哪个值、哪个凝胶和哪个状态）的所有信息。对于此示例数据，这意味着有 3 行和 22 列。
FID IID Value1.G1.B1 Value2.G1.B1 Value1.G2.B1 Value2.G2.B1 Value1.G1.B2 Value2.G1.B2 Value1.G2.B2
1 N123 123 888 443 446 540 345 325 345
2 N123 456 323 761 446 765 888 443 345
3 K541 789 875 325 446 345 323 761 345
Value2.G2.B2 Value1.G1.B3 Value2.G1.B3 Value1.G2.B3 Value2.G2.B3 Value1.G1.B4 Value2.G1.B4 Value1.G2.B4
1 345 765 761 765 765 875 443 875
2 540 345 325 765 345 765 761 875
3 765 8039 649 765 169 830 975 875
Value2.G2.B4 Value1.G1.Total Value2.G1.Total Value1.G2.Total Value2.G2.Total
1 540 875 325 323 345
2 765 875 443 323 540
3 875 849 323 323 431
您可以从 data.table 的开发版本中尝试 melt、dcast，即 v1.9.5，它可以采用多个值列。它可以从 here
安装
要从 'wide' 转换为 'long'，请在 'data.table' 上使用 melt（setDT(dat) - 将 'data.frame' 转换为 'data.table') 并将 'Value1' 和 'Value2' 的索引指定为 measure.vars
中的 'list'
library(data.table)#v1.9.5+
dM <- melt(setDT(dat), measure.vars=list(c(4,6), c(5,7)), variable.name='Gel')
dM[order(Status)]
# FID IID Status Gel value1 value2
# 1: N123 123 B1 1 888 443
# 2: N123 456 B1 1 323 761
# 3: K541 789 B1 1 875 325
# 4: N123 123 B1 2 446 540
# 5: N123 456 B1 2 446 765
# 6: K541 789 B1 2 446 345
# 7: N123 123 B2 1 345 325
# 8: N123 456 B2 1 888 443
# 9: K541 789 B2 1 323 761
#10: N123 123 B2 2 345 345
#11: N123 456 B2 2 345 540
#12: K541 789 B2 2 345 765
#13: N123 123 B3 1 765 761
#14: N123 456 B3 1 345 325
#15: K541 789 B3 1 8039 649
#16: N123 123 B3 2 765 765
#17: N123 456 B3 2 765 345
#18: K541 789 B3 2 765 169
#19: N123 123 B4 1 875 443
#20: N123 456 B4 1 765 761
#21: K541 789 B4 1 830 975
#22: N123 123 B4 2 875 540
#23: N123 456 B4 2 875 765
#24: K541 789 B4 2 875 875
#25: N123 123 Total 1 875 325
#26: N123 456 Total 1 875 443
#27: K541 789 Total 1 849 323
#28: N123 123 Total 2 323 345
#29: N123 456 Total 2 323 540
#30: K541 789 Total 2 323 431
我们可以用dcast将'long'格式转换为'wide'格式。在这里，我们在 value.var
中指定多个值列
dC <- dcast(dM, FID+IID~Gel+Status, value.var=c('value1', 'value2'))
dC
# FID IID 1_B1_value1 1_B2_value1 1_B3_value1 1_B4_value1 1_Total_value1
#1: K541 789 875 323 8039 830 849
#2: N123 123 888 345 765 875 875
#3: N123 456 323 888 345 765 875
# 2_B1_value1 2_B2_value1 2_B3_value1 2_B4_value1 2_Total_value1 1_B1_value2
#1: 446 345 765 875 323 325
#2: 446 345 765 875 323 443
#3: 446 345 765 875 323 761
# 1_B2_value2 1_B3_value2 1_B4_value2 1_Total_value2 2_B1_value2 2_B2_value2
#1: 761 649 975 323 345 765
#2: 325 761 443 325 540 345
#3: 443 325 761 443 765 540
# 2_B3_value2 2_B4_value2 2_Total_value2
#1: 169 875 431
#2: 765 540 345
#3: 345 765 540
我们也可以从原始数据集中得到宽格式
dcast(setDT(dat), FID+IID~Status, value.var=names(dat)[4:7])
