[英]SAS Macro variable in R
I am very familiar with the SAS programing environment. 我对SAS编程环境非常熟悉。 I am currently trying to learn to program in R. I have found that using SAS Macros reduces the amount of repetitive code in my programming.
我目前正在尝试学习使用R编程。我发现使用SAS宏可以减少编程中重复代码的数量。 Particularly, I have found useful adjusting parts of datasets names and variable names using macro variables.
特别是,我发现使用宏变量有用的调整数据集名称和变量名称的部分。 However, in RI haven't found something that can replicate this.
然而,在RI中没有找到可以复制这个的东西。
For example, in SAS I could write a simple macro to run proc means on two datasets like this: 例如,在SAS中,我可以编写一个简单的宏来在两个数据集上运行proc,如下所示:
%macro means(dataset_suffix = , var1_suffix= );
proc means data = data&dataset_suffix;
var var1&var1_suffix;
run;
%mend means;
%means(dataset_suffix = _suf1, var1_suffix = _suf2);
%means(dataset_suffix = _suf3, var1_suffix = _suf4);
running this code executes the macro two times resulting in the following code being run 运行此代码会执行两次宏,从而导致运行以下代码
proc means data = data_suf1;
var var_suf2;
run;
proc means data = data_suf3;
var var_suf4;
run;
I have looked into R's user defined functions as well as using lists. 我已经研究了R的用户定义函数以及使用列表。 I know there isn't a procedure in R that is directly comparable to proc means.
我知道R中没有一个与proc手段直接相似的程序。 However, this focus of my question is how to use macro variables to reference different objects in R that have similar prefixes but different suffixes.
然而,我的问题的焦点是如何使用宏变量来引用R中具有相似前缀但不同后缀的不同对象。 I have also considered using the paste function.
我也考虑过使用粘贴功能。 Any help with this would be most appreciated.
任何有关这方面的帮助将非常感激。
It always takes some adjustment coming from a macro-heavy language (SAS or Stata) to one that has real variables (R). 它总是需要一些调整,从宏观重型语言(SAS或Stata)到具有实变量(R)的语言。 In the end, you'll find that real variables are more powerful and less error-prone.
最后,您会发现真正的变量更强大,更不容易出错。
Just about everything in R
is a first-class object. 几乎
R
中的所有东西都是一流的对象。 And a list
can store just about any object. list
可以存储任何对象。 That means you can have lists of model objects, data.frames
, whatever you want. 这意味着您可以拥有模型对象列表,
data.frames
,无论您想要什么。
datasets <- list( one=data.frame(x=runif(100),y=runif(100) ), two=data.frame(x=runif(100),y=runif(100) ) )
lm(y~x, data=datasets$one)
modelList <- lapply( datasets, function(dat) lm(y~x, data=dat) )
Returns a list of model results: 返回模型结果列表:
> modelList
$one
Call:
lm(formula = y ~ x, data = dat)
Coefficients:
(Intercept) x
0.46483 0.06038
$two
Call:
lm(formula = y ~ x, data = dat)
Coefficients:
(Intercept) x
0.48379 0.00948
Which you can then operate on: 然后你可以操作:
sapply(modelList,coef)
one two
(Intercept) 0.46482610 0.483785135
x 0.06038169 0.009480099
Starting to see the power yet? 开始看电力了吗? :-)
:-)
You could do the same thing with loops, but *apply
commands save you a lot of book-keeping code. 您可以使用循环执行相同的操作,但
*apply
命令可以为您节省大量的簿记代码。
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.