ggplot2 - 在尺寸图例中显示多个键（形状）
ggplot2 - Show multiple keys (shapes) in size legend
我遇到了与显示图例相关的问题。我什至不确定我能用 ggplot 解决这个问题，但由于我是 R 的基本用户，我相信这只是我的无知。
设置
我正在尝试从两个不同的数据集绘制两组不同的点，在同一组美学上：x、y、大小。两组点相对于 y 轴及其形状的垂直微移发生变化。
问题
情节如期而至（见代码下方的图片）。我唯一无法解决的问题与传说有关。特别是，我想让尺寸图例显示我在图表中使用的两种形状（在本例中为圆形和三角形）。有什么办法吗？我在网上浏览了一下，但没有找到任何关于这个问题的问题。
数据和代码
这里我把我正在使用的数据作为一个可重现的例子（通过 dput() 获得）。
# Packages
library(tidyverse)
library(ggtext)
library(janitor)
library(delabj)
library(wesanderson)
library(forcats)
# Basic data
basedata<-structure(list(country = structure(c("Argentina", "Argentina",
"Argentina", "Argentina", "Argentina", "Argentina", "Argentina",
"Argentina", "Argentina", "Argentina", "Argentina", "Argentina",
"Argentina", "Argentina"), format.stata = "%44s"), iso = structure(c("ARG",
"ARG", "ARG", "ARG", "ARG", "ARG", "ARG", "ARG", "ARG", "ARG",
"ARG", "ARG", "ARG", "ARG"), label = "iso_3", format.stata = "%9s"),
region = structure(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"), label = "Region", format.stata = "%26s"),
income_group = structure(c("Upper middle income", "Upper middle income",
"Upper middle income", "Upper middle income", "Upper middle income",
"Upper middle income", "Upper middle income", "Upper middle income",
"Upper middle income", "Upper middle income", "Upper middle income",
"Upper middle income", "Upper middle income", "Upper middle income"
), label = "Income group", format.stata = "%19s"), gdp = structure(c(519871519807.795,
519871519807.795, 519871519807.795, 519871519807.795, 519871519807.795,
519871519807.795, 519871519807.795, 519871519807.795, 519871519807.795,
519871519807.795, 519871519807.795, 519871519807.795, 519871519807.795,
519871519807.795), label = "(first) gdp_o", format.stata = "%9.0g"),
assessment = structure(c(2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1,
1, 2, 1), label = "Initial assessment", format.stata = "%12.0g", labels = c(liberalising = 1,
restrictive = 2)), start_date = structure(c(18341, 18349,
18354, 18366, 18393, 18393, 18270, 18270, 18339, 18354, 18354,
18393, 18393, 18351), label = "Announcement date", class = "Date", format.stata = "%td"),
duration = structure(c(357, 349, 344, 63, 33, 305, 156, 156,
87, 62, 344, 305, 33, 359), format.stata = "%9.0g"), GTAinterventiontype = structure(c("Export licensing requirement",
"Export licensing requirement", "Import Internal taxation",
"Import Internal taxation", "Import Internal taxation", "Import Internal taxation",
"Import licensing requirement", "Import licensing requirement",
"Import licensing requirement", "Import licensing requirement",
"Import tariff", "Import tariff", "Import tariff", "Import-related, Unknown measure"
), label = "GTA intervention type", format.stata = "%38s"),
any_food = structure(c(0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,
0, 0), label = "Product: Food", format.stata = "%8.0g", labels = c(`FALSE` = 0,
`TRUE` = 1)), any_medical = structure(c(1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1), label = "Product: Any medical product", format.stata = "%8.0g", labels = c(`FALSE` = 0,
`TRUE` = 1)), food_position = structure(c("Net Exporter",
"Net Exporter", "Net Exporter", "Net Exporter", "Net Exporter",
"Net Exporter", "Net Exporter", "Net Exporter", "Net Exporter",
"Net Exporter", "Net Exporter", "Net Exporter", "Net Exporter",
