当前位置： 文江博客话题详情

如何将数据从长格式重塑为宽格式

发布于 2024-11-05 07:07:36 字数 825 浏览 7 评论 0原文

我在重新排列以下数据框时遇到问题：

set.seed(45)
dat1 <- data.frame(
    name = rep(c("firstName", "secondName"), each=4),
    numbers = rep(1:4, 2),
    value = rnorm(8)
    )

dat1
       name  numbers      value
1  firstName       1  0.3407997
2  firstName       2 -0.7033403
3  firstName       3 -0.3795377
4  firstName       4 -0.7460474
5 secondName       1 -0.8981073
6 secondName       2 -0.3347941
7 secondName       3 -0.5013782
8 secondName       4 -0.1745357

我想重新调整它的形状，以便每个唯一的“名称”变量都是一个行名称，“值”作为该行的观察值，“数字”作为列名称。有点像这样：

     name          1          2          3         4
1  firstName  0.3407997 -0.7033403 -0.3795377 -0.7460474
5 secondName -0.8981073 -0.3347941 -0.5013782 -0.1745357

我看过 melt 和 cast 以及其他一些东西，但似乎没有一个能完成这项工作。

原文

I'm having trouble rearranging the following data frame:

set.seed(45)
dat1 <- data.frame(
    name = rep(c("firstName", "secondName"), each=4),
    numbers = rep(1:4, 2),
    value = rnorm(8)
    )

dat1
       name  numbers      value
1  firstName       1  0.3407997
2  firstName       2 -0.7033403
3  firstName       3 -0.3795377
4  firstName       4 -0.7460474
5 secondName       1 -0.8981073
6 secondName       2 -0.3347941
7 secondName       3 -0.5013782
8 secondName       4 -0.1745357

I want to reshape it so that each unique "name" variable is a rowname, with the "values" as observations along that row and the "numbers" as colnames. Sort of like this:

     name          1          2          3         4
1  firstName  0.3407997 -0.7033403 -0.3795377 -0.7460474
5 secondName -0.8981073 -0.3347941 -0.5013782 -0.1745357

I've looked at melt and cast and a few other things, but none seem to do the job.

如果你对这篇内容有疑问，欢迎到本站社区发帖提问 参与讨论，获取更多帮助，或者扫码二维码加入 Web 技术交流群。

扫码二维码加入Web技术交流群

发布评论

需要 登录 才能够评论， 你可以免费 注册 一个本站的账号。

评论（15）

梦屿孤独相伴 2024-11-12 07:07:36

使用reshape函数：

reshape(dat1, idvar = "name", timevar = "numbers", direction = "wide")

Using reshape function:

reshape(dat1, idvar = "name", timevar = "numbers", direction = "wide")
回复 原文
等往事风中吹 2024-11-12 07:07:36

新的（2014 年）tidyr 包也简单地做到了这一点，gather()/spread() 是 melt 的术语/投射。

编辑： 现在，2019 年，tidyr v 1.0 已启动，并将 spread 和 gather 设置为弃用路径，而是改为 pivot_wider< /code> 和 pivot_longer，您可以找到描述在此答案中。如果您想简要了解传播/聚集的短暂生命，请继续阅读。

library(tidyr)
spread(dat1, key = numbers, value = value)

来自 github，

tidyr 是 reshape2 的重构，旨在配合 tidy 数据框架，并与 magrittr 和 < code>dplyr 构建可靠的数据分析管道。

正如 reshape2 的效果比 reshape 的效果要少，tidyr 的效果也比 reshape2 的效果要少。它是专门为整理数据而设计的，而不是像 reshape2 那样进行一般的重塑，或者像 reshape 那样进行一般的聚合。特别是，内置方法仅适用于数据帧，并且 tidyr 不提供边距或聚合。

The new (in 2014) tidyr package also does this simply, with gather()/spread() being the terms for melt/cast.

Edit: Now, in 2019, tidyr v 1.0 has launched and set spread and gather on a deprecation path, preferring instead pivot_wider and pivot_longer, which you can find described in this answer. Read on if you want a brief glimpse into the brief life of spread/gather.

library(tidyr)
spread(dat1, key = numbers, value = value)

From github,

tidyr is a reframing of reshape2 designed to accompany the tidy data framework, and to work hand-in-hand with magrittr and dplyr to build a solid pipeline for data analysis.

Just as reshape2 did less than reshape, tidyr does less than reshape2. It's designed specifically for tidying data, not the general reshaping that reshape2 does, or the general aggregation that reshape did. In particular, built-in methods only work for data frames, and tidyr provides no margins or aggregation.

