问题描述
我有一个数据框,其中包含一个变量,其中包含与其他变量中的元素匹配时要删除的元素-请参见下面的一个小示例:
I have a data frame with a variable containing elements to drop if they match to an element in another variable - see a small example below:
df <- data.frame(pair = c(1, 1, 2, 2, 3, 3),
animal = rep(c("dog", "cat"), 3),
value = seq(1, 12, 2),
drop = c("no", "no", "dog", "dog", "cat", "cat"))
pair animal value drop
1 1 dog 1 no
2 1 cat 3 no
3 2 dog 5 dog
4 2 cat 7 dog
5 3 dog 9 cat
6 3 cat 11 cat
我试图根据 animal
的值是否与 drop
的值匹配来过滤数据帧.我想要类似 filter(df,animal!= drop)
之类的东西来删除仅animal值与drop值匹配的行:
I'm trying to want to filter the data frame according to whether the value of animal
matches the value of drop
. I want something like filter(df, animal != drop)
to remove rows where only the value of animal matches the value of drop:
pair animal value drop
1 1 dog 1 no
2 1 cat 3 no
4 2 cat 7 dog
5 3 dog 9 cat
我还尝试编写一个简单的循环来测试动物匹配是否每行都下降,如果为true,则删除该行,但是我无法使其正常工作.(我对循环不是很自信,并且如果可能的话,我不愿使用一个循环,因为我的数据帧很大,但是我感到绝望了!)
I also tried writing a simple loop to test whether animal matches drop for each row and remove the row if true, but I couldn't get it working. (I'm not very confident with loops and would prefer not to use one if possible as my data frame is very large but I was getting desperate!)
for(i in nrow(df)){
if(df$animal[i] == df$drop[i]){
df <- df[-i,]
return(df)
}
}
是否有使用dplyr做到这一点的方法?
Is there a way of doing this using dplyr?
推荐答案
使用 filter(df,animal!= drop)
是正确的.但是,由于未在 data.frame()
调用中指定 stringsAsFactors = F
,因此所有字符串都将转换为因数,从而增加了不同级别集的错误.因此,添加 stringsAsFactors = F
应该可以解决此问题
The use of filter(df, animal != drop)
is correct. However, as you haven't specified stringsAsFactors = F
in your data.frame()
call, all strings are converted to factors, raising the error of different level sets. Thus adding stringsAsFactors = F
, should solve this
df <- data.frame(pair = c(1, 1, 2, 2, 3, 3),
animal = rep(c("dog", "cat"), 3),
value = seq(1, 12, 2),
drop = c("no", "no", "dog", "dog", "cat", "cat"),
stringsAsFactors = F)
df %>%
filter(animal != drop)
pair animal value drop
1 1 dog 1 no
2 1 cat 3 no
3 2 cat 7 dog
4 3 dog 9 cat
为避免使用此不希望出现的字符串来影响行为,我强烈建议使用 tibble
To avoid issues with this undesired string to factor behaviour I highly recommend the use of tibble
如果没有机会更改数据的创建方式,我在这里提供@akrun的解决方案:
In case that one does not have the opportunity to change how the data is created I here include @akrun's solution:
library(dplyr)
df %>%
mutate_at(vars(animal, drop), as.character) %>%
filter(animal != drop)
# pair animal value drop
#1 1 dog 1 no
#2 1 cat 3 no
#3 2 cat 7 dog
#4 3 dog 9 cat
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