我试图重现我在出版物 ( Mayor-Fernández et al 2012 ) 中发现的一个图表，显示了几个地区随时间变化的失业率。它结合了热图 (?)、时间序列图（下面板）和箱线图（右面板）。
为了更好地了解情节，请参阅http://www2.dse.unibo.it/wp/WP835.pdf中的第 7 页
有人可以指导我正确的方向或分享一些代码吗？非常感谢您的帮助和时间！
区域的时间趋势可视化
机器算法验证
r
时间序列
数据可视化
ggplot2
2022-03-28 23:34:51
2个回答
您可以轻松地制作每个图。坚持你的例子，我将使用 1999 年至 2011 年间欧洲国家的失业数据（来自欧盟统计局），称为unempd（对不起，它很长！）：
> dput(unempd)
structure(list(Year = c(1999L, 2000L, 2001L, 2002L, 2003L, 2004L,
2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 2000L, 2001L,
2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L,
2011L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L,
2007L, 2008L, 2009L, 2010L, 2011L, 1999L, 2000L, 2001L, 2002L,
2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L,
1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L,
2008L, 2009L, 2010L, 2011L, 1999L, 2000L, 2001L, 2002L, 2003L,
2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 1999L,
2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L,
2009L, 2010L, 2011L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L,
2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 1999L, 2000L,
2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L,
2010L, 2011L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L,
2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 1999L, 2000L, 2001L,
2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L,
2011L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L,
2008L, 2009L, 2010L, 2011L, 1999L, 2000L, 2001L, 2002L, 2003L,
2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 1999L,
2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L,
2009L, 2010L, 2011L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L,
2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 1999L, 2000L,
2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L,
2010L, 2011L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L,
2007L, 2008L, 2009L, 2010L, 2011L, 1999L, 2000L, 2001L, 2002L,
2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L,
1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L,
2008L, 2009L, 2010L, 2011L, 1999L, 2000L, 2001L, 2002L, 2003L,
2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 1999L,
2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L,
2009L, 2010L, 2011L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L,
2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 1999L, 2000L,
2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L,
2010L, 2011L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L,
2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 1999L, 2000L, 2001L,
2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L,
2011L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L,
2007L, 2008L, 2009L, 2010L, 2011L, 1999L, 2000L, 2001L, 2002L,
2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L,
1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L,
2008L, 2009L, 2010L, 2011L, 1999L, 2000L, 2001L, 2002L, 2003L,
2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 1999L,
2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L,
2009L, 2010L, 2011L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L,
2008L, 2009L, 2010L, 2011L), Country = structure(c(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, 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,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 28L, 28L,
28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 25L, 25L, 25L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L,
27L, 27L, 27L, 27L, 27L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L,
26L, 26L, 26L, 26L, 26L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L,
29L, 29L, 29L, 29L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L,
31L, 31L, 31L, 31L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("Austria",
"Belgium", "Bulgaria", "Croatia", "Cyprus", "Czech Republic",
"Denmark", "Estonia", "Finland", "France", "Germany", "Greece",
