dplyr：引用变量名称的变异标准评估 [英] dplyr: standard evaluation for mutate with quoted variable names

问题描述

我如何使用 mutate （我的推测是我正在寻找标准评估，因此 mutate _ ，但我不完全相信这一点）当使用接受变量名称列表的函数，如：

  createSum = function（data，variableNames）{
 data％>％
 mutate_（sumvar = interp（〜sum（var，na.rm = TRUE ）
 var = as.name（paste（as.character（variableNames），collapse =，））））
 
} 
  

这是一个将功能剥离到其核心逻辑的MWE，并展示了我要实现的目标：

 库（dplyr）
库（lazyeval）
 
＃函数使给定列名的随机表
 makeTable = function （colNames，sampleSize）{
 liSample = lapply（colNames，function（week）{
 sample = rnorm（sampleSize）
}）
名称（liSample）= as.char actor（colNames）
 return（tbl_df（data.frame（liSample，check.names = FALSE）））
} 
 
＃创建一些需要列名称模式的示例数据
 weekDates = seq.Date（from = as.Date（2014-01-01），
 to = as.Date（2014-08-01），by =week） 
 dfTest = makeTable（weekDates，10）
 
＃test mutate on this table 
 dfTest％>％
 mutate_（sumvar = interp（〜sum na.rm = TRUE），
 var = as.name（paste（as.character（weekDates），collapse =，））））
  

此处的预期输出将由以下内容返回：

  rowSums（ dfTest [，as.character（weekDates）]）
  

解决方案

我认为这是你之后的

  createSum = function（data，variableNames）{
 data％> ％
 mutate_（sumvar = paste（as.character（variableNames），collapse =+））
} 
 createSum（dfTest，我们只提供一个字符值，而不是 interp   



< c $ c>，因为您不能将名称列表作为单个参数传递给函数。另外， sum（）会做一些不希望的崩溃，因为操作不是逐行执行，它们一次被传递到向量列。

此示例的另一个问题是您在data.frame中设置 check.names = FALSE ，这意味着您已经创建了无法列出的列名称是有效的符号。如果您喜欢

  createSum（dfTest，paste0（`，weekDates， `））
  

但一般来说，最好不要使用无效的名称。 >

How would I go about using mutate (my presumption is that I am looking for standard evaluation in my case, and hence mutate_, but I am not entirely confident on this point) when using a function that accepts a list of variable names, such as this:

createSum = function(data, variableNames) {
  data %>% 
    mutate_(sumvar = interp(~ sum(var, na.rm = TRUE), 
                            var = as.name(paste(as.character(variableNames), collapse =","))))

}

Here is an MWE that strips the function to its core logic and demonstrates what I am trying to achieve:

library(dplyr)
library(lazyeval)

# function to make random table with given column names
makeTable = function(colNames, sampleSize) {
  liSample = lapply(colNames, function(week) {
    sample = rnorm(sampleSize)
  })
  names(liSample) = as.character(colNames)
  return(tbl_df(data.frame(liSample, check.names = FALSE)))
}

# create some sample data with the column name patterns required
weekDates = seq.Date(from = as.Date("2014-01-01"),
                     to = as.Date("2014-08-01"), by = "week")
dfTest = makeTable(weekDates, 10)

# test mutate on this table
dfTest %>% 
  mutate_(sumvar = interp(~ sum(var, na.rm = TRUE), 
                          var = as.name(paste(as.character(weekDates), collapse =","))))

Expected output here is what would be returned by:

rowSums(dfTest[, as.character(weekDates)])

解决方案

I think this is what you're after

createSum = function(data, variableNames) {
    data %>% 
        mutate_(sumvar = paste(as.character(variableNames), collapse ="+"))
}
createSum(dfTest, weekDates)

where we just supply a character value rather than interp because you can't pass in a list of names as a single parameter to a function. Plus, sum() would do some undesired collapsing because operations are not performed rowwise, they are passed in columns of vectors at a time.

The other problem with this example is that you set check.names=FALSE in your data.frame which means that you've created column names that cannot be valid symbols. You can explicitly wrap your variable names in back-ticks if you like

createSum(dfTest , paste0("`", weekDates,"`"))

but in general it would be better not to use invalid names.

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