更好地组织图表上的数据答案

【问题标题】：Better Organization Of Data on Graph更好地组织图表上的数据
【发布时间】：2021-07-21 03:54:49
【问题描述】：

我的数据被组织成

Country   Year Region1 Region2...
Mexico    2007 10000   20000
Mexico    2008 1000.   1000
Guatemala 2007 500     2000
Guatemala 2008 1000    200

x <- read_csv("Downloads/BorderYearData.csv", skip = 1)
x[is.na(x)] = 0
x$CITIZENSHIP <- str_to_title(x$CITIZENSHIP) #Fixes Country Name from CAPS to Title Format
x$Region <- countrycode(sourcevar = x$CITIZENSHIP, origin = "country.name", destination = "region") #Applies a region to each country
Latin_America <- x %>% filter(Region == "Latin America & Caribbean")
South_West <- Latin_America #Only includes Latin American countries from 2007-2019
ggplot(South_West, aes(x = Year, y = YUM, col = CITIZENSHIP)) + geom_line() + geom_point() + scale_y_log10()  + ggtitle("Yuma Apprehensions By Country") + xlab("Year") + ylab("Apprehensions") #Plots the Years vs. Yuma Region filtered by Latin American countries

Year Vs. Yuma Region filtered by Latin American Countries 我面临的主要问题是我创建的图表有太多标签，因为它需要显示大约 42 个国家，并且使图表看起来非常小。我想知道是否有办法让图表看起来更大或更好地显示数据？

【问题讨论】：

标签： r ggplot2 country-codes

【解决方案1】：

根据你的价值观在情节上“重叠”的程度，也许你可以用国家名称而不是图例来标记线条，例如

最小可重现示例（“假”数据集）：

South_West <- structure(list(CITIZENSHIP = structure(c(1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L), .Label = c("Argentina", 
"Brazil", "Guatemala", "Uruguay", "Haiti", "Jamaica", "Barbados", 
"Ecuador", "Honduras", "Trinidad"), class = "factor"), Year = c(2005L, 
2005L, 2005L, 2005L, 2005L, 2005L, 2005L, 2005L, 2005L, 2005L, 
2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 
2006L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 
2007L, 2007L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 
2008L, 2008L, 2008L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 
2009L, 2009L, 2009L, 2009L, 2010L, 2010L, 2010L, 2010L, 2010L, 
2010L, 2010L, 2010L, 2010L, 2010L, 2011L, 2011L, 2011L, 2011L, 
2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2013L, 2013L, 
2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2014L, 
2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 
2015L), Region = c("Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean", 
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean"
), YUM = c(1.78122037672438, 2.37322455388494, 2.13142191176303, 
1.27564440481365, 3.42383766197599, 2.15245464001782, 1.88633515499532, 
1.9733019429259, 2.4353325588163, 1.32718274137005, 2.16036220546812, 
2.72933556581847, 3.22234436636791, 4.95644223992713, 1.46167525206693, 
3.81690606242046, 3.79422693513334, 2.37372375791892, 4.51981934485957, 
0.319619702640921, 2.22136777942069, 0.657447576522827, 3.38299622060731, 
2.99594825366512, 4.82561674318276, 4.61055635008961, 0.999588350532576, 
3.0135670141317, 4.56902083824389, 2.70756668644026, 1.8570082646329, 
1.19101525633596, 2.51976897357963, 3.210360937519, 3.66874974104576, 
4.38053260440938, 3.89196885516867, 3.19129517069086, 0.931762574473396, 
0.365887695224956, 4.02607865049504, 3.81519310059957, 4.43216874962673, 
3.50513802957721, 1.79077092558146, 1.65124102495611, 3.7168695544824, 
3.58231659629382, 2.9938762774691, 1.7820083280094, 2.81697046244517, 
2.64568548649549, 0.321083774324507, 0.201700357720256, 0.345256101572886, 
1.84380351682194, 1.43193824333139, 3.95378023153171, 1.0867537453305, 
1.90293225925416, 1.73558907699771, 3.74970692326315, 0.281770154833794, 
0.723997309105471, 3.25781691004522, 0.0901412731036544, 4.84373902203515, 
1.00294897332788, 4.80122401262634, 2.29895253665745, 0.393149207811803, 
1.70218026614748, 0.637223341036588, 1.07310341205448, 0.570325846783817, 
3.75451701926067, 1.13961286842823, 1.49829157744534, 4.191464248579, 
0.374115689191967, 1.77846394828521, 2.76220632251352, 0.591986104846001, 
2.77725224383175, 0.694206092739478, 0.182232277002186, 2.90164744481444, 
4.46320472285151, 2.61500569409691, 0.592531125294045, 4.52516902470961, 
2.69278525840491, 1.3527360453736, 2.26024812087417, 3.52698272559792, 
1.79418036015704, 1.70009316992946, 0.136871858267114, 1.7957616772037, 
3.17168297595344, 0.466970148263499, 1.65315211634152, 4.09610666916706, 
0.326487204292789, 3.25591150205582, 1.31828204495832, 0.486518631223589, 
2.16823832946829, 0.225406098179519, 2.96029959921725)), out.attrs = list(
    dim = structure(10:11, .Names = c("CITIZENSHIP", "Year")), 
    dimnames = list(CITIZENSHIP = c("CITIZENSHIP=Argentina", 
    "CITIZENSHIP=Brazil", "CITIZENSHIP=Guatemala", "CITIZENSHIP=Uruguay", 
    "CITIZENSHIP=Haiti", "CITIZENSHIP=Jamaica", "CITIZENSHIP=Barbados", 
    "CITIZENSHIP=Ecuador", "CITIZENSHIP=Honduras", "CITIZENSHIP=Trinidad"
    ), Year = c("Year=2005", "Year=2006", "Year=2007", "Year=2008", 
    "Year=2009", "Year=2010", "Year=2011", "Year=2012", "Year=2013", 
    "Year=2014", "Year=2015"))), class = "data.frame", row.names = c(NA, 
-110L))

