R 각종통계3: 워드클라우드
0. csv 파일 준비
x<-read.table(“c:/image/sales.csv”,sep=”,”,header=TRUE)
sales.csv
|
no,id,sex,age,goods_name,account,sale_price,total_price,sale_day,sale_week,sale_wh_day |
1. R 소스
library(wordcloud)
item.x <-sort(x$goods_name, decreasing = TRUE)
item.df <-x[x$goods_name%in% names(item.x[1:450]), ]
item.table <-table(x$goods_name)
word.name <-names(item.table)
item.table <-as.matrix(item.table)
freqs <-rowSums(item.table)
col <-brewer.pal(8, ‘Dark2’)
wordcloud(x$goods_name, min.freq = 1, scale = c(5, 0.2), col = col , random.order = FALSE)
2. 자바 소스
//구매횟수 상위 450개의 세분류 상품추출
rc.voidEval(“library(wordcloud)”);
rc.voidEval(“item.x <-sort(x$goods_name, decreasing = TRUE)”);
rc.voidEval(“item.df <-x[x$goods_name%in% names(item.x[1:450]), ]”);
//woldcloud 수행
rc.voidEval(“item.table <-table(x$goods_name)”);
//세분류명지정
rc.voidEval(“word.name <-names(item.table)”);
//빈도수지정
rc.voidEval(“item.table <-as.matrix(item.table)”);
rc.voidEval(“freqs <-rowSums(item.table)”);
//색지정
rc.voidEval(“col <-brewer.pal(8, ‘Dark2’)”);
//wordcloud 함수실행
rc.voidEval(“wordcloud(words = word.name, freq = freqs, min.freq = 1, scale = c(5, 0.2), col = col , random.order = FALSE)”);
3. 짧은 버젼
3-1. R 소스
library(wordcloud)
wordcloud(x$goods_name, min.freq = 1, scale = c(5, 0.2), col = rainbow(100) , random.order = FALSE)
3-2. 자바 소스
rc.voidEval(“library(wordcloud)”);
rc.voidEval(“wordcloud(x$goods_name, min.freq = 1, scale = c(5, 0.2), col = rainbow(100) , random.order = FALSE)”);