使用 ggplot2 Rstudio 进行时间序列可视化（国家级）

Question

我刚刚在学习 R 基础知识，我想请您帮忙处理数据可视化，尤其是时间序列。我正在研究从 2009 年到 2019 年每个国家/地区特定类别政党（右翼民粹主义者）的投票份额如何随着时间的推移而变化。这是我的数据集：

dput(votesharespop)
structure(list(country = c("Austria", "Belgium", "Bulgaria", 
"Czech Republic", "Denmark", "Estonia", "Finland", "France", 
"Germany", "Great Britain", "Greece", "Hungary", "Italy", "Lithuania", 
"Luxembourg", "Netherlands", "Poland", "Romania", "Portugal", 
"Slovakia", "Slovenia", "Spain", "Sweden", "Austria", "Belgium", 
"Bulgaria", "Czech Republic", "Denmark", "Estonia", "Finland", 
"France", "Germany", "Great Britain", "Greece", "Hungary", "Italy", 
"Lithuania", "Luxembourg", "Netherlands", "Poland", "Romania", 
"Portugal", "Slovakia", "Slovenia", "Spain", "Sweden", "Austria", 
"Belgium", "Bulgaria", "Czech Republic", "Denmark", "Estonia", 
"Finland", "France", "Germany", "Great Britain", "Greece", "Hungary", 
"Italy", "Lithuania", "Luxembourg", "Netherlands", "Poland", 
"Romania", "Portugal", "Slovakia", "Slovenia", "Spain", "Sweden"
), year = c(2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
2009, 2009, 2009, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 
2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 
2014, 2014, 2014, 2014, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 
2019, 2019, 2019, 2019, 2019), vote_share = c(17.3, 15.7, 16.7, 
4.3, 15.3, 0, 9.8, 8.1, 1.7, 22.7, 7.2, 71.2, 45.5, 12.2, 7.4, 
17, 27.4, 8.7, 0, 5.6, 35.2, 0, 3.3, 20.2, 7.6, 16.8, 4.8, 26.6, 
5.3, 12.9, 28.7, 0.4, 28.6, 6.2, 66.2, 26.7, 14.3, 7.5, 13.3, 
31.8, 2.7, 0, 3.6, 28.8, 1.6, 9.7, 17.2, 13.8, 14.6, 10, 10.8, 
12.7, 13.8, 26.8, 11, 34.9, 6.2, 62.2, 49.5, 2.7, 10, 14.5, 49.1, 
0, 1.5, 7.3, 30.3, 6.2, 15.3), continent = c("Europe", "Europe", 
"Europe", "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", 
"Europe", "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", 
"Europe", "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", 
"Europe", "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", 
"Europe", "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", 
"Europe", "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", 
"Europe", "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", 
"Europe", "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", 
"Europe", "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", 
"Europe", "Europe", "Europe", "Europe")), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -69L))

我的目标是得到这样的东西（没有互动）：

或类似方面的内容，但适用于每个国家/地区。

非常感谢您的关注。

Answer 1

数据

votesharespop <- structure(list(country = c("Austria", "Belgium", "Bulgaria", 
                           "Czech Republic", "Denmark", "Estonia", "Finland", "France", 
                           "Germany", "Great Britain", "Greece", "Hungary", "Italy", "Lithuania", 
                           "Luxembourg", "Netherlands", "Poland", "Romania", "Portugal", 
                           "Slovakia", "Slovenia", "Spain", "Sweden", "Austria", "Belgium", 
                           "Bulgaria", "Czech Republic", "Denmark", "Estonia", "Finland", 
                           "France", "Germany", "Great Britain", "Greece", "Hungary", "Italy", 
                           "Lithuania", "Luxembourg", "Netherlands", "Poland", "Romania", 
                           "Portugal", "Slovakia", "Slovenia", "Spain", "Sweden", "Austria", 
                           "Belgium", "Bulgaria", "Czech Republic", "Denmark", "Estonia", 
                           "Finland", "France", "Germany", "Great Britain", "Greece", "Hungary", 
                           "Italy", "Lithuania", "Luxembourg", "Netherlands", "Poland", 
                           "Romania", "Portugal", "Slovakia", "Slovenia", "Spain", "Sweden"
), year = c(2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
            2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
            2009, 2009, 2009, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 
            2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 
            2014, 2014, 2014, 2014, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 
            2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 
            2019, 2019, 2019, 2019, 2019), vote_share = c(17.3, 15.7, 16.7, 
                                                          4.3, 15.3, 0, 9.8, 8.1, 1.7, 22.7, 7.2, 71.2, 45.5, 12.2, 7.4, 
                                                          17, 27.4, 8.7, 0, 5.6, 35.2, 0, 3.3, 20.2, 7.6, 16.8, 4.8, 26.6, 
                                                          5.3, 12.9, 28.7, 0.4, 28.6, 6.2, 66.2, 26.7, 14.3, 7.5, 13.3, 
                                                          31.8, 2.7, 0, 3.6, 28.8, 1.6, 9.7, 17.2, 13.8, 14.6, 10, 10.8, 
                                                          12.7, 13.8, 26.8, 11, 34.9, 6.2, 62.2, 49.5, 2.7, 10, 14.5, 49.1, 
                                                          0, 1.5, 7.3, 30.3, 6.2, 15.3), continent = c("Europe", "Europe", 
                                                                                                       "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", 
                                                                                                       "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", 
                                                                                                       "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", 
                                                                                                       "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", 
                                                                                                       "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", 
                                                                                                       "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", 
                                                                                                       "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", 
                                                                                                       "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", 
                                                                                                       "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", "Europe", 
                                                                                                       "Europe", "Europe", "Europe", "Europe")), class = c("tbl_df", 
                                                                                                                                                           "tbl", "data.frame"), row.names = c(NA, -69L))

