将数据帧正确转换为 R 中规则的事务
correct converting dataframe into transactions for arules in R
我必须在 R 中执行关联规则，我找到了示例
这里
http://www.salemmarafi.com/code/market-basket-analysis-with-r/
在这个例子中，他们使用 data(Groceries)
但他们给出了原始数据集 Groceries.csv
structure(list(chocolate = structure(c(9L, 13L, 1L, 8L, 16L,
2L, 14L, 11L, 7L, 15L, 17L, 5L, 10L, 4L, 3L, 6L, 2L, 18L, 12L
), .Label = c("bottled water", "canned beer", "chicken,citrus fruit,tropical fruit,root vegetables,whole milk,frozen fish,rollsbuns",
"chicken,pip fruit,other vegetables,whole milk,dessert,yogurt,whippedsour cream,rollsbuns,pasta,soda,waffles",
"citrus fruit,pip fruit,root vegetables,other vegetables,whole milk,cream cheese ,domestic eggs,brown bread,margarine,baking powder,waffles",
"frankfurter,citrus fruit,onions,other vegetables,whole milk,rollsbuns,sugar,soda",
"frankfurter,rollsbuns,bottled water,fruitvegetable juice,hygiene articles",
"frankfurter,sausage,butter,whippedsour cream,rollsbuns,margarine,spices",
"fruitvegetable juice", "hamburger meat,other vegetables,whole milk,curd,yogurt,rollsbuns,pastry,semi-finished bread,margarine,bottled water,fruitvegetable juice",
"meat,citrus fruit,berries,root vegetables,whole milk,soda",
"packaged fruitvegetables,whole milk,curd,yogurt,domestic eggs,brown bread,mustard,pickled vegetables,bottled water,misc. beverages",
"pickled vegetables,coffee", "root vegetables", "tropical fruit,margarine,rum",
"tropical fruit,pip fruit,onions,other vegetables,whole milk,domestic eggs,sugar,soups,tea,soda,hygiene articles,napkins",
"tropical fruit,root vegetables,herbs,whole milk,butter milk,whippedsour cream,flour,hygiene articles",
"turkey,pip fruit,salad dressing,pastry"), class = "factor")), .Names = "chocolate", class = "data.frame", row.names = c(NA,
-19L))
我加载这个数据
g=read.csv("g.csv",sep=";")
所以我必须将其转换为像 arule 要求的交易
#'@importClassesFrom arules transactions
trans = as(g, "transactions")
让我们检查数据（杂货）
> str(Groceries)
Formal class 'transactions' [package "arules"] with 3 slots
..@ data :Formal class 'ngCMatrix' [package "Matrix"] with 5 slots
.. .. ..@ i : int [1:43367] 13 60 69 78 14 29 98 24 15 29 ...
.. .. ..@ p : int [1:9836] 0 4 7 8 12 16 21 22 27 28 ...
.. .. ..@ Dim : int [1:2] 169 9835
.. .. ..@ Dimnames:List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. .. ..@ factors : list()
..@ itemInfo :'data.frame': 169 obs. of 3 variables:
.. ..$ labels: chr [1:169] "frankfurter" "sausage" "liver loaf" "ham" ...
.. ..$ level2: Factor w/ 55 levels "baby food","bags",..: 44 44 44 44 44 44 44 42 42 41 ...
.. ..$ level1: Factor w/ 10 levels "canned food",..: 6 6 6 6 6 6 6 6 6 6 ...
..@ itemsetInfo:'data.frame': 0 obs. of 0 variables
>
以及我从原始 csv 转换而来的数据
> str(trans)
Formal class 'transactions' [package "arules"] with 3 slots
..@ data :Formal class 'ngCMatrix' [package "Matrix"] with 5 slots
.. .. ..@ i : int [1:9835] 1265 6162 6377 4043 3585 6475 4431 3535 4401 6490 ...
.. .. ..@ p : int [1:9836] 0 1 2 3 4 5 6 7 8 9 ...
.. .. ..@ Dim : int [1:2] 7011 9835
.. .. ..@ Dimnames:List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. .. ..@ factors : list()
..@ itemInfo :'data.frame': 7011 obs. of 3 variables:
.. ..$ labels : chr [1:7011] "tr=abrasive cleaner" "tr=abrasive cleaner,napkins" "tr=artif. sweetener" "tr=artif. sweetener,coffee" ...
.. ..$ variables: Factor w/ 1 level "tr": 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ levels : Factor w/ 7011 levels "abrasive cleaner",..: 1 2 3 4 5 6 7 8 9 10 ...
..@ itemsetInfo:'data.frame': 9835 obs. of 1 variable:
.. ..$ transactionID: chr [1:9835] "1" "2" "3" "4" ...
