按颜色识别点
Identifying points by color
我正在学习这里的教程：https://www.rpubs.com/loveb/som。本教程介绍如何在虹膜数据上使用 Kohonen 网络（也称为 SOM，一种机器学习算法）。
我运行教程中的这段代码：
library(kohonen) #fitting SOMs
library(ggplot2) #plots
library(GGally) #plots
library(RColorBrewer) #colors, using predefined palettes
iris_complete <-iris[complete.cases(iris),]
iris_unique <- unique(iris_complete) # Remove duplicates
#scale data
iris.sc = scale(iris_unique[, 1:4]) #Levels/Factors cannot be scaled... But used in predictive SOM:s using xyf. Later.
#build grid
iris.grid = somgrid(xdim = 10, ydim=10, topo="hexagonal", toroidal = TRUE)
set.seed(33) #for reproducability
iris.som <- som(iris.sc, grid=iris.grid, rlen=700, alpha=c(0.05,0.01), keep.data = TRUE)
#plot 1
plot(iris.som, type="count")
#plot2
var <- 1 #define the variable to plot
plot(iris.som, type = "property", property = getCodes(iris.som)[,var], main=colnames(getCodes(iris.som))[var], palette.name=terrain.colors)
以上代码在虹膜数据上拟合了一个 Kohonen 网络。数据集中的每个观察值都分配给下图中的每个“彩色圆圈”（也称为“神经元”）。
我的问题：在这些图中，您如何确定哪些观察值被分配给了哪些圆圈？假设我想知道哪些观察属于下面用黑色三角形勾勒出的圆圈：
这可以吗？现在，我正在尝试使用 iris.som$classif 以某种方式追踪哪些点在哪个圆圈中。有更好的方法吗？
更新：@Jonny Phelps 向我展示了如何识别三角形内的观察结果（请参阅下面的答案）。但我仍然不确定是否可以识别不规则形状。例如。
在之前的 post (Labelling Points on a Plot (R Language)) 中，一位用户向我展示了如何为网格上的每个圆分配任意数字：
根据上图，您如何使用“som$classif”语句找出哪些观测值位于圆圈 92、91、82、81、72 和 71 中？
谢谢
据我所知，使用 iris.som$unit.classif 和 iris.som$grid 是在绘图网格中隔离圆圈的方法。我假设分类器值与 iris.som$grid 的行索引匹配，因此这将需要更多验证。如果这对您的问题有帮助，请告诉我:)
findTriangle <- function(top_row, top_column, side_length, iris.som,
reverse=FALSE){
# top_row: row index of the top most triangle value
# top_column: column index...
# side_length: how many rows does the triangle occupy?
# iris.som: the som object
# reverse: set to TRUE to flip the triangle
# make the grid
grid_pts <- as.data.frame(iris.som$grid$pts)
grid_pts$column <- rep(1:iris.som$grid$xdim, by=iris.som$grid$ydim)
grid_pts$row <- rep(1:iris.som$grid$ydim, each=iris.som$grid$xdim)
grid_pts$classif <- 1:nrow(grid_pts)
# starting point - top most point of the triangle
# use reverse for triangles the other way around
grid_pts$triangle <- FALSE
grid_pts[grid_pts$column == top_column & grid_pts$row == top_row, ][["triangle"]] <- TRUE
# loop through the remaining rows and fill out the triangle
value_row <- top_row
value_start_column <- grid_pts[grid_pts$triangle == TRUE,]$x
value_end_column <- grid_pts[grid_pts$triangle == TRUE,]$x
if(reverse){
row_move <- -1
}else{
row_move <- 1
}
# update triangle
for(row in 1:(side_length-1)){
value_row <- value_row + row_move
value_start_column <- value_start_column - 0.5
value_end_column <- value_end_column + 0.5
grid_pts[grid_pts$row == value_row &
grid_pts$x >= value_start_column &
grid_pts$x <= value_end_column, ]$triangle <- TRUE
}
# visualise
pl <- ggplot(grid_pts, aes(x=x, y=rev(row), col=as.factor(triangle))) +
geom_point(size=7) +
scale_color_manual(values=c("grey", "indianred")) +
theme_void()
print(pl)
return(grid_pts)
}
# take the grid and pick out the triangle
top_row <- 2
top_column <- 6
side_length <- 4
reverse <- FALSE # set to TRUE to flip the triangle ie go from the bottom