回复 原文
执笔绘流年 2024-11-12 07:07:36

您可以使用 reshape() 函数或 reshape 包中的 melt() / cast() 函数来完成此操作。对于第二个选项，示例代码是

library(reshape)
cast(dat1, name ~ numbers)

Or using reshape2

library(reshape2)
dcast(dat1, name ~ numbers)

You can do this with the reshape() function, or with the melt() / cast() functions in the reshape package. For the second option, example code is

library(reshape)
cast(dat1, name ~ numbers)

Or using reshape2

library(reshape2)
dcast(dat1, name ~ numbers)
回复 原文
是你 2024-11-12 07:07:36

如果关注性能，另一种选择是使用 data.table 的 reshape2 的 Melt & 扩展。 dcast 函数

（参考：使用 data.tables 进行高效重塑）

library(data.table)

setDT(dat1)
dcast(dat1, name ~ numbers, value.var = "value")

#          name          1          2         3         4
# 1:  firstName  0.1836433 -0.8356286 1.5952808 0.3295078
# 2: secondName -0.8204684  0.4874291 0.7383247 0.5757814

并且，从 data.table v1.9.6 开始，我们可以在多个列上进行转换

## add an extra column
dat1[, value2 := value * 2]

## cast multiple value columns
dcast(dat1, name ~ numbers, value.var = c("value", "value2"))

#          name    value_1    value_2   value_3   value_4   value2_1   value2_2 value2_3  value2_4
# 1:  firstName  0.1836433 -0.8356286 1.5952808 0.3295078  0.3672866 -1.6712572 3.190562 0.6590155
# 2: secondName -0.8204684  0.4874291 0.7383247 0.5757814 -1.6409368  0.9748581 1.476649 1.1515627

Another option if performance is a concern is to use data.table's extension of reshape2's melt & dcast functions

(Reference: Efficient reshaping using data.tables)

library(data.table)

setDT(dat1)
dcast(dat1, name ~ numbers, value.var = "value")

#          name          1          2         3         4
# 1:  firstName  0.1836433 -0.8356286 1.5952808 0.3295078
# 2: secondName -0.8204684  0.4874291 0.7383247 0.5757814

And, as of data.table v1.9.6 we can cast on multiple columns

## add an extra column
dat1[, value2 := value * 2]

## cast multiple value columns
dcast(dat1, name ~ numbers, value.var = c("value", "value2"))

#          name    value_1    value_2   value_3   value_4   value2_1   value2_2 value2_3  value2_4
# 1:  firstName  0.1836433 -0.8356286 1.5952808 0.3295078  0.3672866 -1.6712572 3.190562 0.6590155
# 2: secondName -0.8204684  0.4874291 0.7383247 0.5757814 -1.6409368  0.9748581 1.476649 1.1515627
回复 原文
り繁华旳梦境 2024-11-12 07:07:36

对于 tidyr，有 pivot_wider() 和 pivot_longer()，它们被概括为从 long -> 进行重塑。宽还是宽->分别长。使用OP的数据：

单列长->宽

library(tidyr)

dat1 %>% 
    pivot_wider(names_from = numbers, values_from = value)

# # A tibble: 2 x 5
#   name          `1`    `2`    `3`    `4`
#   <fct>       <dbl>  <dbl>  <dbl>  <dbl>
# 1 firstName   0.341 -0.703 -0.380 -0.746
# 2 secondName -0.898 -0.335 -0.501 -0.175

多列长-> Wide

pivot_wider() 还能够进行更复杂的枢轴操作。例如，您可以同时旋转多个列：

# create another column for showing the functionality
dat2 <- dat1 %>% 
    dplyr::rename(valA = value) %>%
    dplyr::mutate(valB = valA * 2) 

dat2 %>% 
    pivot_wider(names_from = numbers, values_from = c(valA, valB))

# # A tibble: 2 × 9
#   name       valA_1 valA_2 valA_3 valA_4 valB_1 valB_2 valB_3 valB_4
#   <chr>       <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
#  1 firstName   0.341 -0.703 -0.380 -0.746  0.682 -1.41  -0.759 -1.49 
#  2 secondName -0.898 -0.335 -0.501 -0.175 -1.80  -0.670 -1.00  -0.349

在文档。

With tidyr, there is pivot_wider() and pivot_longer() which are generalized to do reshaping from long -> wide or wide -> long, respectively. Using the OP's data:

single column long -> wide

library(tidyr)

dat1 %>% 
    pivot_wider(names_from = numbers, values_from = value)

# # A tibble: 2 x 5
#   name          `1`    `2`    `3`    `4`
#   <fct>       <dbl>  <dbl>  <dbl>  <dbl>
# 1 firstName   0.341 -0.703 -0.380 -0.746
# 2 secondName -0.898 -0.335 -0.501 -0.175

multiple columns long -> wide

pivot_wider() is also capable of more complex pivot operations. For example, you can pivot multiple columns simultaneously:

# create another column for showing the functionality
dat2 <- dat1 %>% 
    dplyr::rename(valA = value) %>%
    dplyr::mutate(valB = valA * 2) 

dat2 %>% 
    pivot_wider(names_from = numbers, values_from = c(valA, valB))

# # A tibble: 2 × 9
#   name       valA_1 valA_2 valA_3 valA_4 valB_1 valB_2 valB_3 valB_4
#   <chr>       <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
#  1 firstName   0.341 -0.703 -0.380 -0.746  0.682 -1.41  -0.759 -1.49 
#  2 secondName -0.898 -0.335 -0.501 -0.175 -1.80  -0.670 -1.00  -0.349

There is much more functionality to be found in the docs.

回复 原文
听风念你 2024-11-12 07:07:36

使用您的示例数据框，我们可以：

xtabs(value ~ name + numbers, data = dat1)

Using your example dataframe, we could:

xtabs(value ~ name + numbers, data = dat1)
回复 原文
一场春暖 2024-11-12 07:07:36

其他两个选项：

基础包：

df <- unstack(dat1, form = value ~ numbers)
rownames(df) <- unique(dat1$name)
df

sqldf 包：

library(sqldf)
sqldf('SELECT name,
      MAX(CASE WHEN numbers = 1 THEN value ELSE NULL END) x1, 
      MAX(CASE WHEN numbers = 2 THEN value ELSE NULL END) x2,
      MAX(CASE WHEN numbers = 3 THEN value ELSE NULL END) x3,
      MAX(CASE WHEN numbers = 4 THEN value ELSE NULL END) x4
      FROM dat1
      GROUP BY name')

Other two options:

Base package:

df <- unstack(dat1, form = value ~ numbers)
rownames(df) <- unique(dat1$name)
df

sqldf package:

library(sqldf)
sqldf('SELECT name,
      MAX(CASE WHEN numbers = 1 THEN value ELSE NULL END) x1, 
      MAX(CASE WHEN numbers = 2 THEN value ELSE NULL END) x2,
      MAX(CASE WHEN numbers = 3 THEN value ELSE NULL END) x3,
      MAX(CASE WHEN numbers = 4 THEN value ELSE NULL END) x4
      FROM dat1
      GROUP BY name')
回复 原文
随遇而安 2024-11-12 07:07:36

使用基本 R aggregate 函数：

aggregate(value ~ name, dat1, I)

# name           value.1  value.2  value.3  value.4
#1 firstName      0.4145  -0.4747   0.0659   -0.5024
#2 secondName    -0.8259   0.1669  -0.8962    0.1681

Using base R aggregate function:

aggregate(value ~ name, dat1, I)

# name           value.1  value.2  value.3  value.4
#1 firstName      0.4145  -0.4747   0.0659   -0.5024
#2 secondName    -0.8259   0.1669  -0.8962    0.1681
回复 原文
请持续率性 2024-11-12 07:07:36

基本的reshape函数工作得很好：

df <- data.frame(
  year   = c(rep(2000, 12), rep(2001, 12)),
  month  = rep(1:12, 2),
  values = rnorm(24)
)
df_wide <- reshape(df, idvar="year", timevar="month", v.names="values", direction="wide", sep="_")
df_wide

其中

  • idvar是分隔行的类的列
  • 的类的列
  • timevar是要转换宽>v.names 是包含数值的列
  • direction 指定宽或长格式
  • 可选的 sep 参数是 timevar 之间使用的分隔符code> 类名和 v.names 中输出data.frame。

如果 idvar 不存在，请在使用 reshape() 函数之前创建一个：

df$id   <- c(rep("year1", 12), rep("year2", 12))
df_wide <- reshape(df, idvar="id", timevar="month", v.names="values", direction="wide", sep="_")
df_wide

只需记住 idvar 是必需的！ timevar 和 v.names 部分很简单。该函数的输出比其他一些函数更可预测，因为所有内容都是明确定义的。

The base reshape function works perfectly fine:

df <- data.frame(
  year   = c(rep(2000, 12), rep(2001, 12)),
  month  = rep(1:12, 2),
  values = rnorm(24)
)
df_wide <- reshape(df, idvar="year", timevar="month", v.names="values", direction="wide", sep="_")
df_wide

Where

  • idvar is the column of classes that separates rows
  • timevar is the column of classes to cast wide
  • v.names is the column containing numeric values
  • direction specifies wide or long format
  • the optional sep argument is the separator used in between timevar class names and v.names in the output data.frame.

