29

[更新：虽然我已经接受了一个答案，但如果您有其他可视化想法（无论是 R 还是其他语言/程序），请添加另一个答案。关于分类数据分析的文本似乎没有太多关于可视化纵向数据，而关于纵向数据分析的文本似乎没有太多关于可视化类别成员随时间推移的主题内变化。对这个问题有更多的答案将使它成为解决标准参考文献中没有得到太多报道的问题的更好资源。]

一位同事刚刚给了我一个纵向分类数据集来查看，我试图弄清楚如何在可视化中捕获纵向方面。我在这里发帖，因为我想在 R 中执行此操作，但请让我知道交叉发布到 Cross-Validated 是否有意义，因为通常不鼓励交叉发布。

快速背景：数据跟踪通过学术咨询计划的学生从学期到学期的学术地位。数据为长格式，有五个变量：“id”、“cohort”、“term”、“standing”和“termGPA”。前两个标识学生和他们在咨询计划中的术语。最后三个是记录学生的学术地位和 GPA 的术语。我在下面粘贴了一些示例数据，使用dput.

我创建了一个马赛克图（见下文），按队列、地位和学期对学生进行分组。这显示了每个学期在每个学术地位类别中的学生比例。但这并没有捕捉到纵向方面——随着时间的推移跟踪个别学生的事实。我想跟踪具有给定学术地位的学生群体随着时间的推移所经历的路径。

例如：在 2009 年秋季（“F09”）获得“AP”（学术缓刑）的学生中，有多少部分在未来仍是 AP，有多少部分进入其他类别（例如，GS，“良好的信誉”）？自进入咨询计划以来，随着时间的推移，群组之间在类别之间的移动方面是否存在差异？

我不太清楚如何在 R 图形中捕捉这种纵向方面。该vcd软件包具有可视化分类数据的功能，但似乎并未解决纵向分类数据。是否有可视化纵向分类数据的“标准”方法？R是否有为此设计的包？长格式适合这种类型的数据还是使用宽格式会更好？

我将不胜感激有关解决此特定问题的建议，以及对文章、书籍等的建议，以了解更多有关可视化纵向分类数据的信息。

这是我用来制作马赛克图的代码。该代码使用下面列出的数据dput。

library(RColorBrewer)

# create a table object for plotting
df1.tab = table(df1$cohort, df1$term, df1$standing,
            dnn=c("Cohort\nAcademic Standing", "Term", "Standing"))

# create a mosaic plot
plot(df1.tab, las=1, dir=c("h","v","h"), 
     col=brewer.pal(8,"Dark2"),
     main="Fall 2009 and Fall 2010 Cohorts")

这是马赛克图（附带问题：是否有任何方法可以使 F10 队列的列直接位于 F09 队列的列下方并具有与 F09 队列的列相同的宽度，即使 F10 队列中的某些术语没有数据？） ：

