3

我正在尝试向我的 Shiny 应用程序添加交互式热图，但我也有使用 ggiraph 的交互式图表。我目前正在使用 d3heatmap 包，但热图不会在应用程序中呈现。我创建了一个玩具示例来说明这一点：

library(shiny)
library(ggiraph)
library(d3heatmap)

ui <- fluidPage(
    d3heatmapOutput('d3'),
    ggiraphOutput('gg')
)

server <- function(input, output, session) {

    # Create heatmap
    output$d3 <- renderD3heatmap({
        d3heatmap(matrix(1:100, nrow = 100, ncol = 100))
    })

    # Create ggiraph
    output$gg <- renderggiraph({
        p <- ggplot(iris, aes(x = Sepal.Length, y = Petal.Width,
                              color = Species, tooltip = iris$Species) ) +
             geom_point_interactive()

        ggiraph(code = {print(p)})
    })
}

shinyApp(ui =  ui, server = server)

一起，只有 ggiraph 渲染，但热图没有。但是，如果您注释掉 ggiraph 代码，则会呈现热图。我尝试切换加载包的顺序，但这仍然不起作用。

我目前在 R 3.2.2 上运行（我必须使用这个版本，因为公司服务器只在这个版本上运行，我的经理和我都没有权限更新它）。我尝试下载 shinyheatmap、heatmaply 和 heatmap.2 包，但由于版本问题，安装不成功。

所以现在，我刚刚使用 pheatmap 来创建热图，但它们不是交互式的（即，当我将鼠标悬停在单个单元格上时，我无法获取值，也无法放大）。是否有任何解决方法，或者是否有其他可以工作的交互式热图包？我想避免将我所有的 ggiraph 图更改为绘图图，因为我的代码中有很多。

如果您需要任何其他信息，请告诉我。我们欢迎所有的建议！

4

2 回答 2

5

（只是为了让您知道我是 ggiraph 的作者） ggiraph 和 d3heatmap 之间存在冲突，因为 ggiraph 使用的是d3.js 版本 4而 d3heatmap 使用的是D3.js 版本 3。我认为没有解决这种冲突的方法。

但是，使用 ggplot2/ggiraph 构建交互式热图并不难。见下文：

library(dplyr)
library(tidyr)
library(ggplot2)
library(ggiraph)
library(ggdendro)


# mydata <- cor(mtcars)
mydata <- matrix(runif(2500, min = -2, max = 2), ncol = 50)
row.names(mydata) <- paste0("row_", seq_len(nrow(mydata)))
colnames(mydata) <- paste0("col_", seq_len(ncol(mydata)))

# dendrogram for rows
hc <- hclust(dist(mydata), "ave")
dhr <- as.dendrogram(hc)
order_r <- rownames(mydata)[hc$order]

# dendrogram for columns
hc <- hclust(dist(t(mydata)), "ave")
dhc <- as.dendrogram(hc)
order_c <- colnames(mydata)[hc$order]

# the data
expr_set <- bind_cols(
  data_frame(rowvar = rownames(mydata)),
  as.data.frame(mydata)
)
expr_set <- gather(expr_set, colvar, measure, -rowvar)
expr_set$rowvar <- factor( expr_set$rowvar, levels = order_r )
expr_set$colvar <- factor( expr_set$colvar, levels = order_c )
expr_set <- arrange(expr_set, rowvar, colvar)

# get data for dendrograms - IMHO, ggdendro is the hero here...
data_c <- dendro_data(dhc, type = "rectangle")
data_c <- segment(data_c) %>% mutate(
  y = y + length(order_r) + .5,
  yend = yend + length(order_r) + .5
)

data_r <- dendro_data(dhr, type = "rectangle")
data_r <- segment(data_r)
data_r <- data_r %>%
  mutate( x_ = y + length(order_c) + .5,
          xend_ = yend + length(order_c) + .5,
          y_ = x,
          yend_ = xend )

expr_set <- expr_set %>% 
  mutate( 
    tooltip = sprintf("Row: %s<br/>Col: %s<br/>measure: %.02f", 
                      rowvar, colvar, measure) ,
    data_id = sprintf("%s_%s", rowvar, colvar)
    )


# all data are tidy and can be now used with ggplot
p <- ggplot(data = expr_set, aes(x = colvar, y = rowvar) ) +
  geom_tile_interactive(aes(fill = measure, tooltip = tooltip, data_id = data_id), colour = "white") +
  scale_fill_gradient(low = "white", high = "#BC120A") +
  geom_segment(
    data = data_c,
    mapping = aes(x = x, y = yend, xend = xend, yend = y),
    colour = "gray20", size = .2) +
  geom_segment(
    data = data_r,
    mapping = aes(x = x_, y = y_, xend = xend_, yend = yend_),
    colour = "gray20", size = .2) +
  coord_equal()

