R中交互式直方图选择的数据提取与汇总

我想使用plotly或其他软件包创建一个交互式直方图，如果更适合R，则使用类似于此示例集的数据：

test<-data.frame(sex=c("m","m","f","f","m","m","f","m","f","m","m","m","f","f","m","m","f","m","f","m","m","m","f","f","m","m","f","m","f","m","m","m","f","f","m","m","f","m","f","m","m","m","f","f","m","m","f","m","f","m","m","m","f","f","m","m","f","m","f","m","m","m","f","f","m","m","f","m","f","m","m","m","f","f","m","m","f","m","f","m"),weight=runif(80,5,9))

我想展示每个性别的体重分布的两个叠加直方图，以及一些汇总统计数据，如标准差、平均值、样本数量、所有性别以及全球范围。 此外，我希望能够更好地使用范围滑块或选择框进行选择，同时将这些汇总统计信息更新到选择中。然后，我希望能够向原始数据集添加一个变量，以指示样本是否是选择的一部分。

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谢谢你的帮助！即使它只是指向一个相关的在线资源，我也很难找到一个解决类似问题的资源。

@DataZhukov这是一个基于您更大数据样本的修订答案。根据回答，我删除了并排思考年龄金字塔，并展示了如何使用{plotly}作为直方图

虽然{plotly}支持交互，但它基于静态html网页的概念。这意味着在查看页面的客户端/用户上不进行活动计算。 对于简单的统计/摘要，您可以查看{crosstalk}&以启用一些动态更新，即客户端计算。 对于一个成熟的动态选择/过滤/重新计算类型的交互，{shinny}是一个不错的选择。但那是另一场球赛

{plotly}允许您通过指定add_text层自由放置文本批注。 我是根据你的数据构造的。您也可以手动以向量的形式定义它

如果使用数据帧作为输入数据结构，请注意{plotly}对变量使用tilde符号

test<-data.frame(sex=c("m","m","f","f","m","m","f","m","f","m","m","m","f","f","m","m","f","m","f","m","m","m","f","f","m","m","f","m","f","m","m","m","f","f","m","m","f","m","f","m","m","m","f","f","m","m","f","m","f","m","m","m","f","f","m","m","f","m","f","m","m","m","f","f","m","m","f","m","f","m","m","m","f","f","m","m","f","m","f","m"),weight=runif(80,5,9))


# calculate mean, sd, etc based on given data
# note you can also define this with simple vectors
total_stats <- test_df %>% 
  summarise(SAMPLE = n(), MEAN_WEIGHT = mean(weight), SD = sd(weight)) %>%
  mutate(sex = "m+f")

group_stats <- test_df %>% group_by(sex) %>%
  summarise(SAMPLE = n(), MEAN_WEIGHT = mean(weight), SD = sd(weight))

my_stats <- bind_rows(total_stats, group_stats) %>%
  mutate(LABEL = paste0(sex, " sample size: ", SAMPLE
                        , " with mean ", round(MEAN_WEIGHT, 2)
                        , " and SD ", round(SD, 2)
                        )
         )

# format your text, e.g. font face and size ---- format to your liking
tf <- list(
  family = "sans serif",
  size = 11
)

这将产生：

显然，您可以自由定义文本批注的x、y位置

默认行为将计数条并排放置。如果要强制覆盖行为，可以绘制两个直方图并强制这两个图形层进行覆盖。对于后者，需要在布局层中设置模式。我还使用了alpha透明度，因为您的数据样本中可能存在重叠计数。文本放置等遵循上述原则

# split test data frame in a male and female df
males <- test %>% filter(sex == "m")
fems  <- test %>% filter(sex == "f")

plot_ly(
     alpha = 0.5     # set alpha to ensure visibility on overlapping counts
   , nbinsx = 20     # set number of bins
  ) %>%
#------------ add a histogram layer per group -------------------
  add_histogram(data = males, x = ~weight, name = "male") %>%
  add_histogram(data = fems,  x = ~weight, name = "female") %>%
#------------ tweak layout --------------------------------------
  layout(
    barmode = "overlay"   # to change side-by-side default to overlay
  )

---

谢谢你的回答，至少在一定程度上帮助了我。我编辑了我的问题，以显示示例数据集，使其更接近我计划使用的实际数据，但它应该是两个直方图，而不是您在这里提出的金字塔图。此外，你的答案中的SD和Mean是否随选择内容更新？对我来说似乎不是这样，而且我还不清楚如何提取或指示图形上的选定数据。我一直在尝试使用shiny来完成这项工作，但没有成功。我修改了我的答案，以备不时之需。简单地说，交互性级别仅限于操纵其显示方式，例如，您可以选择要显示/不显示或放大的图层“男/女”。客户端没有活动的重新计算，即静态。html小部件对此提供了一些支持。然而，如果你想把你的绘图和这些小部件结合起来，你需要使用一个与{crosstalk}兼容的小部件，现在它是有限的。如果您想使用数据的主动筛选并重新计算stats:=动态，那么Shining是一个不错的选择。祝你好运

# split test data frame in a male and female df
males <- test %>% filter(sex == "m")
fems  <- test %>% filter(sex == "f")

plot_ly(
     alpha = 0.5     # set alpha to ensure visibility on overlapping counts
   , nbinsx = 20     # set number of bins
  ) %>%
#------------ add a histogram layer per group -------------------
  add_histogram(data = males, x = ~weight, name = "male") %>%
  add_histogram(data = fems,  x = ~weight, name = "female") %>%
#------------ tweak layout --------------------------------------
  layout(
    barmode = "overlay"   # to change side-by-side default to overlay
  )