在此处输入图像描述

这是用于创建表格和绘图的数据：

df1 =
structure(list(id = c(101L, 102L, 103L, 104L, 105L, 106L, 107L, 
108L, 109L, 110L, 111L, 112L, 113L, 114L, 115L, 116L, 117L, 118L, 
119L, 120L, 121L, 122L, 123L, 124L, 125L, 101L, 102L, 103L, 104L, 
105L, 106L, 107L, 108L, 109L, 110L, 111L, 112L, 113L, 114L, 115L, 
116L, 117L, 118L, 119L, 120L, 121L, 122L, 123L, 124L, 125L, 101L, 
102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L, 112L, 
113L, 114L, 115L, 116L, 117L, 118L, 119L, 120L, 121L, 122L, 123L, 
124L, 125L, 101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 
110L, 111L, 112L, 113L, 114L, 115L, 116L, 117L, 118L, 119L, 120L, 
121L, 122L, 123L, 124L, 125L, 101L, 102L, 103L, 104L, 105L, 106L, 
107L, 108L, 109L, 110L, 111L, 112L, 113L, 114L, 115L, 116L, 117L, 
118L, 119L, 120L, 121L, 122L, 123L, 124L, 125L, 101L, 102L, 103L, 
104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L, 112L, 113L, 114L, 
115L, 116L, 117L, 118L, 119L, 120L, 121L, 122L, 123L, 124L, 125L, 
101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L, 
112L, 113L, 114L, 115L, 116L, 117L, 118L, 119L, 120L, 121L, 122L, 
123L, 124L, 125L), cohort = structure(c(1L, 1L, 1L, 1L, 2L, 1L, 
1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 
1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 
2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 
1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 
1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L), .Label = c("F09", "F10"), class = c("ordered", 
"factor")), term = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L), .Label = c("S09", "F09", "S10", 
"F10", "S11", "F11", "S12"), class = c("ordered", "factor")), 
    standing = structure(c(2L, 4L, 1L, 4L, NA, 4L, 1L, NA, NA, 
    NA, NA, 2L, 2L, 1L, 4L, 4L, 1L, 3L, NA, NA, 4L, 3L, 1L, 4L, 
    NA, 2L, 1L, 3L, 3L, NA, 1L, 2L, NA, NA, NA, NA, 2L, 4L, 3L, 
    4L, 4L, 4L, 2L, NA, NA, 4L, 2L, 4L, 4L, NA, 3L, 4L, 6L, 6L, 
    1L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 4L, 6L, 4L, 4L, 1L, 4L, 1L, 
    2L, 4L, 3L, 1L, 4L, 1L, 6L, 1L, 6L, 6L, 7L, 4L, 4L, 2L, 2L, 
    4L, 2L, 6L, 4L, 6L, 7L, 4L, 2L, 4L, 1L, 2L, 4L, 6L, 6L, 4L, 
    2L, 2L, 3L, 6L, 6L, 7L, 4L, 4L, 3L, 4L, 4L, 6L, 2L, 1L, 6L, 
    6L, 4L, 2L, 1L, 7L, 2L, 4L, 6L, 6L, 4L, 4L, 3L, 6L, 4L, 6L, 
    2L, 4L, 4L, 6L, 4L, 4L, 6L, 3L, 2L, 6L, 6L, 4L, 2L, 6L, 3L, 
    4L, 4L, 6L, 6L, 4L, 4L, 5L, 6L, 4L, 6L, 4L, 4L, 4L, 5L, 4L, 
    4L, 6L, 6L, 2L, 6L, 6L, 4L, 3L, 6L, 6L, 4L, 4L, 6L, 6L, 4L, 
    4L), .Label = c("AP", "CP", "DQ", "GS", "DM", "NE", "WD"), class = "factor"), 
    termGPA = c(1.433, 1.925, 1, 1.68, NA, 1.579, 1.233, NA, 
    NA, NA, NA, 2.009, 1.675, 0, 1.5, 1.86, 0.5, 0.94, NA, NA, 
    1.777, 1.1, 1.133, 1.675, NA, 2, 1.25, 1.66, 0, NA, 1.525, 
    2.25, NA, NA, NA, NA, 1.66, 2.325, 0, 2.308, 1.6, 1.825, 
    2.33, NA, NA, 2.65, 2.65, 2.85, 3.233, NA, 1.25, 1.575, NA, 
    NA, 1, 2.385, 3.133, 0, 0, 1.729, 1.075, 0, 4, NA, 2.74, 
    0, 1.369, 2.53, 0, 2.65, 2.75, 0, 0.333, 3.367, 1, NA, 0.1, 
    NA, NA, 1, 2.2, 2.18, 2.31, 1.75, 3.073, 0.7, NA, 1.425, 
    NA, 2.74, 2.9, 0.692, 2, 0.75, 1.675, 2.4, NA, NA, 3.829, 
    2.33, 2.3, 1.5, NA, NA, NA, 2.69, 1.52, 0.838, 2.35, 1.55, 
    NA, 1.35, 0.66, NA, NA, 1.35, 1.9, 1.04, NA, 1.464, 2.94, 
    NA, NA, 3.72, 2.867, 1.467, NA, 3.133, NA, 1, 2.458, 1.214, 
