如何在ggplot2中分隔图例，以使它们不重叠? [英] How can I separate legends in ggplot2 so they don't overlap?
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问题描述
我无法弄清楚如何为geom_point和geom_line数据创建两个单独的图例.当前，我有一个图例描述了不同线型的含义，但是当我尝试添加图例来描述不同点形状的含义(蓝色三角形和圆形)时，蓝色圆圈会简单地重叠线型图例.非常感谢任何可以帮助我为线型和点形制作两个单独图例的人.以下是我的代码，数据和当前图形的图片:
I cannot figure out how to make two separate legends for my geom_point and my geom_line data. Currently, I have a legend describing what the different linetypes mean, but when I try to add a legend to describe what the different point shapes mean (blue triangles and circles), blue circles simply overlap the linetype legend. Many thanks to anyone who can help me make two separate legends for linetype and point shape. Below is my code, my data, and a picture of my current graph:
这是我的数据，称为数据":
Here's my data, called "Data":
site_name Watershed Fish_IBI_Tool2_percent Fish_IBI_Tool7_percent Exceptional_2_percent Poor_2_percent
1 Piersons Six Mile Creek & Schutz Lake 12.888889 NA 42.22222 0
2 Wassermann Six Mile Creek & Schutz Lake -17.111111 NA 42.22222 0
3 Church Six Mile Creek & Schutz Lake NA NA NA NA
4 Steiger Six Mile Creek & Schutz Lake -35.777778 NA 42.22222 0
5 Zumbra Six Mile Creek & Schutz Lake 2.666667 NA 42.22222 0
6 Stone Six Mile Creek & Schutz Lake NA NA NA NA
7 East Auburn Six Mile Creek & Schutz Lake -35.333333 NA 42.22222 0
8 West Auburn Six Mile Creek & Schutz Lake -35.333333 NA 42.22222 0
9 Turbid Six Mile Creek & Schutz Lake NA NA NA NA
10 Schutz Six Mile Creek & Schutz Lake -49.333333 NA 42.22222 0
11 Brownie Minnehaha Creek NA NA NA NA
12 Calhoun Minnehaha Creek -6.888889 NA 42.22222 0
13 Cedar Minnehaha Creek 6.666667 NA 42.22222 0
14 Harriet Minnehaha Creek -6.222222 NA 42.22222 0
15 Hiawatha Minnehaha Creek NA NA NA NA
16 Lake of the Isles Minnehaha Creek NA 7.50000 NA NA
17 Nokomis Minnehaha Creek -4.888889 NA 42.22222 0
18 Minnehaha Creek NA NA NA NA
19 Taft Minnehaha Creek NA NA NA NA
20 Mud Six Mile Creek & Schutz Lake NA 13.61111 NA NA
21 Parley Six Mile Creek & Schutz Lake NA 45.83333 NA NA
Degraded_2_percent Exceptional_4_percent Poor_4_percent Degraded_4_percent Poor_7_percent Degraded_7_percent
1 -51.11111 NA NA NA NA NA
2 -51.11111 NA NA NA NA NA
3 NA 55.26316 0 -76.31579 NA NA
4 -51.11111 NA NA NA NA NA
5 -51.11111 NA NA NA NA NA
6 NA 55.26316 0 -76.31579 NA NA
7 -51.11111 NA NA NA NA NA
8 -51.11111 NA NA NA NA NA
9 NA 55.26316 0 -76.31579 NA NA
10 -51.11111 NA NA NA NA NA
11 NA 55.26316 0 -76.31579 NA NA
12 -51.11111 NA NA NA NA NA
13 -51.11111 NA NA NA NA NA
14 -51.11111 NA NA NA NA NA
15 NA 55.26316 0 -76.31579 NA NA
16 NA NA NA NA 0 -52.77778
17 -51.11111 NA NA NA NA NA
18 NA NA NA NA 0 -52.77778
19 NA 55.26316 0 -76.31579 NA NA
20 NA NA NA NA 0 -52.77778
21 NA NA NA NA 0 -52.77778
这是我的数据的副本:
structure(list(site_name = structure(c(10L, 13L, 18L, 14L, 12L,
19L, 16L, 17L, 20L, 15L, 5L, 4L, 1L, 3L, 6L, 8L, 2L, 9L, 7L,
22L, 21L), .Label = c("Cedar", "Nokomis", "Harriet", "Calhoun",
"Brownie", "Hiawatha", "Taft", "Lake of the Isles", " ", "Piersons",
"Kelser's Pond", "Zumbra", "Wassermann", "Steiger", "Schutz",
