R语言-Plotly入门到精通

plotly

Plotly，一个用于做分析和可视化的在线平台，曾被网友称为“有史以来最牛逼”可视化神器（具体无从考据，只是在搜索资料时看到的），为何有如此称号？因为它功能强大。

其功能强大到不仅与多个主流绘图软件的对接，而且还可以像Excel那样实现交互式制图，而且图表种类齐全，并可以实现在线分享以及开源，等等；

本课程会帮助大家掌握这样一门技能，在自己日常的学习工作中，能够快速的，优雅的对数据进行交互式的可视化。

本课程的内容大纲如下：

1. 简介与准备
2. 绘制基本的图表
3. 绘制统计图表
4. 绘制科学图表
5. 绘制金融图表
6. 绘制3D图表
7. 绘制动画
8. 图形的细节调整

1准备

准备工作很简单，准备好Rstudio，然后使用以下代码进行安装：

install.packages("plotly")

然后我们就可以开始了

2. 绘制基本图形

基本图表包括了很多内容，分别罗列如下：

1. 散点图
2. 气泡图
3. 线图
4. 填充图
5. 条形图
6. 并图
7. 桑基图

2.1 散点图

使用的数据是

library(plotly)

p <- plot_ly(data = iris, x = ~Sepal.Length, y = ~Petal.Length)
p

散点图可以修改的三个参数颜色，形状,现在来修改以下

p <- plot_ly(data = iris, x = ~Sepal.Length, y = ~Petal.Length,color = ~Species ,symbol =  ~Species)
p

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plot.ly/r/reference/#scatter

## No scatter mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode

2.2 气泡图

使用的数据是mtcars，气泡图是用多一维的数据来表示点的大小

p <- plot_ly(data = mtcars, x = ~mpg, y = ~wt,size = ~vs)
p

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plot.ly/r/reference/#scatter

## No scatter mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode

## Warning: `line.width` does not currently support multiple values.

修改一下颜色

p <- plot_ly(data = mtcars, x = ~mpg, y = ~wt,size = ~vs,color = ~am)
p

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plot.ly/r/reference/#scatter

## No scatter mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode

## Warning: `line.width` does not currently support multiple values.

2.3 线图

p <- plot_ly(data = mtcars, x = ~mpg, y = ~cyl,type = "scatter",mode = 'lines')
p

添加不同的颜色

p <- plot_ly(data = mtcars, x = ~mpg, y = ~cyl,type = "scatter",mode = 'lines',color = ~am)
p

## Warning: line.color doesn't (yet) support data arrays

## Warning: line.color doesn't (yet) support data arrays

2.4 填充图形

绘制填充图关键的参数是fill,在第二条曲线的调用中，将内部填充设置fill为区域图“tozeroy”。有关fill选项结帐的更多信息和选项，请访问https://plot.ly/r/reference/#scatter-fill

density <- density(diamonds$carat)

p <- plot_ly(x = ~density$x, y = ~density$y, type = 'scatter', mode = 'lines', fill = 'toself') # tozeroy tozerox
p

绘制两条填充曲线

diamonds1 <- diamonds[which(diamonds$cut == "Fair"),]
density1 <- density(diamonds1$carat)

diamonds2 <- diamonds[which(diamonds$cut == "Ideal"),]
density2 <- density(diamonds2$carat)

p <- plot_ly(x = ~density1$x, y = ~density1$y, type = 'scatter', mode = 'lines', name = 'Fair cut', fill = 'tozeroy') %>%
  add_trace(x = ~density2$x, y = ~density2$y, name = 'Ideal cut', fill = 'tozeroy')
p

2.5 条形图

基本的条形图绘制，将type参数设置为bar

p <- plot_ly(
  x = c("giraffes", "orangutans", "monkeys"),
  y = c(20, 14, 23),
  name = "SF Zoo",
  type = "bar"
)

p

分组条形图

Animals <- c("giraffes", "orangutans", "monkeys")
SF_Zoo <- c(20, 14, 23)
LA_Zoo <- c(12, 18, 29)
data <- data.frame(Animals, SF_Zoo, LA_Zoo)

p <- plot_ly(data, x = ~Animals, y = ~SF_Zoo, type = 'bar', name = 'SF Zoo') %>%
  add_trace(y = ~LA_Zoo, name = 'LA Zoo') 

p

2.6 饼图

p <- plot_ly(iris, labels = ~Species, values = ~Sepal.Length, type = 'pie')
p

甜甜圈图

require(tidyverse)

## Loading required package: tidyverse

## ─ Attaching packages ────────────────────────── tidyverse 1.2.1 ─

## ✔ tibble  2.0.0     ✔ purrr   0.2.5
## ✔ tidyr   0.8.2     ✔ dplyr   0.7.8
## ✔ readr   1.3.1     ✔ stringr 1.3.1
## ✔ tibble  2.0.0     ✔ forcats 0.3.0

## Warning: package 'tidyr' was built under R version 3.4.4

## Warning: package 'readr' was built under R version 3.4.4

## Warning: package 'purrr' was built under R version 3.4.4

## Warning: package 'dplyr' was built under R version 3.4.4

## Warning: package 'stringr' was built under R version 3.4.4

## ─ Conflicts ─────────────────────────── tidyverse_conflicts() ─
## ✖ dplyr::filter() masks plotly::filter(), stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

mtcars$manuf <- sapply(strsplit(rownames(mtcars), " "), "[[", 1)

p <- mtcars %>%
  group_by(manuf) %>%
  summarize(count = n()) %>%
  plot_ly(labels = ~manuf, values = ~count) %>%
  add_pie(hole = 0.6)

2.7 桑基图

library(plotly)
library(rjson)

## Warning: package 'rjson' was built under R version 3.4.4

json_file <- "https://raw.githubusercontent.com/plotly/plotly.js/master/test/image/mocks/sankey_energy_dark.json"
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

p <- plot_ly(
  type = "sankey",

  orientation = "h",
  
  node = list(
    label = json_data$data[[1]]$node$label
    
  ),
  
  link = list(
    source = json_data$data[[1]]$link$source,
    target = json_data$data[[1]]$link$target,
    value =  json_data$data[[1]]$link$value
  )
)

p

我们来看一下具体是如何实现：首先 type = “sankey” 制定了绘制的是sankey，桑基图。然后orientation=’h’表明的是绘制出的图形是横向的。node 参数指定了绘制图形的节点。link里面参数制定了绘制节点的流向以及流量的大小。

我们自己来构造数据，来实现一个自己的例子：

图形的节点选取为字母表的前六个，然后设置了7个连接，分别为:(0,1),(1,2),(2,3),(3,4),c(0,2),c(0,3),c(0,4).每个连接的流量为：sample(c(1,2,3,4,5,6,7))

p <- plot_ly(
  type = "sankey",
  
  orientation = "h",
  
  node = list(
    label = letters[1:5]
    
  ),
  
  link = list(
    source = c(0,1,2,3,0,0,0),
    target = c(1,2,3,4,2,3,4),
  value =  sample(c(1,2,3,4,5,6,7))
)
) 
p

3. 统计图表

统计图表包括

1. 误差棒
2. 方块图
3. 直方图
4. 2d直方图
5. 二维直方轮廓图
6. 小提琴图
7. Splom图

3.1 绘制误差棒图

误差棒图本质上是带有误差线的条形图

library(plyr)

## -------------------------------------------------------------------------

## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)

## -------------------------------------------------------------------------

## 
## Attaching package: 'plyr'

## The following objects are masked from 'package:dplyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize

## The following object is masked from 'package:purrr':
## 
##     compact

## The following objects are masked from 'package:plotly':
## 
##     arrange, mutate, rename, summarise

data_mean <- ddply(ToothGrowth, c("supp", "dose"), summarise, length = mean(len))
data_sd <- ddply(ToothGrowth, c("supp", "dose"), summarise, length = sd(len))
data <- data.frame(data_mean, data_sd$length)
data <- rename(data, c("data_sd.length" = "sd"))
data$dose <- as.factor(data$dose)


p <- plot_ly(data = data[which(data$supp == 'OJ'),], x = ~dose, y = ~length, type = 'bar',
             error_y = ~list(array = sd,
                             color = '#000000')) %>% 
   add_trace(data = data[which(data$supp == 'VC'),], name = 'VC')

p

绘制带有误差棒的散点图

p <- plot_ly(data = data[which(data$supp == 'OJ'),], x = ~dose, y = ~length, type = 'scatter', mode = 'markers',
        name = 'OJ',
        error_y = ~list(array = sd,
                        color = '#000000')) %>%
  add_trace(data = data[which(data$supp == 'VC'),], name = 'VC')
p

3.2 箱线图

箱线图的基本绘制方式

p <- plot_ly(y = ~rnorm(50), type = "box") %>%
  add_trace(y = ~rnorm(50, 1))
p

添加抖动点

p <- plot_ly(y = ~rnorm(50), type = "box", boxpoints = "all", jitter = 0.3,
        pointpos = -1.8)
p

添加多个箱线图

p <- plot_ly(ggplot2::diamonds, y = ~price, color = ~cut, type = "box")
p

3.3 直方图

绘制基本的直方图

library(plotly)
p <- plot_ly(x = ~rnorm(50), type = "histogram")

p

归一化直方图

p <- plot_ly(x = ~rnorm(50),
             type = "histogram",
             histnorm = "probability")

p

叠加直方图

p <- plot_ly(alpha = 0.6) %>%
  add_histogram(x = ~rnorm(500)) %>%
  add_histogram(x = ~rnorm(500) + 1) %>%
  layout(barmode = "overlay")
p

3.4 2D直方图

首先，我们构造一个二维正态分布的数据：

# 构造二维正太分布
s <- matrix(c(1, -.75, -.75, 1), ncol = 2)
obs <- mvtnorm::rmvnorm(500, sigma = s)

#  绘制一维的直方图
plot_ly(x = ~obs[,1], type = "histogram")

p <- plot_ly(x = obs[,1], y = obs[,2])
p

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plot.ly/r/reference/#scatter

## No scatter mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode

# 这个时候是默认散点图的
pp <- subplot(
  p %>% add_markers(alpha = 0.2),
  p %>% add_histogram2d()
)
pp

3.5 二维轮廓直方图

二维轮廓直方图的基本绘制：

s <- matrix(c(1, -.75, -.75, 1), ncol = 2)
obs <- mvtnorm::rmvnorm(500, sigma = s)

p <- plot_ly(x = obs[,1], y = obs[,2]) %>% 
  add_trace(type='histogram2dcontour')

2D直方图轮廓子图

x <- rnorm(1000)
y <- rnorm(1000)
s <- subplot(
  plot_ly(x = x, color = I("black"), type = 'histogram'), 
  plotly_empty(), 
  plot_ly(x = x, y = y, type = 'histogram2dcontour', showscale = F), 
  plot_ly(y = y, color = I("black"), type = 'histogram'),
  nrows = 2, heights = c(0.2, 0.8), widths = c(0.8, 0.2), 
  shareX = TRUE, shareY = TRUE, titleX = FALSE, titleY = FALSE
)

## Warning: No trace type specified and no positional attributes specified

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plot.ly/r/reference/#scatter

## No scatter mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode

s

3.6 小提琴图

library(plotly)
p <- plot_ly(x = ~rnorm(50), type = "violin",box = list(
      visible = T
    ),meanline = list(
      visible = T
    ))
p