"Net Exporter"), format.stata = "%12s"), meds_position = structure(c("Net Importer",
"Net Importer", "Net Importer", "Net Importer", "Net Importer",
"Net Importer", "Net Importer", "Net Importer", "Net Importer",
"Net Importer", "Net Importer", "Net Importer", "Net Importer",
"Net Importer"), format.stata = "%12s"), month = c(3, 3,
4, 4, 5, 5, 1, 1, 3, 4, 4, 5, 5, 3), Announcement = c("March",
"March", "April", "April", "May", "May", "January", "January",
"March", "April", "April", "May", "May", "March"), Domain = c("Medical",
"Medical", "Medical", "Medical", "Medical", "Medical", "Food & Meds",
"Food & Meds", "Medical", "Medical", "Medical", "Medical",
"Medical", "Medical"), gdp_group_avg = c("Low Income", "Low Income",
"Low Income", "Low Income", "Low Income", "Low Income", "Low Income",
"Low Income", "Low Income", "Low Income", "Low Income", "Low Income",
"Low Income", "Low Income"), gdp_group_med = c("High Income",
"High Income", "High Income", "High Income", "High Income",
"High Income", "High Income", "High Income", "High Income",
"High Income", "High Income", "High Income", "High Income",
"High Income"), shp_point = c(23, 23, 21, 21, 23, 21, 23,
21, 21, 21, 21, 21, 23, 21), length_cat = c("More than 3 months",
"More than 3 months", "More than 3 months", "[1-3] months",
"[1-3] months", "More than 3 months", "More than 3 months",
"More than 3 months", "[1-3] months", "[1-3] months", "More than 3 months",
"More than 3 months", "[1-3] months", "More than 3 months"
), type = structure(c(2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 1L), .Label = c("Liberalizig", "Restrictive"
), class = "factor")), row.names = c(NA, -14L), class = "data.frame")
# Subset 1 - assessment == 1
prova1<-structure(list(country = c("Argentina", "Argentina", "Argentina",
"Argentina", "Argentina", "Argentina", "Argentina", "Argentina",
"Argentina"), iso = c("ARG", "ARG", "ARG", "ARG", "ARG", "ARG",
"ARG", "ARG", "ARG"), 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"), income_group = c("Upper middle income",
"Upper middle income", "Upper middle income", "Upper middle income",
"Upper middle income", "Upper middle income", "Upper middle income",
"Upper middle income", "Upper middle income"), gdp = c(519871519807.795,
519871519807.795, 519871519807.795, 519871519807.795, 519871519807.795,
519871519807.795, 519871519807.795, 519871519807.795, 519871519807.795
), assessment = c(1, 1, 1, 1, 1, 1, 1, 1, 1), start_date = structure(c(18354,
18366, 18393, 18270, 18339, 18354, 18354, 18393, 18351), class = "Date"),
duration = c(344, 63, 305, 156, 87, 62, 344, 305, 359), GTAinterventiontype = c("Import Internal taxation",
"Import Internal taxation", "Import Internal taxation", "Import licensing requirement",
"Import licensing requirement", "Import licensing requirement",
"Import tariff", "Import tariff", "Import-related, Unknown measure"
), any_food = c(0, 0, 0, 1, 0, 0, 0, 0, 0), any_medical = c(1,
1, 1, 1, 1, 1, 1, 1, 1), food_position = c("Net Exporter",
"Net Exporter", "Net Exporter", "Net Exporter", "Net Exporter",
"Net Exporter", "Net Exporter", "Net Exporter", "Net Exporter"
), meds_position = c("Net Importer", "Net Importer", "Net Importer",
"Net Importer", "Net Importer", "Net Importer", "Net Importer",
"Net Importer", "Net Importer"), month = c(4, 4, 5, 1, 3,
4, 4, 5, 3), Announcement = c("April", "April", "May", "January",
