dplyr面具GGally并打破ggparcoord [英] dplyr masks GGally and breaks ggparcoord
问题描述
在新的会话中,
执行
<$ p $的文档中提供的 ggparcoord(。) p>
图书馆(GGally)
数据(diamonds,package =ggplot2)
diamonds.samp< - diamonds [sample(1:dim(diamonds) [1],100),]
ggparcoord(data = diamonds.samp,columns = c(1,5:10))
结果为以下情节:
再次,从一个新的会话开始,并使用加载的
code> library(GGally)
library(dplyr)
data(diamonds,package =ggplot2)
diamonds.samp< - diamonds [sample :dim(diamonds)[1],100),]
ggparcoord(data = diamonds.samp,columns = c(1,5:10))
结果:
错误:(list)对象不能强制键入'double'
请注意库(。)语句的顺序不。
问题
- 代码示例有问题吗?
- 有没有办法克服这个问题(通过某些命名空间函数)?
- 还是这个错误?
我需要 dplyr 和 ggparcoord(。),但这个最小的例子反映了我面临的问题。
版本
- R @ 3.2.3
- dplyr @ 0.4.3
- GGally @ 1.0.1
- ggplot @ 2.0.0
更新
包装Joran提供的优秀答案:
答案
- 代码示例实际上是错误的,因为 ggparcoord(。) expec ts数据框架不是钻石数据集(如果加载dplyr)给出的一个
tbl_df 。 - 问题是通过将 tbl_df 胁迫到数据框架来解决。
- 不,这不是一个错误。
工作代码示例:
图书馆(GGally)
库(dplyr)
数据(diamonds,package =ggplot2)
diamonds.samp< - diamonds [sample(1:dim )[1],100),]
ggparcoord(data = as.data.frame(diamonds.samp),columns = c(1,5:10))
将我的评论转换为答案...
这里的GGally包正在做出合理的假设,即在数据框上使用 [
应该按照它始终如一的方式运行。然而,这一切都在哈德利经文中,钻石
数据集是一个 tbl_df
以及 data.frame
。
当加载 dplyr 时, [
的行为被覆盖,以便 drop = FALSE
始终是 tbl_df
的默认值。因此,在 GGally 中有一个地方,其中 data [,cut]
预计将返回一个向量,而是返回另一个数据帧。 / p>
...具体来说,尝试执行时,您的示例中会抛出错误:
code> data [,fact.var]< - as.numeric(data [,fact.var])。
由于 data [,fact.var]
仍然是一个数据框,因此列表 as.numeric
将无法正常工作。
至于你的结论这不是一个bug,我会说....也许。大概。至少GGally 软件包作者应该怎么做才能解决这个问题。您只需要注意,使用非Hadley书面包的 tbl_df
可能会破坏事物。
As您注意到,删除额外的类属性可以修复问题,因为它将R返回到使用正常的 [
方法。
Given a fresh session, executing a small ggparcoord(.) example provided in the documentation of the function
library(GGally)
data(diamonds, package="ggplot2")
diamonds.samp <- diamonds[sample(1:dim(diamonds)[1], 100), ]
ggparcoord(data = diamonds.samp, columns = c(1, 5:10))
results into the following plot:
Again, starting in a fresh session and executing the same script with the loaded dplyr
library(GGally)
library(dplyr)
data(diamonds, package="ggplot2")
diamonds.samp <- diamonds[sample(1:dim(diamonds)[1], 100), ]
ggparcoord(data = diamonds.samp, columns = c(1, 5:10))
results in:
Error: (list) object cannot be coerced to type 'double'
Note that the order of the library(.) statements does not matter.
Questions
- Is there something wrong with the code samples?
- Is there a way to overcome the problem (over some namespace functions)?
- Or is this a bug?
I need both dplyr and ggparcoord(.) in a bigger analysis but this minimal example reflects the problem i am facing.
Versions
- R @ 3.2.3
- dplyr @ 0.4.3
- GGally @ 1.0.1
- ggplot @ 2.0.0
UPDATE
To wrap the excellent answer given by Joran up:
Answers
- The code samples are in fact wrong as ggparcoord(.) expects a data.frame not a tbl_df as given by the diamonds data set (if dplyr is loaded).
- The problem is solved by coercing the tbl_df to a data.frame.
- No it is not a bug.
Working code sample:
library(GGally)
library(dplyr)
data(diamonds, package="ggplot2")
diamonds.samp <- diamonds[sample(1:dim(diamonds)[1], 100), ]
ggparcoord(data = as.data.frame(diamonds.samp), columns = c(1, 5:10))
Converting my comments to an answer...
The GGally package here is making the reasonable assumption that using [
on a data frame should behave the way it always does and always has. However, this all being in the Hadley-verse, the diamonds
data set is a tbl_df
as well as a data.frame
.
When dplyr is loaded, the behavior of [
is overridden such that drop = FALSE
is always the default for a tbl_df
. So there's a place in GGally where data[,"cut"]
is expected to return a vector, but instead it returns another data frame.
...specifically, the error is thrown in your example while attempting to execute:
data[, fact.var] <- as.numeric(data[, fact.var]).
Since data[,fact.var]
remains a data frame, and hence a list, as.numeric
won't work.
As for your conclusion that this isn't a bug, I'd say....maybe. Probably. At least there probably isn't anything the GGally package author ought to do to address it. You just have to be aware that using tbl_df
's with non-Hadley written packages may break things.
As you noted, removing the extra class attributes fixes the problem, as it returns R to using the normal [
method.
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