Question

我有2个像这样的数据框

df1 <- data.frame(Colors = c("Yellow","Pink","Green","Blue","White","Red"
                            ,"Cyan","Brown","Violet","Orange","Gray"))

df2 <- data.frame(Colors = c("Yellow,Pink","Green","Gold","White","Red,Cyan,Brown",
                             "Violet","Magenta","Gray"))

我正在尝试合并这两个数据帧并返回df2中的行，这些行也存在于df1中。我还需要确保

所需的输出是

          Colors
     Yellow,Pink
           Green
           White
  Red,Cyan,Brown
          Violet
            Gray

如果我df <- inner_join(df2,df1)，然后我没有得到行Yellow,Pink＆amp; Red,Cyan,Brown

我在这里缺少什么？有人能指出我正确的方向吗？

Answer 1

在每个拆分项目上使用R的基本pmatch解决方案：

split_list <- strsplit(as.character(df2$Colors),",")
keep_lgl   <- sapply(split_list,function(x) !anyNA(pmatch(x,df1$Colors)))
df2[keep_lgl,,drop=FALSE]

#           Colors
# 1    Yellow,Pink
# 2          Green
# 4          White
# 5 Red,Cyan,Brown
# 6         Violet
# 8           Gray

注意：仅当df1中有所有颜色时才匹配一系列颜色。

一些tidyverse方法：

library(tidyverse)
df2 %>% mutate(keep=Colors) %>%
  separate_rows(Colors) %>%
  add_count(keep) %>%
  inner_join(df1) %>%
  add_count(keep) %>% # doesn't do anything here but important in general
  filter(n==nn)   %>% # same
  distinct(keep)  %>%
  rename(Colors=keep)

# # A tibble: 6 x 1
# Colors
# <fctr>
# 1    Yellow,Pink
# 2          Green
# 3          White
# 4 Red,Cyan,Brown
# 5         Violet
# 6           Gray

df2 %>% mutate(keep=Colors) %>%
  separate_rows(Colors) %>%
  left_join(df1 %>% mutate(Colors2=Colors,.)) %>%
  group_by(keep) %>%
  summarize(filt=anyNA(Colors2)) %>%
  filter(!filt) %>%
  select(-2)

# # A tibble: 6 x 1
#             keep
#           <fctr>
# 1           Gray
# 2          Green
# 3 Red,Cyan,Brown
# 4         Violet
# 5          White
# 6    Yellow,Pink

Answer 2

您可以使用regex_inner_join个套件中的fuzzyjoin加入df1和df2。最后，从df2列中选择唯一的行。

library(dplyr)
library(fuzzyjoin)

regex_inner_join(df2, df1, by=c(Colors = "Colors")) %>%
  select(Colors = Colors.x) %>% distinct()

#           Colors
# 1    Yellow,Pink
# 2          Green
# 3          White
# 4 Red,Cyan,Brown
# 5         Violet
# 6           Gray

# Just to demonstrate, result of joined tables using regex_inner_join. One,
# can work-out to convert data in desired format afterwards.

regex_inner_join(df2, df1, by=c(Colors = "Colors")) 
#         Colors.x Colors.y
# 1    Yellow,Pink   Yellow
# 2    Yellow,Pink     Pink
# 3          Green    Green
# 4          White    White
# 5 Red,Cyan,Brown      Red
# 6 Red,Cyan,Brown     Cyan
# 7 Red,Cyan,Brown    Brown
# 8         Violet   Violet
# 9           Gray     Gray

合并2个数据帧，其中一个数据帧中的逗号分隔值

2 个答案: