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你见过随月份变化的温度拆线图 你还见过可以鼠标点选显示详细信息、开关分组的散点图 互联网中铺天盖地的词云 线图、柱状图、堆叠图任意切换，不再为选择类型纠结 甚至是随心所欲的力导向布局图 今天不是带你来看图，而是带你画图的。只安成功安装recharts包，半小时带实现以上全部图型。 相关内容太多，文字传达不便，故录了一个小视频，帮助大家理解和实操。 Recharts包简介、安装和使用视频教程 文字代码看不清，快访问 https://v.qq.com/x/page/m0546uuyeib.html 在线观看1080p高清，更可后台回复'recharts'获得高清视频下载链接，本地播放效果更佳。 R—交互式图表recharts包 recharts 是从Yihui Xie fork而来。它基于百度Echarts2的最后一个稳定发布版(v2.2.7)开发。本文档始终反映recharts最新的特性(Github)。基于Echarts3的recharts2包仍在开发中。 recharts是一个用于可视化的R加载包，它提供了一套面向JavaScript库ECharts2的接口。此包的目的是让R用户即便不精通HTML或JavaScript，也能用很少的代码做出Echarts交互图——当然，懂一点JavaScript的话会更如虎添翼。下面这个散点图展示了本包的基本语法： Website: http://madlogos./recharts 原始代码可以访问官网教程更详细，也可以按下文我节选的部分操作，配合视频教程，学习和理解。 请将本文的代码，用Rstudio保存为recharts.r的文件中。 手动设置工作目录：Session - Set Work Directory - To Source File Location # 1. 依赖关系检查、安装和加载# 1.1 安装CRAN来源常用包# 我要北京使用清华镜像下载超快，比官方快100倍，下载几乎不用等待，大家下载有问题可以更新自己较快的国内镜像，如中科大、英荔教育、兰大、同济，详见https://cran./mirrors.htmlsite='https://mirrors.tuna./CRAN'# 参数解析、数据变换、绘图和开发包安装、安装依赖、ggplot主题package_list <- c('optparse','reshape2','ggplot2','devtools','bindrcpp',="" ="" ="" ="" ="" ="" ="" ="" ="" 'ggthemes')for(p="" in="" package_list){="" if(!suppresswarnings(suppressmessages(require(p,="" character.only="TRUE," quietly="TRUE," warn.conflicts="FALSE)))){" ="" install.packages(p,="" repos="site)" ="" suppresswarnings(suppressmessages(library(p,="" character.only="TRUE," quietly="TRUE," warn.conflicts="FALSE)))" }}#="" 1.2="" 安装bioconductor常用包#="" 参数解析、数据变换、绘图和开发包安装package_list=""><- c('digest')for(p="" in="" package_list){="" if(!suppresswarnings(suppressmessages(require(p,="" character.only="TRUE," quietly="TRUE," warn.conflicts="FALSE)))){" ="" source('https:///bioclite.r')="" ="" bioclite(p)="" ="" suppresswarnings(suppressmessages(library(p,="" character.only="TRUE," quietly="TRUE," warn.conflicts="FALSE)))" }}#="" 1.3="" 安装github常用包#="" 参数解析、数据变换、绘图和开发包安装package_list=""><- c('kassambara/ggpubr','madlogos/recharts')for(p="" in="" package_list){="" q="unlist(strsplit(p,split" =="" '/'))[2]="" if(!suppresswarnings(suppressmessages(require(q,="" character.only="TRUE," quietly="TRUE," warn.conflicts="FALSE)))){" ="" install_github(p)="" ="" suppresswarnings(suppressmessages(library(q,="" character.only="TRUE," quietly="TRUE," warn.conflicts="FALSE)))" }}#="" 2.="" 开始画图#="" 2.1="" 散点图/气泡图#="" 测试数据iris来自masslibrary(mass)#="" 查看测试数据，不同鸢尾个体花萼片和花瓣长度head(iris)#="" 绘制花萼宽为x轴，花辨宽为y轴散点图echartr(iris,="" x="Sepal.Width," y="Petal.Width)#" 图片为交互相图像，鼠标悬停有参考线和坐标，点可选并显示数值，#="" 可切换为数据视图和局部缩放，可另存为png和html交互图#="" 多个维度：series控制分组形状和着色echartr(iris,="" x="Sepal.Width," y="Petal.Width," series="Species)#" 图中分组可以按图例开关，同时作标轴跟随移动#="" 气泡图：weight控制气泡大小为花瓣长，type选择图表类型scatter/point/bubble类型echartr(iris,="" sepal.width,="" petal.width,="" series="Species," ="" ="" ="" weight="Petal.Length," type='bubble' )#="" 散点图：点着连续数值对应颜色#="" 将数据点按花瓣大小着色，类似热图echartr(iris,="" sepal.width,="" petal.width,="" weight="Petal.Length)" %="">% setDataRange(calculable=TRUE, splitNumber=0, labels=c('Big','Small'), color=c('red', 'yellow', 'green'), valueRange=c(0, 2.5))# 2.2 折线图# 先改造下内置数据集：aq <- airqualityhead(aq)aq$date=""><- as.date(paste('1973',="" aq$month,="" aq$day,="" sep='-' ))aq$day=""><- as.character(aq$day)aq$month=""><- factor(aq$month,="" labels="c('May'," 'jun',="" 'jul',="" 'aug',="" 'sep'))head(aq)#="" 绘制时间-温度变化折线图，设置标题和符号类型为空echartr(aq,="" date,="" temp,="" type='line' )="" %="">% setTitle('NY Temperature May - Sep 1973') %>% setSymbols('none')# 设置按月分组，符号为空心圆echartr(aq, Day, Temp, Month, type='line') %>% setTitle('NY Temperature May - Sep 1973, by Month') %>% setSymbols('emptycircle')# 记得可以点选图例开关分组哟# 带有时间轴，时间为月，可播放的小动图，是不是B格满满：echartr(aq, Day, Temp, t=Month, type='line') %>% setTitle('NY Temperature May - Sep 1973, by Month') %>% setSymbols('emptycircle')# 堆叠面积图：type属性控制面积，subtype控制堆叠stackechartr(aq, Day, Temp, Month, type='area', subtype='stack') %>% setTitle('NY Temperature May - Sep 1973, by Month') %>% setSymbols('emptycircle')# 2.3 饼图# 基于泰坦尼克数据，重构内置数据集# 显示数据结构，包括孩子、成人的生或死共4个表，包括1/2/3等舱和船员中性别分布str(Titanic)# 表格按行求和，再进行转换长表达titanic <- data.table::melt(apply(titanic,="" c(1,4),="" sum))#="" 修改列名names(titanic)=""><- c('class',="" 'survived',="" 'count')#="" knitr格式化表达knitr::kable(titanic)#="" 画饼图，按舱级别class分组显示数值和比例echartr(titanic,="" class,="" count,="" type='pie' )="" %="">% setTitle('Titanic: N by Cabin Class')# 右上角按扭可以切换为漏斗图# 多个饼图：按Class分面，每面中显示存活率echartr(titanic, Survived, Count, facet=Class, type='pie') %>% setTitle('Titanic: Survival Outcome by Cabin Class')# 环图，中空饼图：按Class分面，每面中显示存活率echartr(titanic, Survived, Count, facet=Class, type='ring') %>% setTitle('Titanic: Survival Outcome by Cabin Class')# 信息图样环图：总和为100%，突出组间比较ds <- data.frame(q="c('68%" feel="" good',="" '29%="" feel="" bad',="" '3%="" have="" no="" feelings'),="" ="" ="" ="" ="" ="" ="" ="" ="" a="c(68," 29,="" 3))g=""><- echartr(ds,="" q,="" a,="" type='ring' ,="" subtype='info' )="" %="">% setTheme('macarons', width=800, height=600) %>% setTitle('How do you feel?','ring_info', pos=c('center','center', 'horizontal'))g# 南丁格尔玫瑰图：中空饼图，高度和比例正相关echartr(titanic, Class, Count, facet=Survived, type='rose', subtype='radius') %>% setTitle('Titanic: Survival Outcome by Cabin Class')# 2.4 雷达图# 筛选内置数据mtcars的某些行和列，重构内置数据集cars = mtcars[c('Merc 450SE','Merc 450SL','Merc 450SLC'), c('mpg','disp','hp','qsec','wt','drat')]cars$model <- rownames(cars)cars=""><- data.table::melt(cars,="" id.vars='model' )names(cars)=""><- c('model',="" 'indicator',="" 'parameter')knitr::kable(cars)#="" 单个雷达图：展示不同车的性能指标echartr(cars,="" indicator,="" parameter,="" model,="" type='radar' ,="" sub='fill' )="" %="">% setTitle('Merc 450SE vs 450SL vs 450SLC')# 多个雷达图：按车型分面，每图展示车性能类型对应的数值echartr(cars, indicator, Parameter, facet=model, type='radar') %>% setTitle('Merc 450SE vs 450SL vs 450SLC')# 2.5 仪表盘图gauge plot# 构造一个数据集：data = data.frame(x=rep(c('KR/min', 'Kph'), 2), y=c(3.3, 56, 9.5, 88), z=c(rep('t1', 2), rep('t2', 2)))# 表格展示数据knitr::kable(data)# 显示表中第一个值echartr(data, x, y, type='gauge')# 多个dashboard：按类型分类，可以显示两种速度类型echartr(data, x, y, facet=x, type='gauge')# 带时间轴：按时间轴动图echartr(data, x, y, facet=x, t=z, type='gauge')# 2.6 柱状混合图# 数据筛选和变换d <- data.table::dcast(mtcars,="" carb+gear~.,="" mean,="" value.var='mpg' )names(d)[3]=""><- 