>
我们在数据（杂货）中看到
transactions in sparse format with
9835 transactions (rows) and
169 items (columns)
在我的反式数据中
9835 transactions (rows) and
7011 items (columns)
即我从 Groceries.csv 获得了 7011 列，同时在嵌入式示例中（169 列）
为什么会这样？这个文件如何正确转换。
我必须理解它，因为我无法使用我的文件
我试过找到类似的主题
但这两个帖子对我没有帮助
这是因为数据在下载时以逗号分隔，而在 g=read.csv("g.csv",sep=";") 中，您是在 semi-colon 上拆分数据。如果从 g.
的定义中删除 sep = ";"，您应该会得到所需的输出
见下文，其中将 sep 定义为 ;:
> trans <- read.transactions("~/Downloads/groceries.csv", format = 'basket', sep = ';')
> str(trans)
Formal class 'transactions' [package "arules"] with 3 slots
..@ data :Formal class 'ngCMatrix' [package "Matrix"] with 5 slots
.. .. ..@ i : int [1:9835] 1265 6162 6377 4043 3585 6475 4431 3535 4401 6490 ...
.. .. ..@ p : int [1:9836] 0 1 2 3 4 5 6 7 8 9 ...
.. .. ..@ Dim : int [1:2] 7011 9835
.. .. ..@ Dimnames:List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. .. ..@ factors : list()
..@ itemInfo :'data.frame': 7011 obs. of 1 variable:
.. ..$ labels: chr [1:7011] "abrasive cleaner" "abrasive cleaner,napkins" "artif. sweetener" "artif. sweetener,coffee" ...
..@ itemsetInfo:'data.frame': 0 obs. of 0 variables
还有这个，它把 sep 定义为 ,:
> trans <- read.transactions("~/Downloads/groceries.csv", format = 'basket', sep = ',')
> str(trans)
Formal class 'transactions' [package "arules"] with 3 slots
..@ data :Formal class 'ngCMatrix' [package "Matrix"] with 5 slots
.. .. ..@ i : int [1:43367] 29 88 118 132 33 157 167 166 38 91 ...
.. .. ..@ p : int [1:9836] 0 4 7 8 12 16 21 22 27 28 ...
.. .. ..@ Dim : int [1:2] 169 9835
.. .. ..@ Dimnames:List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. .. ..@ factors : list()
..@ itemInfo :'data.frame': 169 obs. of 1 variable:
.. ..$ labels: chr [1:169] "abrasive cleaner" "artif. sweetener" "baby cosmetics" "baby food" ...
..@ itemsetInfo:'data.frame': 0 obs. of 0 variables
我必须在 R 中执行关联规则，我找到了示例
这里
http://www.salemmarafi.com/code/market-basket-analysis-with-r/
在这个例子中，他们使用 data(Groceries)
但他们给出了原始数据集 Groceries.csv
structure(list(chocolate = structure(c(9L, 13L, 1L, 8L, 16L,
2L, 14L, 11L, 7L, 15L, 17L, 5L, 10L, 4L, 3L, 6L, 2L, 18L, 12L
), .Label = c("bottled water", "canned beer", "chicken,citrus fruit,tropical fruit,root vegetables,whole milk,frozen fish,rollsbuns",
"chicken,pip fruit,other vegetables,whole milk,dessert,yogurt,whippedsour cream,rollsbuns,pasta,soda,waffles",
"citrus fruit,pip fruit,root vegetables,other vegetables,whole milk,cream cheese ,domestic eggs,brown bread,margarine,baking powder,waffles",
"frankfurter,citrus fruit,onions,other vegetables,whole milk,rollsbuns,sugar,soda",
"frankfurter,rollsbuns,bottled water,fruitvegetable juice,hygiene articles",
"frankfurter,sausage,butter,whippedsour cream,rollsbuns,margarine,spices",
"fruitvegetable juice", "hamburger meat,other vegetables,whole milk,curd,yogurt,rollsbuns,pastry,semi-finished bread,margarine,bottled water,fruitvegetable juice",
"meat,citrus fruit,berries,root vegetables,whole milk,soda",
"packaged fruitvegetables,whole milk,curd,yogurt,domestic eggs,brown bread,mustard,pickled vegetables,bottled water,misc. beverages",
"pickled vegetables,coffee", "root vegetables", "tropical fruit,margarine,rum",
"tropical fruit,pip fruit,onions,other vegetables,whole milk,domestic eggs,sugar,soups,tea,soda,hygiene articles,napkins",
"tropical fruit,root vegetables,herbs,whole milk,butter milk,whippedsour cream,flour,hygiene articles",
"turkey,pip fruit,salad dressing,pastry"), class = "factor")), .Names = "chocolate", class = "data.frame", row.names = c(NA,
-19L))
我加载这个数据
g=read.csv("g.csv",sep=";")
所以我必须将其转换为像 arule 要求的交易
#'@importClassesFrom arules transactions
trans = as(g, "transactions")
让我们检查数据（杂货）
> str(Groceries)
Formal class 'transactions' [package "arules"] with 3 slots
..@ data :Formal class 'ngCMatrix' [package "Matrix"] with 5 slots
.. .. ..@ i : int [1:43367] 13 60 69 78 14 29 98 24 15 29 ...