# FID IID B1_Value1.G1 B2_Value1.G1 B3_Value1.G1 B4_Value1.G1 Total_Value1.G1
#1: K541 789 875 323 8039 830 849
#2: N123 123 888 345 765 875 875
#3: N123 456 323 888 345 765 875
# B1_Value2.G1 B2_Value2.G1 B3_Value2.G1 B4_Value2.G1 Total_Value2.G1
#1: 325 761 649 975 323
#2: 443 325 761 443 325
#3: 761 443 325 761 443
# B1_Value1.G2 B2_Value1.G2 B3_Value1.G2 B4_Value1.G2 Total_Value1.G2
#1: 446 345 765 875 323
#2: 446 345 765 875 323
#3: 446 345 765 875 323
# B1_Value2.G2 B2_Value2.G2 B3_Value2.G2 B4_Value2.G2 Total_Value2.G2
#1: 345 765 169 875 431
#2: 540 345 765 540 345
#3: 765 540 345 765 540
更新
只需仔细检查 OP 的预期输出 ('form1')
merge(dM,form1,by=c("FID","IID","Gel","Status"),suffixes=c("akrun","real"))
# FID IID Status Gel value1 value2 Value1 Value2
# 1: K541 789 B1 1 875 325 875 325
# 2: K541 789 B2 1 323 761 323 761
# 3: K541 789 B3 1 8039 649 8039 649
# 4: K541 789 B4 1 830 975 830 975
# 5: K541 789 Total 1 849 323 849 323
# 6: K541 789 B1 2 446 345 446 345
# 7: K541 789 B2 2 345 765 345 765
# 8: K541 789 B3 2 765 169 765 169
# 9: K541 789 B4 2 875 875 875 875
# 10: K541 789 Total 2 323 431 323 431
# 11: N123 123 B1 1 888 443 888 443
# 12: N123 123 B2 1 345 325 345 325
# 13: N123 123 B3 1 765 761 765 761
# 14: N123 123 B4 1 875 443 875 443
# 15: N123 123 Total 1 875 325 875 325
# 16: N123 123 B1 2 446 540 446 540
# 17: N123 123 B2 2 345 345 345 345
# 18: N123 123 B3 2 765 765 765 765
# 19: N123 123 B4 2 875 540 875 540
# 20: N123 123 Total 2 323 345 323 345
# 21: N123 456 B1 1 323 761 323 761
# 22: N123 456 B2 1 888 443 888 443
# 23: N123 456 B3 1 345 325 345 325
# 24: N123 456 B4 1 765 761 765 761
# 25: N123 456 Total 1 875 443 875 443
# 26: N123 456 B1 2 446 765 446 765
# 27: N123 456 B2 2 345 540 345 540
# 28: N123 456 B3 2 765 345 765 345
# 29: N123 456 B4 2 875 765 875 765
# 30: N123 456 Total 2 323 540 323 540
使用?reshape
Status <- rep(c(paste0("B",seq(1,4)),"Total"),3)
FID <- c(rep("N123",10),rep("K541",5))
IID <- c(rep(123,5),rep(456,5),rep(789,5))
Value1.G1 <- c(rep(c(888,345,765,875,875,323),2),8039,830,849)
Value2.G1 <- c(rep(c(443,325,761),4),649,975,323)
Value1.G2 <- rep(c(446,345,765,875,323),3)
Value2.G2 <- c(rep(c(540,345,765),4),169,875,431)
dat <-data.frame(FID,IID,Status,Value1.G1,Value2.G1,Value1.G2,Value2.G2)
dat
对于长格式
l <- reshape(dat, direction = 'long', varying = list(c(4,6), c(5,7)),
v.names = c('Value1', 'Value2'), timevar = 'Gel')
l[order(l$Status), ]
