从 data.frame 命令中的列表指定列名称

发布于 2024-08-30 19:41:33 字数 1021 浏览 5 评论 0原文

我有一个名为 cols 的列表,其中包含列名称:

cols <- c('Column1','Column2','Column3')

我想重现这个命令,但调用列表:

data.frame(Column1=rnorm(10))

以下是我尝试时发生的情况:

>; data.frame(cols[1]=rnorm(10))

错误:“data.frame(I(cols[1])=”中出现意外的“=”

同样的事情发生如果我将 cols[1] 包装在 I()eval() 中,

如何将该项目从向量输入到 中。 >data.frame() 命令?

更新:

对于一些背景,我定义了一个函数calc.means(),它接受一个数据框和一个列表变量并执行大型且复杂的 ddply 操作,在变量指定的级别进行总结。

我尝试使用 data.frame() 命令执行的操作是向上返回聚合级别。在最顶部,在每一步重新运行 calc.means() 并使用 rbind() 将结果相互粘合,我需要添加带有 ' 的虚拟列。为了让 rbind 正常工作,

我基本上将类似 cast 的边距功能滚动到 ddply 中,并且我不想每次运行都重新输入列名称。这是完整的代码:

cols <- c('Col1','Col2','Col3')
rbind ( calc.means(dat,cols),
    data.frame(cols[1]='All', calc.means(dat, cols[2:3])),
    data.frame(cols[1]='All', cols[2]='All', calc.means(dat, cols[3]))
)

I have a list called cols with column names in it:

cols <- c('Column1','Column2','Column3')

I'd like to reproduce this command, but with a call to the list:

data.frame(Column1=rnorm(10))

Here's what happens when I try it:

> data.frame(cols[1]=rnorm(10))

Error: unexpected '=' in "data.frame(I(cols[1])="

The same thing happens if I wrap cols[1] in I() or eval().

How can I feed that item from the vector into the data.frame() command?

Update:

For some background, I have defined a function calc.means() that takes a data frame and a list of variables and performs a large and complicated ddply operation, summarizing at the level specified by the variables.

What I'm trying to do with the data.frame() command is walk back up the aggregation levels to the very top, re-running calc.means() at each step and using rbind() to glue the results onto one another. I need to add dummy columns with 'All' values in order to get the rbind to work properly.

I'm rolling cast-like margin functionality into ddply, basically, and I'd like to not retype the column names for each run. Here's the full code:

cols <- c('Col1','Col2','Col3')
rbind ( calc.means(dat,cols),
    data.frame(cols[1]='All', calc.means(dat, cols[2:3])),
    data.frame(cols[1]='All', cols[2]='All', calc.means(dat, cols[3]))
)

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评论(4

抱着落日 2024-09-06 19:41:33

使用可以使用结构

cols <- c("a","b")

foo <- structure(list(c(1, 2 ), c(3, 3)), .Names = cols, row.names = c(NA, -2L), class = "data.frame")

但我不明白你为什么这样做!

Use can use structure:

cols <- c("a","b")

foo <- structure(list(c(1, 2 ), c(3, 3)), .Names = cols, row.names = c(NA, -2L), class = "data.frame")

I don't get why you are doing this though!

亣腦蒛氧 2024-09-06 19:41:33

我不确定如何直接执行此操作,但您可以简单地跳过在 data.frame() 命令中分配名称的步骤。假设您将 data.frame() 的结果存储在名为 foo 的变量中,您可以

简单地执行以下操作:
names(foo) <- cols

在创建数据框后

I'm not sure how to do it directly, but you could simply skip the step of assigning names in the data.frame() command. Assuming you store the result of data.frame() in a variable named foo, you can simply do:

names(foo) <- cols

after the data frame is created

蹲墙角沉默 2024-09-06 19:41:33

有一个技巧。你可能会弄乱列表:

cols_dummy <- setNames(rep(list("All"), 3), cols)

然后,如果你使用 one paren 来调用列表,那么你应该得到你想要的东西

data.frame(cols_dummy[1], calc.means(dat, cols[2:3]))

你可以将它即时用作 setNames(list("All") ), cols[1]) 但我认为它不太优雅。

例子:

some_names <- list(name_A="Dummy 1", name_B="Dummy 2") # equivalent of cols_dummy from above
data.frame(var1=rnorm(3), some_names[1])
#        var1  name_A
# 1 -1.940169 Dummy 1
# 2 -0.787107 Dummy 1
# 3 -0.235160 Dummy 1

There is one trick. You could mess with lists:

cols_dummy <- setNames(rep(list("All"), 3), cols)

Then if you use call to list with one paren then you should get what you want

data.frame(cols_dummy[1], calc.means(dat, cols[2:3]))

You could use it on-the-fly as setNames(list("All"), cols[1]) but I think it's less elegant.