If no idvar exists, create one before using the reshape() function:

df$id   <- c(rep("year1", 12), rep("year2", 12))
df_wide <- reshape(df, idvar="id", timevar="month", v.names="values", direction="wide", sep="_")
df_wide

Just remember that idvar is required! The timevar and v.names part is easy. The output of this function is more predictable than some of the others, as everything is explicitly defined.

回复 原文
谢绝鈎搭 2024-11-12 07:07:36

Win-Vector 的天才数据科学家（他们开发了 vtreat、seplyr 和 replyr）推出了非常强大的新软件包，名为 cdata< /代码>。它实现了本文档中描述的“协调数据”原则，并且在这篇博客文章中。这个想法是，无论您如何组织数据，都应该可以使用“数据坐标”系统来识别各个数据点。以下是约翰·蒙特 (John Mount) 最近发表的博客文章的摘录：

整个系统基于两个原语或运算符
cdata::moveValuesToRowsD() 和 cdata::moveValuesToColumnsD()。这些
运算符有主元、非主元、one-hot 编码、转置、移动
多行和多列，以及许多其他简单的特殊变换
案例。

很容易根据以下方式编写许多不同的操作
cdata 原语。这些运算符可以在内存中或大数据中工作
规模（使用数据库和 Apache Spark；对于大数据，请使用
cdata::moveValuesToRowsN() 和 cdata::moveValuesToColumnsN()
变体）。转换由控制表控制，该控制表
它本身就是变换的图表（或图片）。

我们将首先构建控制表（参见博客文章了解详细信息），然后执行数据从行到列的移动。

library(cdata)
# first build the control table
pivotControlTable <- buildPivotControlTableD(table = dat1, # reference to dataset
                        columnToTakeKeysFrom = 'numbers', # this will become column headers
                        columnToTakeValuesFrom = 'value', # this contains data
                        sep="_")                          # optional for making column names

# perform the move of data to columns
dat_wide <- moveValuesToColumnsD(tallTable =  dat1, # reference to dataset
                    keyColumns = c('name'),         # this(these) column(s) should stay untouched 
                    controlTable = pivotControlTable# control table above
                    ) 
dat_wide

#>         name  numbers_1  numbers_2  numbers_3  numbers_4
#> 1  firstName  0.3407997 -0.7033403 -0.3795377 -0.7460474
#> 2 secondName -0.8981073 -0.3347941 -0.5013782 -0.1745357

There's very powerful new package from genius data scientists at Win-Vector (folks that made vtreat, seplyr and replyr) called cdata. It implements "coordinated data" principles described in this document and also in this blog post. The idea is that regardless how you organize your data, it should be possible to identify individual data points using a system of "data coordinates". Here's a excerpt from the recent blog post by John Mount:

The whole system is based on two primitives or operators
cdata::moveValuesToRowsD() and cdata::moveValuesToColumnsD(). These
operators have pivot, un-pivot, one-hot encode, transpose, moving
multiple rows and columns, and many other transforms as simple special
cases.

It is easy to write many different operations in terms of the
cdata primitives. These operators can work-in memory or at big data
scale (with databases and Apache Spark; for big data use the
cdata::moveValuesToRowsN() and cdata::moveValuesToColumnsN()
variants). The transforms are controlled by a control table that
itself is a diagram of (or picture of) the transform.

We will first build the control table (see blog post for details) and then perform the move of data from rows to columns.

library(cdata)
# first build the control table
pivotControlTable <- buildPivotControlTableD(table = dat1, # reference to dataset
                        columnToTakeKeysFrom = 'numbers', # this will become column headers
                        columnToTakeValuesFrom = 'value', # this contains data
                        sep="_")                          # optional for making column names

# perform the move of data to columns
dat_wide <- moveValuesToColumnsD(tallTable =  dat1, # reference to dataset
                    keyColumns = c('name'),         # this(these) column(s) should stay untouched 
                    controlTable = pivotControlTable# control table above
                    ) 
dat_wide

#>         name  numbers_1  numbers_2  numbers_3  numbers_4
#> 1  firstName  0.3407997 -0.7033403 -0.3795377 -0.7460474
#> 2 secondName -0.8981073 -0.3347941 -0.5013782 -0.1745357
回复 原文
流绪微梦 2024-11-12 07:07:36

更简单的方法！

devtools::install_github("yikeshu0611/onetree") #install onetree package

library(onetree)
widedata=reshape_toWide(data = dat1,id = "name",j = "numbers",value.var.prefix = "value")
widedata

        name     value1     value2     value3     value4
   firstName  0.3407997 -0.7033403 -0.3795377 -0.7460474
  secondName -0.8981073 -0.3347941 -0.5013782 -0.1745357

