ggplot 有两个图例，颜色图例中显示错误的形状
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【中文标题】ggplot 有两个图例，颜色图例中显示错误的形状【英文标题】：ggplot has two legends and the wrong shape shows up in the color legend 【发布时间】：2016-12-29 15:07:51 【问题描述】：我正在使用 R 中的 ggplot 创建图表，但图例未正确显示。首先，我得到了两个图例，一个用于颜色，一个用于线型。尽管我在 scale_color_manual 和 scale_linetype_manual 中放置了相同的项目，但这些项目都出现了，正如关于该主题的其他几篇文章中所建议的那样。此外，颜色图例为三个项目中的每一个都显示相同的形状（带点和 x 的线），而它们应该都不同（前两个应该是带点的线，而第三个应该是 x没有线）。
这是一个可重现的例子。
library(ggplot2)
library(dplyr)
#specify color palette
b.navHexRGB <- c(green=rgb(149,214,0, maxColorValue=255),
red=rgb(229,60,46, maxColorValue=255),
gray=rgb(85,87,89, maxColorValue=255),
dark_green=rgb(100,140,26, maxColorValue=255),
yellow=rgb(255,183,24, maxColorValue=255),
purple=rgb(139,24,155, maxColorValue=255),
blue=rgb(0,147,201, maxColorValue = 255))
#create plot
ggplot(data = df, aes(x=as.character(bill_yrmo), y=mean_kwh)) +
geom_line(aes(group = treatment, colour = treatment, linetype = treatment),
size = .9) +
geom_point(aes(group = treatment, colour=treatment),
size = 1.5) +
geom_point(data = df %>% mutate(treatment= 'Indicates the difference is statistically significant'),
aes(y=stat_sig, colour=treatment),
size = 2.5,
shape=4,
na.rm=T) +
guides(colour=guide_legend(nrow=3)) +
scale_color_manual(name= "Variable",values=c(palette(b.navHexRGB)), breaks=c("Control","Recipient","Indicates the difference is statistically significant")) +
scale_linetype_manual(name="Variable",values=c(1,2), breaks=c("Control","Recipient","Indicates the difference is statistically significant")) +
ylab("Average Daily Consumption (kWh)") +
xlab("Year-Month") +
theme_bw() +
theme(legend.title = element_blank(),
legend.justification = c(0,0),
legend.position = "bottom",
legend.key = element_rect(fill = "white",colour = "white"),
#legend.key.width = unit(1.1, "cm"),
axis.text.x = element_text(angle=45, hjust=1, color="black"),
axis.text.y = element_text(color="black"),
axis.title.y = element_text(vjust=1)
)
数据
df <- structure(list(treatment = structure(c(1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L), .Label = c("Control", "Recipient"), class = "factor"),
bill_month = c(9, 9, 10, 10, 11, 11, 12, 12, 1, 1, 2, 2,
3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8), bill_year = c(2013,
2013, 2013, 2013, 2013, 2013, 2013, 2013, 2014, 2014, 2014,
2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014,
2014, 2014, 2014), bill_yrmo = c(201309, 201309, 201310,
201310, 201311, 201311, 201312, 201312, 201401, 201401, 201402,
201402, 201403, 201403, 201404, 201404, 201405, 201405, 201406,
201406, 201407, 201407, 201408, 201408), mean_kwh = c(34.1891698781763,
34.8263665605318, 22.998584869823, 23.6329516672246, 21.0428206185862,
21.7774153609304, 25.4992975653725, 25.8397296039854, 28.74368522348,
29.200670842288, 29.8474912589325, 30.373483172434, 26.7411627390396,
26.4600472396878, 21.628265542195, 21.3047667878863, 19.502019234349,
19.062337524723, 24.1381516068859, 24.3165665754673, 27.8915927136898,
28.3625761820341, 26.8570348685593, 27.1359185596385), p.value = c(9.36594553258583e-07,
9.36594553258583e-07, 1.76373182797948e-13, 1.76373182797948e-13,
2.12425701682086e-15, 2.12425701682086e-15, 0.00415203493379312,
0.00415203493379312, 0.00109178463449181, 0.00109178463449181,
0.00122110380638705, 0.00122110380638705, 0.0438138636035026,
0.0438138636035026, 0.00140538140516743, 0.00140538140516743,
5.74367939388898e-07, 5.74367939388898e-07, 0.100848768452669,
0.100848768452669, 0.000172505914392074, 0.000172505914392074,
0.145110211153141, 0.145110211153141), stat_sig = c(19, 19,
19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19,
19, NA, NA, 19, 19, NA, NA)), .Names = c("treatment", "bill_month",
"bill_year", "bill_yrmo", "mean_kwh", "p.value", "stat_sig"), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -24L))
【问题讨论】：
只是一个指定的调色板。 【参考方案1】：我遇到了类似的问题。在 scale_color_manual()、scale_shape_manual() 和 scale_linetype_manual() 中指定 name="Variable" 解决了这个问题。 values 参数的命名向量并没有改变我的结果。
【讨论】：
【参考方案2】：谢谢大家。我将上面的两个响应结合起来得到了我需要的东西。
