导入和分析R中的非矩形.csv文件
我从Mathematica转到R,在输入过程中我不需要预测数据结构,特别是在导入之前我不需要预测数据的矩形.
我有很多文件.csv文件格式如下:
tasty,chicken,cinnamon
not_tasty,butter,pepper,onion,cardamom,cayenne
tasty,olive_oil,pepper
okay,olive_oil,onion,potato,black_pepper
not_tasty,tomato,fenugreek,pepper,onion,potato
tasty,butter,cheese,wheat,ham
行具有不同的长度,仅包含字符串.
在R中,我该如何处理这个问题？
你试过什么？
我尝试过read.table:
dataImport <- read.table("data.csv", header = FALSE)
class(dataImport)
##[1] "data.frame"
dim(dataImport)
##[1] 6 1
dataImport[1]
##[1] tasty,chicken,cinnamon
##6 Levels: ...
我从文档中将其解释为一个单独的列,每个成分列表作为一个独特的行.我可以按如下方式提取前三行,每行都class factor包含但看起来包含的数据多于我的预期:
dataImport[c(1,2,3),1]
## my rows
rowOne <- dataImport[c(1),1];
class(rowOne)
## "factor"
rowOne
## [1] tasty,chicken,cinnamon
## 6 Levels: not_tasty,butter,cheese [...]
这就是我现在所追求的这个问题,我希望read.table对这个数据结构的适用性提出建议.
我的目标是按每行的第一个元素对数据进行分组,并分析每种类型的配方之间的差异.如果它有助于影响数据结构建议,在Mathematica中我会做以下事情:
dataImport=Import["data.csv"];
tasty = Cases[dataImport, {"tasty", ingr__} :> {ingr}]
回答讨论
@ G.Grothendieck提供了使用read.table和后续处理的解决方案reshape2- 这看起来非常有用,我稍后会进行调查.这里的一般建议解决了我的问题,因此接受.
@ MrFlick建议使用该tm软件包对以后的分析有用DataframeSource
函数read.table尝试read.table用fill=TRUE:
d1 <- read.table("data.csv", sep = ",", as.is = TRUE, fill = TRUE)
赠送:
> d1
V1 V2 V3 V4 V5 V6
1 tasty chicken cinnamon
2 not_tasty butter pepper onion cardamom cayenne
3 tasty olive_oil pepper
4 okay olive_oil onion potato black_pepper
5 not_tasty tomato fenugreek pepper onion potato
6 tasty butter cheese wheat ham
与NA一起阅读
或者用NA值填充空单元格na.strings = "":
d2 <- read.table("data.csv", sep = ",", as.is = TRUE, fill = TRUE, na.strings = "")
赠送:
> d2
V1 V2 V3 V4 V5 V6
1 tasty chicken cinnamon <NA> <NA> <NA>
2 not_tasty butter pepper onion cardamom cayenne
3 tasty olive_oil pepper <NA> <NA> <NA>
4 okay olive_oil onion potato black_pepper <NA>
5 not_tasty tomato fenugreek pepper onion potato
6 tasty butter cheese wheat ham <NA>
长表
如果你想要长形式:
library(reshape2)
long <- na.omit(melt(d2, id.var = c("id", "V1"))[-3])
long <- long[order(long$id), ]
赠送:
> long
id V1 value
1 1 tasty chicken
7 1 tasty cinnamon
2 2 not_tasty butter
8 2 not_tasty pepper
14 2 not_tasty onion
20 2 not_tasty cardamom
26 2 not_tasty cayenne
3 3 tasty olive_oil
9 3 tasty pepper
4 4 okay olive_oil
10 4 okay onion
16 4 okay potato
22 4 okay black_pepper
5 5 not_tasty tomato
11 5 not_tasty fenugreek
17 5 not_tasty pepper
23 5 not_tasty onion
29 5 not_tasty potato
6 6 tasty butter
12 6 tasty cheese
18 6 tasty wheat
24 6 tasty ham
宽格0/1二进制变量
要将变量部分表示为0/1二进制变量,请尝试以下操作:
wide <- cast(id + V1 ~ value, data = long)
wide[-(1:2)] <- 0 + !is.na(wide[-(1:2)])
给这个:
在数据框中列出
不同的表示形式是数据框中的以下列表,因此它ag$value是字符向量列表:
ag <- aggregate(value ~., transform(long, value = as.character(value)), c)
ag <- ag[order(ag$id), ]
giving:
> ag
id V1 value
4 1 tasty chicken, cinnamon
1 2 not_tasty butter, pepper, onion, cardamom, cayenne
5 3 tasty olive_oil, pepper
3 4 okay olive_oil, onion, potato, black_pepper
2 5 not_tasty tomato, fenugreek, pepper, onion, potato
6 6 tasty butter, cheese, wheat, ham
> str(ag)
'data.frame': 6 obs. of 3 variables:
$ id : int 1 2 3 4 5 6
$ V1 : chr "tasty" "not_tasty" "tasty" "okay" ...
$ value:List of 6
..$ 15: chr "chicken" "cinnamon"
..$ 1 : chr "butter" "pepper" "onion" "cardamom" ...
..$ 17: chr "olive_oil" "pepper"
..$ 11: chr "olive_oil" "onion" "potato" "black_pepper"
..$ 6 : chr "tomato" "fenugreek" "pepper" "onion" ...
..$ 19: chr "butter" "cheese" "wheat" "ham"