R中具有多个变量的频率表 [英] frequency table with several variables in R

问题描述

我正在尝试复制一个经常用于官方统计的表格，但到目前为止没有成功。给定这样的数据帧：

I am trying to replicate a table often used in official statistics but no success so far. Given a dataframe like this one:

d1 <- data.frame( StudentID = c("x1", "x10", "x2", 
                          "x3", "x4", "x5", "x6", "x7", "x8", "x9"),
             StudentGender = c('F', 'M', 'F', 'M', 'F', 'M', 'F', 'M', 'M', 'M'),
             ExamenYear    = c('2007','2007','2007','2008','2008','2008','2008','2009','2009','2009'),
             Exam          = c('algebra', 'stats', 'bio', 'algebra', 'algebra', 'stats', 'stats', 'algebra', 'bio', 'bio'),
             participated  = c('no','yes','yes','yes','no','yes','yes','yes','yes','yes'),  
             passed      = c('no','yes','yes','yes','no','yes','yes','yes','no','yes'),
             stringsAsFactors = FALSE)

我想创建一个表格，显示每年，所有学生（全部）和女性人数，参与人数以及谁过去了请注意，下面的其中指所有学生。

I would like to create a table showing PER YEAR , the number of all students (all) and those who are female, those who participated and those who passed. Please note "ofwhich" below refers to all students.

我想到的一张表格如下：

A table I have in mind would look like that:

cbind(All = table(d1$ExamenYear),
  participated      = table(d1$ExamenYear, d1$participated)[,2],
  ofwhichFemale     = table(d1$ExamenYear, d1$StudentGender)[,1],
  ofwhichpassed     = table(d1$ExamenYear, d1$passed)[,2])

我确信R中有一种更好的方法可以解决这种问题。

I am sure there is a better way to this kind of thing in R.

注意：我已经看过LaTex解决方案，但是我不使用它会起作用对我来说，因为我需要在Excel中导出表。

Note: I have seen LaTex solutions, but I am not use this will work for me as I need to export the table in Excel .

预先感谢

推荐答案

使用 plyr ：

require(plyr)
ddply(d1, .(ExamenYear), summarize,
      All=length(ExamenYear),
      participated=sum(participated=="yes"),
      ofwhichFemale=sum(StudentGender=="F"),
      ofWhichPassed=sum(passed=="yes"))

其中给出：

  ExamenYear All participated ofwhichFemale ofWhichPassed
1       2007   3            2             2             2
2       2008   4            3             2             3
3       2009   3            3             0             2

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