# FID IID Status Gel Value1 Value2 id
# 1.1 N123 123 B1 1 888 443 1
# 6.1 N123 456 B1 1 323 761 6
# 11.1 K541 789 B1 1 875 325 11
# 1.2 N123 123 B1 2 446 540 1
# 6.2 N123 456 B1 2 446 765 6
# 11.2 K541 789 B1 2 446 345 11
# 2.1 N123 123 B2 1 345 325 2
# 7.1 N123 456 B2 1 888 443 7
# 12.1 K541 789 B2 1 323 761 12
# 2.2 N123 123 B2 2 345 345 2
# 7.2 N123 456 B2 2 345 540 7
# 12.2 K541 789 B2 2 345 765 12
# 3.1 N123 123 B3 1 765 761 3
# 8.1 N123 456 B3 1 345 325 8
# 13.1 K541 789 B3 1 8039 649 13
# 3.2 N123 123 B3 2 765 765 3
# 8.2 N123 456 B3 2 765 345 8
# 13.2 K541 789 B3 2 765 169 13
# 4.1 N123 123 B4 1 875 443 4
# 9.1 N123 456 B4 1 765 761 9
# 14.1 K541 789 B4 1 830 975 14
# 4.2 N123 123 B4 2 875 540 4
# 9.2 N123 456 B4 2 875 765 9
# 14.2 K541 789 B4 2 875 875 14
# 5.1 N123 123 Total 1 875 325 5
# 10.1 N123 456 Total 1 875 443 10
# 15.1 K541 789 Total 1 849 323 15
# 5.2 N123 123 Total 2 323 345 5
# 10.2 N123 456 Total 2 323 540 10
# 15.2 K541 789 Total 2 323 431 15
宽
reshape(dat, direction = 'wide', timevar = 'Status', idvar = names(dat)[1:2])
# FID IID Value1.G1.B1 Value2.G1.B1 Value1.G2.B1 Value2.G2.B1 Value1.G1.B2
# 1 N123 123 888 443 446 540 345
# 6 N123 456 323 761 446 765 888
# 11 K541 789 875 325 446 345 323
# Value2.G1.B2 Value1.G2.B2 Value2.G2.B2 Value1.G1.B3 Value2.G1.B3 Value1.G2.B3
# 1 325 345 345 765 761 765
# 6 443 345 540 345 325 765
# 11 761 345 765 8039 649 765
# Value2.G2.B3 Value1.G1.B4 Value2.G1.B4 Value1.G2.B4 Value2.G2.B4 Value1.G1.Total
# 1 765 875 443 875 540 875
# 6 345 765 761 875 765 875
# 11 169 830 975 875 875 849
# Value2.G1.Total Value1.G2.Total Value2.G2.Total
# 1 325 323 345
# 6 443 323 540
# 11 323 323 431
我得到了一个 table，我需要将其转换成另外两种形式。谁能帮我找到一个系统的 R-way 来转换它而不需要一堆嵌套的 for 循环？
这是 table 的简化版本：
Status <- rep(c(paste0("B",seq(1,4)),"Total"),3)
FID <- c(rep("N123",10),rep("K541",5))
IID <- c(rep(123,5),rep(456,5),rep(789,5))
Value1.G1 <- c(rep(c(888,345,765,875,875,323),2),8039,830,849)
Value2.G1 <- c(rep(c(443,325,761),4),649,975,323)
Value1.G2 <- rep(c(446,345,765,875,323),3)
Value2.G2 <- c(rep(c(540,345,765),4),169,875,431)
dat <-data.frame(FID,IID,Status,Value1.G1,Value2.G1,Value1.G2,Value2.G2)
print(dat)
FID IID Status Value1.G1 Value2.G1 Value1.G2 Value2.G2
1 N123 123 B1 888 443 446 540
2 N123 123 B2 345 325 345 345
3 N123 123 B3 765 761 765 765
4 N123 123 B4 875 443 875 540
5 N123 123 Total 875 325 323 345
6 N123 456 B1 323 761 446 765
7 N123 456 B2 888 443 345 540
8 N123 456 B3 345 325 765 345
9 N123 456 B4 765 761 875 765
10 N123 456 Total 875 443 323 540
11 K541 789 B1 875 325 446 345
12 K541 789 B2 323 761 345 765
13 K541 789 B3 8039 649 765 169
14 K541 789 B4 830 975 875 875
15 K541 789 Total 849 323 323 431
简而言之，除了前三列，每个单元格中的数字都是特定样本的特定值（在本例中为Value1或Value2）（123， 456, 和 789), 在特定凝胶上 (1 和 2), 在特定状态下 (B1,B2,B3、B4 和 Total）。前三列 (FID、IID、Status) 列出有关样本 (FID、IID) 和状态 (Status ).