代码

library(ggplot2)
library(ggthemes) # to access theme_hc()

ggplot(data = votesharespop, mapping = aes(x = year, y = vote_share, color = country)) + # specify data, x-axis, y-axis and grouping variable
  geom_line() + # a line per group
  geom_point() + # points per group
  theme_hc() +  # a ggtheme, similar to your example
  labs(title = "Variation of vote shares of right wing populists, 2009 to 2019", # plot title
       subtitle = "Add a subtitle of your choice", # plot subtitle
       caption = "Add a caption of your choice") + # plot caption
  theme(legend.position = "right", # move legend to the right hand side of the plot
        axis.title.x = element_blank(), # remove x axis title
        axis.title.y = element_blank(), # remove y axis title
        legend.title = element_blank(), # remove legend title
        plot.title = element_text(size = 20, color = "gray40"), # change size and color of plot title
        plot.subtitle = element_text(color = "gray40"), # change color of subtitle
        plot.caption = element_text(color = "gray40", hjust = 0)) + # change color of caption and left-align
  scale_y_continuous(breaks = seq(0, 80, by = 20)) + # specify min, max and break distance for y axis
  scale_x_continuous(breaks = seq(2009, 2019, by = 5)) + # specify min, max and break distance for x axis
  expand_limits(y = c(0, 80))

输出

但是请注意，对于多个组，颜色可能很难区分。最好使用 facet_wrap

代码

ggplot(data = votesharespop, mapping = aes(x = year, y = vote_share, color = country)) + # specify data, x-axis, y-axis and grouping variable
  geom_line() + # a line per group
  geom_point() + # points per group
  theme_hc() +  # a ggtheme, similar to your example
  labs(title = "Variation of vote shares of right wing populists, 2009 to 2019", # plot title
       subtitle = "Add a subtitle of your choice", # plot subtitle
       caption = "Add a caption of your choice") + # plot caption
  theme(legend.position = "right", # move legend to the right hand side of the plot
        axis.title.x = element_blank(), # remove x axis title
        axis.title.y = element_blank(), # remove y axis title
        legend.title = element_blank(), # remove legend title
        plot.title = element_text(size = 20, color = "gray40"), # change size and color of plot title
        plot.subtitle = element_text(color = "gray40"), # change color of subtitle
        plot.caption = element_text(color = "gray40", hjust = 0)) + # change color of caption and left-align
  scale_y_continuous(breaks = seq(0, 75, by = 25)) + # specify min, max and break distance for y axis
  scale_x_continuous(breaks = seq(2009, 2019, by = 5)) + # specify min, max and break distance for x axis
  expand_limits(y = c(0, 75)) + # adjust y axis limits
  facet_wrap(~ country) + # facet wrap
  theme(legend.position = "none") + # remove legend, since not needed anymore in facet_wrap
  theme(panel.spacing.x = unit(4, "mm")) # avoid overlapping of x axis text

输出

使用 ggplot2 Rstudio 进行时间序列可视化（国家级）

Time series visualization with ggplot2 Rstudio (country-level)

r

ggplot2

time-series

data-visualization

facet