分组的小提琴图

p <- plot_ly(data = iris,x = ~Petal.Width,color = ~Species, type = "violin",box = list(
      visible = T
    ),meanline = list(
      visible = T
    ))
p

4. 科学图表

1. 雷达图
2. 热图

4.1 雷达图

基本的雷达图

p <- plot_ly(
  
    type = 'scatterpolar',
    r = c(39, 28, 8, 7, 28, 39),
    theta = c('A','B','C', 'D', 'E', 'A'),
    fill = 'toself'
  )
p

## No scatterpolar mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode

多轨迹的雷达图

p <- plot_ly(
    type = 'scatterpolar',
    fill = 'toself'
  ) %>%
  add_trace(
    r = c(39, 28, 8, 7, 28, 39),
    theta = c('A','B','C', 'D', 'E', 'A'),
    name = 'Group A'
  ) %>%
  add_trace(
    r = c(1.5, 10, 39, 31, 15, 1.5),
    theta = c('A','B','C', 'D', 'E', 'A'),
    name = 'Group B'
  )
p

## No scatterpolar mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
## No scatterpolar mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
## No scatterpolar mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode

4.2 热图

基本热图

p <- plot_ly(z = volcano, type = "heatmap")
p

设置颜色

p <- plot_ly(z = volcano, colors = "Greys", type = "heatmap")
p

想要知道有哪些颜色可以选择，可以（参见RColorBrewer::brewer.pal.info有效名称）

5. 金融图表

1. 时间序列图
2. 蜡烛图
3. OHLC图

5.1 时间序列

today <- Sys.Date()
tm <- seq(0, 600, by = 10) # 生成600天的数据
x <- today - tm
y <- rnorm(length(x))
p <- plot_ly(x = ~x, y = ~y, mode = 'lines', text = paste(tm, "days from today"))
p

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plot.ly/r/reference/#scatter

带有时区的时间序列

now_lt <- as.POSIXlt(Sys.time(), tz = "GMT")
tm <- seq(0, 600, by = 10)
x <- now_lt - tm
y <- rnorm(length(x))
p <- plot_ly(x = ~x, y = ~y, mode = 'lines', text = paste(tm, "seconds from now in GMT"))

p

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plot.ly/r/reference/#scatter

5.2 蜡烛图

绘制蜡烛图首先要使用quantmod绘制

library(quantmod)

## Warning: package 'quantmod' was built under R version 3.4.4

## Loading required package: xts

## Warning: package 'xts' was built under R version 3.4.4

## Loading required package: zoo

## Warning: package 'zoo' was built under R version 3.4.4

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

## 
## Attaching package: 'xts'

## The following objects are masked from 'package:dplyr':
## 
##     first, last

## Loading required package: TTR

## Warning: package 'TTR' was built under R version 3.4.4

## Version 0.4-0 included new data defaults. See ?getSymbols.

getSymbols("AAPL",src='yahoo')

## 'getSymbols' currently uses auto.assign=TRUE by default, but will
## use auto.assign=FALSE in 0.5-0. You will still be able to use
## 'loadSymbols' to automatically load data. getOption("getSymbols.env")
## and getOption("getSymbols.auto.assign") will still be checked for
## alternate defaults.
## 
## This message is shown once per session and may be disabled by setting 
## options("getSymbols.warning4.0"=FALSE). See ?getSymbols for details.

## 
## WARNING: There have been significant changes to Yahoo Finance data.
## Please see the Warning section of '?getSymbols.yahoo' for details.
## 
## This message is shown once per session and may be disabled by setting
## options("getSymbols.yahoo.warning"=FALSE).