"March", "April", "April", "May", "March"), Domain = c("Medical",
"Medical", "Medical", "Food & Meds", "Medical", "Medical",
"Medical", "Medical", "Medical"), gdp_group_avg = c("Low Income",
"Low Income", "Low Income", "Low Income", "Low Income", "Low Income",
"Low Income", "Low Income", "Low Income"), gdp_group_med = c("High Income",
"High Income", "High Income", "High Income", "High Income",
"High Income", "High Income", "High Income", "High Income"
), shp_point = c(21, 21, 21, 21, 21, 21, 21, 21, 21), length_cat = c("More than 3 months",
"[1-3] months", "More than 3 months", "More than 3 months",
"[1-3] months", "[1-3] months", "More than 3 months", "More than 3 months",
"More than 3 months"), type = structure(c(1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L), .Label = c("Liberalizig", "Restrictive"
), class = "factor")), row.names = c(3L, 4L, 6L, 8L, 9L,
10L, 11L, 12L, 14L), class = "data.frame")
# Subset 2 - assessment == 2
prova2<-structure(list(country = c("Argentina", "Argentina", "Argentina",
"Argentina", "Argentina"), iso = c("ARG", "ARG", "ARG", "ARG",
"ARG"), region = c("Latin America & Caribbean", "Latin America & Caribbean",
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean"
), income_group = c("Upper middle income", "Upper middle income",
"Upper middle income", "Upper middle income", "Upper middle income"
), gdp = c(519871519807.795, 519871519807.795, 519871519807.795,
519871519807.795, 519871519807.795), assessment = c(2, 2, 2,
2, 2), start_date = structure(c(18341, 18349, 18393, 18270, 18393
), class = "Date"), duration = c(357, 349, 33, 156, 33), GTAinterventiontype = c("Export licensing requirement",
"Export licensing requirement", "Import Internal taxation", "Import licensing requirement",
"Import tariff"), any_food = c(0, 0, 0, 1, 0), any_medical = c(1,
1, 1, 1, 1), food_position = c("Net Exporter", "Net Exporter",
"Net Exporter", "Net Exporter", "Net Exporter"), meds_position = c("Net Importer",
"Net Importer", "Net Importer", "Net Importer", "Net Importer"
), month = c(3, 3, 5, 1, 5), Announcement = c("March", "March",
"May", "January", "May"), Domain = c("Medical", "Medical", "Medical",
"Food & Meds", "Medical"), gdp_group_avg = c("Low Income", "Low Income",
"Low Income", "Low Income", "Low Income"), gdp_group_med = c("High Income",
"High Income", "High Income", "High Income", "High Income"),
shp_point = c(23, 23, 23, 23, 23), length_cat = c("More than 3 months",
"More than 3 months", "[1-3] months", "More than 3 months",
"[1-3] months"), type = structure(c(2L, 2L, 2L, 2L, 2L), .Label = c("Liberalizig",
"Restrictive"), class = "factor")), row.names = c(1L, 2L,
5L, 7L, 13L), class = "data.frame")
请注意 prova1 和 prova2 只是 basedata 的两个子集。剧情代码如下ig
countrydata %>%
ggplot(aes(x = start_date, y = fct_rev(GTAinterventiontype), shape = type)) +
geom_point(data = prova1, aes(color = fct_rev(GTAinterventiontype),size=duration, shape = fct_rev(type)), alpha = 0.65, position = position_nudge(y = +0.05)) +
geom_point(data = prova2, aes(color = fct_rev(GTAinterventiontype),size=duration, shape = fct_rev(type)), alpha = 0.65, position = position_nudge(y = -0.05)) +
scale_shape(drop=FALSE) +
guides(color = FALSE,
shape = guide_legend(order = 1, nrow = 2, ncol = 1),
size = guide_legend(order = 2)) +
delabj::theme_delabj() +
delabj::scale_color_delabj() +
