亲爱的StackOverflow社区，
我有一个来自我的大学项目的数据集，我正在尝试解析并运行一些计算。它看起来类似于：
Month,1,2,3,3,4,4,5,6,7
x.1,0,0,0,0,0,0,0,0,0
x.2,0,0,0,0,0,0,0,0,0
x.3,0,0,0,6,5,5,,,15
x.4,0,0,0,7,7,,,,15
x.5,1,1,1,11,7,5,,,0
x.6,1,1,1,14,6,,,,0
x.7,1,1,1,17,5,,,,15
x.8,1,1,1,21,4,,,,15
x.9,0,0,0,1,1,1,1,1,0
x.10,0,0,0,1,1,1,1,1,0
x.11,1,0,0,1,1,1,1,1,0
x.12,0,0,0,0,0,0,0,0,1
x.13,0,0,0,0,0,0,0,0,0
x.14,0,1,0,0,0,0,0,0,0
x.20,orchid,,,orchid,rose,orchid,orchid,orchid,
x.23,0,0,0,1,1,1,1,1,1
x.24,,,,,buttercup,buttercup,buttercup,buttercup,lilac
x.25,0,0,0,1,1,0,1,1,1
x.26,,,,17,,,,,15
x.27,,,,999,,,,,15
我尝试然后像这样导入它：
data <- read.csv("~/data_munging/data.csv", header=F)
my_matrix <- as.matrix(data)
这里的问题是数据集的第一列实际上是变量的名称，as.matrix()不会将其读作行（变量）名称。
（某些数据也有漏洞，但我会留下另一个问题）。
我是R的新手，我想知道我做错了什么？
更新
根据Justin的评论，这里是如何导入数据集及其产生的str()：
> sample_data <- read.csv("~/data_munging/sample_data.csv", header=F)
> str(sample_data)
'data.frame': 28 obs. of 10 variables:
$ V1 : Factor w/ 28 levels "Month","x.1","x.10",..: 1 2 13 22 23 24 25 26 27 28 ...
$ V2 : Factor w/ 4 levels "","0","1","orchid": 3 2 2 2 2 3 3 3 3 2 ...
$ V3 : int 2 0 0 0 0 1 1 1 1 0 ...
$ V4 : int 3 0 0 0 0 1 1 1 1 0 ...
$ V5 : Factor w/ 12 levels "","0","1","11",..: 8 2 2 9 10 4 5 6 7 3 ...
$ V6 : Factor w/ 9 levels "","0","1","4",..: 4 2 2 5 7 7 6 5 4 3 ...
$ V7 : Factor w/ 7 levels "","0","1","4",..: 4 2 2 5 1 5 1 1 1 3 ...
$ V8 : Factor w/ 6 levels "","0","1","5",..: 4 2 2 1 1 1 1 1 1 3 ...
$ V9 : Factor w/ 6 levels "","0","1","6",..: 4 2 2 1 1 1 1 1 1 3 ...
$ V10: Factor w/ 6 levels "","0","1","15",..: 5 2 2 4 4 2 2 4 4 2 ...
我认为它应该是一个矩阵的原因是因为这种方式它将Month读作一个因子，它的级别是行名而不是飞蛾（一年中的一个月）。
更新2：现在使用CSV格式的原始数据集。
答案 0 :(得分：4)
矩阵和数据帧的转置方法返回矩阵。：
tdat <- t( read.table(text="Month,1,2,3,3,4,4,5,6,7
x.1,0,0,0,0,0,0,0,0,0
x.2,0,0,0,0,0,0,0,0,0
x.3,0,0,0,6,5,5,,,15
x.4,0,0,0,7,7,,,,15
x.5,1,1,1,11,7,5,,,0
x.6,1,1,1,14,6,,,,0
x.7,1,1,1,17,5,,,,15
x.8,1,1,1,21,4,,,,15
x.9,0,0,0,1,1,1,1,1,0
x.10,0,0,0,1,1,1,1,1,0
x.11,1,0,0,1,1,1,1,1,0
x.12,0,0,0,0,0,0,0,0,1
x.13,0,0,0,0,0,0,0,0,0
x.14,0,1,0,0,0,0,0,0,0
x.20,orchid,,,orchid,rose,orchid,orchid,orchid,
x.23,0,0,0,1,1,1,1,1,1
x.24,,,,,buttercup,buttercup,buttercup,buttercup,lilac
x.25,0,0,0,1,1,0,1,1,1
x.26,,,,17,,,,,15
x.27,,,,999,,,,,15", sep=",", header=FALSE, as.is=TRUE) )
# It might not be immediately obvious that the transpose function converts to matrix
newdat <- tdat[-1, ]
colnames(newdat) <- dat[1,]
newdat <- as.data.frame(newdat)
# when converted back , everything is factors. Will need to convert to get numeric
newdat[ , -grep("20|24", names(newdat) ) ] <-
lapply(newdat[ , -grep("20|24", names(newdat) )],
function(x) as.numeric( as.character(x) ))
# Need to use grep to convert character-names to numeric so can use negative indexing
# and used the redundant `as.numeric(as.character(x))` to illustrate good practice.
导致：
> newdat
Month x.1 x.2 x.3 x.4 x.5 x.6 x.7 x.8 x.9 x.10 x.11 x.12 x.13 x.14 x.20 x.23 x.24 x.25 x.26 x.27
V2 3 2 2 3 3 4 4 3 3 2 2 3 2 2 3 orchid 2 2 1 1
V3 1 1 1 2 2 2 2 2 2 1 1 1 1 1 2 <NA> 1 <NA> 1 NA NA
V4 2 1 1 2 2 2 2 2 2 1 1 1 1 1 1 <NA> 1 <NA> 1 NA NA
V5 4 2 2 6 5 5 5 5 5 3 3 3 2 2 3 orchid 3 3 3 3
V6 5 2 2 5 5 7 6 6 6 3 3 3 2 2 3 rose 3 buttercup 3 1 1
V7 5 2 2 5 1 6 1 1 1 3 3 3 2 2 3 orchid 3 buttercup 2 1 1
V8 6 2 2 1 1 1 1 1 1 3 3 3 2 2 3 orchid 3 buttercup 3 1 1
V9 7 2 2 1 1 1 1 1 1 3 3 3 2 2 3 orchid 3 buttercup 3 1 1
V10 8 2 2 4 4 3 3 4 4 2 2 2 3 2 3 3 lilac 3 2 2
我注意到有一个999值可能是一个缺失值指示器，以及两个不同的值因素列中缺失。这是read.table如何输入列的副作用。它“认为”V3和V4列是数字并且处理顺序逗号作为真正的缺失，而所有其他列（在转置之前）被视为因子或字符变量并且连续逗号变成“”不同作为_NA_character或因子的NA。