r - R highcharter：将热图付诸实践

Question

我正在尝试使用highcharterR 包“运动插件”来制作热图的运动图表。即我希望热图随时间变化，使用带有播放/暂停按钮的滑块（参见下面的链接）。

我可以为特定年份创建一个简单的热图，例如：

df <- tibble(year = c(rep(2016, 6), rep(2017, 6)),
         xVar = rep(c("a", "a", "b", "b", "c", "c"), 2),
         yVar = rep(c("d", "e"), 6),
         heatVar = rnorm(12))

df %>%
  filter(year == 2016) %>%
  hchart(type = "heatmap", hcaes(x = xVar, y = yVar, value = heatVar)) %>%
  hc_legend(layout = "vertical", verticalAlign = "top", align = "right")

hc_motion(enabled = TRUE, ...)但是，我正在努力使用该函数将其制作为动态图表（在此示例中滑过 2016 年、2017 年） 。

我已阅读并关注以下链接：

https://www.r-bloggers.com/adding-motion-to-choropleths/

http://jkunst.com/highcharter/plugins.html

但无论我如何定义我的系列，我都没有得到预期的结果。任何人都可以指出我应该如何定义xVar,yVar系列以及如何hc_motion使用该功能使其工作？

---

更新：

按照这个答案，我设法做到了这一点shiny，但我仍然希望避免这种解决方案：

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

  output$heatmap <- renderHighchart({

  df <- tibble(year = c(rep(2016, 6), rep(2017, 6)),
             xVar = rep(c("a", "a", "b", "b", "c", "c"), 2),
             yVar = rep(c("d", "e"), 6),
             heatVar = rnorm(12))

  # filter data based on selected year
  df.select <- dplyr::filter(df, year == input$year) 

  # chart
  hchart(df.select, type = "heatmap", hcaes(x = xVar, y = yVar, value = heatVar))



  })

})

ui <- shinyUI(fluidPage(

  # Application title
  titlePanel("Highcharts Heatmap Motion Chart"),

  # Sidebar with a slider input for the selected year
  sidebarLayout(
    sidebarPanel(
      sliderInput("year",
                  "Year:",
                  min = 2016,
                  max = 2017,
                  step = 1,
                  value = 2016,
                  animate = TRUE,
                  sep = "")
    ),

    # Show a bubble plot for the selected year
    mainPanel(
      highchartOutput("heatmap")
    )
  )
))

shinyApp(ui = ui, server = server)

Answer 1

这种方法肯定不是最干净的，因为它需要创建初始位置（如标准图表），然后为每个点创建 te 序列。

http://rpubs.com/jbkunst/questions-42945062

所以添加动作插件的结构是：

模拟数据

library(highcharter)
library(dplyr)
library(purrr)


years <- 10
nx <- 5
ny <- 6
df <- data_frame(year = rep(c(2016 + 1:years - 1), each = nx * ny), xVar = rep(1:nx, 
  times = years * ny), yVar = rep(1:ny, times = years * nx))

df <- df %>% group_by(xVar, yVar) %>% mutate(heatVar = cumsum(rnorm(length(year))))

获取初始值

df_start <- df %>% arrange(year) %>% distinct(xVar, yVar, .keep_all = TRUE)
df_start
#> Source: local data frame [30 x 4]
#> Groups: xVar, yVar [30]
#> 
#>     year  xVar  yVar    heatVar
#>    <dbl> <int> <int>      <dbl>
#> 1   2016     1     1  0.5894443
#> 2   2016     2     2 -1.0991727
#> 3   2016     3     3  1.1209292
#> 4   2016     4     4  0.4936719
#> 5   2016     5     5 -0.4614157
#> # ... with 25 more rows

对固定变量进行分组以创建具有序列的列表

df_seqc <- df %>% group_by(xVar, yVar) %>% do(sequence = list_parse(select(., 
  value = heatVar)))
df_seqc
#> Source: local data frame [30 x 3]
#> Groups: <by row>
#> 
#> # A tibble: 30 × 3
#>     xVar  yVar    sequence
#> *  <int> <int>      <list>
#> 1      1     1 <list [10]>
#> 2      1     2 <list [10]>
#> 3      1     3 <list [10]>
#> 4      1     4 <list [10]>
#> 5      1     5 <list [10]>
#> # ... with 25 more rows

加入

data <- left_join(df_start, df_seqc)
#> Joining, by = c("xVar", "yVar")
data
#> Source: local data frame [30 x 5]
#> Groups: xVar, yVar [?]
#> 
#>     year  xVar  yVar    heatVar    sequence
#>    <dbl> <int> <int>      <dbl>      <list>
#> 1   2016     1     1  0.5894443 <list [10]>
#> 2   2016     2     2 -1.0991727 <list [10]>
#> 3   2016     3     3  1.1209292 <list [10]>
#> 4   2016     4     4  0.4936719 <list [10]>
#> 5   2016     5     5 -0.4614157 <list [10]>
#> # ... with 25 more rows

和图表

limits <- (unlist(data$sequence)) %>% {
  c(min(.), max(.))
}
limits
#> [1] -5.332709  6.270384

hc1 <- hchart(data, type = "heatmap", hcaes(x = xVar, y = yVar, value = heatVar))

hc2 <- hchart(data, type = "heatmap", hcaes(x = xVar, y = yVar, value = heatVar)) %>% 
  hc_motion(enabled = TRUE, series = 0, startIndex = 0, labels = unique(df$year)) %>% 
  hc_legend(layout = "vertical", verticalAlign = "top", align = "right") %>% 
  hc_colorAxis(min = limits[1], max = limits[2])