我从一个同事那里继承了一个数据集，我想在R中以长格式（当前为宽格式数据）进行分析。这是数据的子集：
structure(list(Phase = structure(c(1L, 1L, 2L, 2L, 3L, 3L, 4L,
4L), .Label = c("1_baseline", "2_first_10mins", "3_second_10mins",
"4_final_10mins"), class = "factor"), MaleID = c(1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L), Weight_g = c(48.91, 84.81, 48.91, 84.81,
48.91, 84.81, 48.91, 84.81), FemaleID = c(1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L), Female_side = structure(c(2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L), .Label = c("Left", "Right"), class = "factor"), Date = structure(c(2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L), .Label = c("7/11/2016 10:53", "7/11/2016 9:26"
), class = "factor"), Trx = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L), .Label = "G1", class = "factor"), Center_All_TotalDistMoved = c(2385,
3284.86, 1612.03, 2656.36, 1674.68, 1174.75, 1543.54, 629.412
), Nose_Left_CumDur = c(112.64, 248.32, 64.24, 91.12, 82.56,
22.8, 71.88, 16.48), Nose_Left_FreqZoneEntry = c(260L, 602L,
60L, 208L, 77L, 16L, 139L, 10L), Nose_Left_LatencyFirstEntry = c(4.6,
0.08, 1571.84, 1560, 2168.16, 2161.96, 2776.28, 2790.72), Nose_Right_CumDur = c(92.4,
40.48, 194.88, 426.96, 188.76, 500.44, 93.92, 559.56), Nose_Right_FreqZoneEntry = c(174L,
100L, 257L, 229L, 277L, 159L, 182L, 54L), Nose_Right_LatencyFirstEntry = c(0.12,
117.44, 1565.2, 1660.72, 2160, 2165.4, 2790.2, 2760.76), Nose_Dead_CumDur = c(327.68,
112.88, 176.84, 25.16, 143.44, 45.32, 314.48, 12.16), Nose_Dead_FreqZoneEntry = c(536L,
722L, 370L, 142L, 437L, 148L, 455L, 52L), Nose_Dead_LatencyFirstEntry = c(0,
0, 1562.92, 1684.04, 2160.6, 2160, 2760, 2760), Center_Left_CumDur = c(73.2,
260.96, 37.24, 102.68, 46.16, 20.32, 32.92, 17.52), Center_Left_FreqZoneEntry = c(23L,
74L, 9L, 8L, 13L, 4L, 14L, 1L), Center_Left_LatencyFirstEntry = c(108.16,
0, 1792.32, 1560, 2170.76, 2162.8, 2804.04, 2792.4), Center_Right_CumDur = c(63.16,
25.44, 154, 415.56, 131.16, 475.4, 64.28, 541.68), Center_Right_FreqZoneEntry = c(10L,
8L, 26L, 26L, 26L, 22L, 29L, 13L), Center_Right_LatencyFirstEntry = c(121.64,
139.04, 1673.12, 1661.84, 2160, 2166.08, 2790.72, 2760), Center_Dead_CumDur = c(441.64,
101.76, 243.56, 62.76, 198, 87.88, 430.28, 32.4), Center_Dead_FreqZoneEntry = c(34L,
72L, 38L, 23L, 37L, 26L, 46L, 8L), Center_Dead_LatencyFirstEntry = c(0,
20.28, 1560, 1706.4, 2160.96, 2160, 2760, 2770.84), Center_All_MeanVelocity = c(3.98271,
5.4766, 2.68744, 4.42786, 2.79113, 1.95818, 2.57738, 1.05056)), class = "data.frame", row.names = c(NA,
-8L))
在每列（具体而言，第8-27列）中，我要提取3个因素/测量值：
基于此处的两篇类似文章（one，two），我尝试对我的数据子集使用以下代码（首先只是一列，然后进行扩展），但是我我对正则表达式感到非常困惑，我认为这是我的问题的一部分（不确定如何基于“ _”作为分隔符来解析3列/值）：
df %>%
pivot_longer(Center_All_TotalDistMoved,
names_to = c("Point", ".value"),
names_pattern = "([^_]+)_(.*)")
这做得很好，并解析出“积分”因素：
# A tibble: 151 x 9
Phase MaleID Weight_g FemaleID Female_side Date Trx Point All_TotalDistMoved
<fct> <int> <fct> <int> <fct> <fct> <fct> <chr> <dbl>
1 1_baseline 1 48.91 1 Right 7/11/2016 9:26 G1 Center 2385
2 1_baseline 2 84.81 2 Right 7/11/2016 10:53 G1 Center 3285.
