我只是在學習 R 基礎知識，我想請你幫忙處理資料可視化，特別是時間序列。我正在研究從 2009 年到 2019 年，每個國家/地區特定類別政黨（右翼民粹主義者）的投票份額如何隨時間變化。這是我的資料集：
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))
我的目標是得到這樣的東西（沒有互動）：
或類似方面，但針對每個國家/地區。
非常感謝您的關注。
uj5u.com熱心網友回復：
資料
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))
代碼
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))
輸出
但是請注意，對于多個組，顏色可能很難區分。最好使用 facet_wrap
代碼
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
輸出
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