[英]Grouping rows or columns of data in R
I'm trying to import some data into R and not having much luck grouping together rows of related data. 我正在尝试将一些数据导入到R中并且没有太多运气将相关数据行组合在一起。
Example: There a set of problems such as {A, B, C, D}. 示例:存在一组问题,例如{A,B,C,D}。 Each problem has two variables of interest which are being measured: "x" and "y".
每个问题都有两个感兴趣的变量:“x”和“y”。 Each variable is analysed in terms of some simple statistics: min, max, mean, stddev.
根据一些简单的统计数据分析每个变量:min,max,mean,stddev。
So, my input data has the form: 所以,我的输入数据有以下形式:
Min Max Mean StdDev
A
x 3 10 6.6 2.1
y 2 5 3.2 1.7
B
x 3 10 6.6 2.1
y 2 5 3.2 1.7
C
x 3 10 6.6 2.1
y 2 5 3.2 1.7
D
x 3 10 6.6 2.1
y 2 5 3.2 1.7
Is there any way to preserve the structure of this data in R? 有没有办法在R中保留这些数据的结构? A similar problem is creating groups of columns (flip the table by 90 degrees to the right for example).
类似的问题是创建列组(例如,将表向右翻转90度)。
You actually have many options: a data frame (relational table), or list. 您实际上有很多选项:数据框(关系表)或列表。 The following code will show how to create a data frame, and then to split it into a list containing the elements {x,y} or {A,B,C,D}:
以下代码将说明如何创建数据框,然后将其拆分为包含元素{x,y}或{A,B,C,D}的列表:
> txt <- " Min Max Mean StdDev
+ A
+ x 3 10 6.6 2.1
+ y 2 5 3.2 1.7
+ B
+ x 3 10 6.6 2.1
+ y 2 5 3.2 1.7
+ C
+ x 3 10 6.6 2.1
+ y 2 5 3.2 1.7
+ D
+ x 3 10 6.6 2.1
+ y 2 5 3.2 1.7
+ "
>
> data <- head(readLines(textConnection(txt)),-1)
> fields <- strsplit(sub("^[ ]+","",data[!nchar(data)==1]),"[ ]+")
> DF <- `names<-`(data.frame(rep(data[nchar(data)==1],each=2), ## letters
+ do.call(rbind,fields[-1])), ## data
+ c("Letter","xy",fields[[1]])) ## colnames
> split(DF,DF$xy)
$x
Letter xy Min Max Mean StdDev
1 A x 3 10 6.6 2.1
3 B x 3 10 6.6 2.1
5 C x 3 10 6.6 2.1
7 D x 3 10 6.6 2.1
$y
Letter xy Min Max Mean StdDev
2 A y 2 5 3.2 1.7
4 B y 2 5 3.2 1.7
6 C y 2 5 3.2 1.7
8 D y 2 5 3.2 1.7
> split(DF,DF$Letter)
$A
Letter xy Min Max Mean StdDev
1 A x 3 10 6.6 2.1
2 A y 2 5 3.2 1.7
$B
Letter xy Min Max Mean StdDev
3 B x 3 10 6.6 2.1
4 B y 2 5 3.2 1.7
$C
Letter xy Min Max Mean StdDev
5 C x 3 10 6.6 2.1
6 C y 2 5 3.2 1.7
$D
Letter xy Min Max Mean StdDev
7 D x 3 10 6.6 2.1
8 D y 2 5 3.2 1.7
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