3 1_baseline 3 VERIFY_NUMBER 3 Right 7/12/2016 9:50 VEH Center 2397.
4 1_baseline 4 69.15 1 Left 7/12/2016 12:32 VEH Center 4011.
5 1_baseline 5 86.15 2 Left 7/13/2016 9:52 G1 Center 2746.
6 1_baseline 6 60.17 3 Left 7/13/2016 11:30 G1 Center 1994.
7 1_baseline 7 46.82 1 Left 7/14/2016 10:10 VEH Center 2910.
8 1_baseline 8 35.52 2 Left 7/14/2016 11:59 VEH Center 1892.
9 1_baseline 9 29.92 3 Right 7/15/2016 9:54 G1 Center 4823.
10 1_baseline 10 28.96 1 Left 7/15/2016 11:32 G1 Center 2119.
但是，由于正则表达式的无知，我无法在没有错误的情况下将上述代码向上扩展到一栏中。此外，我不确定如何在其余各列中执行相同的操作，并且仍将数据与MaleID和Phase匹配。 理想情况下，数据框应该是这样：
MaleID Weight_g Treatment Phase FemaleID FemaleLocation Date Point Zone TotalDistMoved CumDur FreqZoneEntry LatencyFirstEntry MeanVelocity
1 1 48.91 G1 Baseline 1 Right 7/11/2016 9:26 Center All 2385 NA NA NA 3.98271
2 1 48.91 G1 Baseline 1 Right 7/11/2016 9:26 Nose Left NA 112.64 260 4.60 NA
3 1 48.91 G1 Baseline 1 Right 7/11/2016 9:26 Nose Right NA 92.40 174 0.12 NA
4 1 48.91 G1 Baseline 1 Right 7/11/2016 9:26 Nose Dead NA 327.68 536 0.00 NA
5 1 48.91 G1 Baseline 1 Right 7/11/2016 9:26 Center Left NA 73.20 23 108.16 NA
6 1 48.91 G1 Baseline 1 Right 7/11/2016 9:26 Center Right NA 63.16 10 121.64 NA
谢谢！
答案 0 :(得分：1)
尝试一下：
代码说明：
names_to参数使用这些名称创建三列。names_pattern参数中，使用(.*)_(.*)_(.*)可以提取三个分隔符。请注意，此正则表达式假定_在列名中仅出现两次。 .*与任何单词匹配，而()用于提取单词。library(tidyr)
library(dplyr)
df %>%
pivot_longer(cols = c(8:27),
names_to = c('point', 'zone', 'measurement'),
names_pattern = '(.*)_(.*)_(.*)',
values_to = 'amount') -> df2
df2
#> # A tibble: 160 x 11
#> Phase MaleID Weight_g FemaleID Female_side Date Trx point zone
#> <fct> <int> <dbl> <int> <fct> <fct> <fct> <chr> <chr>
#> 1 1_ba~ 1 48.9 1 Right 7/11~ G1 Cent~ All
#> 2 1_ba~ 1 48.9 1 Right 7/11~ G1 Nose Left
#> 3 1_ba~ 1 48.9 1 Right 7/11~ G1 Nose Left
#> 4 1_ba~ 1 48.9 1 Right 7/11~ G1 Nose Left
#> 5 1_ba~ 1 48.9 1 Right 7/11~ G1 Nose Right
#> 6 1_ba~ 1 48.9 1 Right 7/11~ G1 Nose Right
#> 7 1_ba~ 1 48.9 1 Right 7/11~ G1 Nose Right
#> 8 1_ba~ 1 48.9 1 Right 7/11~ G1 Nose Dead
#> 9 1_ba~ 1 48.9 1 Right 7/11~ G1 Nose Dead
#> 10 1_ba~ 1 48.9 1 Right 7/11~ G1 Nose Dead
#> # ... with 150 more rows, and 2 more variables: measurement <chr>, amount <dbl>
由reprex package（v0.3.0）于2020-07-10创建