如何从组合变量名称创建新的分类变量
how to create a new categorical variable from a combined varibale name
我正在处理以下数据框：
ID GR SES COND `P3(400-450).FCz` `P3(400-450).Cz` `P3(400-450).Pz` `LPPearly(500-700).FCz` `LPPearly(500-700).Cz` `LPPearly(500-7~
<chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 01 RP V NEG-CTR -11.6 -5.17 11.9 -11.8 -5.96 8.24
2 01 RP V NEG-NOC -11.1 -5.53 12.1 -9.15 -5.11 9.51
3 01 RP V NEU-NOC -4.00 0.622 13.0 -7.58 -2.82 9.42
4 04 RP V NEG-CTR -0.314 2.23 15.1 -4.02 -1.44 5.06
5 04 RP V NEG-NOC 0.239 3.97 15.4 -6.60 -3.15 5.03
6 04 RP V NEU-NOC 5.04 7.76 16.7 -2.03 0.160 9.08
7 06 RP V NEG-CTR -0.214 -1.30 3.14 4.47 3.52 7.56
8 06 RP V NEG-NOC -2.96 -3.60 -0.0893 2.54 2.10 5.50
9 06 RP V NEU-NOC -1.97 -1.92 2.15 2.23 2.71 4.91
10 07 RP V NEG-CTR -2.83 2.19 13.7 -0.884 5.50 13.0
如果我想从它们出现的变量名称中拆分符号 .FCz、.Cz、.Pz 以创建一个名为 electrode 的新变量，其中应列出这些符号，我应该怎么做？
提前致谢
这里是数据集
> dput(head(data))
structure(list(ID = c("01", "01", "01", "04", "04", "04"), GR = c("RP",
"RP", "RP", "RP", "RP", "RP"), SES = c("V", "V", "V", "V", "V",
"V"), COND = c("NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC",
"NEU-NOC"), `P3(400-450).FCz` = c(-11.6312151716924, -11.1438413285935,
-3.99591470944713, -0.314155675382471, 0.238885648959708, 5.03749946898385
), `P3(400-450).Cz` = c(-5.16524399006139, -5.53112490175437,
0.621502123415388, 2.23100741241039, 3.96990710862955, 7.75899775608441
), `P3(400-450).Pz` = c(11.8802266972569, 12.1053426662461, 12.955441582096,
15.0981004360619, 15.4046229884164, 16.671036999147), `LPPearly(500-700).FCz` = c(-11.7785042972793,
-9.14927207125904, -7.58190508537766, -4.01515836011381, -6.60165385653499,
-2.02861964460179), `LPPearly(500-700).Cz` = c(-5.96429031525769,
-5.10918437158799, -2.81732229625975, -1.43557366487622, -3.14872157912645,
0.160393685024631), `LPPearly(500-700).Pz` = c(8.23981597718437,
9.51261484648731, 9.42367409925817, 5.06332653216481, 5.02619159395405,
9.07903916629231), `LPP1(500-1000).FCz` = c(-5.67295796971287,
-4.3918290080777, -2.96652960658775, 0.159183652691071, -1.78361184935376,
1.97377908783621), `LPP1(500-1000).Cz` = c(-0.774461731301161,
-0.650009462761383, 1.14010250644923, 1.51403741206392, 0.25571835554024,
3.76051565494304), `LPP1(500-1000).Pz` = c(9.99385579756163,
11.1212652173052, 10.6989716871958, 3.7899021820967, 4.59413830322224,
8.52123662617732), `LPP2(1000-1500).FCz` = c(-0.198736254963744,
-3.16101041766438, 0.895992279831378, 3.11042068112836, 2.27800090558473,
3.83846437952292), `LPP2(1000-1500).Cz` = c(2.96437294922766,
-2.12913230708907, 2.94619035115619, 3.44844607014521, 3.02403433835637,
4.7045767546583), `LPP2(1000-1500).Pz` = c(6.28027312932027,
5.24535230966772, 7.68162285335806, 1.08242973465635, 2.99896314000211,
5.36085942954182), `LPP2(1000-1500).POz` = c(7.91468942320841,
9.94838815736199, 10.2186482048953, 1.07455889922813, 1.65917850515029,
3.22422743232682)), row.names = c(NA, 6L), class = "data.frame")
>
可能我们需要 pivot_longer 将 'wide' 重塑为 'long' 指定 cols 的列名包含 ( 后跟数字 ( \d+)，在分隔符处拆分（. 通过指定 names_sep 。names_to 和 .value 将保留 . 之前的列值并且新列 'electrode' 在 .
之后保留列名的后缀部分
library(dplyr)
library(tidyr)
data %>%
pivot_longer(cols = matches("\(\d+-\d+"),
names_to = c(".value", "electrode"), names_sep = "\.")
