在R中為多因子列創建頻率表

Question

我是R的新手。我正在編寫一本關於我工作的常用功能/特性的語法的單獨手冊。 我的示例數據框如下：

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

我的原始數據框有21列。

如果我想找到平均值（將其視為序數變量）：

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

我想為我的數據幀中的所有變量制作頻率表。 我搜索了互聯網和許多論壇，並看到最近的命令是使用sapply。 但是當我這樣做時，它給了所有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

問題如何根據上表對數據幀中的所有列（即因子）制作一個頻率表來制作頻率表？

PS很抱歉，如果這似乎是瑣事，但我搜索了2天沒有答案，並嘗試所有可能的組合。 也許我沒有足夠的搜索=（

非常感謝。

Answer 1

你快到了。 只需對你的功能進行一次小改動就能讓你在那里。 在x的function(x) ...需要通過傳遞給table()調用：

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

稍微重復一下代碼可能會讓它更易於閱讀：

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

來得有點晚了，但這是一個reshape2可能解決方案。 recast可能非常簡單，但是我們需要在這里處理空因子水平，所以我們需要指定兩個factorsAsStrings = FALSE在melt factorsAsStrings = FALSE和dcast drop = FALSE ，而recast不能將參數傳遞給melt （僅限於dcast ），所以這里

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

如果我們不關心空白水平，那么快速解決方案就是這樣

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，如果速度是一個問題，我們可以使用data.atble做同樣的data.atble

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

為什么不呢：

> 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

我們稱之為'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