使用循环提取一系列整数

本文介绍了使用循环提取一系列整数的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我有一些数据要提取整数出现的频率.这是一些示例数据:

I have some data where I want to extract the frequency with which the integers appear. Here is some sample data:

df <- read.table(header=T, text="A B C D 1 1 5 3 1 2 1 2 3 2 3 2 3 5 3 4 1 4 5 3 5 3 1 4 2 6 5 2 5 1 ") df

我可以遍历这些并获得如下计数:

I can loop through these and get the counts as follows:

for (i in 1:5){ print(colSums(df==i)) }

但是每次我尝试存储输出时，都会出现错误.将结果输出存储在数据框中的最巧妙的方法是什么?我想我对循环存储数据的方式感到困惑.谢谢你的帮助.

But every time I try to store the output I get an error. What is the neatest way to store the resultant output in a dataframe? I think I'm getting confused about the way to store data that's run through a loop. Thanks for your help.

推荐答案

我们可以使用mtabulate

library(qdapTools) t(mtabulate(df)) # A B C D #1 3 1 0 2 #2 1 2 0 2 #3 1 1 2 2 #4 0 1 1 0 #5 1 1 3 0

在base R中，我们还可以unlist数据集，复制列名，并使用table(不使用任何循环，显式(for)或隐式(lapply)).

In base R, we can also unlist the dataset, replicate the column names, and use table (not using any loop, explicit (for) or implicit (lapply).

table(unlist(df),names(df)[col(df)]) # A B C D # 1 3 1 0 2 # 2 1 2 0 2 # 3 1 1 2 2 # 4 0 1 1 0 # 5 1 1 3 0

或者就像@nicola提到的那样，我们可以使用rep(应该更快)代替col(df)

Or as @nicola mentioned, the instead of col(df), we can use rep (should be faster)

table(unlist(df), rep(names(df),each=nrow(df)))