library(tidyverse)
library(scales)

ggplot(South_West, aes(x = Year, y = YUM, col = CITIZENSHIP)) +
  geom_line() +
  geom_point() +
  geom_text(data = . %>% filter(Year == max(Year)),
            aes(x = Inf, y = YUM, label = CITIZENSHIP),
            hjust = -0.1, vjust = 0.4) +
  coord_cartesian(expand = FALSE, clip = "off") +
  scale_y_log10() +
  scale_x_continuous(breaks = scales::pretty_breaks(10)) +
  ggtitle("Yuma Apprehensions By Country") +
  xlab("Year") +
  ylab("Apprehensions") +
  theme(legend.position = "none",
        plot.margin = unit(c(1,6,1,1), "lines"))

【讨论】：

感谢您的帮助！快速提问，名字似乎重叠，你知道分散文本的方法吗？
是的 - @Uwe 下面的回答是防止数据标签重叠的绝佳解决方案

【解决方案2】：

在Jared's excellent answer 中，某些标签确实重叠，例如巴巴多斯和阿根廷。这可以使用ggrepel 包解决（请参阅详细的ggrepel examples page）。

以Jared's answer and data 为基础，

library(ggplot2)
library(ggrepel)
library(dplyr)

ggplot(South_West, aes(x = Year, y = YUM, colour = CITIZENSHIP)) +
  geom_line() +
  geom_point() +
  geom_label_repel(data = . %>% filter(Year == max(Year)),
                   aes(x = Year, y = YUM, label = CITIZENSHIP), 
                   xlim = c(-Inf, Inf), 
                   segment.curvature = -0.1,
                   nudge_x = 0.3, direction = "y", hjust = "left") +
  coord_cartesian(expand = TRUE, clip = "off") +
  scale_y_log10() +
  scale_x_continuous(breaks = scales::pretty_breaks(10)) +
  scale_colour_discrete(guide = "none") +
  ggtitle("Yuma Apprehensions By Country") +
  xlab("Year") +
  ylab("Apprehensions") +
  theme(plot.margin = unit(c(1,6,1,1), "lines"))

【讨论】：