#delabj::legend_none() +
labs(shape = 'Type',
size = "Duration",
x="",
y="",
title = paste("ARG", "Med-related Measures by Announcement date", sep = ": "),
subtitle = "Bubbles are proportional to expected duration of the measure",
caption = "")
结果图就是这个。
任何想法、建议，甚至不可行的警告（如果确实如此）都非常受欢迎！
试试这个。基本思想是复制尺寸图例的中断和符号。在第二步中，我通过 guide_legend 调整符号。也许不完美，但在尝试了一些我能想到的最好的方法之后。
library(tidyverse)
library(ggtext)
library(janitor)
library(delabj)
library(wesanderson)
library(forcats)
# Breaks, labels and symbols
breaks <- c(100, 200, 300)
n_breaks <- length(breaks)
labels <- c(breaks, rep("", n_breaks))
shapes <- c(rep(16, n_breaks), rep(17, n_breaks))
breaks2 <- rep(breaks, 2)
basedata %>%
ggplot(aes(x = start_date, y = fct_rev(GTAinterventiontype), shape = type)) +
geom_point(data = prova1, aes(color = fct_rev(GTAinterventiontype), size=duration, shape = fct_rev(type)), alpha = 0.65, position = position_nudge(y = +0.05)) +
scale_size_continuous(breaks = breaks2, labels = labels,
guide = guide_legend(order = 2, nrow = 2, byrow = TRUE,
override.aes = list(shape = shapes),
direction = "horizontal", label.vjust = -.5)) +
geom_point(data = prova2, aes(color = fct_rev(GTAinterventiontype), size=duration, shape = fct_rev(type)), alpha = 0.65, position = position_nudge(y = -0.05)) +
scale_shape(drop=FALSE) +
guides(color = FALSE,
shape = guide_legend(order = 1, nrow = 2, ncol = 1)) +
delabj::theme_delabj() +
delabj::scale_color_delabj() +
#delabj::legend_none() +
labs(shape = 'Type',
size = "Duration",
x="",
y="",
title = paste("ARG", "Med-related Measures by Announcement date", sep = ": "),
subtitle = "Bubbles are proportional to expected duration of the measure",
caption = "")
我遇到了与显示图例相关的问题。我什至不确定我能用 ggplot 解决这个问题，但由于我是 R 的基本用户，我相信这只是我的无知。
设置
我正在尝试从两个不同的数据集绘制两组不同的点，在同一组美学上：x、y、大小。两组点相对于 y 轴及其形状的垂直微移发生变化。
问题
情节如期而至（见代码下方的图片）。我唯一无法解决的问题与传说有关。特别是，我想让尺寸图例显示我在图表中使用的两种形状（在本例中为圆形和三角形）。有什么办法吗？我在网上浏览了一下，但没有找到任何关于这个问题的问题。
数据和代码
这里我把我正在使用的数据作为一个可重现的例子（通过 dput() 获得）。
# Packages
library(tidyverse)
library(ggtext)
library(janitor)
library(delabj)
library(wesanderson)
library(forcats)
# Basic data
basedata<-structure(list(country = structure(c("Argentina", "Argentina",
"Argentina", "Argentina", "Argentina", "Argentina", "Argentina",
"Argentina", "Argentina", "Argentina", "Argentina", "Argentina",
"Argentina", "Argentina"), format.stata = "%44s"), iso = structure(c("ARG",
"ARG", "ARG", "ARG", "ARG", "ARG", "ARG", "ARG", "ARG", "ARG",
"ARG", "ARG", "ARG", "ARG"), label = "iso_3", format.stata = "%9s"),
region = structure(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"), label = "Region", format.stata = "%26s"),
income_group = structure(c("Upper middle income", "Upper middle income",
"Upper middle income", "Upper middle income", "Upper middle income",
"Upper middle income", "Upper middle income", "Upper middle income",
"Upper middle income", "Upper middle income", "Upper middle income",
"Upper middle income", "Upper middle income", "Upper middle income"
), label = "Income group", format.stata = "%19s"), gdp = structure(c(519871519807.795,
519871519807.795, 519871519807.795, 519871519807.795, 519871519807.795,
519871519807.795, 519871519807.795, 519871519807.795, 519871519807.795,
519871519807.795, 519871519807.795, 519871519807.795, 519871519807.795,
519871519807.795), label = "(first) gdp_o", format.stata = "%9.0g"),