如果你想从宽变回长，只需将宽变回长，而不会改变对象。

reshape_toLong(data = widedata,id = "name",j = "numbers",value.var.prefix = "value")

        name numbers      value
   firstName       1  0.3407997
  secondName       1 -0.8981073
   firstName       2 -0.7033403
  secondName       2 -0.3347941
   firstName       3 -0.3795377
  secondName       3 -0.5013782
   firstName       4 -0.7460474
  secondName       4 -0.1745357

much easier way!

devtools::install_github("yikeshu0611/onetree") #install onetree package

library(onetree)
widedata=reshape_toWide(data = dat1,id = "name",j = "numbers",value.var.prefix = "value")
widedata

        name     value1     value2     value3     value4
   firstName  0.3407997 -0.7033403 -0.3795377 -0.7460474
  secondName -0.8981073 -0.3347941 -0.5013782 -0.1745357

if you want to go back from wide to long, only change Wide to Long, and no changes in objects.

reshape_toLong(data = widedata,id = "name",j = "numbers",value.var.prefix = "value")

        name numbers      value
   firstName       1  0.3407997
  secondName       1 -0.8981073
   firstName       2 -0.7033403
  secondName       2 -0.3347941
   firstName       3 -0.3795377
  secondName       3 -0.5013782
   firstName       4 -0.7460474
  secondName       4 -0.1745357
回复 原文
甜味拾荒者 2024-11-12 07:07:36

即使您缺少对并且不需要排序（as.matrix(dat1)[,1:2] 可以替换为 cbind(dat1[,1], dat1[,2]))：

> set.seed(45);dat1=data.frame(name=rep(c("firstName","secondName"),each=4),numbers=rep(1:4,2),value=rnorm(8))
> u1=unique(dat1[,1]);u2=unique(dat1[,2])
> m=matrix(nrow=length(u1),ncol=length(u2),dimnames=list(u1,u2))
> m[as.matrix(dat1)[,1:2]]=dat1[,3]
> m
                    1          2          3          4
firstName   0.3407997 -0.7033403 -0.3795377 -0.7460474
secondName -0.8981073 -0.3347941 -0.5013782 -0.1745357

如果缺少对并且需要排序，则此方法不起作用，但如果对已排序，则它会更短：

> u1=unique(dat1[,1]);u2=unique(dat1[,2])
> dat1=dat1[order(dat1[,1],dat1[,2]),] # not actually needed in this case
> matrix(dat1[,3],length(u1),,T,list(u1,u2))
                    1          2          3          4
firstName   0.3407997 -0.7033403 -0.3795377 -0.7460474
secondName -0.8981073 -0.3347941 -0.5013782 -0.1745357

这是第一种方法的函数版本（添加as.data.frame 使其与 tibbles 一起工作）：

l2w=function(x,row=1,col=2,val=3,sort=F){
  u1=unique(x[,row])
  u2=unique(x[,col])
  if(sort){u1=sort(u1);u2=sort(u2)}
  out=matrix(nrow=length(u1),ncol=length(u2),dimnames=list(u1,u2))
  out[cbind(x[,row],x[,col])]=x[,val]
  out
}

或者如果您只有下三角形的值，您可以这样做：

> euro=as.matrix(eurodist)[1:3,1:3]
> lower=data.frame(V1=rownames(euro)[row(euro)[lower.tri(euro)]],V2=colnames(euro)[col(euro)[lower.tri(euro)]],V3=euro[lower.tri(euro)])
> lower
         V1        V2   V3
1 Barcelona    Athens 3313
2  Brussels    Athens 2963
3  Brussels Barcelona 1318
> n=unique(c(lower[,1],lower[,2]))
> full=rbind(lower,setNames(lower[,c(2,1,3)],names(lower)),data.frame(V1=n,V2=n,V3=0))
> full
         V1        V2   V3
1 Barcelona    Athens 3313
2  Brussels    Athens 2963
3  Brussels Barcelona 1318
4    Athens Barcelona 3313
5    Athens  Brussels 2963
6 Barcelona  Brussels 1318
7    Athens    Athens    0
8 Barcelona Barcelona    0
9  Brussels  Brussels    0
> l2w(full,sort=T)
          Athens Barcelona Brussels
Athens         0      3313     2963
Barcelona   3313         0     1318
Brussels    2963      1318        0

或者这是另一种方法：

> rc=as.matrix(lower[-3])
> n=sort(unique(c(rc)))
> m=matrix(0,length(n),length(n),,list(n,n))
> m[rc]=lower[,3]
> m[rc[,2:1]]=lower[,3]
> m
          Athens Barcelona Brussels
Athens         0      3313     2963
Barcelona   3313         0     1318
Brussels    2963      1318        0