grid_pts <- findTriangle(top_row, top_column, side_length, iris.som, reverse)
# now add the classifier and merge to get the co-ordinates
iris.sc2 <- as.data.frame(iris.sc)
iris.sc2$classif <- iris.som$unit.classif
iris.sc2 <- merge(iris.sc2, grid_pts, by=c("classif"), all.x=TRUE)
# filter to the points in the triangle
iris.sc2[iris.sc2$triangle==TRUE,]
输出数据：
classif Sepal.Length Sepal.Width Petal.Length Petal.Width x y column row triangle
21 16 -1.01537328 0.5506423 -1.3287735 -1.3042249 6.0 1.732051 6 2 TRUE
22 16 -1.01537328 0.3214643 -1.4419091 -1.3042249 6.0 1.732051 6 2 TRUE
39 25 -0.89501479 1.0089981 -1.3287735 -1.3042249 5.5 2.598076 5 3 TRUE
40 25 -0.77465630 1.0089981 -1.2722057 -1.3042249 5.5 2.598076 5 3 TRUE
41 25 -0.77465630 0.7798202 -1.3287735 -1.3042249 5.5 2.598076 5 3 TRUE
42 25 -1.01537328 0.7798202 -1.2722057 -1.3042249 5.5 2.598076 5 3 TRUE
43 25 -0.89501479 0.7798202 -1.2722057 -1.3042249 5.5 2.598076 5 3 TRUE
44 26 -0.89501479 0.5506423 -1.1590702 -0.9108454 6.5 2.598076 6 3 TRUE
45 26 -1.01537328 0.7798202 -1.2156380 -1.0419719 6.5 2.598076 6 3 TRUE
58 36 -0.53393933 0.7798202 -1.2722057 -1.0419719 6.0 3.464102 6 4 TRUE
59 36 -0.41358084 1.0089981 -1.3853413 -1.3042249 6.0 3.464102 6 4 TRUE
60 36 -0.53393933 0.7798202 -1.1590702 -1.3042249 6.0 3.464102 6 4 TRUE
61 37 -1.01537328 1.0089981 -1.2156380 -0.7797188 7.0 3.464102 7 4 TRUE
62 37 -1.01537328 1.0089981 -1.3853413 -1.1730984 7.0 3.464102 7 4 TRUE
63 37 -0.89501479 1.0089981 -1.3287735 -1.1730984 7.0 3.464102 7 4 TRUE
74 44 0.06785311 0.3214643 0.5945312 0.7937995 4.5 4.330127 4 5 TRUE
75 46 -0.65429782 1.4673539 -1.2722057 -1.3042249 6.5 4.330127 6 5 TRUE
76 46 -0.53393933 1.4673539 -1.2722057 -1.3042249 6.5 4.330127 6 5 TRUE
77 47 -0.89501479 1.6965319 -1.0459346 -1.0419719 7.5 4.330127 7 5 TRUE
78 47 -0.89501479 1.6965319 -1.2156380 -1.3042249 7.5 4.330127 7 5 TRUE
79 47 -0.89501479 1.4673539 -1.2722057 -1.0419719 7.5 4.330127 7 5 TRUE
80 47 -0.89501479 1.6965319 -1.2722057 -1.1730984 7.5 4.330127 7 5 TRUE
网格上的验证绘图：
我在我的 post 中详细说明了示例，但是，不是在鸢尾花数据集上，但我想这没问题：R, SOM, Kohonen Package, Outlier Detection 并且还添加了您可能需要的代码片段。他们显示
- 如何生成数据、添加离群值并在图上描绘它们
- 如何训练 SOM
- 如何进行聚类
- 如何使用层次聚类将聚类边界添加到 SOM 图
- 最后，我添加了 SOM 预测的集群，以将它们与我生成数据的真实集群进行比较
我认为这回答了您的问题。将 SOM 的性能与 t-SNE 进行比较也很好。我只使用 SOM 作为对我生成的数据和真实葡萄酒数据集的实验。如果你有两个以上的变量，准备热图也很好。祝您分析一切顺利！
编辑：现在有了 Shiny App！
plotly 解决方案也是可能的，您可以将鼠标悬停在单个神经元上以显示相关的虹膜行名（此处称为 id）。根据您的 iris.som 数据和 Jonny Phelps 的网格方法，您可以将行号作为连接字符串分配给各个神经元，并在鼠标悬停时显示这些：
library(ggplot2)
library(plotly)
ga <- data.frame(g=iris.som$unit.classif,
sample=seq_len(dim(iris.som$data[[1]])[1]))
grid_pts <- as.data.frame(iris.som$grid$pts)
grid_pts$column <- rep(1:iris.som$grid$xdim, by=iris.som$grid$ydim)
grid_pts$row <- rep(1:iris.som$grid$ydim, each=iris.som$grid$xdim)
grid_pts$classif <- 1:nrow(grid_pts)
grid_pts$id <- sapply(seq_along(grid_pts$classif),
function(x) paste(ga$sample[ga$g==x], collapse=", "))
grid_pts$count <- sapply(seq_along(grid_pts$classif),
function(x) length(ga$sample[ga$g==x]))
grid_pts$count <- factor(grid_pts$count, levels=0:max(grid_pts$count))
p1 <- ggplot(grid_pts, aes(x=x, y=y, colour=count, row=row, column=column, id=id)) +