# cosmetics
p <- p + theme_minimal() +
  theme(
    legend.position = "right",
    panel.grid.minor = element_line(color = "transparent"),
    panel.grid.major = element_line(color = "transparent"),
    axis.ticks.length   = unit(2, units = "mm"),
    plot.title = element_text(face = "bold", hjust = 0.5, size = 12),
    axis.title = element_text(size = 9, colour = "gray30"),
    axis.text.y = element_text(hjust = 1, size = 5, colour = "gray40"),
    axis.text.x = element_text(angle = 90, hjust = 1, size = 5, colour = "gray40"),
    legend.title=element_text(face = "bold", hjust = 0.5, size=8),
    legend.text=element_text(size=6)
  )



ggiraph(ggobj = p)

在此处输入图像描述

希望能帮助到你

于 2017-06-22T22:54:11.187 回答
1

我知道这个问题已经回答了一段时间，但我遇到了同样的问题，我无法使用ggplot2，因为我的Shiny应用程序运行速度很慢。该heatmaply软件包分配得更快，更容易实施。我执行了一个迷你基准测试（n = 20）。平均ggplot2耗时 64 秒。只需heatmaply2秒。两种方法都使用. 我希望这有帮助的'ave'方法 。hclust

ggplot vs heatmaply的迷你基准n = 20

这是我使用的代码：

library(tidyr)
library(ggplot2)
library(ggiraph)
library(ggdendro)
library(heatmaply)

# mydata <- cor(mtcars)
create_data <- function(){
df <- matrix(runif(2500, min = -2, max = 2), ncol = 50)
row.names(df) <- paste0("row_", seq_len(nrow(df)))
colnames(df) <- paste0("col_", seq_len(ncol(df)))
return(df)
}

gg2heat <- function(mydata){
# dendrogram for rows
hc <- hclust(dist(mydata), "ave")
dhr <- as.dendrogram(hc)
order_r <- rownames(mydata)[hc$order]

# dendrogram for columns
hc <- hclust(dist(t(mydata)), "ave")
dhc <- as.dendrogram(hc)
order_c <- colnames(mydata)[hc$order]

# the data
expr_set <- bind_cols(
data_frame(rowvar = rownames(mydata)),
as.data.frame(mydata)
)
expr_set <- gather(expr_set, colvar, measure, -rowvar)
expr_set$rowvar <- factor( expr_set$rowvar, levels = order_r )
expr_set$colvar <- factor( expr_set$colvar, levels = order_c )
expr_set <- arrange(expr_set, rowvar, colvar)

# get data for dendrograms - IMHO, ggdendro is the hero here...
data_c <- dendro_data(dhc, type = "rectangle")
data_c <- segment(data_c) %>% mutate(
y = y + length(order_r) + .5,
yend = yend + length(order_r) + .5
)

data_r <- dendro_data(dhr, type = "rectangle")
data_r <- segment(data_r)
data_r <- data_r %>%
mutate( x_ = y + length(order_c) + .5,
       xend_ = yend + length(order_c) + .5,
       y_ = x,
       yend_ = xend )

expr_set <- expr_set %>% 
mutate( 
 tooltip = sprintf("Row: %s<br/>Col: %s<br/>measure: %.02f", 
                   rowvar, colvar, measure) ,
 data_id = sprintf("%s_%s", rowvar, colvar)
)


# all data are tidy and can be now used with ggplot
p <- ggplot(data = expr_set, aes(x = colvar, y = rowvar) ) +
geom_tile_interactive(aes(fill = measure, tooltip = tooltip, data_id = data_id), colour = "white") +
scale_fill_gradient(low = "white", high = "#BC120A") +
geom_segment(
 data = data_c,
 mapping = aes(x = x, y = yend, xend = xend, yend = y),
 colour = "gray20", size = .2) +
geom_segment(
 data = data_r,
 mapping = aes(x = x_, y = y_, xend = xend_, yend = yend_),
 colour = "gray20", size = .2) +
coord_equal()

# cosmetics
p <- p + theme_minimal() +
theme(
 legend.position = "right",
 panel.grid.minor = element_line(color = "transparent"),
 panel.grid.major = element_line(color = "transparent"),
 axis.ticks.length   = unit(2, units = "mm"),
 plot.title = element_text(face = "bold", hjust = 0.5, size = 12),
 axis.title = element_text(size = 9, colour = "gray30"),
 axis.text.y = element_text(hjust = 1, size = 5, colour = "gray40"),
 axis.text.x = element_text(angle = 90, hjust = 1, size = 5, colour = "gray40"),
 legend.title=element_text(face = "bold", hjust = 0.5, size=8),
 legend.text=element_text(size=6)
)
ggiraph(ggobj = p)
}

htmp_gg <- c()
htmp_maply <-c() 


for (i in 1:20){
df <- create_data()
time_gg <- (system.time(gg2heat(df)))[3]
htmp_gg<- append(htmp_gg, values = time_gg)
time_heatmaply <- (system.time(heatmaply::heatmaply(df, hclust_method = 'ave')))[3]
htmp_maply<- append(htmp_maply, values = time_heatmaply)
rm(df)
}

score <- data.frame(htmp_gg, htmp_maply)%>% gather(key = 'method', value = 'time')


p <- ggplot(score, aes(x = method, y = time, fill = method))+geom_violin()+ stat_summary(fun.y=median, geom="point", size=2, color="black")
print(p)
于 2019-10-04T14:48:41.843 回答