'mean.mpg'd$carb=""><- as.character(d$carb)head(d)#="" 绘图，按gear分组，三组分别为柱状图和线图echartr(d,="" carb,="" 'mean.mpg',="" gear,="" type="c('vbar'," 'vbar',="" 'line'))="" %="">% setSymbols('emptycircle')# 可以按右上角点选切换线图、柱状图、堆叠柱状图# 3 修改图的细节# 3.1 简单的两组散点图示例g = echartr(mtcars, wt, mpg, factor(am, labels=c('Automatic', 'Manual')))g# 3.2 可以调用低级函数setSeries来修改第二组，点大小为8，并旋转30度g %>% setSeries(series=2, symbolSize=8, symbolRotate=30)# 3.3 给两个数据系列分别添加各自的均数标注线g %>% addMarkLine(data=data.frame(type='average', name1='Avg'))# 3.4 标注点markPoint# 给第一个数据系列(‘Automatic’)标出最大值的点。g %>% addMarkPoint(series=1, data=data.frame(type='max', name='Max'))# 3.5 添加标题(红色)和副标题(超级链接到 https://stat./R-manual/R-devel/library/datasets/html/mtcars.html)。link <- 'https://stat./r-manual/r-devel/library/datasets/html/mtcars.html'g="" %="">% setTitle('wt vs mpg', paste0('[Motor Trend](', link, ')'), textStyle=list(color='red'))# 3.6 修改图例(青柠色/绿黄色)，初始化时只选中第一系列(‘Automatic’)，可以手动选择g %>% setLegend(selected='Automatic', textStyle=list(color='lime'))# 3.7 修改工具箱显示语言为英文，并置于交互图右上角，垂直显示。g %>% setToolbox(lang='en', pos=2)# 3.8 添加缩放漫游控件(初始时不显示).g %>% setDataZoom()# 3.9 调整坐标轴，使x-和y-坐标交叉于零点。g %>% setXAxis(min=0) %>% setYAxis(min=0)# 主题Theme: 使用’dark’主题。可以选择的自带主题包括“macarons”, “infographic”, “blue”, “dark”, “gray”, “green”, “helianthus”, “macarons2”, “mint”, “red”, “roma”, “sakura”, “shine”, 和 “vintage”。# 拖曳重算(Calculable)是Echarts特有的交互方式。在某些图(如饼图)中，效果比较好。g %>% setTheme('dark', calculable=TRUE)# 图标Symbols:把第1系列(‘Automatic’)的图标改为’heart’，第2系列(‘Manual’)的图标改为’star6’。g %>% setSymbols(c('heart', 'star6'))# 合起来Altogether: 你可以把上述步骤用%>%合起来。如果你对JavaScript很熟悉，你可以把JavaScript片段包在JS()函数中，以获得更好的效果。g %>% setSeries(series=2, symbolSize=8, symbolRotate=30) %>% addMarkLine(data=data.frame(type='average', name1='Avg')) %>% addMarkPoint(series=1, data=data.frame(type='max', name='Max')) %>% setTitle('wt vs mpg', paste0('[Motor Trend](', link, ')'), textStyle=list(color='red')) %>% setLegend(selected='Automatic', textStyle=list(color='lime')) %>% setToolbox(lang='en', pos=2) %>% setDataZoom() %>% setTheme('dark', calculable=TRUE) %>% setSymbols(c('heart', 'star6'))# 4. 高B格类图# 4.1 和弦图Chord Chartmat <- as.data.frame(rbind(="" c(11975,="" 5871,="" 8916,="" 2868),="" c(="" 1951,="" 10048,="" 2060,="" 6171),="" c(="" 8010,="" 16145,="" 8090,="" 8045),="" c(="" 1013,="" ="" 990,="" 940,="" 6907)))names(mat)=""><- c('group1',="" 'group2',="" 'group3',="" 'group4')mat$name=""><- names(mat)echartr(mat,="" x="name," y="c(group1," group2,="" group3,="" group4),="" type='chord' ,="" ="" ="" ="" subtype='ribbon + asc + descsub + hidelab + scaletext' )="" %="">% setTitle('测试数据', subtitle='From d3.js', pos=5)# 4.2 力导向布局图Force Chart# 准备数据grpmtx <- matrix(c(11975,="" 5871,="" 8916,="" 2868,="" 1951,="" 10048,="" 2060,="" 6171,="" 8010,="" 16145,="" ="" ="" ="" ="" ="" ="" ="" ="" ="" 8090,="" 8045,="" 1013,="" 990,="" 940,="" 6907),="" byrow="TRUE," nrow="4)grpmtx"><- as.data.frame(grpmtx)names(grpmtx)=""><- paste0('group',="" 1:4)grpmtx$name=""><- paste0('group',="" 1:4)knitr::kable(grpmtx,="" align="c('lllll'))#" 