geom_point(size=8) +
scale_colour_manual(values=c("grey50", heat.colors(length(unique(grid_pts$count))))) +
theme_void() +
theme(plot.margin=unit(c(1,rep(.3, 3)),"cm"))
ggplotly(p1)
这是一个完整的 Shiny 应用程序，它允许套索选择并显示 table 和数据：
invisible(suppressPackageStartupMessages(
lapply(c("shiny","dplyr","ggplot2", "plotly", "kohonen", "GGally", "DT"),
require, character.only=TRUE)))
iris_complete <- iris[complete.cases(iris),]
iris_unique <- unique(iris_complete) # Remove duplicates
#scale data
iris.sc = scale(iris_unique[, 1:4]) #Levels/Factors cannot be scaled... But used in predictive SOM:s using xyf. Later.
#build grid
iris.grid = somgrid(xdim = 10, ydim=10, topo="hexagonal", toroidal = TRUE)
set.seed(33) #for reproducability
iris.som <- som(iris.sc, grid=iris.grid, rlen=700, alpha=c(0.05,0.01), keep.data = TRUE)
ga <- data.frame(g=iris.som$unit.classif,
sample=seq_len(dim(iris.som$data[[1]])[1]))
grid_pts <- as.data.frame(iris.som$grid$pts)
grid_pts$column <- rep(1:iris.som$grid$xdim, by=iris.som$grid$ydim)
grid_pts$row <- rep(1:iris.som$grid$ydim, each=iris.som$grid$xdim)
grid_pts$classif <- 1:nrow(grid_pts)
grid_pts$id <- sapply(seq_along(grid_pts$classif),
function(x) paste(ga$sample[ga$g==x], collapse=", "))
grid_pts$count <- sapply(seq_along(grid_pts$classif),
function(x) length(ga$sample[ga$g==x]))
grid_pts$count <- factor(grid_pts$count, levels=0:max(grid_pts$count))
# Shiny app, adapted from https://gist.github.com/dgrapov/128e3be71965bf00495768e47f0428b9
ui <- fluidPage(
fluidRow(
column(12, plotlyOutput("plot", height = "600px")),
column(12, DT::dataTableOutput('data_table'))
)
)
server <- function(input, output){
output$plot <- renderPlotly({
req(data())
p <- ggplot(data = data()$data,
aes(x=x, y=y, classif=classif, colour=count, row=row, column=column, id=id)) +
geom_point(size=8) +
scale_colour_manual(
values=c("grey50", heat.colors(length(unique(grid_pts$count))))
) +
theme_void() +
theme(plot.margin=unit(c(1, rep(.3, 3)), "cm"))
obj <- data()$sel
if(nrow(obj) != 0) {
p <- p + geom_point(data=obj, mapping=aes(x=x, y=y, classif=classif,
count=count, row=row, column=column, id=id), color="blue",
size=5, inherit.aes=FALSE)
}
ggplotly(p, source="p1") %>% layout(dragmode = "lasso")
})
selected <- reactive({
event_data("plotly_selected", source = "p1")
})
output$data_table <- DT::renderDataTable(
data()$sel, filter='top', options=list(
pageLength=5, autoWidth=TRUE
)
)
data <- reactive({
tmp <- grid_pts
sel <- tryCatch(filter(grid_pts, paste(x, y, sep="_") %in%
paste(selected()$x, selected()$y, sep="_")),
error=function(e){NULL})
list(data=tmp, sel=sel)
})
}
shinyApp(ui,server)
我正在学习这里的教程：https://www.rpubs.com/loveb/som。本教程介绍如何在虹膜数据上使用 Kohonen 网络（也称为 SOM，一种机器学习算法）。
我运行教程中的这段代码：
library(kohonen) #fitting SOMs
library(ggplot2) #plots
library(GGally) #plots
library(RColorBrewer) #colors, using predefined palettes
iris_complete <-iris[complete.cases(iris),]
iris_unique <- unique(iris_complete) # Remove duplicates
#scale data
iris.sc = scale(iris_unique[, 1:4]) #Levels/Factors cannot be scaled... But used in predictive SOM:s using xyf. Later.