    NA, 3.325, 2.315, NA, 1, 2.233, NA, NA, 2.567, 1, NA, 0, 
    3.325, 2.077, NA, NA, 3.85, 2.718, 1.385, NA, 2.333, NA, 
    2.675, 1.267, 1.6, 1.388, 3.433, 0.838, NA, NA, 0, NA, NA, 
    2.6, 0, NA, NA, 1, 2.825, NA, NA, 3.838, 2.883)), .Names = c("id", 
"cohort", "term", "standing", "termGPA"), row.names = c("101.F09.s09", 
"102.F09.s09", "103.F09.s09", "104.F09.s09", "105.F10.s09", "106.F09.s09", 
"107.F09.s09", "108.F10.s09", "109.F10.s09", "110.F10.s09", "111.F10.s09", 
"112.F09.s09", "113.F09.s09", "114.F09.s09", "115.F09.s09", "116.F09.s09", 
"117.F09.s09", "118.F09.s09", "119.F10.s09", "120.F10.s09", "121.F09.s09", 
"122.F09.s09", "123.F09.s09", "124.F09.s09", "125.F10.s09", "101.F09.f09", 
"102.F09.f09", "103.F09.f09", "104.F09.f09", "105.F10.f09", "106.F09.f09", 
"107.F09.f09", "108.F10.f09", "109.F10.f09", "110.F10.f09", "111.F10.f09", 
"112.F09.f09", "113.F09.f09", "114.F09.f09", "115.F09.f09", "116.F09.f09", 
"117.F09.f09", "118.F09.f09", "119.F10.f09", "120.F10.f09", "121.F09.f09", 
"122.F09.f09", "123.F09.f09", "124.F09.f09", "125.F10.f09", "101.F09.s10", 
"102.F09.s10", "103.F09.s10", "104.F09.s10", "105.F10.s10", "106.F09.s10", 
"107.F09.s10", "108.F10.s10", "109.F10.s10", "110.F10.s10", "111.F10.s10", 
"112.F09.s10", "113.F09.s10", "114.F09.s10", "115.F09.s10", "116.F09.s10", 
"117.F09.s10", "118.F09.s10", "119.F10.s10", "120.F10.s10", "121.F09.s10", 
"122.F09.s10", "123.F09.s10", "124.F09.s10", "125.F10.s10", "101.F09.f10", 
"102.F09.f10", "103.F09.f10", "104.F09.f10", "105.F10.f10", "106.F09.f10", 
"107.F09.f10", "108.F10.f10", "109.F10.f10", "110.F10.f10", "111.F10.f10", 
"112.F09.f10", "113.F09.f10", "114.F09.f10", "115.F09.f10", "116.F09.f10", 
"117.F09.f10", "118.F09.f10", "119.F10.f10", "120.F10.f10", "121.F09.f10", 
"122.F09.f10", "123.F09.f10", "124.F09.f10", "125.F10.f10", "101.F09.s11", 
"102.F09.s11", "103.F09.s11", "104.F09.s11", "105.F10.s11", "106.F09.s11", 
"107.F09.s11", "108.F10.s11", "109.F10.s11", "110.F10.s11", "111.F10.s11", 
"112.F09.s11", "113.F09.s11", "114.F09.s11", "115.F09.s11", "116.F09.s11", 
"117.F09.s11", "118.F09.s11", "119.F10.s11", "120.F10.s11", "121.F09.s11", 
"122.F09.s11", "123.F09.s11", "124.F09.s11", "125.F10.s11", "101.F09.f11", 
"102.F09.f11", "103.F09.f11", "104.F09.f11", "105.F10.f11", "106.F09.f11", 
"107.F09.f11", "108.F10.f11", "109.F10.f11", "110.F10.f11", "111.F10.f11", 
"112.F09.f11", "113.F09.f11", "114.F09.f11", "115.F09.f11", "116.F09.f11", 
"117.F09.f11", "118.F09.f11", "119.F10.f11", "120.F10.f11", "121.F09.f11", 
"122.F09.f11", "123.F09.f11", "124.F09.f11", "125.F10.f11", "101.F09.s12", 
"102.F09.s12", "103.F09.s12", "104.F09.s12", "105.F10.s12", "106.F09.s12", 
"107.F09.s12", "108.F10.s12", "109.F10.s12", "110.F10.s12", "111.F10.s12", 
"112.F09.s12", "113.F09.s12", "114.F09.s12", "115.F09.s12", "116.F09.s12", 
"117.F09.s12", "118.F09.s12", "119.F10.s12", "120.F10.s12", "121.F09.s12", 
"122.F09.s12", "123.F09.s12", "124.F09.s12", "125.F10.s12"), reshapeLong = structure(list(
    varying = list(c("s09as", "f09as", "s10as", "f10as", "s11as", 
    "f11as", "s12as"), c("s09termGPA", "f09termGPA", "s10termGPA", 
    "f10termGPA", "s11termGPA", "f11termGPA", "s12termGPA")), 
    v.names = c("standing", "termGPA"), idvar = c("id", "cohort"
    ), timevar = "term"), .Names = c("varying", "v.names", "idvar", 
"timevar")), class = "data.frame")
4