基 R 中的另一种简单方法是使用 xtabs。 xtabs 的结果基本上只是一个具有奇特类名的矩阵，但您可以使用 class(x)=NULL;attr(x,"call" 使其看起来像常规矩阵)=NULL;dimnames(x)=unname(dimnames(x))：

> x=xtabs(value~name+numbers,dat1);x
            numbers
name                  1          2          3          4
  firstName   0.3407997 -0.7033403 -0.3795377 -0.7460474
  secondName -0.8981073 -0.3347941 -0.5013782 -0.1745357
> str(x)
 'xtabs' num [1:2, 1:4] 0.341 -0.898 -0.703 -0.335 -0.38 ...
 - attr(*, "dimnames")=List of 2
  ..$ name   : chr [1:2] "firstName" "secondName"
  ..$ numbers: chr [1:4] "1" "2" "3" "4"
 - attr(*, "call")= language xtabs(formula = value ~ name + numbers, data = dat1)
> class(x)
[1] "xtabs" "table"
> class(as.matrix(x)) # `as.matrix` has no effect because `x` is already a matrix
[1] "xtabs" "table"
> class(x)=NULL;class(x)
[1] "matrix" "array"
> attr(x,"call")=NULL;dimnames(x)=unname(dimnames(x))
> x # now it looks like a regular matrix
                    1          2          3          4
firstName   0.3407997 -0.7033403 -0.3795377 -0.7460474
secondName -0.8981073 -0.3347941 -0.5013782 -0.1745357
> str(x)
 num [1:2, 1:4] 0.341 -0.898 -0.703 -0.335 -0.38 ...
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:2] "firstName" "secondName"
  ..$ : chr [1:4] "1" "2" "3" "4"

通常 as.data.frame(x) 将 xtabs 的结果转换回来到长格式，但您可以使用 class(x)=NULL 来避免它：

> x=xtabs(value~name+numbers,dat1);as.data.frame(x)
        name numbers       Freq
1  firstName       1  0.3407997
2 secondName       1 -0.8981073
3  firstName       2 -0.7033403
4 secondName       2 -0.3347941
5  firstName       3 -0.3795377
6 secondName       3 -0.5013782
7  firstName       4 -0.7460474
8 secondName       4 -0.1745357
> class(x)=NULL;as.data.frame(x)
                    1          2          3          4
firstName   0.3407997 -0.7033403 -0.3795377 -0.7460474
secondName -0.8981073 -0.3347941 -0.5013782 -0.1745357

这会将宽格式的数据转换为长格式（unlist 将数据帧转换为向量，并且< code>c 将矩阵转换为向量）：

w2l=function(x)data.frame(V1=rownames(x)[row(x)],V2=colnames(x)[col(x)],V3=unname(c(unlist(x))))

This works even if you have missing pairs and it doesn't require sorting (as.matrix(dat1)[,1:2] can be replaced with cbind(dat1[,1],dat1[,2])):

> set.seed(45);dat1=data.frame(name=rep(c("firstName","secondName"),each=4),numbers=rep(1:4,2),value=rnorm(8))
> u1=unique(dat1[,1]);u2=unique(dat1[,2])
> m=matrix(nrow=length(u1),ncol=length(u2),dimnames=list(u1,u2))
> m[as.matrix(dat1)[,1:2]]=dat1[,3]
> m
                    1          2          3          4
firstName   0.3407997 -0.7033403 -0.3795377 -0.7460474
secondName -0.8981073 -0.3347941 -0.5013782 -0.1745357

This doesn't work if you have missing pairs and it requires sorting, but it's a bit shorter in case the pairs are already sorted:

> u1=unique(dat1[,1]);u2=unique(dat1[,2])
> dat1=dat1[order(dat1[,1],dat1[,2]),] # not actually needed in this case
> matrix(dat1[,3],length(u1),,T,list(u1,u2))
                    1          2          3          4
firstName   0.3407997 -0.7033403 -0.3795377 -0.7460474
secondName -0.8981073 -0.3347941 -0.5013782 -0.1745357

Here's a function version of the first approach (add as.data.frame to make it work with tibbles):

l2w=function(x,row=1,col=2,val=3,sort=F){
  u1=unique(x[,row])
  u2=unique(x[,col])
  if(sort){u1=sort(u1);u2=sort(u2)}
  out=matrix(nrow=length(u1),ncol=length(u2),dimnames=list(u1,u2))
  out[cbind(x[,row],x[,col])]=x[,val]
  out
}

Or if you only have the values of the lower triangle, you can do this:

> euro=as.matrix(eurodist)[1:3,1:3]
> lower=data.frame(V1=rownames(euro)[row(euro)[lower.tri(euro)]],V2=colnames(euro)[col(euro)[lower.tri(euro)]],V3=euro[lower.tri(euro)])
> lower
         V1        V2   V3
1 Barcelona    Athens 3313
2  Brussels    Athens 2963
3  Brussels Barcelona 1318
> n=unique(c(lower[,1],lower[,2]))
> full=rbind(lower,setNames(lower[,c(2,1,3)],names(lower)),data.frame(V1=n,V2=n,V3=0))
> full
         V1        V2   V3
1 Barcelona    Athens 3313
2  Brussels    Athens 2963
3  Brussels Barcelona 1318
4    Athens Barcelona 3313
5    Athens  Brussels 2963
6 Barcelona  Brussels 1318
7    Athens    Athens    0
8 Barcelona Barcelona    0
9  Brussels  Brussels    0
> l2w(full,sort=T)
          Athens Barcelona Brussels
Athens         0      3313     2963
Barcelona   3313         0     1318
Brussels    2963      1318        0

Or here's another approach:

> rc=as.matrix(lower[-3])
> n=sort(unique(c(rc)))
> m=matrix(0,length(n),length(n),,list(n,n))
> m[rc]=lower[,3]
> m[rc[,2:1]]=lower[,3]
> m
          Athens Barcelona Brussels
Athens         0      3313     2963
Barcelona   3313         0     1318
Brussels    2963      1318        0

Another simple method in base R is to use xtabs. The result of xtabs is basically just a matrix with a fancy class name, but you can make it look like a regular matrix with class(x)=NULL;attr(x,"call")=NULL;dimnames(x)=unname(dimnames(x)):

> x=xtabs(value~name+numbers,dat1);x
            numbers
name                  1          2          3          4
  firstName   0.3407997 -0.7033403 -0.3795377 -0.7460474
  secondName -0.8981073 -0.3347941 -0.5013782 -0.1745357
> str(x)
 'xtabs' num [1:2, 1:4] 0.341 -0.898 -0.703 -0.335 -0.38 ...
 - attr(*, "dimnames")=List of 2
  ..$ name   : chr [1:2] "firstName" "secondName"
  ..$ numbers: chr [1:4] "1" "2" "3" "4"
 - attr(*, "call")= language xtabs(formula = value ~ name + numbers, data = dat1)
> class(x)
[1] "xtabs" "table"
> class(as.matrix(x)) # `as.matrix` has no effect because `x` is already a matrix
[1] "xtabs" "table"
> class(x)=NULL;class(x)
[1] "matrix" "array"
> attr(x,"call")=NULL;dimnames(x)=unname(dimnames(x))
> x # now it looks like a regular matrix
                    1          2          3          4
firstName   0.3407997 -0.7033403 -0.3795377 -0.7460474
secondName -0.8981073 -0.3347941 -0.5013782 -0.1745357
> str(x)
 num [1:2, 1:4] 0.341 -0.898 -0.703 -0.335 -0.38 ...
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:2] "firstName" "secondName"
  ..$ : chr [1:4] "1" "2" "3" "4"

Normally as.data.frame(x) converts the result of xtabs back to long format, but you can avoid it with class(x)=NULL:

> x=xtabs(value~name+numbers,dat1);as.data.frame(x)
        name numbers       Freq
1  firstName       1  0.3407997
2 secondName       1 -0.8981073
3  firstName       2 -0.7033403
4 secondName       2 -0.3347941
5  firstName       3 -0.3795377
6 secondName       3 -0.5013782
7  firstName       4 -0.7460474
8 secondName       4 -0.1745357
> class(x)=NULL;as.data.frame(x)
                    1          2          3          4
firstName   0.3407997 -0.7033403 -0.3795377 -0.7460474
secondName -0.8981073 -0.3347941 -0.5013782 -0.1745357

This converts data in wide fromat to long format (unlist converts a dataframe to a vector and c converts a matrix to a vector):

w2l=function(x)data.frame(V1=rownames(x)[row(x)],V2=colnames(x)[col(x)],V3=unname(c(unlist(x))))
回复 原文
残龙傲雪 2024-11-12 07:07:36

通过链接问题来到这里 将三列数据框重塑为矩阵（“长”为“宽”格式）。这个问题已经结束，所以我在这里写了一个替代解决方案。

我找到了一个替代解决方案，对于寻求将三列转换为矩阵的人来说可能有用。我指的是 de CoupleR (2.3.2) 包。下面是从他们的 网站 复制

的 生成一种表，其中行来自id_cols、names_from 中的列以及values_from 中的值。

用法

pivot_wider_profile(
data,
id_cols,
names_from,
values_from,
values_fill = NA,
to_matrix = FALSE,
to_sparse = FALSE,
...
)

Came here via a linked question Reshape three column data frame to matrix ("long" to "wide" format). That question is closed, so I writing an alternative solution here.