"East Auburn", "West Auburn", "Church", "Stone", "Turbid", "Parley",
"Mud"), class = "factor"), Watershed = c("Six Mile Creek & Schutz Lake",
"Six Mile Creek & Schutz Lake", "Six Mile Creek & Schutz Lake",
"Six Mile Creek & Schutz Lake", "Six Mile Creek & Schutz Lake",
"Six Mile Creek & Schutz Lake", "Six Mile Creek & Schutz Lake",
"Six Mile Creek & Schutz Lake", "Six Mile Creek & Schutz Lake",
"Six Mile Creek & Schutz Lake", "Minnehaha Creek", "Minnehaha Creek",
"Minnehaha Creek", "Minnehaha Creek", "Minnehaha Creek", "Minnehaha Creek",
"Minnehaha Creek", "Minnehaha Creek", "Minnehaha Creek", "Six Mile Creek & Schutz Lake",
"Six Mile Creek & Schutz Lake"), Fish_IBI_Tool2_percent = c(12.88888889,
-17.11111111, NA, -35.77777778, 2.666666667, NA, -35.33333333,
-35.33333333, NA, -49.33333333, NA, -6.888888889, 6.666666667,
-6.222222222, NA, NA, -4.888888889, NA, NA, NA, NA), Fish_IBI_Tool7_percent = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 7.5,
NA, NA, NA, 13.61111111, 45.83333333), Exceptional_2_percent = c(42.22222222,
42.22222222, NA, 42.22222222, 42.22222222, NA, 42.22222222, 42.22222222,
NA, 42.22222222, NA, 42.22222222, 42.22222222, 42.22222222, NA,
NA, 42.22222222, NA, NA, NA, NA), Poor_2_percent = c(0L, 0L,
NA, 0L, 0L, NA, 0L, 0L, NA, 0L, NA, 0L, 0L, 0L, NA, NA, 0L, NA,
NA, NA, NA), Degraded_2_percent = c(-51.11111111, -51.11111111,
NA, -51.11111111, -51.11111111, NA, -51.11111111, -51.11111111,
NA, -51.11111111, NA, -51.11111111, -51.11111111, -51.11111111,
NA, NA, -51.11111111, NA, NA, NA, NA), Exceptional_4_percent = c(NA,
NA, 55.26315789, NA, NA, 55.26315789, NA, NA, 55.26315789, NA,
55.26315789, NA, NA, NA, 55.26315789, NA, NA, NA, 55.26315789,
NA, NA), Poor_4_percent = c(NA, NA, 0L, NA, NA, 0L, NA, NA, 0L,
NA, 0L, NA, NA, NA, 0L, NA, NA, NA, 0L, NA, NA), Degraded_4_percent = c(NA,
NA, -76.31578947, NA, NA, -76.31578947, NA, NA, -76.31578947,
NA, -76.31578947, NA, NA, NA, -76.31578947, NA, NA, NA, -76.31578947,
NA, NA), Poor_7_percent = c(NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 0L, NA, 0L, NA, 0L, 0L), Degraded_7_percent = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -52.77777778,
NA, -52.77777778, NA, -52.77777778, -52.77777778)), .Names = c("site_name",
"Watershed", "Fish_IBI_Tool2_percent", "Fish_IBI_Tool7_percent",
"Exceptional_2_percent", "Poor_2_percent", "Degraded_2_percent",
"Exceptional_4_percent", "Poor_4_percent", "Degraded_4_percent",
"Poor_7_percent", "Degraded_7_percent"), class = "data.frame", row.names = c(NA,
-21L))
这是我的代码:
#Import, fix up, and subset Data data.frame
Data = read.csv("Lakes_data_for_R2.csv",
stringsAsFactors=FALSE)
colnames(Data)[1] <- "site_name"
#Order site_name
Data$site_name <- factor(Data$site_name, levels = c("Cedar",
"Nokomis",
"Harriet",
"Calhoun",
"Brownie",
"Hiawatha",
"Taft",
"Lake of the Isles",
" ",
"Piersons",
"Kelser's Pond",
"Zumbra",
"Wassermann",
"Steiger",
"Schutz",
"East Auburn",
"West Auburn",
"Church",
"Stone",
"Turbid",
"Parley",
"Mud"))
#Load ggplot
library(ggplot2)
#Make plot
ggplot() +
geom_point(data = Data, aes(x = site_name, y = Fish_IBI_Tool2, shape = "tool2"), size = 5, color = "blue", show_guide = TRUE) +