-输出
# A tibble: 24 × 9
ID GR SES COND electrode `P3(400-450)` `LPPearly(500-700)` `LPP1(500-1000)` `LPP2(1000-1500)`
<chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 01 RP V NEG-CTR FCz -11.6 -11.8 -5.67 -0.199
2 01 RP V NEG-CTR Cz -5.17 -5.96 -0.774 2.96
3 01 RP V NEG-CTR Pz 11.9 8.24 9.99 6.28
4 01 RP V NEG-CTR POz NA NA NA 7.91
5 01 RP V NEG-NOC FCz -11.1 -9.15 -4.39 -3.16
6 01 RP V NEG-NOC Cz -5.53 -5.11 -0.650 -2.13
7 01 RP V NEG-NOC Pz 12.1 9.51 11.1 5.25
8 01 RP V NEG-NOC POz NA NA NA 9.95
9 01 RP V NEU-NOC FCz -4.00 -7.58 -2.97 0.896
10 01 RP V NEU-NOC Cz 0.622 -2.82 1.14 2.95
# … with 14 more rows
我正在处理以下数据框：
ID GR SES COND `P3(400-450).FCz` `P3(400-450).Cz` `P3(400-450).Pz` `LPPearly(500-700).FCz` `LPPearly(500-700).Cz` `LPPearly(500-7~
<chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 01 RP V NEG-CTR -11.6 -5.17 11.9 -11.8 -5.96 8.24
2 01 RP V NEG-NOC -11.1 -5.53 12.1 -9.15 -5.11 9.51
3 01 RP V NEU-NOC -4.00 0.622 13.0 -7.58 -2.82 9.42
4 04 RP V NEG-CTR -0.314 2.23 15.1 -4.02 -1.44 5.06
5 04 RP V NEG-NOC 0.239 3.97 15.4 -6.60 -3.15 5.03
6 04 RP V NEU-NOC 5.04 7.76 16.7 -2.03 0.160 9.08
7 06 RP V NEG-CTR -0.214 -1.30 3.14 4.47 3.52 7.56
8 06 RP V NEG-NOC -2.96 -3.60 -0.0893 2.54 2.10 5.50
9 06 RP V NEU-NOC -1.97 -1.92 2.15 2.23 2.71 4.91
10 07 RP V NEG-CTR -2.83 2.19 13.7 -0.884 5.50 13.0
如果我想从它们出现的变量名称中拆分符号 .FCz、.Cz、.Pz 以创建一个名为 electrode 的新变量，其中应列出这些符号，我应该怎么做？
提前致谢
这里是数据集
> dput(head(data))
structure(list(ID = c("01", "01", "01", "04", "04", "04"), GR = c("RP",
"RP", "RP", "RP", "RP", "RP"), SES = c("V", "V", "V", "V", "V",
"V"), COND = c("NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC",
"NEU-NOC"), `P3(400-450).FCz` = c(-11.6312151716924, -11.1438413285935,
-3.99591470944713, -0.314155675382471, 0.238885648959708, 5.03749946898385
), `P3(400-450).Cz` = c(-5.16524399006139, -5.53112490175437,
0.621502123415388, 2.23100741241039, 3.96990710862955, 7.75899775608441
), `P3(400-450).Pz` = c(11.8802266972569, 12.1053426662461, 12.955441582096,
15.0981004360619, 15.4046229884164, 16.671036999147), `LPPearly(500-700).FCz` = c(-11.7785042972793,
-9.14927207125904, -7.58190508537766, -4.01515836011381, -6.60165385653499,
-2.02861964460179), `LPPearly(500-700).Cz` = c(-5.96429031525769,
-5.10918437158799, -2.81732229625975, -1.43557366487622, -3.14872157912645,
0.160393685024631), `LPPearly(500-700).Pz` = c(8.23981597718437,
9.51261484648731, 9.42367409925817, 5.06332653216481, 5.02619159395405,
9.07903916629231), `LPP1(500-1000).FCz` = c(-5.67295796971287,
-4.3918290080777, -2.96652960658775, 0.159183652691071, -1.78361184935376,
1.97377908783621), `LPP1(500-1000).Cz` = c(-0.774461731301161,
-0.650009462761383, 1.14010250644923, 1.51403741206392, 0.25571835554024,
3.76051565494304), `LPP1(500-1000).Pz` = c(9.99385579756163,
11.1212652173052, 10.6989716871958, 3.7899021820967, 4.59413830322224,
8.52123662617732), `LPP2(1000-1500).FCz` = c(-0.198736254963744,
-3.16101041766438, 0.895992279831378, 3.11042068112836, 2.27800090558473,
3.83846437952292), `LPP2(1000-1500).Cz` = c(2.96437294922766,
-2.12913230708907, 2.94619035115619, 3.44844607014521, 3.02403433835637,
4.7045767546583), `LPP2(1000-1500).Pz` = c(6.28027312932027,
5.24535230966772, 7.68162285335806, 1.08242973465635, 2.99896314000211,
5.36085942954182), `LPP2(1000-1500).POz` = c(7.91468942320841,
9.94838815736199, 10.2186482048953, 1.07455889922813, 1.65917850515029,
3.22422743232682)), row.names = c(NA, 6L), class = "data.frame")
>
可能我们需要 pivot_longer 将 'wide' 重塑为 'long' 指定 cols 的列名包含 ( 后跟数字 ( \d+)，在分隔符处拆分（. 通过指定 names_sep 。names_to 和 .value 将保留 . 之前的列值并且新列 'electrode' 在 .
library(dplyr)
library(tidyr)
data %>%
pivot_longer(cols = matches("\(\d+-\d+"),
names_to = c(".value", "electrode"), names_sep = "\.")
-输出
# A tibble: 24 × 9
ID GR SES COND electrode `P3(400-450)` `LPPearly(500-700)` `LPP1(500-1000)` `LPP2(1000-1500)`
<chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 01 RP V NEG-CTR FCz -11.6 -11.8 -5.67 -0.199
2 01 RP V NEG-CTR Cz -5.17 -5.96 -0.774 2.96
3 01 RP V NEG-CTR Pz 11.9 8.24 9.99 6.28
4 01 RP V NEG-CTR POz NA NA NA 7.91
5 01 RP V NEG-NOC FCz -11.1 -9.15 -4.39 -3.16
6 01 RP V NEG-NOC Cz -5.53 -5.11 -0.650 -2.13
7 01 RP V NEG-NOC Pz 12.1 9.51 11.1 5.25
8 01 RP V NEG-NOC POz NA NA NA 9.95
9 01 RP V NEU-NOC FCz -4.00 -7.58 -2.97 0.896
10 01 RP V NEU-NOC Cz 0.622 -2.82 1.14 2.95
# … with 14 more rows