#build grid
iris.grid = somgrid(xdim = 10, ydim=10, topo="hexagonal", toroidal = TRUE)
set.seed(33) #for reproducability
iris.som <- som(iris.sc, grid=iris.grid, rlen=700, alpha=c(0.05,0.01), keep.data = TRUE)
#plot 1
plot(iris.som, type="count")
#plot2
var <- 1 #define the variable to plot
plot(iris.som, type = "property", property = getCodes(iris.som)[,var], main=colnames(getCodes(iris.som))[var], palette.name=terrain.colors)
以上代码在虹膜数据上拟合了一个 Kohonen 网络。数据集中的每个观察值都分配给下图中的每个“彩色圆圈”（也称为“神经元”）。
我的问题：在这些图中，您如何确定哪些观察值被分配给了哪些圆圈？假设我想知道哪些观察属于下面用黑色三角形勾勒出的圆圈：
这可以吗？现在，我正在尝试使用 iris.som$classif 以某种方式追踪哪些点在哪个圆圈中。有更好的方法吗？
更新：@Jonny Phelps 向我展示了如何识别三角形内的观察结果（请参阅下面的答案）。但我仍然不确定是否可以识别不规则形状。例如。
在之前的 post (Labelling Points on a Plot (R Language)) 中，一位用户向我展示了如何为网格上的每个圆分配任意数字：
根据上图，您如何使用“som$classif”语句找出哪些观测值位于圆圈 92、91、82、81、72 和 71 中？
谢谢
据我所知，使用 iris.som$unit.classif 和 iris.som$grid 是在绘图网格中隔离圆圈的方法。我假设分类器值与 iris.som$grid 的行索引匹配，因此这将需要更多验证。如果这对您的问题有帮助，请告诉我:)
findTriangle <- function(top_row, top_column, side_length, iris.som,
reverse=FALSE){
# top_row: row index of the top most triangle value
# top_column: column index...
# side_length: how many rows does the triangle occupy?
# iris.som: the som object
# reverse: set to TRUE to flip the triangle
# make the grid
grid_pts <- as.data.frame(iris.som$grid$pts)
grid_pts$column <- rep(1:iris.som$grid$xdim, by=iris.som$grid$ydim)
grid_pts$row <- rep(1:iris.som$grid$ydim, each=iris.som$grid$xdim)
grid_pts$classif <- 1:nrow(grid_pts)
# starting point - top most point of the triangle
# use reverse for triangles the other way around
grid_pts$triangle <- FALSE
grid_pts[grid_pts$column == top_column & grid_pts$row == top_row, ][["triangle"]] <- TRUE
# loop through the remaining rows and fill out the triangle
value_row <- top_row
value_start_column <- grid_pts[grid_pts$triangle == TRUE,]$x
value_end_column <- grid_pts[grid_pts$triangle == TRUE,]$x
if(reverse){
row_move <- -1
}else{
row_move <- 1
}
# update triangle
for(row in 1:(side_length-1)){
value_row <- value_row + row_move
value_start_column <- value_start_column - 0.5
value_end_column <- value_end_column + 0.5
grid_pts[grid_pts$row == value_row &
grid_pts$x >= value_start_column &
grid_pts$x <= value_end_column, ]$triangle <- TRUE
}
# visualise
pl <- ggplot(grid_pts, aes(x=x, y=rev(row), col=as.factor(triangle))) +
geom_point(size=7) +
scale_color_manual(values=c("grey", "indianred")) +
theme_void()
print(pl)
return(grid_pts)
}
# take the grid and pick out the triangle
top_row <- 2
top_column <- 6
side_length <- 4
reverse <- FALSE # set to TRUE to flip the triangle ie go from the bottom
grid_pts <- findTriangle(top_row, top_column, side_length, iris.som, reverse)
# now add the classifier and merge to get the co-ordinates
iris.sc2 <- as.data.frame(iris.sc)
iris.sc2$classif <- iris.som$unit.classif