初一
我需要创建的第一个表格将标题分解成它们的组成部分。 （澄清点："Gel" 列指的是列名称中 "G" 之后的前一个数值。）
FID IID Status Value1 Value2 Gel
1 N123 123 B1 888 443 1
2 N123 456 B1 323 761 1
3 K541 789 B1 875 325 1
4 N123 123 B1 446 540 2
5 N123 456 B1 446 765 2
6 K541 789 B1 446 345 2
7 N123 123 B2 345 325 1
8 N123 456 B2 888 443 1
9 K541 789 B2 323 761 1
10 N123 123 B2 345 345 2
11 N123 456 B2 345 540 2
12 K541 789 B2 345 765 2
13 N123 123 B3 765 761 1
14 N123 456 B3 345 325 1
15 K541 789 B3 8039 649 1
16 N123 123 B3 765 765 2
17 N123 456 B3 765 345 2
18 K541 789 B3 765 169 2
19 N123 123 B4 875 443 1
20 N123 456 B4 765 761 1
21 K541 789 B4 830 975 1
22 N123 123 B4 875 540 2
23 N123 456 B4 875 765 2
24 K541 789 B4 875 875 2
25 N123 123 Total 875 325 1
26 N123 456 Total 875 443 1
27 K541 789 Total 849 323 1
28 N123 123 Total 323 345 2
29 N123 456 Total 323 540 2
30 K541 789 Total 323 431 2
中学
我需要创建的第二种形式要求每个唯一的个人 ID (IID) 有一行包含它的所有值信息。在这种情况下，列名称指示有关值及其特定条件（即哪个值、哪个凝胶和哪个状态）的所有信息。对于此示例数据，这意味着有 3 行和 22 列。
FID IID Value1.G1.B1 Value2.G1.B1 Value1.G2.B1 Value2.G2.B1 Value1.G1.B2 Value2.G1.B2 Value1.G2.B2
1 N123 123 888 443 446 540 345 325 345
2 N123 456 323 761 446 765 888 443 345
3 K541 789 875 325 446 345 323 761 345
Value2.G2.B2 Value1.G1.B3 Value2.G1.B3 Value1.G2.B3 Value2.G2.B3 Value1.G1.B4 Value2.G1.B4 Value1.G2.B4
1 345 765 761 765 765 875 443 875
2 540 345 325 765 345 765 761 875
3 765 8039 649 765 169 830 975 875
Value2.G2.B4 Value1.G1.Total Value2.G1.Total Value1.G2.Total Value2.G2.Total
1 540 875 325 323 345
2 765 875 443 323 540
3 875 849 323 323 431
您可以从 data.table 的开发版本中尝试 melt、dcast，即 v1.9.5，它可以采用多个值列。它可以从 here
要从 'wide' 转换为 'long'，请在 'data.table' 上使用 melt（setDT(dat) - 将 'data.frame' 转换为 'data.table') 并将 'Value1' 和 'Value2' 的索引指定为 measure.vars
library(data.table)#v1.9.5+
dM <- melt(setDT(dat), measure.vars=list(c(4,6), c(5,7)), variable.name='Gel')
dM[order(Status)]
# FID IID Status Gel value1 value2
# 1: N123 123 B1 1 888 443
# 2: N123 456 B1 1 323 761
# 3: K541 789 B1 1 875 325
# 4: N123 123 B1 2 446 540
# 5: N123 456 B1 2 446 765
# 6: K541 789 B1 2 446 345
# 7: N123 123 B2 1 345 325
# 8: N123 456 B2 1 888 443
# 9: K541 789 B2 1 323 761
#10: N123 123 B2 2 345 345
#11: N123 456 B2 2 345 540
#12: K541 789 B2 2 345 765
#13: N123 123 B3 1 765 761
#14: N123 456 B3 1 345 325
#15: K541 789 B3 1 8039 649
#16: N123 123 B3 2 765 765
#17: N123 456 B3 2 765 345
#18: K541 789 B3 2 765 169
#19: N123 123 B4 1 875 443
#20: N123 456 B4 1 765 761
#21: K541 789 B4 1 830 975
#22: N123 123 B4 2 875 540
#23: N123 456 B4 2 875 765
#24: K541 789 B4 2 875 875
