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我正在通过按零售商名称对采购进行分组来分析我的银行对帐单,然后可以使用dplyr
函数分析生成的数据框。我的方法使用自定义函数,但我很想知道是否有更高效的方法。例如,是否有任何包可以使用数据帧列之间的复杂匹配逻辑来连接数据框?R银行对帐单分组
debug(FindRetailer)
FindRetailer<-function(Purchase){
P <- toupper(Purchase)
for(z in 1:length(RetailerNames)){
Retailer<-toupper(RetailerNames[z])
HasFound=grepl(Retailer,P)
if(HasFound==TRUE){
return(str_to_title(Retailer))
}
}
return("Donno")
}
Statement <- data.frame(
Purchase = c("abc Aldi xyz","a Kmart bcd","a STARBUCKS ghju","abcd MacD efg"),
Amount = c(235,23,789,45))
RetailerNames<- c("Aldi","Kmart","Starbucks","MacD")
# what I need
Result <- data.frame(
Purchase = c("abc Aldi xyz","a KMART bcd","a STARBUCKS mmm","abcd MACD efg"),
Amount = c(235,23,789,45),
Retailer = c("Aldi","Kmart","Starbucks","Macd"))
# this works using custom function
NewStatment<-Statement %>%
rowwise() %>%
mutate(Retailer=FindRetailer(Purchase))
# is this possible: join dataframes using complex string matching?
# this doesn't work yet
TestMethod<-Statement %>%
left_join(RetailerNames,by="Statement.Purchase %in% RetailerNames")
谢谢,我以为会有一个简单的解决方案。我会看看''fuzzyjoin''也 – Zeus
我编辑的解决方案,因为我的原始只是因为幸运的巧合。我目前的解决方案涉及将零售商名称向量折叠为正则表达式字符串 – yeedle
感谢您的纠正和模糊逻辑方法 – Zeus