assessment = structure(c(2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1,
1, 2, 1), label = "Initial assessment", format.stata = "%12.0g", labels = c(liberalising = 1,
restrictive = 2)), start_date = structure(c(18341, 18349,
18354, 18366, 18393, 18393, 18270, 18270, 18339, 18354, 18354,
18393, 18393, 18351), label = "Announcement date", class = "Date", format.stata = "%td"),
duration = structure(c(357, 349, 344, 63, 33, 305, 156, 156,
87, 62, 344, 305, 33, 359), format.stata = "%9.0g"), GTAinterventiontype = structure(c("Export licensing requirement",
"Export licensing requirement", "Import Internal taxation",
"Import Internal taxation", "Import Internal taxation", "Import Internal taxation",
"Import licensing requirement", "Import licensing requirement",
"Import licensing requirement", "Import licensing requirement",
"Import tariff", "Import tariff", "Import tariff", "Import-related, Unknown measure"
), label = "GTA intervention type", format.stata = "%38s"),
any_food = structure(c(0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,
0, 0), label = "Product: Food", format.stata = "%8.0g", labels = c(`FALSE` = 0,
`TRUE` = 1)), any_medical = structure(c(1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1), label = "Product: Any medical product", format.stata = "%8.0g", labels = c(`FALSE` = 0,
`TRUE` = 1)), food_position = structure(c("Net Exporter",
"Net Exporter", "Net Exporter", "Net Exporter", "Net Exporter",
"Net Exporter", "Net Exporter", "Net Exporter", "Net Exporter",
"Net Exporter", "Net Exporter", "Net Exporter", "Net Exporter",
"Net Exporter"), format.stata = "%12s"), meds_position = structure(c("Net Importer",
"Net Importer", "Net Importer", "Net Importer", "Net Importer",
"Net Importer", "Net Importer", "Net Importer", "Net Importer",
"Net Importer", "Net Importer", "Net Importer", "Net Importer",
"Net Importer"), format.stata = "%12s"), month = c(3, 3,
4, 4, 5, 5, 1, 1, 3, 4, 4, 5, 5, 3), Announcement = c("March",
"March", "April", "April", "May", "May", "January", "January",
"March", "April", "April", "May", "May", "March"), Domain = c("Medical",
"Medical", "Medical", "Medical", "Medical", "Medical", "Food & Meds",
"Food & Meds", "Medical", "Medical", "Medical", "Medical",
"Medical", "Medical"), gdp_group_avg = c("Low Income", "Low Income",
"Low Income", "Low Income", "Low Income", "Low Income", "Low Income",
"Low Income", "Low Income", "Low Income", "Low Income", "Low Income",
"Low Income", "Low Income"), gdp_group_med = c("High Income",
"High Income", "High Income", "High Income", "High Income",
"High Income", "High Income", "High Income", "High Income",
"High Income", "High Income", "High Income", "High Income",
"High Income"), shp_point = c(23, 23, 21, 21, 23, 21, 23,
21, 21, 21, 21, 21, 23, 21), length_cat = c("More than 3 months",
"More than 3 months", "More than 3 months", "[1-3] months",
"[1-3] months", "More than 3 months", "More than 3 months",
"More than 3 months", "[1-3] months", "[1-3] months", "More than 3 months",
"More than 3 months", "[1-3] months", "More than 3 months"
), type = structure(c(2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 1L), .Label = c("Liberalizig", "Restrictive"
), class = "factor")), row.names = c(NA, -14L), class = "data.frame")
# Subset 1 - assessment == 1
prova1<-structure(list(country = c("Argentina", "Argentina", "Argentina",
