[英]Creating new variables with dplyr::mutate() without conflicting names
I am writing a series of functions that use dplyr
internally to manipulate data.我正在编写一系列在内部使用
dplyr
来操作数据的函数。
There are a number of places where I'd like to add new variables to the data set as I work with it.在使用数据集时,我想在很多地方向数据集添加新变量。 However, I am not sure how to name these new variables so as to avoid overwriting variables already in the data, given that I don't know what's in the data set being passed.
但是,我不确定如何命名这些新变量以避免覆盖数据中已有的变量,因为我不知道传递的数据集中有什么。
In base RI can do this:在基础 RI 中可以这样做:
df <- data.frame(a = 1:5)
df[, ncol(df)+1] <- 6:10
and it will select a name for the newly-added variable that doesn't conflict with any existing names.它将为新添加的变量选择一个不与任何现有名称冲突的名称。 I'd like to do this in
dplyr
rather than breaking up the consistent application of dplyr
to go back to base-R.我想这样做是
dplyr
而不是分手的一致应用dplyr
回到基地-R。
All the solutions I've thought of so far feel very kludgy, or require the use of a bunch of base-R futzing anyway that isn't any better than just adding the variable in base-R:到目前为止,我想到的所有解决方案都感觉非常笨拙,或者无论如何都需要使用一堆 base-R futzing,这并不比仅在 base-R 中添加变量好:
names()
vector and use one of many methods to generate a name that isn't in the vectornames()
向量并使用多种方法之一生成不在向量中的名称 Is there a straightforward way to do this in dplyr
?在
dplyr
是否有一种直接的方法可以做到这dplyr
? Getting it to work in mutate
would be ideal, although I suppose bind_cols
or tibble::add_column
would also be fine.让它在
mutate
工作将是理想的,尽管我认为bind_cols
或tibble::add_column
也可以。
Some things I have tried that don't work:我尝试过的一些方法不起作用:
df <- data.frame(a = 1:5)
# Gives the new variable a fixed title which might already be in there
df %>% mutate(6:10)
df %>% tibble::add_column(6:10)
df %>% mutate(NULL = 6:10)
# Error
df %>% bind_cols(6:10)
df %>% mutate( = 6:10)
df %>% mutate(!!NULL := 6:10)
# And an example of the kind of function I'm looking at:
# This function returns the original data arranged in a random order
# and also the random variable used to arrange it
arrange_random <- function(df) {
df <- df %>%
mutate(randomorder = runif(n())) %>%
arrange(randomorder)
return(df)
}
# No naming conflict, no problem!
data <- data.frame(a = 1:5)
arrange_random(data)
# Uh-oh, the original data gets lost!
data <- data.frame(randomorder = 1:5)
arrange_random(data)
I am posting this solution for now.我现在发布这个解决方案。 This sounds like a case of not knowing one's data very well, so I think one good approach is to include an
if-else
statement in the function.这听起来像是一个不太了解自己数据的情况,所以我认为一个好方法是在函数中包含一个
if-else
语句。 The logic is that the user chooses some arbitrary new name to add as a suffix to their original variable name, but the function will return an error if the new name is already included in the original data.逻辑是用户选择一些任意的新名称作为其原始变量名称的后缀,但如果新名称已包含在原始数据中,则该函数将返回错误。 Otherwise, the function runs and returns the original data plus the newly mutated data.
否则,该函数运行并返回原始数据和新变异的数据。
df <- data.frame(a = 1:5, b=11:15, c=21:25)
# define function with if-else statement to catch any possible duplicates
addnew <- function(data,name='newvar'){
if(sum(grepl(name,names(data),ignore.case=T))>0)
{stop('Error! Possible duplicate names with your new variable names')} else{
data1 <- data %>% mutate_all(list( ~ runif(n())))
names(data1) <- paste0(names(data1),'_',name)
bind_cols(data,data1)
}
}
addnew(df,'new')
a b c a_new b_new c_new
1 1 11 21 0.2875775 0.0455565 0.9568333
2 2 12 22 0.7883051 0.5281055 0.4533342
3 3 13 23 0.4089769 0.8924190 0.6775706
4 4 14 24 0.8830174 0.5514350 0.5726334
5 5 15 25 0.9404673 0.4566147 0.1029247
# try with new data that should throw an error
df <- data.frame(a_new = 1:5,b=11:15,c=21:25)
addnew(df,'new')
Error in addnew(df, "new") :
Error! Possible duplicate names with your new variable names
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