R闪亮图和d3heatmap兼容性问题

Question

我试图添加一个交互式热图到我的闪亮的应用程序，但我也有互动的图形使用ggi相图。我目前正在使用d3heatmap包，但是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)

加在一起，只有计时器呈现，而热图不呈现。但是，如果您注释掉了ggi相图代码，heatmap就会呈现。我试着改变加载包的顺序，但仍然没有效果。

我目前正在R3.2.2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更新它的权限)。我尝试下载shinyheatmap、heatmaply和heatmap.2包，但是由于版本控制问题，安装失败了。

所以现在，我已经使用pheatmap创建了热图，但是它们不是交互的(也就是说，当我在单个单元格上悬停时无法得到值，并且不能放大)。有什么解决办法吗，还是有其他的交互式热图包可以工作？我希望避免将我的所有图形更改为平面图，因为我的代码中有很多这样的图形。

如果你需要其他信息，请告诉我。任何建议都将不胜感激！

Answer 1

(让你知道我是d3heatmap的作者)因为ggi相图使用的是d3.js版本4，d3heatmap使用的是D3.js版本3，所以两者之间存在冲突。我认为没有解决这一冲突的办法。

然而，使用ggplot2 2/ggi相图构建交互式热图并不是那么困难。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)

​

​

希望它能帮上忙

Answer 2

我知道这个问题是在一段时间前回答的，但我遇到了同样的问题，我无法使用ggplot2，因为它只是缓慢地处理我的Shiny应用程序。heatmaply包的分配速度更快，实现起来更容易。我执行了一个迷你基准测试(n= 20)。ggplot2的平均时间为64秒。用heatmaply只花了2秒。这两种方法都使用了'ave'方法hclust.I，希望这是有帮助的。

的迷你基准n= 20

下面是我使用的代码：

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)