最未被充分利用的数据可视化 [英] Most underused data visualization

问题描述

直方图和散点图是可视化数据和变量之间关系的好方法，但最近我一直想知道我缺少哪些可视化技术.你认为最未被充分利用的情节类型是什么?

答案应该:

1. 不是很常用练习.
2. 无需过多解释即可理解背景讨论.
3. 适用于许多常见情况.
4. 包括可重现的代码以创建一个例子(最好在 R 中).链接的图像将是不错.

解决方案

我非常同意其他海报:，它轻松提供创建交互式 3D 图形的能力.网上有很多这方面的例子(包括在 rgl 文档中).

R-Wiki 有一个很好的例子 如何使用 rgl 绘制 3D 散点图.

GGobi

另一个值得了解的包是rggobi.有 一本关于这个主题的 Springer 书，以及许多很棒的在线文档/示例，包括在 <一个 href="http://lookingatdata.com/jsm-2009/" rel="noreferrer">查看数据" 课程.

Histograms and scatterplots are great methods of visualizing data and the relationship between variables, but recently I have been wondering about what visualization techniques I am missing. What do you think is the most underused type of plot?

Answers should:

1. Not be very commonly used in practice.
2. Be understandable without a great deal of background discussion.
3. Be applicable in many common situations.
4. Include reproducible code to create an example (preferably in R). A linked image would be nice.

解决方案

I really agree with the other posters: Tufte's books are fantastic and well worth reading.

First, I would point you to a very nice tutorial on ggplot2 and ggobi from "Looking at Data" earlier this year. Beyond that I would just highlight one visualization from R, and two graphics packages (which are not as widely used as base graphics, lattice, or ggplot):

Heat Maps

I really like visualizations that can handle multivariate data, especially time series data. Heat maps can be useful for this. One really neat one was featured by David Smith on the Revolutions blog. Here is the ggplot code courtesy of Hadley:

stock <- "MSFT"
start.date <- "2006-01-12"
end.date <- Sys.Date()
quote <- paste("http://ichart.finance.yahoo.com/table.csv?s=",
                stock, "&a=", substr(start.date,6,7),
                "&b=", substr(start.date, 9, 10),
                "&c=", substr(start.date, 1,4), 
                "&d=", substr(end.date,6,7),
                "&e=", substr(end.date, 9, 10),
                "&f=", substr(end.date, 1,4),
                "&g=d&ignore=.csv", sep="")    
stock.data <- read.csv(quote, as.is=TRUE)
stock.data <- transform(stock.data,
  week = as.POSIXlt(Date)$yday %/% 7 + 1,
  wday = as.POSIXlt(Date)$wday,
  year = as.POSIXlt(Date)$year + 1900)

library(ggplot2)
ggplot(stock.data, aes(week, wday, fill = Adj.Close)) + 
  geom_tile(colour = "white") + 
  scale_fill_gradientn(colours = c("#D61818","#FFAE63","#FFFFBD","#B5E384")) + 
  facet_wrap(~ year, ncol = 1)

Which ends up looking somewhat like this:

RGL: Interactive 3D Graphics

Another package that is well worth the effort to learn is RGL, which easily provides the ability to create interactive 3D graphics. There are many examples online for this (including in the rgl documentation).

The R-Wiki has a nice example of how to plot 3D scatter plots using rgl.

GGobi

Another package that is worth knowing is rggobi. There is a Springer book on the subject, and lots of great documentation/examples online, including at the "Looking at Data" course.

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