3 回答 3

32

以下是绘制数据的一些想法。我使用了ggplot2，并且在某些地方重新格式化了数据。

图1

在此处输入图像描述 我使用堆叠条形图来模拟您的马赛克图并解决对齐问题。

图 2

在此处输入图像描述 每个学生的数据点由一条灰线连接，这让人想起平行坐标图。为点着色显示分类地位。在 y 轴上使用 GPA 有助于分散点以减少过度绘制，并显示站立和 GPA 的相关性。一个主要问题是许多有效standing数据点因为缺少匹配的 termGPA 值而丢失。

图 3

在此处输入图像描述 在这里，我创建了一个名为 initial_standing 的新变量，用于分面。每个面板都包含在队列和 initial_standing 中匹配的学生。将 id 绘制为文本会使该图有点混乱，但在某些情况下可能很有用。

图 4

在此处输入图像描述 这个图就像一个热图，其中每一行都是一个学生。我控制id轴的顺序以强制 initial_standing 和群组分组保持在一起。如果您有更多行，您可能需要考虑按某种类型的聚类对行进行排序。 

library(ggplot2)

# Create new data frame for determining initial standing.
standing_data = data.frame(id=unique(df1$id), initial_standing=NA, cohort=NA)

for (i in 1:nrow(standing_data)) {
    id = standing_data$id[i]
    subdat = df1[df1$id == id, ]
    subdat = subdat[complete.cases(subdat), ]
    initial_standing = subdat$standing[which.min(subdat$term)]
    standing_data[i, "initial_standing"] = as.character(initial_standing)
    standing_data[i, "cohort"] = as.character(subdat$cohort[1])
}

standing_data$cohort = factor(standing_data$cohort, levels=levels(df1$cohort))
standing_data$initial_standing = factor(standing_data$initial_standing,
                                        levels=levels(df1$standing))

# Add the new column (initial_standing) to df1.
df1 = merge(df1, standing_data[, c("id", "initial_standing")], by="id")

# Remove rows where standing is missing. Make some plots tidier.
df1 = df1[!is.na(df1$standing), ]

# Create id factor, controlling the sort order of the levels.     
id_order = order(standing_data$initial_standing, standing_data$cohort)
df1$id = factor(df1$id, levels=as.character(standing_data$id)[id_order])


p1 = ggplot(df1, aes(x=term, fill=standing)) +
     geom_bar(position="fill", colour="grey20", size=0.5, width=1.0) +
     facet_grid(cohort ~ .) +
     scale_fill_brewer(palette="Set1")

p2 = ggplot(df1, aes(x=term, y=termGPA, group=id)) + 
     geom_line(colour="grey70") + 
     geom_point(aes(colour=standing), size=4) + 
     facet_grid(cohort ~ .) +
     scale_colour_brewer(palette="Set1")

p3 = ggplot(df1, aes(x=term, y=termGPA, group=id)) +
     geom_line(colour="grey70") + 
     geom_point(aes(colour=standing), size=4) + 
     geom_text(aes(label=id), hjust=-0.30, size=3) +
     facet_grid(initial_standing ~ cohort) +
     scale_colour_brewer(palette="Set1")


p4 = ggplot(df1, aes(x=term, y=id, fill=standing)) + 
     geom_tile(colour="grey20") +
     facet_grid(initial_standing ~ ., space="free_y", scales="free_y") +
     scale_fill_brewer(palette="Set1") +
     opts(panel.grid.major=theme_blank()) +
     opts(panel.grid.minor=theme_blank())

ggsave("plot_1.png", p1, width=10, height=6.25, dpi=80)
ggsave("plot_2.png", p2, width=10, height=6.25, dpi=80)
ggsave("plot_3.png", p3, width=10, height=6.25, dpi=80)
ggsave("plot_4.png", p4, width=10, height=6.25, dpi=80)
于 2012-07-17T07:38:09.610 回答
9