.. .. ..@ p : int [1:9836] 0 4 7 8 12 16 21 22 27 28 ...
.. .. ..@ Dim : int [1:2] 169 9835
.. .. ..@ Dimnames:List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. .. ..@ factors : list()
..@ itemInfo :'data.frame': 169 obs. of 3 variables:
.. ..$ labels: chr [1:169] "frankfurter" "sausage" "liver loaf" "ham" ...
.. ..$ level2: Factor w/ 55 levels "baby food","bags",..: 44 44 44 44 44 44 44 42 42 41 ...
.. ..$ level1: Factor w/ 10 levels "canned food",..: 6 6 6 6 6 6 6 6 6 6 ...
..@ itemsetInfo:'data.frame': 0 obs. of 0 variables
>
以及我从原始 csv 转换而来的数据
> str(trans)
Formal class 'transactions' [package "arules"] with 3 slots
..@ data :Formal class 'ngCMatrix' [package "Matrix"] with 5 slots
.. .. ..@ i : int [1:9835] 1265 6162 6377 4043 3585 6475 4431 3535 4401 6490 ...
.. .. ..@ p : int [1:9836] 0 1 2 3 4 5 6 7 8 9 ...
.. .. ..@ Dim : int [1:2] 7011 9835
.. .. ..@ Dimnames:List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. .. ..@ factors : list()
..@ itemInfo :'data.frame': 7011 obs. of 3 variables:
.. ..$ labels : chr [1:7011] "tr=abrasive cleaner" "tr=abrasive cleaner,napkins" "tr=artif. sweetener" "tr=artif. sweetener,coffee" ...
.. ..$ variables: Factor w/ 1 level "tr": 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ levels : Factor w/ 7011 levels "abrasive cleaner",..: 1 2 3 4 5 6 7 8 9 10 ...
..@ itemsetInfo:'data.frame': 9835 obs. of 1 variable:
.. ..$ transactionID: chr [1:9835] "1" "2" "3" "4" ...
>
我们在数据（杂货）中看到
transactions in sparse format with
9835 transactions (rows) and
169 items (columns)
在我的反式数据中
9835 transactions (rows) and
7011 items (columns)
即我从 Groceries.csv 获得了 7011 列，同时在嵌入式示例中（169 列）
为什么会这样？这个文件如何正确转换。 我必须理解它，因为我无法使用我的文件
我试过找到类似的主题
但这两个帖子对我没有帮助
这是因为数据在下载时以逗号分隔，而在 g=read.csv("g.csv",sep=";") 中，您是在 semi-colon 上拆分数据。如果从 g.
sep = ";"，您应该会得到所需的输出
见下文，其中将 sep 定义为 ;:
> trans <- read.transactions("~/Downloads/groceries.csv", format = 'basket', sep = ';')
> str(trans)
Formal class 'transactions' [package "arules"] with 3 slots
..@ data :Formal class 'ngCMatrix' [package "Matrix"] with 5 slots
.. .. ..@ i : int [1:9835] 1265 6162 6377 4043 3585 6475 4431 3535 4401 6490 ...
.. .. ..@ p : int [1:9836] 0 1 2 3 4 5 6 7 8 9 ...
.. .. ..@ Dim : int [1:2] 7011 9835
.. .. ..@ Dimnames:List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. .. ..@ factors : list()
..@ itemInfo :'data.frame': 7011 obs. of 1 variable:
.. ..$ labels: chr [1:7011] "abrasive cleaner" "abrasive cleaner,napkins" "artif. sweetener" "artif. sweetener,coffee" ...
..@ itemsetInfo:'data.frame': 0 obs. of 0 variables
还有这个，它把 sep 定义为 ,:
> trans <- read.transactions("~/Downloads/groceries.csv", format = 'basket', sep = ',')
> str(trans)
Formal class 'transactions' [package "arules"] with 3 slots
..@ data :Formal class 'ngCMatrix' [package "Matrix"] with 5 slots
.. .. ..@ i : int [1:43367] 29 88 118 132 33 157 167 166 38 91 ...
.. .. ..@ p : int [1:9836] 0 4 7 8 12 16 21 22 27 28 ...
.. .. ..@ Dim : int [1:2] 169 9835
.. .. ..@ Dimnames:List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. .. ..@ factors : list()
..@ itemInfo :'data.frame': 169 obs. of 1 variable:
.. ..$ labels: chr [1:169] "abrasive cleaner" "artif. sweetener" "baby cosmetics" "baby food" ...
..@ itemsetInfo:'data.frame': 0 obs. of 0 variables