iris.sc2 <- merge(iris.sc2, grid_pts, by=c("classif"), all.x=TRUE)
# filter to the points in the triangle
iris.sc2[iris.sc2$triangle==TRUE,]
输出数据：
classif Sepal.Length Sepal.Width Petal.Length Petal.Width x y column row triangle
21 16 -1.01537328 0.5506423 -1.3287735 -1.3042249 6.0 1.732051 6 2 TRUE
22 16 -1.01537328 0.3214643 -1.4419091 -1.3042249 6.0 1.732051 6 2 TRUE
39 25 -0.89501479 1.0089981 -1.3287735 -1.3042249 5.5 2.598076 5 3 TRUE
40 25 -0.77465630 1.0089981 -1.2722057 -1.3042249 5.5 2.598076 5 3 TRUE
41 25 -0.77465630 0.7798202 -1.3287735 -1.3042249 5.5 2.598076 5 3 TRUE
42 25 -1.01537328 0.7798202 -1.2722057 -1.3042249 5.5 2.598076 5 3 TRUE
43 25 -0.89501479 0.7798202 -1.2722057 -1.3042249 5.5 2.598076 5 3 TRUE
44 26 -0.89501479 0.5506423 -1.1590702 -0.9108454 6.5 2.598076 6 3 TRUE
45 26 -1.01537328 0.7798202 -1.2156380 -1.0419719 6.5 2.598076 6 3 TRUE
58 36 -0.53393933 0.7798202 -1.2722057 -1.0419719 6.0 3.464102 6 4 TRUE
59 36 -0.41358084 1.0089981 -1.3853413 -1.3042249 6.0 3.464102 6 4 TRUE
60 36 -0.53393933 0.7798202 -1.1590702 -1.3042249 6.0 3.464102 6 4 TRUE
61 37 -1.01537328 1.0089981 -1.2156380 -0.7797188 7.0 3.464102 7 4 TRUE
62 37 -1.01537328 1.0089981 -1.3853413 -1.1730984 7.0 3.464102 7 4 TRUE
63 37 -0.89501479 1.0089981 -1.3287735 -1.1730984 7.0 3.464102 7 4 TRUE
74 44 0.06785311 0.3214643 0.5945312 0.7937995 4.5 4.330127 4 5 TRUE
75 46 -0.65429782 1.4673539 -1.2722057 -1.3042249 6.5 4.330127 6 5 TRUE
76 46 -0.53393933 1.4673539 -1.2722057 -1.3042249 6.5 4.330127 6 5 TRUE
77 47 -0.89501479 1.6965319 -1.0459346 -1.0419719 7.5 4.330127 7 5 TRUE
78 47 -0.89501479 1.6965319 -1.2156380 -1.3042249 7.5 4.330127 7 5 TRUE
79 47 -0.89501479 1.4673539 -1.2722057 -1.0419719 7.5 4.330127 7 5 TRUE
80 47 -0.89501479 1.6965319 -1.2722057 -1.1730984 7.5 4.330127 7 5 TRUE
网格上的验证绘图：
我在我的 post 中详细说明了示例，但是，不是在鸢尾花数据集上，但我想这没问题：R, SOM, Kohonen Package, Outlier Detection 并且还添加了您可能需要的代码片段。他们显示
- 如何生成数据、添加离群值并在图上描绘它们
- 如何训练 SOM
- 如何进行聚类
- 如何使用层次聚类将聚类边界添加到 SOM 图
- 最后，我添加了 SOM 预测的集群，以将它们与我生成数据的真实集群进行比较
我认为这回答了您的问题。将 SOM 的性能与 t-SNE 进行比较也很好。我只使用 SOM 作为对我生成的数据和真实葡萄酒数据集的实验。如果你有两个以上的变量，准备热图也很好。祝您分析一切顺利！
编辑：现在有了 Shiny App！
plotly 解决方案也是可能的，您可以将鼠标悬停在单个神经元上以显示相关的虹膜行名（此处称为 id）。根据您的 iris.som 数据和 Jonny Phelps 的网格方法，您可以将行号作为连接字符串分配给各个神经元，并在鼠标悬停时显示这些：
library(ggplot2)
library(plotly)
ga <- data.frame(g=iris.som$unit.classif,
sample=seq_len(dim(iris.som$data[[1]])[1]))
grid_pts <- as.data.frame(iris.som$grid$pts)
grid_pts$column <- rep(1:iris.som$grid$xdim, by=iris.som$grid$ydim)
grid_pts$row <- rep(1:iris.som$grid$ydim, each=iris.som$grid$xdim)
grid_pts$classif <- 1:nrow(grid_pts)
grid_pts$id <- sapply(seq_along(grid_pts$classif),
function(x) paste(ga$sample[ga$g==x], collapse=", "))
grid_pts$count <- sapply(seq_along(grid_pts$classif),