"Hungary", "Iceland", "Ireland", "Italy", "Latvia", "Lithuania",
"Luxembourg", "Malta", "Netherlands", "Norway", "Poland", "Portugal",
"Romania", "Slovakia", "Slovenia", "Spain", "Sweden", "Switzerland",
"United Kingdom"), class = "factor"), Unemployment = c(8.6, 7,
6.6, 7.5, 8.2, 8.4, 8.4, 8.2, 7.5, 7, 7.9, 8.3, 7.1, 3.6, 4.1,
18.2, 13.7, 12, 10.1, 9, 6.9, 5.6, 6.8, 10.2, 11.2, 8.8, 8.8,
8.2, 7.3, 7.8, 8.3, 7.9, 7.1, 5.3, 4.4, 6.7, 7.3, 6.7, 5.6, 4.6,
4.6, 4.6, 5.4, 5.5, 4.8, 3.9, 3.8, 3.3, 6, 7.4, 7.6, 8.9, 7.9,
7.8, 8.5, 9.8, 10.7, 11.1, 10.2, 8.6, 7.5, 7.7, 7.1, 5.9, 11.6,
13.6, 12.6, 10.3, 10, 9.7, 7.9, 5.9, 4.7, 5.5, 13.8, 16.9, 12.5,
5.8, 4.3, 3.9, 4.4, 4.7, 4.5, 4.3, 4.4, 4.6, 6, 11.7, 13.5, 14.4,
12.1, 11.4, 10.8, 10.3, 9.7, 10.5, 9.8, 8.9, 8.3, 7.7, 9.5, 12.5,
17.7, 15.7, 13.9, 10.5, 11.5, 11.5, 11, 9.2, 8.5, 8.3, 11.3,
18, 20.1, 21.6, 12, 10.2, 9.1, 9.2, 8.9, 9.3, 9.3, 9.3, 8.4,
7.8, 9.5, 9.7, 9.7, 11.4, 10.6, 9.5, 9, 8.7, 8, 7.7, 6.8, 6.1,
6.7, 7.8, 8.4, 8.4, 5, 4, 3.3, 4.1, 4.3, 5.3, 4.5, 3.9, 3.7,
5.3, 6.2, 7.7, 13.8, 14.2, 13.1, 12.1, 10.5, 10.4, 8.9, 6.8,
6, 7.5, 17.1, 18.7, 15.4, 13.4, 15.9, 16.8, 13.7, 12.4, 11.4,
8.3, 5.6, 4.3, 5.8, 13.7, 17.8, 15.4, 2.4, 2.3, 1.8, 2.6, 3.7,
5.1, 4.5, 4.7, 4.1, 5.1, 5.1, 4.4, 4.9, 7, 6.4, 5.7, 5.8, 5.9,
6.1, 7.2, 7.5, 7.4, 7.8, 10, 11.2, 10.9, 6.3, 7.1, 6.9, 7.6,
7.2, 7.3, 7.3, 6.4, 6, 7, 6.9, 6.5, 3.6, 2.9, 2.3, 2.8, 3.7,
4.6, 4.7, 3.9, 3.2, 2.8, 3.4, 4.5, 4.4, 3.7, 3.5, 3.6, 4, 4.3,
4.9, 5.2, 4.7, 4.4, 3.8, 4.8, 4.4, 4.1, 12.3, 16.1, 18.2, 19.9,
19.6, 19, 17.7, 13.9, 9.6, 7.1, 8.2, 9.6, 9.6, 4.5, 4, 4, 5,
6.3, 6.7, 7.6, 7.7, 8, 7.6, 9.5, 10.8, 12.7, 6.9, 7.2, 6.6, 8.4,
7, 8.1, 7.2, 7.3, 6.4, 5.8, 6.9, 7.3, 7.4, 7.4, 6.7, 6.2, 6.3,
6.7, 6.3, 6.5, 6, 4.8, 4.4, 5.9, 7.2, 8.2, 16.4, 18.8, 19.3,
18.7, 17.6, 18.2, 16.3, 13.4, 11.1, 9.5, 12, 14.4, 13.5, 10.2,
9.8, 9.1, 9.1, 9, 8.8, 8.4, 7.7, 6.9, 6.4, 8.2, 8.4, 7.8, 7.6,
5.4, 4.8, 5.1, 5.7, 6.5, 7.5, 7.1, 6.2, 6.2, 8.4, 8.4, 7.5, 6,
5.6, 5, 5.1, 5, 4.7, 4.8, 5.4, 5.3, 5.6, 7.6, 7.8, 8, 2.2, 1.9,
1.9, 3, 3.3, 3, 2.5, 2.8, 2.3, 2.9, 7.2, 7.6, 7, 3.2, 3.3, 3.5,
3.8, 4, 4.2, 4.4, 3.4, 2.5, 2.5, 3.1, 3.5, 3.2, 3.1, 2.7, 2.5,
2.9, 4.1, 4.3, 4.4, 4, 3.7, 3.3, 4.1, 4.5, 4.1, 15.1, 13.9, 13.7,
12.6, 11.1, 9.6, 8.4, 9.1, 11.8, 13.4)), .Names = c("Year", "Country",
"Unemployment"), class = "data.frame", row.names = c(NA, -397L
))
您可以使用以下方法制作热图：
library(ggplot2)
hmplot <- ggplot(unempd, aes(Year, Country, fill=Unemployment))
hmplot + geom_tile(colour="white") + scale_fill_gradient(low="light blue", high="dark blue") +
ylab("") + xlab("") + opts(legend.position="none")
产生以下情节：
然后制作时间序列图，您可以使用[我刚刚使用包中的函数对geom_line(stat="identity")国家/地区的年度数据进行平均，这显然不是整个欧洲失业率的合法反映，但希望能起到说明作用.. .]。ddplyplyr
library(plyr)
unempxyr <- ddply(unempd, .(Year), summarise, meanunemp = mean(Unemployment))
tsplot <- ggplot(unempxyr, aes(Year, meanunemp))
tsplot + geom_line(stat="identity") + ylab("Level") + xlab("") +
scale_y_continuous(lim=c(5,10)) + theme_bw()
这导致了这个图形：
最后，对于“箱线图”，我再次用于ddply计算每个国家/地区的箱线图统计数据：
countryxemp <- ddply(unempd, .(Country), summarise,
minemp = fivenum(Unemployment)[1],
q2emp = fivenum(Unemployment)[2],
medemp = fivenum(Unemployment)[3],
q3emp = fivenum(Unemployment)[4],
maxemp = fivenum(Unemployment)[5]
)
bplot <- ggplot(countryxemp, aes(medemp, Country)) + geom_point()
bplot + geom_errorbarh(aes(xmin=minemp, xmax=q2emp), colour=I("black"), height=0) +
geom_errorbarh(aes(xmin=q3emp, xmax=maxemp), colour=I("black"), height=0) +
ylab("") + xlab("Levels\n (internal)") + theme_bw()
这导致了这个图形：
这是否足够接近您想要的？以文章的方式将情节放在一起是另一回事。我不确定是否可以通过gridExtra::grid.arrange()或类似的方式...？
我知道你的答案被标记为 R，但如果你对 Excel 解决方案持开放态度，那么制作相同类型的图形相对容易：
从我的角度来看，Excel 比 R 有几个优势。首先有更多的人可以访问它，然后可以使用 R（我认为我就是一个很好的例子），所以你可以通过该软件获得更多的受众。此外，在 Excel 中，这是一个交互式图表，因此顶行（在此示例中为荷兰）可用作任何系列的选择器，并且在更改时重新填充该行中的所有相关区域，加上较低的目标系列图表。
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