来自大型文档集的R术语频率
我有一个这样的数据框来自大型文档集的R术语频率,r,sorting,text-mining,R,Sorting,Text Mining,我有一个这样的数据框 ID content 1 hello you how are you 1 you are ok 2 test 我需要通过id获得内容中每个单词的频率，这些单词是空格分隔的。这基本上是在列中查找唯一的术语，并查找按Id分组的频率和显示 ID hello you how are ok test 1 1 3 1 2 1 0 2
ID content
1 hello you how are you
1 you are ok
2 test
ID hello you how are ok test
1 1 3 1 2 1 0
2 0 0 0 0 0 1
test<- unique(unlist(strsplit(temp$val, split=" ")))
df<- cbind(temp, sapply(test, function(y) apply(temp, 1, function(x) as.integer(y %in% unlist(strsplit(x, split=" "))))))
test您可以使用data.table
library(data.table)
setDT(df1)[, unlist(strsplit(content, split = " ")), by = ID
][, dcast(.SD, ID ~ V1)]
# ID are hello how ok test you
#1: 1 2 1 1 1 0 3
#2: 2 0 0 0 0 1 0
在第一部分中，我们按ID
的组使用unlist（strsplit（content，split=”“）
，它给出了以下输出：
# ID V1
#1: 1 hello
#2: 1 you
#3: 1 how
#4: 1 are
#5: 1 you
#6: 1 you
#7: 1 are
#8: 1 ok
#9: 2 test
在下一步中，我们使用dcast
将数据扩展为宽格式
数据
df1 <- structure(list(ID = c(1L, 1L, 2L), content = c("hello you how are you",
"you are ok", "test")), .Names = c("ID", "content"), class = "data.frame", row.names = c(NA,
-3L))
df1您可以使用data.table
library(data.table)
setDT(df1)[, unlist(strsplit(content, split = " ")), by = ID
][, dcast(.SD, ID ~ V1)]
# ID are hello how ok test you
#1: 1 2 1 1 1 0 3
#2: 2 0 0 0 0 1 0
在第一部分中，我们按ID
的组使用unlist（strsplit（content，split=”“）
，它给出了以下输出：
# ID V1
#1: 1 hello
#2: 1 you
#3: 1 how
#4: 1 are
#5: 1 you
#6: 1 you
#7: 1 are
#8: 1 ok
#9: 2 test
在下一步中，我们使用dcast
将数据扩展为宽格式
数据
df1 <- structure(list(ID = c(1L, 1L, 2L), content = c("hello you how are you",
"you are ok", "test")), .Names = c("ID", "content"), class = "data.frame", row.names = c(NA,
-3L))
df1一个用于文本挖掘的包怎么样
# your data
text <- read.table(text = "
ID content
1 'hello you how are you'
1 'you are ok'
2 'test'", header = T, stringsAsFactors = FALSE) # remember the stringAsFactors life saver!
#您的数据
text为文本挖掘制作的包怎么样
# your data
text <- read.table(text = "
ID content
1 'hello you how are you'
1 'you are ok'
2 'test'", header = T, stringsAsFactors = FALSE) # remember the stringAsFactors life saver!
#您的数据
文本