在研究我的问题时，我发现了一些其他选项，我将在此处列出。

许多相对较新的 R 包被设计用于可视化和分析“生活史”或“多状态序列”数据。这个想法是，随着时间的推移，人们（或物体）进入和退出各种类别——例如，职业变化、婚姻和离婚、健康和疾病，或者就我而言，大学学术地位的类别。

用于可视化序列或生活史数据的 R 包包括@timriffe 在上面的评论中提到的biograph和TraMineR。传记包的作者 Frans Willekens 有一本关于传记包的书，传记。使用 R 对生命史进行多态分析，将于今年秋天由 Springer 发表。TraMineR 在上面的链接中有详细的用户手册，还有一篇较短的JSS 文章。JSS 在风险分析的上下文中还有一个关于多状态模型的特殊问题，讨论了用于多状态建模的其他 R 包。

我还发现了一些专门的软件，旨在可视化类别之间随时间的变化。Parallel Sets是一个简单的免费程序，用于生成基本的可视化，尽管它的灵活性有限。Lifeflow更加复杂。它也是免费的，但您必须向创建者发送电子邮件请求副本。

一旦我有机会尝试这些工具，我会在这个答案中添加更多细节。

于 2012-07-18T18:03:12.017 回答
5

我希望在我写一个 R 包来解决这个问题之前找到@bdemarest 的答案，但是由于 OP 要求额外的更新，我将分享一个更多的解决方案。bdemarest 在图 4 中建议的是我一直称之为水平线图的类型。

在开发longCatEDAR 包时，我们发现对数据进行排序对于制作有用的绘图至关重要（example(sorter)有关技术细节，请参阅下面评论中链接的报告），尤其是在问题规模变大的情况下。例如，我们从 3 年（>1000 天）数千名参与者的每日饮酒数据（禁欲、使用、滥用）开始解决问题。

将水平线图应用于@eipi10 的数据的代码如下。图 1 按 分层term，图 2 按第一个状态分层，与 @bdemarest 的图 4 一样，尽管由于分层排序，结果并不相同。

图1

按项分层的水平线图

图 2

按初始状态分层的水平线图

# libraries
install.packages('longCatEDA')
library(longCatEDA)
library(RColorBrewer)

# transform data long to wide
dfw <- reshape(df1,
           timevar = 'term',
           idvar = c('id', 'cohort'),
           direction = 'wide')

# set up objects required by longCat()
y <- dfw[,seq(3,15,by=2)]
Labels <- levels(df1$standing)
tLabels <- levels(df1$term)
groupLabels <- levels(dfw$cohort)

# use the same colors as bdemarest
cols <- brewer.pal(7, "Set1")

# plot the longCat object
png('plot1.png', width=10, height=6.25, units='in', res=100)
par(bg='cornsilk3', mar=c(5.1, 4.1, 4.1, 8.1), xpd=TRUE)
lc <- longCat(y=y, Labels=Labels, tLabels=tLabels, id=dfw$id) 
longCatPlot(lc, cols=cols, xlab='Term', lwd=8, legendBuffer=0)
legend(8.1, 25, legend=Labels, col=cols, lty=1, lwd=4)
dev.off()

# stratify by term
png('plot2.png', width=10, height=6.25, units='in', res=100)
par(bg='cornsilk3', mar=c(5.1, 4.1, 4.1, 8.1), xpd=TRUE)
lc.g <- sorter(lc, group=dfw$cohort, groupLabels=groupLabels)
longCatPlot(lc.g, cols=cols, xlab='Term', lwd=8, legendBuffer=0) 
legend(8.1, 25, legend=Labels, col=cols, lty=1, lwd=4)
dev.off()

# stratify by first status, akin to Figure 4 by bdemarest
png('plot2.png', width=10, height=6.25, units='in', res=100)
par(bg='cornsilk3', mar=c(5.1, 4.1, 4.1, 8.1), xpd=TRUE)
first <- apply(!is.na(y), 1, function(x) which(x)[1])
first <- y[cbind(seq_along(first), first)]
lc.1 <- sorter(lc, group=factor(first), groupLabels = sort(unique(first)))
longCatPlot(lc.1, cols=cols, xlab='Term', lwd=8, legendBuffer=0) 
legend(8.1, 25, legend=Labels, col=cols, lty=1, lwd=4)
dev.off()
于 2017-01-23T17:31:59.323 回答