为 R 中的多个因子列创建频率表

Question

我是 R 的新手。我正在为我的工作编写一份关于常用函数/特性的语法的单独手册。我的示例数据框如下：

x.sample <-
structure(list(Q9_A = structure(c(5L, 3L, 5L, 3L, 5L, 3L, 1L, 
5L, 5L, 5L), .Label = c("Impt", "Neutral", "Not Impt at all", 
"Somewhat Impt", "Very Impt"), class = "factor"), Q9_B = structure(c(5L, 
5L, 5L, 3L, 5L, 5L, 3L, 5L, 3L, 3L), .Label = c("Impt", "Neutral", 
"Not Impt at all", "Somewhat Impt", "Very Impt"), class = "factor"), 
Q9_C = structure(c(3L, 5L, 5L, 3L, 5L, 5L, 3L, 5L, 5L, 3L
), .Label = c("Impt", "Neutral", "Not Impt at all", "Somewhat Impt", 
"Very Impt"), class = "factor")), .Names = c("Q9_A", "Q9_B", 
"Q9_C"), row.names = c(NA, 10L), class = "data.frame")

> x.sample
          Q9_A            Q9_B            Q9_C
1        Very Impt       Very Impt Not Impt at all
2  Not Impt at all       Very Impt       Very Impt
3        Very Impt       Very Impt       Very Impt
4  Not Impt at all Not Impt at all Not Impt at all
5        Very Impt       Very Impt       Very Impt
6  Not Impt at all       Very Impt       Very Impt
7             Impt Not Impt at all Not Impt at all
8        Very Impt       Very Impt       Very Impt
9        Very Impt Not Impt at all       Very Impt
10       Very Impt Not Impt at all Not Impt at all

我的原始数据框有 21 列。

如果我想求均值（将其视为序数变量）：

> sapply(x.sample,function(x) mean(as.numeric(x), na.rm=TRUE))
Q9_A Q9_B Q9_C 
 4.0  4.2  4.2

我想为我的数据框中的所有变量制作一个频率表。我搜索了互联网和许多论坛，发现最近的命令是使用 sapply。但是当我这样做的时候，它全是 0。

> sapply(x.sample,function(x) table(factor(x.sample, levels=c("Not Impt at all", "Somewhat Impt",            "Neutral", "Impt", "Very Impt"), ordered=TRUE)))
                Q9_A Q9_B Q9_C
Not Impt at all    0    0    0
Somewhat Impt      0    0    0
Neutral            0    0    0
Impt               0    0    0
Very Impt          0    0    0

问题 如何使用 sapply 根据上表为数据框中的所有列（即因子）制作频率图表？

PS 很抱歉，如果这看起来很琐碎，但我已经搜索了 2 天没有答案并尝试了所有可能的组合。可能是我搜索的不够仔细=(

非常感谢。

Answer 1

你快到了。只需对您的功能进行一点小改动，您就可以实现这一目标。 function(x) ... 中的 x 需要传递给 table() 调用：

levs <- c("Not Impt at all", "Somewhat Impt", "Neutral", "Impt", "Very Impt")
sapply(x.sample, function(x) table(factor(x, levels=levs, ordered=TRUE)))

对代码稍加修改可能也会使其更易于阅读：

sapply(lapply(x.sample,factor,levels=levs,ordered=TRUE), table)

#                Q9_A Q9_B Q9_C
#Not Impt at all    3    4    4
#Somewhat Impt      0    0    0
#Neutral            0    0    0
#Impt               1    0    0
#Very Impt          6    6    6

Answer 2

来晚了，但这里有一个reshape2 可能的解决方案。使用recast 可能非常简单，但我们需要在这里处理空因子级别，因此我们需要在melt 和dcast 中指定factorsAsStrings = FALSE 和dcast，而recast 不能传递参数到melt（只到dcast），就这样

library(reshape2)
x.sample$indx <- 1 
dcast(melt(x.sample, "indx", factorsAsStrings = FALSE), value ~ variable, drop = FALSE)
#             value Q9_A Q9_B Q9_C
# 1            Impt    1    0    0
# 2         Neutral    0    0    0
# 3 Not Impt at all    3    4    4
# 4   Somewhat Impt    0    0    0
# 5       Very Impt    6    6    6

如果我们不关心空关卡，那么一个快速的解决方案就是

recast(x.sample, value ~ variable, id.var = "indx")
#             value Q9_A Q9_B Q9_C
# 1            Impt    1    0    0
# 2 Not Impt at all    3    4    4
# 3       Very Impt    6    6    6

---

或者，如果速度是一个问题，我们可以使用data.atble 来做同样的事情

library(data.table)
dcast(melt(setDT(x.sample), measure.vars = names(x.sample), value.factor = TRUE), 
           value ~ variable, drop = FALSE)
#              value Q9_A Q9_B Q9_C
# 1:            Impt    1    0    0
# 2:         Neutral    0    0    0
# 3: Not Impt at all    3    4    4
# 4:   Somewhat Impt    0    0    0
# 5:       Very Impt    6    6    6

Answer 3

为什么不只是：

> sapply(x.sample, table)
                Q9_A Q9_B Q9_C
Impt               1    0    0
Neutral            0    0    0
Not Impt at all    3    4    4
Somewhat Impt      0    0    0
Very Impt          6    6    6

我们称它为“tbl”；

tbl[ order(match(rownames(tbl), c("Not Impt at all", "Somewhat Impt", 
                                  "Neutral", "Impt", "Very Impt")) )   , ]
                Q9_A Q9_B Q9_C
Not Impt at all    3    4    4
Somewhat Impt      0    0    0
Neutral            0    0    0
Impt               1    0    0
Very Impt          6    6    6