#25: N123 123 Total 1 875 325
#26: N123 456 Total 1 875 443
#27: K541 789 Total 1 849 323
#28: N123 123 Total 2 323 345
#29: N123 456 Total 2 323 540
#30: K541 789 Total 2 323 431
我们可以用dcast将'long'格式转换为'wide'格式。在这里，我们在 value.var
dC <- dcast(dM, FID+IID~Gel+Status, value.var=c('value1', 'value2'))
dC
# FID IID 1_B1_value1 1_B2_value1 1_B3_value1 1_B4_value1 1_Total_value1
#1: K541 789 875 323 8039 830 849
#2: N123 123 888 345 765 875 875
#3: N123 456 323 888 345 765 875
# 2_B1_value1 2_B2_value1 2_B3_value1 2_B4_value1 2_Total_value1 1_B1_value2
#1: 446 345 765 875 323 325
#2: 446 345 765 875 323 443
#3: 446 345 765 875 323 761
# 1_B2_value2 1_B3_value2 1_B4_value2 1_Total_value2 2_B1_value2 2_B2_value2
#1: 761 649 975 323 345 765
#2: 325 761 443 325 540 345
#3: 443 325 761 443 765 540
# 2_B3_value2 2_B4_value2 2_Total_value2
#1: 169 875 431
#2: 765 540 345
#3: 345 765 540
我们也可以从原始数据集中得到宽格式
dcast(setDT(dat), FID+IID~Status, value.var=names(dat)[4:7])
# FID IID B1_Value1.G1 B2_Value1.G1 B3_Value1.G1 B4_Value1.G1 Total_Value1.G1
#1: K541 789 875 323 8039 830 849
#2: N123 123 888 345 765 875 875
#3: N123 456 323 888 345 765 875
# B1_Value2.G1 B2_Value2.G1 B3_Value2.G1 B4_Value2.G1 Total_Value2.G1
#1: 325 761 649 975 323
#2: 443 325 761 443 325
#3: 761 443 325 761 443
# B1_Value1.G2 B2_Value1.G2 B3_Value1.G2 B4_Value1.G2 Total_Value1.G2
#1: 446 345 765 875 323
#2: 446 345 765 875 323
#3: 446 345 765 875 323
# B1_Value2.G2 B2_Value2.G2 B3_Value2.G2 B4_Value2.G2 Total_Value2.G2
#1: 345 765 169 875 431
#2: 540 345 765 540 345
#3: 765 540 345 765 540
更新
只需仔细检查 OP 的预期输出 ('form1')
merge(dM,form1,by=c("FID","IID","Gel","Status"),suffixes=c("akrun","real"))
# FID IID Status Gel value1 value2 Value1 Value2
# 1: K541 789 B1 1 875 325 875 325
# 2: K541 789 B2 1 323 761 323 761
# 3: K541 789 B3 1 8039 649 8039 649
# 4: K541 789 B4 1 830 975 830 975
# 5: K541 789 Total 1 849 323 849 323
# 6: K541 789 B1 2 446 345 446 345
# 7: K541 789 B2 2 345 765 345 765
# 8: K541 789 B3 2 765 169 765 169
# 9: K541 789 B4 2 875 875 875 875
# 10: K541 789 Total 2 323 431 323 431
# 11: N123 123 B1 1 888 443 888 443
# 12: N123 123 B2 1 345 325 345 325
# 13: N123 123 B3 1 765 761 765 761
# 14: N123 123 B4 1 875 443 875 443
# 15: N123 123 Total 1 875 325 875 325
# 16: N123 123 B1 2 446 540 446 540
# 17: N123 123 B2 2 345 345 345 345
# 18: N123 123 B3 2 765 765 765 765
# 19: N123 123 B4 2 875 540 875 540
# 20: N123 123 Total 2 323 345 323 345
# 21: N123 456 B1 1 323 761 323 761
# 22: N123 456 B2 1 888 443 888 443
# 23: N123 456 B3 1 345 325 345 325
# 24: N123 456 B4 1 765 761 765 761
# 25: N123 456 Total 1 875 443 875 443
# 26: N123 456 B1 2 446 765 446 765
# 27: N123 456 B2 2 345 540 345 540
# 28: N123 456 B3 2 765 345 765 345
# 29: N123 456 B4 2 875 765 875 765
# 30: N123 456 Total 2 323 540 323 540
使用?reshape