Example:

some_names <- list(name_A="Dummy 1", name_B="Dummy 2") # equivalent of cols_dummy from above
data.frame(var1=rnorm(3), some_names[1])
#        var1  name_A
# 1 -1.940169 Dummy 1
# 2 -0.787107 Dummy 1
# 3 -0.235160 Dummy 1
一身软味 2024-09-06 19:41:33

我相信 allocate() 函数就是您的答案:

cols <- c('Col1','Col2','Col3')
data.frame(assign(cols[1], rnorm(10)))

返回:

   assign.cols.1...rnorm.10..
1                 -0.02056822
2                 -0.03675639
3                  1.06249599
4                  0.41763399
5                  0.38873118
6                  1.01779018
7                  1.01379963
8                  1.86119518
9                  0.35760039
10                 1.14742560

使用 lapply() 或 sapply() 函数,您应该能够循环 cbind() 过程。类似于:

operation <- sapply(cols, function(x) data.frame(assign(x, rnorm(10))))
final     <- data.frame(lapply(operation, cbind))

返回:

   Col1.assign.x..rnorm.10.. Col2.assign.x..rnorm.10.. Col3.assign.x..rnorm.10..
1                0.001962187                -0.3561499               -0.22783816
2               -0.706804781                -0.4452781               -1.09950505
3               -0.604417525                -0.8425018               -0.73287079
4               -1.287038060                 0.2545236               -1.18795684
5                0.232084366                -1.0831463                0.40799046
6               -0.148594144                 0.4963714               -1.34938144
7                0.442054119                 0.2856748                0.05933736
8                0.984615916                -0.0795147               -1.91165189
9                1.222310749                -0.1743313                0.18256877
10              -0.231885977                -0.2273724               -0.43247570

然后,清理列名称:

colnames(final) <- cols

返回:

          Col1       Col2        Col3
1   0.19473248  0.2864232  0.93115072
2  -1.08473526 -1.5653469  0.09967827
3  -1.90968422 -0.9678024 -1.02167873
4  -1.11962371  0.4549290  0.76692067
5  -2.13776949  3.0360777 -1.48515698
6   0.64240694  1.3441656  0.47676056
7  -0.53590163  1.2696336 -1.19845723
8   0.09158526 -1.0966833  0.91856639
9  -0.05018762  1.0472368  0.15475583
10  0.27152070 -0.2148181 -1.00551111

干杯,

亚当

I believe the assign() function is your answer:

cols <- c('Col1','Col2','Col3')
data.frame(assign(cols[1], rnorm(10)))

Returns:

   assign.cols.1...rnorm.10..
1                 -0.02056822
2                 -0.03675639
3                  1.06249599
4                  0.41763399
5                  0.38873118
6                  1.01779018
7                  1.01379963
8                  1.86119518
9                  0.35760039
10                 1.14742560

With the lapply() or sapply() function, you should be able to loop the cbind() process. Something like:

operation <- sapply(cols, function(x) data.frame(assign(x, rnorm(10))))
final     <- data.frame(lapply(operation, cbind))

Returns:

   Col1.assign.x..rnorm.10.. Col2.assign.x..rnorm.10.. Col3.assign.x..rnorm.10..
1                0.001962187                -0.3561499               -0.22783816
2               -0.706804781                -0.4452781               -1.09950505
3               -0.604417525                -0.8425018               -0.73287079
4               -1.287038060                 0.2545236               -1.18795684
5                0.232084366                -1.0831463                0.40799046
6               -0.148594144                 0.4963714               -1.34938144
7                0.442054119                 0.2856748                0.05933736
8                0.984615916                -0.0795147               -1.91165189
9                1.222310749                -0.1743313                0.18256877
10              -0.231885977                -0.2273724               -0.43247570

Then, to clean up the column names:

colnames(final) <- cols

Returns:

          Col1       Col2        Col3
1   0.19473248  0.2864232  0.93115072
2  -1.08473526 -1.5653469  0.09967827
3  -1.90968422 -0.9678024 -1.02167873
4  -1.11962371  0.4549290  0.76692067
5  -2.13776949  3.0360777 -1.48515698
6   0.64240694  1.3441656  0.47676056
7  -0.53590163  1.2696336 -1.19845723
8   0.09158526 -1.0966833  0.91856639
9  -0.05018762  1.0472368  0.15475583
10  0.27152070 -0.2148181 -1.00551111

Cheers,

Adam

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