dplyr 字符串匹配和替换基于 R 中的查找 table

Question

我正在尝试实现我之前在 Excel 中实现的功能，但找不到实现它的方法。

我有两个数据集：一个是我的基础数据集，另一个是查找 table。我的基地有两列，人名和姓氏。我的查找 table 也有前两列，但它还包括替换名字。

People <- data.frame(
  Fname = c("Tom","Tom","Jerry","Ben","Rod","John","Perry","Rod"),
  Sname = c("Harper","Kingston","Ribery","Ghazali","Baker","Falcon","Jefferson","Lombardy")
)

Lookup <- data.frame(
  Fname = c("Tom","Tom","Rod","Rod"),
  Sname = c("Harper","Kingston","Baker","Lombardy"),
  NewFname = c("Tommy","Tim","Roderick","Robert")
)

我想做的是用 NewFname 替换 Fname，这取决于两个条件：Fname 和 Sname 在两个数据帧中都匹配。这是因为我有一个数据集，其中包含其他 40,000 行数据需要处理。最终，我希望得到以下数据框：

People <- data.frame(
  Fname = c("Tommy","Tim","Jerry","Ben","Roderick","John","Perry","Robert"),
  Sname = c("Harper","Kingston","Ribery","Ghazali","Baker","Falcon","Jefferson","Lombardy")
)

但是，我想要一个功能解决方案，这样我就不必单独手动输入条件和替换名称。到目前为止，我有以下（有问题的）解决方案，它涉及在 dplyr 中使用 mutate 生成一个新列，但它不起作用

 People %>%
  mutate(NewName = if_else(
    Fname == Lookup$Fname & Sname == Lookup$Sname, NewFname, Fname
  ))

Answer 1

只需使用 left_join，然后在 !is.na()

上使用 mutate

library(dplyr)
People %>% 
  left_join(Lookup, by = c("Fname", "Sname")) %>% 
  mutate(Fname = ifelse(!is.na(NewFname), NewFname, Fname))
# Fname     Sname       NewFname
# 1    Tommy    Harper    Tommy
# 2      Tim  Kingston      Tim
# 3    Jerry    Ribery     <NA>
# 4      Ben   Ghazali     <NA>
# 5 Roderick     Baker Roderick
# 6     John    Falcon     <NA>
# 7    Perry Jefferson     <NA>
# 8   Robert  Lombardy   Robert

我离开 NewFname 只是为了弄清楚发生了什么。

数据：

People <- data.frame(
  Fname = c("Tom","Tom","Jerry","Ben","Rod","John","Perry","Rod"),
  Sname = c("Harper","Kingston","Ribery","Ghazali","Baker","Falcon","Jefferson","Lombardy"), stringsAsFactors = F
)

Lookup <- data.frame(
  Fname = c("Tom","Tom","Rod","Rod"),
  Sname = c("Harper","Kingston","Baker","Lombardy"),
  NewFname = c("Tommy","Tim","Roderick","Robert"), stringsAsFactors = F
)

dplyr 字符串匹配和替换基于 R 中的查找 table

dplyr string match and replace based on lookup table in R

r

str-replace

dplyr