## [1] "AAPL"

# basic example of ohlc charts
df <- data.frame(Date=index(AAPL),coredata(AAPL))
df <- tail(df, 30)

p <- df %>%
  plot_ly(x = ~Date, type="candlestick",
          open = ~AAPL.Open, close = ~AAPL.Close,
          high = ~AAPL.High, low = ~AAPL.Low)

p

6. 3D图

1. 3D散点图
2. 3D线图

6.1 3D散点图

mtcars$am[which(mtcars$am == 0)] <- 'Automatic'
mtcars$am[which(mtcars$am == 1)] <- 'Manual'
mtcars$am <- as.factor(mtcars$am)

p <- plot_ly(mtcars, x = ~wt, y = ~hp, z = ~qsec, color = ~am, colors = c('#BF382A', '#0C4B8E'))
p

## No trace type specified:
##   Based on info supplied, a 'scatter3d' trace seems appropriate.
##   Read more about this trace type -> https://plot.ly/r/reference/#scatter3d

## No scatter3d mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode

绘制3D泡泡图

mtcars$am[which(mtcars$am == 0)] <- 'Automatic'
mtcars$am[which(mtcars$am == 1)] <- 'Manual'
mtcars$am <- as.factor(mtcars$am)

p <- plot_ly(mtcars, x = ~wt, y = ~hp, z = ~qsec,size =  ~disp, color = ~am, colors = c('#BF382A', '#0C4B8E'))
p

## No trace type specified:
##   Based on info supplied, a 'scatter3d' trace seems appropriate.
##   Read more about this trace type -> https://plot.ly/r/reference/#scatter3d

## No scatter3d mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

6.2 3D线图

p <- plot_ly(mtcars, x = ~wt, y = ~hp, z = ~qsec,type = "scatter3d",mode = 'lines')
p

3D随机游走图

data <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/_3d-line-plot.csv')

p <- plot_ly(data, x = ~x1, y = ~y1, z = ~z1, type = 'scatter3d', mode = 'lines',
        line = list(color = '#1f77b4', width = 1)) %>%
  add_trace(x = ~x2, y = ~y2, z = ~z2,
            line = list(color = 'rgb(44, 160, 44)', width = 1)) %>%
  add_trace(x = ~x3, y = ~y3, z = ~z3,
            line = list(color = 'bcbd22', width = 1))
p

7. 绘制动画图

原理就是，frames指向一个数字列表，每个数字都会在实例化时进行循环

df <- data.frame(
  x = c(1,2,1), 
  y = c(1,2,1), 
  f = c(1,2,3)
)

p <- df %>%
  plot_ly(
    x = ~x,
    y = ~y,
    frame = ~f,
    type = 'scatter',
    mode = 'markers',
    showlegend = F
  )
p

更加复杂的一个例子：

library(gapminder)

p <- gapminder %>%
  plot_ly(
    x = ~gdpPercap, 
    y = ~lifeExp, 
    size = ~pop, 
    color = ~continent, 
    frame = ~year, 
    text = ~country, 
    hoverinfo = "text",
    type = 'scatter',
    mode = 'markers'
  ) %>%
  layout(
    xaxis = list(
      type = "log"
    )
  )

p

我们来分析一下这个图

8. 细节调整

接下来我们来介绍一些绘制图形的一些细节调整，这些调整方式大多是通用的，因此，放在本课程的最后一个部分进行讲解

使用到的数据集是mycars数据集合，首先绘制一个散点图：

p <- plot_ly(data = mtcars,x = ~wt,y = ~mpg,type = "scatter")
p

## No scatter mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode

这里显示需要设置mode,mode有三个参数可以选： “lines”, “markers”, “text” ，也可以设置类似于这样“lines + markers”

p <- plot_ly(data = mtcars,x = ~wt,y = ~mpg,type = "scatter",mode = "markers")
p

改变颜色

将颜色设置成为红色

p <- plot_ly(data = mtcars,x = ~wt,y = ~mpg,type = "scatter",mode = "markers",color = I("red"))
p

通过marker参数设置颜色和其他选项

p <- plot_ly(data = mtcars,x = ~wt,y = ~mpg,type = "scatter",mode = "markers",marker = list(color = 'green',size = 10))
p

