string 将数据框字符串列拆分为多列
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Split data frame string column into multiple columns
提问by jkebinger
I'd like to take data of the form
我想获取表格的数据
before = data.frame(attr = c(1,30,4,6), type=c('foo_and_bar','foo_and_bar_2'))
attr type
1 1 foo_and_bar
2 30 foo_and_bar_2
3 4 foo_and_bar
4 6 foo_and_bar_2
and use split()on the column "type" from above to get something like this:
并split()在type上面的“ ”列上使用以获得如下内容：
attr type_1 type_2
1 1 foo bar
2 30 foo bar_2
3 4 foo bar
4 6 foo bar_2
I came up with something unbelievably complex involving some form of applythat worked, but I've since misplaced that. It seemed far too complicated to be the best way. I can use strsplitas below, but then unclear how to get that back into 2 columns in the data frame.
我想出了一些令人难以置信的复杂的东西，其中涉及某种形式的apply工作，但后来我把它放错了地方。这似乎太复杂了，不是最好的方法。我可以使用strsplit如下，但不清楚如何将其恢复到数据框中的 2 列。
> strsplit(as.character(before$type),'_and_')
[[1]]
[1] "foo" "bar"
[[2]]
[1] "foo" "bar_2"
[[3]]
[1] "foo" "bar"
[[4]]
[1] "foo" "bar_2"
Thanks for any pointers. I've not quite groked R lists just yet.
感谢您的指点。我还没有完全了解 R 列表。
回答by hadley
Use stringr::str_split_fixed
用 stringr::str_split_fixed
library(stringr)
str_split_fixed(before$type, "_and_", 2)
回答by hadley
Another option is to use the new tidyr package.
另一种选择是使用新的 tidyr 包。
library(dplyr)
library(tidyr)
before <- data.frame(
attr = c(1, 30 ,4 ,6 ),
type = c('foo_and_bar', 'foo_and_bar_2')
)
before %>%
separate(type, c("foo", "bar"), "_and_")
## attr foo bar
## 1 1 foo bar
## 2 30 foo bar_2
## 3 4 foo bar
## 4 6 foo bar_2
回答by David Arenburg
5 years later adding the obligatory data.tablesolution
5 年后添加强制性data.table解决方案
library(data.table) ## v 1.9.6+
setDT(before)[, paste0("type", 1:2) := tstrsplit(type, "_and_")]
before
# attr type type1 type2
# 1: 1 foo_and_bar foo bar
# 2: 30 foo_and_bar_2 foo bar_2
# 3: 4 foo_and_bar foo bar
# 4: 6 foo_and_bar_2 foo bar_2
We could also both make sure that the resulting columns will have correct types andimprove performance by adding type.convertand fixedarguments (since "_and_"isn't really a regex)
我们还可以通过添加和参数来确保结果列具有正确的类型并提高性能（因为不是真正的正则表达式）type.convertfixed"_and_"
setDT(before)[, paste0("type", 1:2) := tstrsplit(type, "_and_", type.convert = TRUE, fixed = TRUE)]
回答by Aniko
Yet another approach: use rbindon out:
另一种方法：使用rbindon out：
before <- data.frame(attr = c(1,30,4,6), type=c('foo_and_bar','foo_and_bar_2'))
out <- strsplit(as.character(before$type),'_and_')
do.call(rbind, out)
[,1] [,2]
[1,] "foo" "bar"
[2,] "foo" "bar_2"
[3,] "foo" "bar"
[4,] "foo" "bar_2"
And to combine:
并结合：
data.frame(before$attr, do.call(rbind, out))
回答by IRTFM
Notice that sapply with "[" can be used to extract either the first or second items in those lists so:
请注意，带有“[”的 sapply 可用于提取这些列表中的第一项或第二项，因此：
before$type_1 <- sapply(strsplit(as.character(before$type),'_and_'), "[", 1)
before$type_2 <- sapply(strsplit(as.character(before$type),'_and_'), "[", 2)
before$type <- NULL
And here's a gsub method:
这是一个 gsub 方法：
before$type_1 <- gsub("_and_.+$", "", before$type)
before$type_2 <- gsub("^.+_and_", "", before$type)
before$type <- NULL
回答by Ramnath
here is a one liner along the same lines as aniko's solution, but using hadley's stringr package:
这是一个与 aniko 的解决方案相同的线，但使用了 hadley 的 stringr 包：
do.call(rbind, str_split(before$type, '_and_'))
回答by A5C1D2H2I1M1N2O1R2T1
To add to the options, you could also use my splitstackshape::cSplitfunction like this:
要添加到选项，您还可以splitstackshape::cSplit像这样使用我的函数：
library(splitstackshape)
cSplit(before, "type", "_and_")
# attr type_1 type_2
# 1: 1 foo bar
# 2: 30 foo bar_2
# 3: 4 foo bar
# 4: 6 foo bar_2
回答by Gavin Simpson
An easy way is to use sapply()and the [function:
一个简单的方法是使用sapply()和[函数：
before <- data.frame(attr = c(1,30,4,6), type=c('foo_and_bar','foo_and_bar_2'))
out <- strsplit(as.character(before$type),'_and_')
For example:
例如：
> data.frame(t(sapply(out, `[`)))
X1 X2
1 foo bar
2 foo bar_2
3 foo bar
4 foo bar_2
sapply()'s result is a matrix and needs transposing and casting back to a data frame. It is then some simple manipulations that yield the result you wanted:
sapply()的结果是一个矩阵，需要转置并转换回数据框。然后是一些简单的操作，产生你想要的结果：
after <- with(before, data.frame(attr = attr))
after <- cbind(after, data.frame(t(sapply(out, `[`))))
names(after)[2:3] <- paste("type", 1:2, sep = "_")
At this point, afteris what you wanted
此时，after就是你想要的
> after
attr type_1 type_2
1 1 foo bar
2 30 foo bar_2
3 4 foo bar
4 6 foo bar_2
回答by Yannis P.