"Argentina", "Argentina", "Argentina", "Argentina", "Argentina",
"Argentina"), iso = c("ARG", "ARG", "ARG", "ARG", "ARG", "ARG",
"ARG", "ARG", "ARG"), 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"), income_group = c("Upper middle income",
"Upper middle income", "Upper middle income", "Upper middle income",
"Upper middle income", "Upper middle income", "Upper middle income",
"Upper middle income", "Upper middle income"), gdp = c(519871519807.795,
519871519807.795, 519871519807.795, 519871519807.795, 519871519807.795,
519871519807.795, 519871519807.795, 519871519807.795, 519871519807.795
), assessment = c(1, 1, 1, 1, 1, 1, 1, 1, 1), start_date = structure(c(18354,
18366, 18393, 18270, 18339, 18354, 18354, 18393, 18351), class = "Date"),
duration = c(344, 63, 305, 156, 87, 62, 344, 305, 359), GTAinterventiontype = c("Import Internal taxation",
"Import Internal taxation", "Import Internal taxation", "Import licensing requirement",
"Import licensing requirement", "Import licensing requirement",
"Import tariff", "Import tariff", "Import-related, Unknown measure"
), any_food = c(0, 0, 0, 1, 0, 0, 0, 0, 0), any_medical = c(1,
1, 1, 1, 1, 1, 1, 1, 1), food_position = c("Net Exporter",
"Net Exporter", "Net Exporter", "Net Exporter", "Net Exporter",
"Net Exporter", "Net Exporter", "Net Exporter", "Net Exporter"
), meds_position = c("Net Importer", "Net Importer", "Net Importer",
"Net Importer", "Net Importer", "Net Importer", "Net Importer",
"Net Importer", "Net Importer"), month = c(4, 4, 5, 1, 3,
4, 4, 5, 3), Announcement = c("April", "April", "May", "January",
"March", "April", "April", "May", "March"), Domain = c("Medical",
"Medical", "Medical", "Food & Meds", "Medical", "Medical",
"Medical", "Medical", "Medical"), gdp_group_avg = c("Low Income",
"Low Income", "Low Income", "Low Income", "Low Income", "Low Income",
"Low Income", "Low Income", "Low Income"), gdp_group_med = c("High Income",
"High Income", "High Income", "High Income", "High Income",
"High Income", "High Income", "High Income", "High Income"
), shp_point = c(21, 21, 21, 21, 21, 21, 21, 21, 21), length_cat = c("More than 3 months",
"[1-3] months", "More than 3 months", "More than 3 months",
"[1-3] months", "[1-3] months", "More than 3 months", "More than 3 months",
"More than 3 months"), type = structure(c(1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L), .Label = c("Liberalizig", "Restrictive"
), class = "factor")), row.names = c(3L, 4L, 6L, 8L, 9L,
10L, 11L, 12L, 14L), class = "data.frame")
# Subset 2 - assessment == 2
prova2<-structure(list(country = c("Argentina", "Argentina", "Argentina",
"Argentina", "Argentina"), iso = c("ARG", "ARG", "ARG", "ARG",
"ARG"), region = c("Latin America & Caribbean", "Latin America & Caribbean",
"Latin America & Caribbean", "Latin America & Caribbean", "Latin America & Caribbean"
), income_group = c("Upper middle income", "Upper middle income",
"Upper middle income", "Upper middle income", "Upper middle income"
), gdp = c(519871519807.795, 519871519807.795, 519871519807.795,
519871519807.795, 519871519807.795), assessment = c(2, 2, 2,
2, 2), start_date = structure(c(18341, 18349, 18393, 18270, 18393
), class = "Date"), duration = c(357, 349, 33, 156, 33), GTAinterventiontype = c("Export licensing requirement",