ggplot(data = avgkwh_pre2, aes(x=as.character(bill_yrmo), y=mean_kwh)) +
geom_point(aes(group = treatment, colour = treatment, shape = treatment),
size = 2) +
geom_line(aes(group = treatment, colour = treatment, linetype = treatment),
size = .9) +
scale_shape_manual(values = c("Recipient" = 16, "Control" = 16, "Indicates the difference is statistically significant" = 4)) +
scale_color_manual(values = c("Recipient" = b.navHexRGB[["gray"]], "Control" = b.navHexRGB[["green"]], "Indicates the difference is statistically significant" = b.navHexRGB[["red"]]),
breaks = c("Control", "Recipient", "Indicates the difference is statistically significant")) +
scale_linetype_manual(values = c("Recipient" = 1,"Control" = 2, "Indicates the difference is statistically significant" = 0),
breaks = c("Control","Recipient","Indicates the difference is statistically significant")) +
ylab("Average Daily Consumption (kWh)") +
xlab("Year-Month") +
ggtitle(paste("Group Starting", rct_start)) +
theme_bw() +
theme(legend.title = element_blank(),
legend.justification = c(0,0),
legend.position = "bottom",
legend.key = element_rect(fill = "white",colour = "white"),
#legend.key.width = unit(1.1, "cm"),
axis.text.x = element_text(angle=45, hjust=1, color="black"),
axis.text.y = element_text(color="black"),
axis.title.y = element_text(vjust=1)
)
【讨论】：
【参考方案3】：我经常发现在调用ggplot 之前排列数据很有用。我rbind
“统计显着”行与主数据框对齐，并将“统计显着”行的“y”美学对齐到与其他数据相同 (mean_kwh = stat_sig)：
dd <- rbind(df, df %>%
mutate(treatment= 'Indicates the difference is statistically significant',
mean_kwh = stat_sig))
然后拨打ggplot。注意“统计显着”也有linetype，只是它是0：
#create plot
ggplot(data = dd, aes(x=as.character(bill_yrmo), y=mean_kwh)) +
geom_point(aes(group = treatment, colour = treatment, shape = treatment),
size = 1.5) +
geom_line(aes(group = treatment, colour = treatment, linetype = treatment),
size = .9) +
scale_shape_manual(values = c(1, 2, 4)) +
scale_color_manual(values = c(palette(b.navHexRGB)),
breaks = c("Control", "Recipient", "Indicates the difference is statistically significant")) +
scale_linetype_manual(values = c(1, 2, 0),
breaks = c("Control","Recipient","Indicates the difference is statistically significant")) +
labs(y = "Average Daily Consumption (kWh)",
x = "Year-Month") +
theme_bw() +
theme(legend.title = element_blank(),
legend.justification = c(0,0),
legend.position = "bottom",
legend.key = element_rect(fill = "white",colour = "white"),
axis.text.x = element_text(angle=45, hjust=1, color="black"),
axis.text.y = element_text(color="black"),
axis.title.y = element_text(vjust=1)
)
输出：
【讨论】：
【参考方案4】：如果情节的一般美学映射在主要的ggplot 调用中处理，则可以简化您对geom_line 和geom_point 的调用。但主要问题是您在 scale 调用中指定颜色和线型的方式。如果您向values 参数提供命名向量，这些函数更不容易出错，这保证了对映射的控制。
这段代码：
ggplot(data = df, aes(x=as.character(bill_yrmo), y=mean_kwh, color = treatment, lty = treatment)) +
geom_line(size = .9, aes(group = treatment)) +
geom_point(size = 1.5) +
geom_point(data = df %>% mutate(treatment= 'Indicates the difference is statistically significant'),
aes(y=stat_sig, colour=treatment),
size = 2.5,
shape=4,
na.rm=T) +
scale_color_manual(name = "Variable", values = c("Recipient" = b.navHexRGB[["gray"]], "Control" = b.navHexRGB[["green"]], "Indicates the difference is statistically significant" = b.navHexRGB[["red"]]), breaks=c("Control","Recipient","Indicates the difference is statistically significant")) +
scale_linetype_manual(name="Variable",values = c("Recipient" = 2, "Control" = 1, "Indicates the difference is statistically significant" = 0), breaks=c("Control","Recipient","Indicates the difference is statistically significant")) +
labs(x = "Year-Month", y = "Average Daily Consumption (kWh)") +
theme_bw() +
theme(legend.title = element_blank(),
legend.justification = c(0,0),
legend.position = "bottom",
legend.key = element_rect(fill = "white",colour = "white"),
legend.direction = "vertical",
axis.text.x = element_text(angle=45, hjust=1, color="black"),
axis.text.y = element_text(color="black"),
axis.title.y = element_text(vjust=1)
)
产生这个情节：
【讨论】：
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