First, you should load recharts:

library(recharts)

1 Introduction

WordCloud has only 1 type: wordCloud

The keys are:

character x represents the words
numeric y represents the frequency of the words
series is not linked with legend, but linked with colors

2 Function Call

echartr(data, x, y, <t>, <type>)

Arg	Requirement
data	source data in the form of data.frame
x	character independent variable. Only the first one is accepted if multiple variables are provided.
y	numeric dependent variable. Only the first one is accepted.
series	data series variable which will be coerced to factors. Only the first one is accepted if multiple variables are provided.
t	timeline variable which will be coerced to factors. Only the first one is accepted if multiple variables are provided.
type	‘wordCloud’

3 Showcase

3.1 Data Preparation

Fetch the Baidu buzz hotword web page and parse it into a data.frame Keyword and Freq.

For this purpose, we composed a function getBaiduHot to parse the Baidu Hot Word Trend web page.

getBaiduHot <- function(url, top=30, HTMLencoding=NULL){
    baiduhot <- paste0(readLines(url), collapse="")
    charset <- gsub('^.+charset=([[:alnum:]-]+?)[^[:alnum:]-].+$', "\\1", 
                    baiduhot)
    if (is.null(HTMLencoding)) if (!is.null(charset)) HTMLencoding <- charset
    baiduhot <- stringr::str_conv(baiduhot, HTMLencoding)
    hotword <- gsub(".+?<a class=\"list-title\"[^>]+?>([^<>]+?)</a>.+?<span class=\"icon-(rise|fair|fall)\">(\\d+?)</span>.+?","\\1\t\\3\t\\2\t", baiduhot)
    hotword <- enc2native(gsub("^(.+?)\t{4,}.+$","\\1", hotword))
    hotword <- t(matrix(unlist(strsplit(hotword,"\t")), nrow=3))
    hotword <- as.data.frame(hotword, stringsAsFactors=FALSE)
    names(hotword) <- c("Keyword", "Freq", "Trend")
    hotword$Freq <- as.numeric(hotword$Freq)
    hotword <- hotword[order(hotword$Freq, decreasing=TRUE),]
    return(hotword[1:top,])
}
hotword <- getBaiduHot("http://top.baidu.com/buzz?b=1", HTMLencoding = 'GBK')
knitr::kable(hotword)

	Keyword	Freq	Trend
11	小姑娘你火了	116955	rise
10	曝美女兵裸照丑闻	115900	fair
12	试探男友谎称绑架	106834	fall
9	富二代玩枪建工厂	76881	rise
13	男子谋生杀猫卖钱	42903	fall
16	儿生日父亲送毒品	39328	fall
14	男子撞脸达尔文	38389	rise
17	老太眼内8条活虫	33053	rise
5	铁路运行图将调整	27491	rise
1	清洁工擦窗困楼外	25871	rise
30	两架小型飞机相撞	21520	fall
46	耐克气垫门曝光	21170	rise
15	敖厂长被威胁事件	20979	rise
6	蒙冤16年回老家	18929	rise
7	离婚冷静期通知书	17932	rise
24	小学课文被指杜撰	12642	fall
2	贾静雯三胎再产女	12292	rise
27	偷上万元发红包	11621	fall
19	香港旺角暴乱罪成	11488	fall
45	安以轩宣布结婚	10655	fall
23	陈妍希短裙秀美腿	10652	fall
3	秘鲁洪灾泥石流	10441	rise
22	10亿建豪华校区	10137	fall
41	沈梦辰揭澡戏内幕	9333	rise
20	李维嘉终于笑了	9205	fall
21	洋妞街头脱衣暴走	9023	rise
18	男婴出生就18岁	8981	rise
25	火锅底料用560次	8295	rise
31	李维嘉被经纪人骗	8161	rise
26	捡到钱包要求陪睡	6526	rise

3.2 Basic Plot

Only provide x and y.

echartr(hotword, Keyword, Freq, type='wordCloud') %>% 
    setTitle('Baidu Hot Word Top30 (realtime)', as.character(Sys.time()))

3.3 Color by Series

We want to group the hot words. Let’s assign a series variable ‘Trend’. The ‘rise’ series and ‘fall’ series are colored differently.

echartr(hotword, Keyword, Freq, Trend, type='wordCloud') %>% 
    setTitle('Baidu Hot Word Top30 (realtime)', as.character(Sys.time()))

3.4 With Timeline

Let’s compare realtime, today, and 7-days hotwords.

First, get the other two web pages and rbind the datasets.

hotword$t <- 'Realtime'
hotword1 <- getBaiduHot("http://top.baidu.com/buzz?b=341&fr=topbuzz_b1",
                        HTMLencoding = 'GBK')
hotword1$t <- 'Today'
hotword2 <- getBaiduHot("http://top.baidu.com/buzz?b=42&c=513&fr=topbuzz_b341",
                        HTMLencoding = 'GBK')
hotword2$t <- '7-days'
hotword <- do.call('rbind', list(hotword, hotword1, hotword2))
hotword$t <- factor(hotword$t, levels=c('Realtime', 'Today', '7-days'))

Then come up with the chart.

g <- echartr(hotword, Keyword, Freq, t=t, type='wordCloud') %>% 
    setTitle('Baidu Hot Word Top30')
g

4 Futher Setup

Then you can configure the widgets, add markLines and/or markPoints, fortify the chart.

4.1 `setTheme`

g %>% setTheme('dark', palette='manyeyes')

You can refer to related functions to play around on your own.

Basic Plots 21 - WordCloud