添加line参数

p <- plot_ly(data = mtcars,x = ~wt,y = ~mpg,type = "scatter",mode = "markers",marker = list(color = 'green',size = 10,line = list(color = "blue",width = 10)))
p

利用连续变量和离散变量设置颜色

p <- plot_ly(data = mtcars,x = ~wt,y = ~mpg,type = "scatter",mode = "markers",color = ~as.factor(cyl))
p

p <- plot_ly(data = mtcars,x = ~wt,y = ~mpg,type = "scatter",mode = "markers",color = ~as.factor(cyl),colors = "Set1")
p

定义自己想要的颜色，这里设置了三种颜色，红，蓝，绿

pal <- c("red","blue","green")

p <- plot_ly(data = mtcars,x = ~wt,y = ~mpg,type = "scatter",mode = "markers",color = ~as.factor(cyl),colors = pal)
p

通过symbol参数，改变散点图形状

p <- plot_ly(data = mtcars,x = ~wt,y = ~mpg,type = "scatter",mode = "markers",symbol = ~as.factor(cyl))
p

通过symbol参数，指定散点图的形状

p <- plot_ly(data = mtcars,x = ~wt,y = ~mpg,type = "scatter",mode = "markers",symbol = ~as.factor(cyl),symbols = c("circle","x","o"))
p

通过连续变量设置颜色

p <- plot_ly(data = mtcars,x = ~wt,y = ~mpg,type = "scatter",mode = "markers",color = ~disp)
p

同时设置颜色和大小，颜色是因子，大小是连续

p <- plot_ly(data = mtcars,x = ~wt,y = ~mpg,type = "scatter",mode = "markers",color = ~as.factor(cyl),size = ~disp)
p

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

图例的调整

是否显示图例

p %>% layout(showlegend = TRUE)

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

p %>% layout(showlegend = FALSE)

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

图例按照什么方向摆放

p %>% layout(legend = list(orientation = "h"))

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

p %>% layout(legend = list(orientation = "v"))

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

图例放在整个图形的什么位置

p %>% layout(legend = list(x = 0.1, y= 0.1))

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

最标轴的调整，添加标题，x，y轴的名字

p %>% layout(legend = list(x = 0.1, y= 0.1),title = "Plot",xaxis = list(title = "weigth"),yaxis = list(title = "MPG"))

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

是否显示坐标轴

p %>% layout(legend = list(x = 0.1, y= 0.1),title = "Plot",xaxis = list(title = "weigth",showgrid = FALSE),yaxis = list(title = "MPG"))

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

修改交互文本，text参数来修改交互式文本

p <- plot_ly(data = mtcars,x = ~wt,y = ~mpg,type = "scatter",mode = "markers",hoverinfo = "text",text = paste("Miles per gallon:",mtcars$wt,"<br>","Weigth:",mtcars$mpg))
p

hoverinfo 是什么含义，指定的是如何显示交互文本

add_annotations()介绍，这个函数可以在途中添加数据

在这里标识除了wt最小的值的那个点

p <- plot_ly(data = mtcars,x = ~wt,y=~mpg)
p %>% add_annotations(x = mtcars$wt[which.min(mtcars$wt)],
                      y = mtcars$mpg[which.min(mtcars$wt)],
                      text = "good milege")

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plot.ly/r/reference/#scatter

## No scatter mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode

这里标示出wt最大的那个点

p %>% add_annotations(y = mtcars$mpg[which.max(mtcars$wt)],
                      x = mtcars$wt[which.max(mtcars$wt)],
                      text = "good milege",showarrow = FALSE)

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plot.ly/r/reference/#scatter

## No scatter mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode

标识多个点

plot_ly(data = mtcars,x=~mpg,y=~wt,type = "scatter",mode = 'markers') %>% add_annotations(x= mtcars$mpg[mtcars$am==0],text = "point")