点和边设置nodes=""><- cbind(yunetwork$nodes[,1],="" na,="" yunetwork$nodes[,2:3],="" ="" ="" ="" ="" ="" ="" ="" stringsasfactors="FALSE)names(nodes)"><- names(yunetwork$links)yu=""><- rbind(yunetwork$links,="" nodes,="" stringsasfactors="FALSE)#" 曲线连接echartr(yu,="" c(source,="" target),="" weight,="" relation,="" type='force' )="" %="">% setTitle('Yu Family of Shaoxing') %>% setTheme(palette=c( 'tan3','green3','green2','lawngreen','olivedrab1'))# 4.3 词云# 获取实时百度热词，不同时间画的都不同getBaiduHot <- function(url,="" top="30," htmlencoding="NULL){" baiduhot=""><- paste0(readlines(url),="" collapse='' )="" charset=""><- gsub('^.+charset="([[:alnum:]-]+?)[^[:alnum:]-].+$'," '\\1',="" ="" ="" ="" ="" ="" ="" ="" ="" baiduhot)="" if="" (is.null(htmlencoding))="" if="" (!is.null(charset))="" htmlencoding=""><- charset="" baiduhot=""><- stringr::str_conv(baiduhot,="" htmlencoding)="" hotword=""><->]+?>([^<>]+?).+?(\\d+?).+?','\\1\t\\3\t\\2\t', baiduhot) hotword <- enc2native(gsub('^(.+?)\t{4,}.+$','\\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$freq,="" decreasing="TRUE),]" return(hotword[1:top,])}hotword=""><- getbaiduhot('http://top.baidu.com/buzz?b="1'," htmlencoding='GBK' )knitr::kable(hotword)#="" 词云展示echartr(hotword,="" keyword,="" freq,="" type='wordCloud' )="" %="">% setTitle('Baidu Hot Word Top30 (realtime)', as.character(Sys.time()))# 按数据系列着色Color by Seriesechartr(hotword, Keyword, Freq, Trend, type='wordCloud') %>% setTitle('Baidu Hot Word Top30 (realtime)', as.character(Sys.time()))# 带时间轴With Timeline# 获取今日和七日两个榜单的网页并转为数据框，合并。hotword$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'))#="" 然后作图。g=""><- echartr(hotword,="" keyword,="" freq,="" t="t," type='wordCloud' )="" %="">% setTitle('Baidu Hot Word Top30')g# 5. System informationsessionInfo()看一下我的环境，安装成功才是作图的前提。 我在WIndows10和Ubuntu 16.04上都安装成功，但都安装多次，最多要反复打开关闭十几次，才完成全部依赖关系安装。 报错了，直接关闭打开重装，很多错误还真不是你的原因。 R version 3.4.1 (2017-06-30)Platform: x86_64-pc-linux-gnu (64-bit)Running under: Ubuntu 16.04.3 LTSMatrix products: defaultBLAS: /usr/lib/openblas-base/libblas.so.3LAPACK: /usr/lib/libopenblasp-r0.2.18.solocale: [1] LC_CTYPE=zh_CN.utf-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 [4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 [7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C [10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C attached base packages:[1] stats graphics grDevices utils datasets methods base other attached packages: [1] recharts_0.2-1 ggpubr_0.1.6.999 magrittr_1.5 digest_0.6.15 ggthemes_3.4.0 bindrcpp_0.2 [7] devtools_1.13.4 ggplot2_2.2.1 reshape2_1.4.3 optparse_1.4.4 loaded via a namespace (and not attached): [1] Rcpp_0.12.15 highr_0.6 pillar_1.1.0 compiler_3.4.1 RColorBrewer_1.1-2 [6] plyr_1.8.4 bindr_0.1 tools_3.4.1 jsonlite_1.5 memoise_1.1.0 [11] tibble_1.4.2 gtable_0.2.0 pkgconfig_2.0.1 rlang_0.1.6 curl_3.1 [16] yaml_2.1.16 withr_2.1.1 stringr_1.2.0 dplyr_0.7.4 knitr_1.19 [21] htmlwidgets_1.0 grid_3.4.1 getopt_1.20.1 glue_1.2.0 data.table_1.10.4-3[26] R6_2.2.2 scales_0.5.0 htmltools_0.3.6 assertthat_0.2.0 colorspace_1.3-2 [31] stringi_1.1.6 lazyeval_0.2.1 munsell_0.4.3主要参考信息
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