如何解决在列中使用多个变量进行更长的数据透视 数据
我的数据如下：
# A tibble: 120 x 5
age death_rate_male life_exp_male death_rate_fem life_exp_fem
<dbl> <dbl> <dbl> <dbl> <dbl>
1 0 0.00630 76.0 0.00523 81.0
2 1 0.000426 75.4 0.000342 80.4
3 2 0.00029 74.5 0.000209 79.4
4 3 0.000229 73.5 0.000162 78.4
5 4 0.000162 72.5 0.000143 77.4
6 5 0.000146 71.5 0.000125 76.5
7 6 0.000136 70.5 0.000113 75.5
8 7 0.000127 69.6 0.000104 74.5
9 8 0.000115 68.6 0.000097 73.5
10 9 0.000103 67.6 0.000093 72.5
# ... with 110 more rows
>
我正在尝试创建一个整洁的表，其中的变量是年龄，性别，预期寿命和死亡率。
我设法通过将数据帧分成两个（一个包含预期寿命，另一个包含死亡率），并用pivot_longer()进行整理，然后附加两个表来做到这一点。
有没有一种方法可以通过单个pivot_longer()命令更优雅地完成此操作？预先谢谢你。
解决方法
我们可以使用names_pattern（根据模式将其捕获为一组）
library(dplyr)
library(tidyr)
df1 %>%
pivot_longer(cols = -age,names_to = c( '.value','grp'),names_pattern = "^(\\w+_\\w+)_(\\w+)")
# A tibble: 20 x 4
# age grp death_rate life_exp
# <int> <chr> <dbl> <dbl>
# 1 0 male 0.0063 76
# 2 0 fem 0.00523 81
# 3 1 male 0.000426 75.4
# 4 1 fem 0.000342 80.4
# 5 2 male 0.00029 74.5
# 6 2 fem 0.000209 79.4
# 7 3 male 0.000229 73.5
# 8 3 fem 0.000162 78.4
# 9 4 male 0.000162 72.5
#10 4 fem 0.000143 77.4
#11 5 male 0.000146 71.5
#12 5 fem 0.000125 76.5
#13 6 male 0.000136 70.5
#14 6 fem 0.000113 75.5
#15 7 male 0.000127 69.6
#16 7 fem 0.000104 74.5
#17 8 male 0.000115 68.6
#18 8 fem 0.000097 73.5
#19 9 male 0.000103 67.6
#20 9 fem 0.000093 72.5
或names_sep（在此处指定模式为下划线，后面没有字符为下划线）
df1 %>%
pivot_longer(cols = -age,names_sep = "_(?=[^_]+$)")
数据
df1 <- structure(list(age = 0:9,death_rate_male = c(0.0063,0.000426,0.00029,0.000229,0.000162,0.000146,0.000136,0.000127,0.000115,0.000103),life_exp_male = c(76,75.4,74.5,73.5,72.5,71.5,70.5,69.6,68.6,67.6),death_rate_fem = c(0.00523,0.000342,0.000209,0.000143,0.000125,0.000113,0.000104,9.7e-05,9.3e-05),life_exp_fem = c(81,80.4,79.4,78.4,77.4,76.5,75.5,72.5)),class = "data.frame",row.names = c("1","2","3","4","5","6","7","8","9","10"))
从akrun借用数据，这是使用reshape
reshape(
setNames(df,gsub("(.*)_(\\w+)","\\1\\.\\2",names(df))),direction = "long",varying = -1
)
这样
age time death_rate life_exp id
1.male 0 male 0.006300 76.0 1
2.male 1 male 0.000426 75.4 2
3.male 2 male 0.000290 74.5 3
4.male 3 male 0.000229 73.5 4
5.male 4 male 0.000162 72.5 5
6.male 5 male 0.000146 71.5 6
7.male 6 male 0.000136 70.5 7
8.male 7 male 0.000127 69.6 8
9.male 8 male 0.000115 68.6 9
10.male 9 male 0.000103 67.6 10
1.fem 0 fem 0.005230 81.0 1
2.fem 1 fem 0.000342 80.4 2
3.fem 2 fem 0.000209 79.4 3
4.fem 3 fem 0.000162 78.4 4
5.fem 4 fem 0.000143 77.4 5
6.fem 5 fem 0.000125 76.5 6
7.fem 6 fem 0.000113 75.5 7
8.fem 7 fem 0.000104 74.5 8
9.fem 8 fem 0.000097 73.5 9
10.fem 9 fem 0.000093 72.5 10
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