R 用于铸造（扩展）多个字符向量列的优雅解决方案

我想将带有联系信息的数据框转换为城市列表，其中类似信息（如电话号码）显示在多个列中

我尝试过使用Reformate2:：dcast和tidyr:：spread，这两种方法都不能解决我的问题。我还检查了堆栈溢出的其他post，例如

尚未找到有效的解决方案。在我看来，这些问题应该是相当直接的，并且可以通过spread或dcast解决

tmp <- tibble(municipality = c("M1", "M2"), 
       name1 = c("n1", "n2"), name2 = c("n3", "n4"), name3 = c(NA, "n5"), # placeholder names
       phone1 = c("p1", "p2"), phone2 = c("p3", "p4"), phone3 = c(NA, "p5")) # placeholder phone numbers

#solution 1
tmp %>% gather("colname", "value", -municipality) %>% 
  filter(municipality == "M1") %>% #too simplify, should be replaced with group_by(municipality)
  na.omit() %>% mutate(colname = str_replace(colname, "\\d", replacement = "")) %>% 
  spread(., key = "colname", value = "value")

#Solution 2
tmp %>% gather("colname", "value", -municipality) %>% 
  filter(municipality == "M1") %>% # same as above
  na.omit() %>% mutate(colname = str_replace(colname, "\\d", replacement = "")) %>% 
  dcast(municipality + value ~colname)

解决方案1导致以下错误： 错误：每行输出必须由唯一的键组合标识

解决方案2产生以下数据帧，这是所需的结果，但需要折叠：

  municipality value name phone
1           M1    n1   n1  <NA>
2           M1    n3   n3  <NA>
3           M1    p1 <NA>    p1
4           M1    p3 <NA>    p3

你在找什么

library(dplyr)
library(tidyr)

tmp %>%
  gather(key, value, -municipality, na.rm = TRUE) %>%
  mutate(key = gsub("\\d+", "", key)) %>%
  group_by(municipality, key) %>%
  mutate(row = row_number()) %>%
  spread(key, value) %>%
  select(-row)

# municipality name  phone
# <chr>        <chr> <chr>
#1 M1           n1    p1   
#2 M1           n3    p3   
#3 M2           n2    p2   
#4 M2           n4    p4   
#5 M2           n5    p5  

---

我们可以使用“聚集”将数据以长格式放入NA值。删除单个列名中的数字，使它们共享同一个键，按市政当局和键创建列组，以将数据扩展为宽格式

我们可以在开发版tidyr的pivot_上优雅地实现这一点

---

确切地请您解释一下为什么需要按键分组并添加行号？@KennethEnevoldsen因为没有公共标识符来排列列，所以我们需要它。如果我们不这样做，我们会得到一个错误。tmp%>%gatherkey，value，-unicitification，na.rm=TRUE%>%mutatekey=gsub\\d+，key%>%spreadkey，value

library(dplyr)
library(tidyr)# 0.8.3.9000
library(stringr)
tmp %>%
   rename_at(-1,  ~str_replace(., "(\\d+$)", "_\\1")) %>%
   pivot_longer(cols = -municipality, names_to = c(".value", "group"), 
        names_sep="_", values_drop_na = TRUE) %>%
   select(-group)
# A tibble: 5 x 3
#  municipality name  phone
#  <chr>        <chr> <chr>
#1 M1           n1    p1   
#2 M1           n3    p3   
#3 M2           n2    p2   
#4 M2           n4    p4   
#5 M2           n5    p5   

library(data.table)
melt(setDT(tmp), measure = patterns("^name", "^phone"), 
   value.name = c("name", "phone"), na.rm = TRUE)[, variable := NULL][]
#.  municipality name phone
#1:           M1   n1    p1
#2:           M2   n2    p2
#3:           M1   n3    p3
#4:           M2   n4    p4
#5:           M2   n5    p5