function(x) length(ga$sample[ga$g==x]))
grid_pts$count <- factor(grid_pts$count, levels=0:max(grid_pts$count))
p1 <- ggplot(grid_pts, aes(x=x, y=y, colour=count, row=row, column=column, id=id)) +
geom_point(size=8) +
scale_colour_manual(values=c("grey50", heat.colors(length(unique(grid_pts$count))))) +
theme_void() +
theme(plot.margin=unit(c(1,rep(.3, 3)),"cm"))
ggplotly(p1)
这是一个完整的 Shiny 应用程序，它允许套索选择并显示 table 和数据：
invisible(suppressPackageStartupMessages(
lapply(c("shiny","dplyr","ggplot2", "plotly", "kohonen", "GGally", "DT"),
require, character.only=TRUE)))
iris_complete <- iris[complete.cases(iris),]
iris_unique <- unique(iris_complete) # Remove duplicates
#scale data
iris.sc = scale(iris_unique[, 1:4]) #Levels/Factors cannot be scaled... But used in predictive SOM:s using xyf. Later.
#build grid
iris.grid = somgrid(xdim = 10, ydim=10, topo="hexagonal", toroidal = TRUE)
set.seed(33) #for reproducability
iris.som <- som(iris.sc, grid=iris.grid, rlen=700, alpha=c(0.05,0.01), keep.data = TRUE)
ga <- data.frame(g=iris.som$unit.classif,
sample=seq_len(dim(iris.som$data[[1]])[1]))
grid_pts <- as.data.frame(iris.som$grid$pts)
grid_pts$column <- rep(1:iris.som$grid$xdim, by=iris.som$grid$ydim)
grid_pts$row <- rep(1:iris.som$grid$ydim, each=iris.som$grid$xdim)
grid_pts$classif <- 1:nrow(grid_pts)
grid_pts$id <- sapply(seq_along(grid_pts$classif),
function(x) paste(ga$sample[ga$g==x], collapse=", "))
grid_pts$count <- sapply(seq_along(grid_pts$classif),
function(x) length(ga$sample[ga$g==x]))
grid_pts$count <- factor(grid_pts$count, levels=0:max(grid_pts$count))
# Shiny app, adapted from https://gist.github.com/dgrapov/128e3be71965bf00495768e47f0428b9
ui <- fluidPage(
fluidRow(
column(12, plotlyOutput("plot", height = "600px")),
column(12, DT::dataTableOutput('data_table'))
)
)
server <- function(input, output){
output$plot <- renderPlotly({
req(data())
p <- ggplot(data = data()$data,
aes(x=x, y=y, classif=classif, colour=count, row=row, column=column, id=id)) +
geom_point(size=8) +
scale_colour_manual(
values=c("grey50", heat.colors(length(unique(grid_pts$count))))
) +
theme_void() +
theme(plot.margin=unit(c(1, rep(.3, 3)), "cm"))
obj <- data()$sel
if(nrow(obj) != 0) {
p <- p + geom_point(data=obj, mapping=aes(x=x, y=y, classif=classif,
count=count, row=row, column=column, id=id), color="blue",
size=5, inherit.aes=FALSE)
}
ggplotly(p, source="p1") %>% layout(dragmode = "lasso")
})
selected <- reactive({
event_data("plotly_selected", source = "p1")
})
output$data_table <- DT::renderDataTable(
data()$sel, filter='top', options=list(
pageLength=5, autoWidth=TRUE
)
)
data <- reactive({
tmp <- grid_pts
sel <- tryCatch(filter(grid_pts, paste(x, y, sep="_") %in%
paste(selected()$x, selected()$y, sep="_")),
error=function(e){NULL})
list(data=tmp, sel=sel)
})
}
shinyApp(ui,server)