Status <- rep(c(paste0("B",seq(1,4)),"Total"),3)
FID <- c(rep("N123",10),rep("K541",5))
IID <- c(rep(123,5),rep(456,5),rep(789,5))
Value1.G1 <- c(rep(c(888,345,765,875,875,323),2),8039,830,849)
Value2.G1 <- c(rep(c(443,325,761),4),649,975,323)
Value1.G2 <- rep(c(446,345,765,875,323),3)
Value2.G2 <- c(rep(c(540,345,765),4),169,875,431)
dat <-data.frame(FID,IID,Status,Value1.G1,Value2.G1,Value1.G2,Value2.G2)
dat
对于长格式
l <- reshape(dat, direction = 'long', varying = list(c(4,6), c(5,7)),
v.names = c('Value1', 'Value2'), timevar = 'Gel')
l[order(l$Status), ]
# FID IID Status Gel Value1 Value2 id
# 1.1 N123 123 B1 1 888 443 1
# 6.1 N123 456 B1 1 323 761 6
# 11.1 K541 789 B1 1 875 325 11
# 1.2 N123 123 B1 2 446 540 1
# 6.2 N123 456 B1 2 446 765 6
# 11.2 K541 789 B1 2 446 345 11
# 2.1 N123 123 B2 1 345 325 2
# 7.1 N123 456 B2 1 888 443 7
# 12.1 K541 789 B2 1 323 761 12
# 2.2 N123 123 B2 2 345 345 2
# 7.2 N123 456 B2 2 345 540 7
# 12.2 K541 789 B2 2 345 765 12
# 3.1 N123 123 B3 1 765 761 3
# 8.1 N123 456 B3 1 345 325 8
# 13.1 K541 789 B3 1 8039 649 13
# 3.2 N123 123 B3 2 765 765 3
# 8.2 N123 456 B3 2 765 345 8
# 13.2 K541 789 B3 2 765 169 13
# 4.1 N123 123 B4 1 875 443 4
# 9.1 N123 456 B4 1 765 761 9
# 14.1 K541 789 B4 1 830 975 14
# 4.2 N123 123 B4 2 875 540 4
# 9.2 N123 456 B4 2 875 765 9
# 14.2 K541 789 B4 2 875 875 14
# 5.1 N123 123 Total 1 875 325 5
# 10.1 N123 456 Total 1 875 443 10
# 15.1 K541 789 Total 1 849 323 15
# 5.2 N123 123 Total 2 323 345 5
# 10.2 N123 456 Total 2 323 540 10
# 15.2 K541 789 Total 2 323 431 15
宽
reshape(dat, direction = 'wide', timevar = 'Status', idvar = names(dat)[1:2])
# FID IID Value1.G1.B1 Value2.G1.B1 Value1.G2.B1 Value2.G2.B1 Value1.G1.B2
# 1 N123 123 888 443 446 540 345
# 6 N123 456 323 761 446 765 888
# 11 K541 789 875 325 446 345 323
# Value2.G1.B2 Value1.G2.B2 Value2.G2.B2 Value1.G1.B3 Value2.G1.B3 Value1.G2.B3
# 1 325 345 345 765 761 765
# 6 443 345 540 345 325 765
# 11 761 345 765 8039 649 765
# Value2.G2.B3 Value1.G1.B4 Value2.G1.B4 Value1.G2.B4 Value2.G2.B4 Value1.G1.Total
# 1 765 875 443 875 540 875
# 6 345 765 761 875 765 875
# 11 169 830 975 875 875 849
# Value2.G1.Total Value1.G2.Total Value2.G2.Total
# 1 325 323 345
# 6 443 323 540
# 11 323 323 431