最后一个下划线后分隔字符串

Question

这确实是这个问题的重复 r-split-string-using-tidyrseparate，但我不能将 MWE 用于我的目的，因为我不知道如何调整正则表达式。我基本上想要同样的东西，但在最后一个下划线之后分割变量。

原因：我的数据中某些列针对相同因素/类型多次出现。我想我可以将数据在类型字符串之前将值变量分开，然后将其再次展开为具有较少列的宽格式。我的问题是我的变量名有不同的几个下划线，我想学习如何在我事先添加的最后一个下划线之后分隔。

微量元素

library(tidyr)
library(data.table)
dt<-data.table(Name=c("A","B","C"),Var_1_EVU=c(2,NA,NA),Var_1_BdS=c(NA,3,4),Var_2_BdS=c(NA,3,4))
dt.long<-melt(dt, id.vars=c("Name"))
dt.long<-separate(dt.long,variable, c("test","type"), sep='/[^_]*$/')
dt.wide<-spread(dt.long,key=Name,value=value) 

我想要类似的东西

   Name type Var1 Var2
1:    A  BdS   NA   NA
2:    A  EVU    2   NA
3:    B  BdS    3    3
4:    B  EVU   NA   NA
5:    C  BdS    4    4
6:    C  EVU   NA   NA

Answer 1

library(tidyr)

df <- data.frame(Name = c("A","B","C"),
                 Var_1_EVU = c(2,NA,NA),
                 Var_1_BdS = c(NA,3,4),
                 Var_2_BdS = c(NA,3,4))

df %>% 
  gather("type", "value", -Name) %>% 
  separate(type, into = c("type", "type_num", "var")) %>% 
  unite(type, type, type_num, sep = "") %>% 
  spread(type, value)

#   Name var Var1 Var2
# 1    A BdS   NA   NA
# 2    A EVU    2   NA
# 3    B BdS    3    3
# 4    B EVU   NA   NA
# 5    C BdS    4    4
# 6    C EVU   NA   NA

tidyr::extract用于处理具有任意数量下划线的变量名的示例...

library(dplyr)
library(tidyr)

df <- data.frame(Name = c("A","B","C"),
                 Var_x_1_EVU = c(2,NA,NA),
                 Var_x_1_BdS = c(NA,3,4),
                 Var_x_y_2_BdS = c(NA,3,4))

df %>% 
  gather("col_name", "value", -Name) %>% 
  extract(col_name, c("var", "type"), "(.*)_(.*)") %>% 
  spread(var, value)

#   Name type Var_x_1 Var_x_y_2
# 1    A  BdS      NA        NA
# 2    A  EVU       2        NA
# 3    B  BdS       3         3
# 4    B  EVU      NA        NA
# 5    C  BdS       4         4
# 6    C  EVU      NA        NA

mutate(n = row_number())您可以通过首先添加行号列/变量来使每个观察唯一，从而避免重复观察的潜在问题，并且您可以通过使用显式调用它tidyr::extract来避免被屏蔽...magrittrtidyr::extract

library(dplyr)
library(tidyr)
library(data.table)
library(magrittr)

dt <- data.table(Name = c("A", "A", "B", "C"),
                 Var_1_EVU = c(1, 2, NA, NA),
                 Var_1_BdS = c(1, NA, 3, 4),
                 Var_x_2_BdS = c(1, NA, 3, 4))

dt %>% 
  mutate(n = row_number()) %>% 
  gather("col_name", "value", -n, -Name) %>% 
  tidyr::extract(col_name, c("var", "type"), "(.*)_(.*)") %>% 
  spread(var, value)

#   Name n type Var_1 Var_x_2
# 1    A 1  BdS     1       1
# 2    A 1  EVU     1      NA
# 3    A 2  BdS    NA      NA
# 4    A 2  EVU     2      NA
# 5    B 3  BdS     3       3
# 6    B 3  EVU    NA      NA
# 7    C 4  BdS     4       4
# 8    C 4  EVU    NA      NA