避免使用dplyr的反向字符 [英] Avoiding backtick characters with dplyr
问题描述
如何编写的参数,选择
而不使用反号码字符?我想这样做,以便我可以从一个变量作为一个字符串传入这个参数。
df< dat [[__ Table]]%>%select(`__ID`)%>%mutate(fk_table =__Table,val = 1)
将选择的参数更改为__ ID
给出了此错误:
错误:所有select()输入必须解析为整数列位置。
以下不要:
*__ID
不幸的是, code> _ 不能避免列名中的字符,因为数据是通过ODBC从关系数据库(FileMaker)下载的,并且需要在保留列名的同时将其写回数据库。 / p>
理想情况下,我希望能够执行以下操作:
colName< - __ID
df< - dat [[__ Table]]%>%select(colName)%>%mutate(fk_table =__Table,val = 1)
我也尝试过 eval(parse())
:
df< - dat [[__ Table]]%>%select(eval(parse(text = __ID)))%>%mutate(fk_table =__Table,val = 1)
它抛出这个错误:
解析错误(text =__ID):< text>:1:1:意外输入
1:_
^
顺便说一句,工作,但然后我回到了一个方块(仍然有反向符号)。
eval(parse(text =`__ID )
关于 R中的反引号字符的引用
您可以使用 as.name()
with select _()
:
colName< - __ID
df< - data.frame(`__ID` = c(1,2,3),'123` = c(4, 5,6),check.names = FALSE)
select_(df,as.name(colName))
How can I write the argument of select
without backtick characters? I would like to do this so that I can pass in this argument from a variable as a character string.
df <- dat[["__Table"]] %>% select(`__ID` ) %>% mutate(fk_table = "__Table", val = 1)
Changing the argument of select to "__ID"
gives this error:
Error: All select() inputs must resolve to integer column positions.
The following do not:
* "__ID"
Unfortunately, the _
characters in column names cannot be avoided since the data is downloaded from a relational database (FileMaker) via ODBC and needs to be written back to the database while preserving the column names.
Ideally, I would like to be able to do the following:
colName <- "__ID"
df <- dat[["__Table"]] %>% select(colName) %>% mutate(fk_table = "__Table", val = 1)
I've also tried eval(parse())
:
df <- dat[["__Table"]] %>% select( eval(parse(text="__ID")) ) %>% mutate(fk_table = "__Table", val = 1)
It throws this error:
Error in parse(text = "__ID") : <text>:1:1: unexpected input
1: _
^
By the way, the following does work, but then I'm back to square one (still with backtick symbol).
eval(parse(text="`__ID`")
References about backtick characters in R
:
You can use as.name()
with select_()
:
colName <- "__ID"
df <- data.frame(`__ID` = c(1,2,3), `123` = c(4,5,6), check.names = FALSE)
select_(df, as.name(colName))
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