"Export licensing requirement", "Import Internal taxation", "Import licensing requirement",
"Import tariff"), any_food = c(0, 0, 0, 1, 0), any_medical = c(1,
1, 1, 1, 1), food_position = c("Net Exporter", "Net Exporter",
"Net Exporter", "Net Exporter", "Net Exporter"), meds_position = c("Net Importer",
"Net Importer", "Net Importer", "Net Importer", "Net Importer"
), month = c(3, 3, 5, 1, 5), Announcement = c("March", "March",
"May", "January", "May"), Domain = c("Medical", "Medical", "Medical",
"Food & Meds", "Medical"), gdp_group_avg = c("Low Income", "Low Income",
"Low Income", "Low Income", "Low Income"), gdp_group_med = c("High Income",
"High Income", "High Income", "High Income", "High Income"),
shp_point = c(23, 23, 23, 23, 23), length_cat = c("More than 3 months",
"More than 3 months", "[1-3] months", "More than 3 months",
"[1-3] months"), type = structure(c(2L, 2L, 2L, 2L, 2L), .Label = c("Liberalizig",
"Restrictive"), class = "factor")), row.names = c(1L, 2L,
5L, 7L, 13L), class = "data.frame")
请注意 prova1 和 prova2 只是 basedata 的两个子集。剧情代码如下ig
countrydata %>%
ggplot(aes(x = start_date, y = fct_rev(GTAinterventiontype), shape = type)) +
geom_point(data = prova1, aes(color = fct_rev(GTAinterventiontype),size=duration, shape = fct_rev(type)), alpha = 0.65, position = position_nudge(y = +0.05)) +
geom_point(data = prova2, aes(color = fct_rev(GTAinterventiontype),size=duration, shape = fct_rev(type)), alpha = 0.65, position = position_nudge(y = -0.05)) +
scale_shape(drop=FALSE) +
guides(color = FALSE,
shape = guide_legend(order = 1, nrow = 2, ncol = 1),
size = guide_legend(order = 2)) +
delabj::theme_delabj() +
delabj::scale_color_delabj() +
#delabj::legend_none() +
labs(shape = 'Type',
size = "Duration",
x="",
y="",
title = paste("ARG", "Med-related Measures by Announcement date", sep = ": "),
subtitle = "Bubbles are proportional to expected duration of the measure",
caption = "")
结果图就是这个。
任何想法、建议，甚至不可行的警告（如果确实如此）都非常受欢迎！
试试这个。基本思想是复制尺寸图例的中断和符号。在第二步中，我通过 guide_legend 调整符号。也许不完美，但在尝试了一些我能想到的最好的方法之后。
library(tidyverse)
library(ggtext)
library(janitor)
library(delabj)
library(wesanderson)
library(forcats)
# Breaks, labels and symbols
breaks <- c(100, 200, 300)
n_breaks <- length(breaks)
labels <- c(breaks, rep("", n_breaks))
shapes <- c(rep(16, n_breaks), rep(17, n_breaks))
breaks2 <- rep(breaks, 2)
basedata %>%
ggplot(aes(x = start_date, y = fct_rev(GTAinterventiontype), shape = type)) +
geom_point(data = prova1, aes(color = fct_rev(GTAinterventiontype), size=duration, shape = fct_rev(type)), alpha = 0.65, position = position_nudge(y = +0.05)) +
scale_size_continuous(breaks = breaks2, labels = labels,
guide = guide_legend(order = 2, nrow = 2, byrow = TRUE,
override.aes = list(shape = shapes),
direction = "horizontal", label.vjust = -.5)) +
geom_point(data = prova2, aes(color = fct_rev(GTAinterventiontype), size=duration, shape = fct_rev(type)), alpha = 0.65, position = position_nudge(y = -0.05)) +
scale_shape(drop=FALSE) +
guides(color = FALSE,
shape = guide_legend(order = 1, nrow = 2, ncol = 1)) +
delabj::theme_delabj() +
delabj::scale_color_delabj() +
#delabj::legend_none() +
labs(shape = 'Type',
size = "Duration",
x="",
y="",
title = paste("ARG", "Med-related Measures by Announcement date", sep = ": "),
subtitle = "Bubbles are proportional to expected duration of the measure",
caption = "")