The subject is almostexhausted, I 'd like though to offer a solution to a slightly more general version where you don't know the number of output columns, a priori. So for example you have
该主题几乎用尽，但我想为稍微更通用的版本提供解决方案，在该版本中您事先不知道输出列的数量。所以例如你有
before = data.frame(attr = c(1,30,4,6), type=c('foo_and_bar','foo_and_bar_2', 'foo_and_bar_2_and_bar_3', 'foo_and_bar'))
attr type
1 1 foo_and_bar
2 30 foo_and_bar_2
3 4 foo_and_bar_2_and_bar_3
4 6 foo_and_bar
We can't use dplyr separate()because we don't know the number of the result columns before the split, so I have then created a function that uses stringrto split a column, given the pattern and a name prefix for the generated columns. I hope the coding patterns used, are correct.
我们不能使用 dplyr，separate()因为我们不知道拆分前结果列的数量，因此我创建了一个stringr用于拆分列的函数，给定模式和生成列的名称前缀。我希望使用的编码模式是正确的。
split_into_multiple <- function(column, pattern = ", ", into_prefix){
cols <- str_split_fixed(column, pattern, n = Inf)
# Sub out the ""'s returned by filling the matrix to the right, with NAs which are useful
cols[which(cols == "")] <- NA
cols <- as.tibble(cols)
# name the 'cols' tibble as 'into_prefix_1', 'into_prefix_2', ..., 'into_prefix_m'
# where m = # columns of 'cols'
m <- dim(cols)[2]
names(cols) <- paste(into_prefix, 1:m, sep = "_")
return(cols)
}
We can then use split_into_multiplein a dplyr pipe as follows:
然后我们可以split_into_multiple在 dplyr 管道中使用，如下所示：
after <- before %>%
bind_cols(split_into_multiple(.$type, "_and_", "type")) %>%
# selecting those that start with 'type_' will remove the original 'type' column
select(attr, starts_with("type_"))
>after
attr type_1 type_2 type_3
1 1 foo bar <NA>
2 30 foo bar_2 <NA>
3 4 foo bar_2 bar_3
4 6 foo bar <NA>
And then we can use gatherto tidy up...
然后我们可以gather用来整理...
after %>%
gather(key, val, -attr, na.rm = T)
attr key val
1 1 type_1 foo
2 30 type_1 foo
3 4 type_1 foo
4 6 type_1 foo
5 1 type_2 bar
6 30 type_2 bar_2
7 4 type_2 bar_2
8 6 type_2 bar
11 4 type_3 bar_3
回答by lmo
Here is a base R one liner that overlaps a number of previous solutions, but returns a data.frame with the proper names.
这是一个基本的 R one liner，它与许多以前的解决方案重叠，但返回一个具有正确名称的 data.frame。
out <- setNames(data.frame(before$attr,
do.call(rbind, strsplit(as.character(before$type),
split="_and_"))),
c("attr", paste0("type_", 1:2)))
out
attr type_1 type_2
1 1 foo bar
2 30 foo bar_2
3 4 foo bar
4 6 foo bar_2
It uses strsplitto break up the variable, and data.framewith do.call/rbindto put the data back into a data.frame. The additional incremental improvement is the use of setNamesto add variable names to the data.frame.
它用于strsplit分解变量，并data.frame使用do.call/rbind将数据放回 data.frame。额外的增量改进是使用setNames将变量名称添加到 data.frame。