I found a alternative solution, perhaps useful for someone looking for converting three columns to a matrix. I am referring to decoupleR (2.3.2) package. Below is copied from their site

Generates a kind of table where the rows come from id_cols, the columns from names_from and the values from values_from.

Usage

pivot_wider_profile(
data,
id_cols,
names_from,
values_from,
values_fill = NA,
to_matrix = FALSE,
to_sparse = FALSE,
...
)
回复 原文
微凉徒眸意 2024-11-12 07:07:36

我最近开始研究 collapse 包，它非常快速且有用。在collapse中，我们可以使用pivot函数来进行这种转换。

library(collapse)
pivot(dat1, "name", "value", "numbers", how = "wider")

#        name          1          2          3          4
#1  firstName  0.3407997 -0.7033403 -0.3795377 -0.7460474
#2 secondName -0.8981073 -0.3347941 -0.5013782 -0.1745357

I have recently started looking into collapse package which is super fast and useful. In collapse we can use the pivot function to do this transformation.

library(collapse)
pivot(dat1, "name", "value", "numbers", how = "wider")

#        name          1          2          3          4
#1  firstName  0.3407997 -0.7033403 -0.3795377 -0.7460474
#2 secondName -0.8981073 -0.3347941 -0.5013782 -0.1745357
回复 原文
缘字诀 2024-11-12 07:07:36

仅使用 dplyr 和 map。

library(dplyr)
library(purrr)
set.seed(45)
dat1 <- data.frame(
  name = rep(c("firstName", "secondName"), each=4),
  numbers = rep(1:4, 2), value = rnorm(8)
)
longer_to_wider <- function(data, name_from, value_from){
  group <- colnames(data)[!(colnames(data) %in% c(name_from,value_from))]
  data %>% group_by(.data[[group]]) %>%
    summarise( name = list(.data[[name_from]]), 
               value = list(.data[[value_from]])) %>%
    {
      d <- data.frame(
        name = .[[name_from]] %>% unlist() %>% unique()
      )
      e <- map_dfc(.[[group]],function(x){
          y <- data_frame(
            x = data %>% filter(.data[[group]] == x) %>% pull(value_from)
          )
          colnames(y) <- x
          y
      })
      cbind(d,e)
    }
}
longer_to_wider(dat1, "name", "value")
#    name          1          2          3          4
# 1  firstName  0.3407997 -0.7033403 -0.3795377 -0.7460474
# 2 secondName -0.8981073 -0.3347941 -0.5013782 -0.1745357

Using only dplyr and map.

library(dplyr)
library(purrr)
set.seed(45)
dat1 <- data.frame(
  name = rep(c("firstName", "secondName"), each=4),
  numbers = rep(1:4, 2), value = rnorm(8)
)
longer_to_wider <- function(data, name_from, value_from){
  group <- colnames(data)[!(colnames(data) %in% c(name_from,value_from))]
  data %>% group_by(.data[[group]]) %>%
    summarise( name = list(.data[[name_from]]), 
               value = list(.data[[value_from]])) %>%
    {
      d <- data.frame(
        name = .[[name_from]] %>% unlist() %>% unique()
      )
      e <- map_dfc(.[[group]],function(x){
          y <- data_frame(
            x = data %>% filter(.data[[group]] == x) %>% pull(value_from)
          )
          colnames(y) <- x
          y
      })
      cbind(d,e)
    }
}
longer_to_wider(dat1, "name", "value")
#    name          1          2          3          4
# 1  firstName  0.3407997 -0.7033403 -0.3795377 -0.7460474
# 2 secondName -0.8981073 -0.3347941 -0.5013782 -0.1745357
回复 原文
~没有更多了~

关于作者

从此见与不见

暂无简介

文章
评论
26 人气
发私信

相关话题

更多

热门标签

操作系统 程序设计 IT运维 Linux系统管理 JavaScript 服务器应用 solaris C/C++ PHP Shell BSD Vue.js aix Oracle Python HTML 系统管理 HTML5 CSS 前端
更多

推荐作者

佚名

文章 0 评论 0

今天

文章 0 评论 0

゛时过境迁

文章 0 评论 0

达拉崩吧

文章 0 评论 0

呆萌少年

文章 0 评论 0

孤者何惧

文章 0 评论 0

更多

友情链接

文江博客
    我们使用 Cookies 和其他技术来定制您的体验包括您的登录状态等。通过阅读我们的 隐私政策 了解更多相关信息。 单击 接受 或继续使用网站，即表示您同意使用 Cookies 和您的相关数据。
    原文