[英]Efficient and generic recoding of categorical variables in R
Imagine a data.table
like this 想象这样一个数据data.table
library(data.table)
DT = data.table(values=c('call', NA, 'letter', 'call', 'e-mail', 'phone'))
print(DT)
values
1: call
2: <NA>
3: letter
4: call
5: e-mail
6: phone
I wish to recode the values by the following mapping 我希望通过以下映射重新编码值
mappings = list(
'by_phone' = c('call', 'phone'),
'by_web' = c('e-mail', 'web-meeting')
)
Ie I want to transform call
into by_phone
etc. NA
should be put to missing
and unknown (by the mapping provided) put to other
. 即我想改造call
到by_phone
等NA
应该付诸missing
和未知的(提供的映射)投入到other
。 For this particular data table I could simply solve my problem by the following 对于这个特定的数据表,我可以通过以下方法简单地解决我的问题
recode_group <- function(values, mappings){
ifelse(values %in% unlist(mappings[1]), names(mappings)[1],
ifelse(values %in% unlist(mappings[2]), names(mappings)[2],
ifelse(is.na(values), 'missing', 'other')
)
)
}
DT[, recoded_group:=recode_group(values, mappings)]
print(DT)
values recoded_group
1: call by_phone
2: <NA> missing
3: letter other
4: call by_phone
5: e-mail by_web
6: phone by_phone
But I am looking for an efficient and generic recode_group
functionality. 但我正在寻找一种有效且通用的recode_group
功能。 Any suggestions? 有什么建议么?
Here's an option with an update-join approach: 这是一个使用update-join方法的选项:
DT[stack(mappings), on = "values", recoded_group := ind]
DT[is.na(values), recoded_group := "missing"]
DT
# values recoded_group
#1: call by_phone
#2: NA missing
#3: letter NA
#4: call by_phone
#5: e-mail by_web
#6: phone by_phone
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