geom_point(data = Data, aes(x = site_name, y = Fish_IBI_Tool7, shape = "tool7"), size = 5, color = "blue", show_guide = TRUE) +
geom_line(data = Data, aes(x = site_name, y = Exceptional_2_percent, group = 1, linetype = "Exceptional", color = "Exceptional"), size = 1) +
geom_line(data = Data, aes(x = site_name, y = Poor_2_percent, group = 1, linetype = "Poor", color = "Poor"), size = 1) +
geom_line(data = Data, aes(x = site_name, y = Degraded_2_percent, group = 1, linetype = "Degraded", color = "Degraded"), size = 1) +
geom_line(data = Data, aes(x = site_name, y = Exceptional_4_percent, group = 1, linetype = "Exceptional", color = "Exceptional"), size = 1) +
geom_line(data = Data, aes(x = site_name, y = Poor_4_percent, group = 1, linetype = "Poor", color = "Poor"), size = 1) +
geom_line(data = Data, aes(x = site_name, y = Degraded_4_percent, group = 1, linetype = "Degraded", color = "Degraded"), size = 1) +
geom_line(data = Data, aes(x = site_name, y = Poor_7_percent, group = 1, linetype = "Poor", color = "Poor"), size = 1) +
geom_line(data = Data, aes(x = site_name, y = Degraded_7_percent, group = 1, linetype = "Degraded", color = "Degraded"), size = 1) +
scale_linetype_manual(values = c("dashed", "twodash", "solid"), breaks = c("Degraded", "Poor", "Exceptional"), name = "Legend") +
scale_color_manual(values = c("red", "springgreen4", "red"), breaks = c("Degraded", "Poor", "Exceptional"), name = "Legend") +
scale_shape_manual(values = c(17, 16), breaks = c("Tool 2", "Tool 7")) +
ylab ("Fish IBI (% difference from Poor threshold)") + xlab("") +
facet_grid(~Watershed, scale = "free", space = "free") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, color = "black"),
axis.text.y = element_text(color = "black"),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor = element_blank(),
axis.line.x = element_line(color = "black"),
axis.line.y = element_line(color = "black"),
panel.background = element_rect(fill = "white"),
legend.key=element_blank(),
legend.title = element_blank(),
text = element_text(size=18),
panel.spacing = unit(2, "lines"))
这是我的图形当前的样子:
And here's what my graph currently looks like:
推荐答案
如上所述，这里的问题是使用 show_guide .但是，也许公平地说，如果将数据重新格式化为较长而不是较宽的格式，则 ggplot2 通常会更好地工作，从而使图例更自然地落入数据之外:
As has been stated, the issue here is the use of show_guide. However, it's also perhaps fair to say that ggplot2 often works better if the data is reformatted to be long rather than wide, such that the legends then fall more naturally out of the data:
library(tidyr)
data_long <- gather(Data, variable, value, -c(site_name, Watershed)) %>%
separate(variable, paste0("var", 1:4), fill = "right")
ggplot(mapping = aes(x = site_name, y = value)) +
geom_point(aes(shape = var3), data = data_long %>% filter(var1 == "Fish"),
colour = "blue", size = 5) +
geom_line(aes(linetype = var1, colour = var1,
group = interaction(var1, var2)),
data = data_long %>% filter(var1 != "Fish"), size = 1) +
scale_linetype_manual("", values = c(Degraded = "dashed", Exceptional = "twodash",
Poor = "solid")) +
scale_color_manual("", values = c(Degraded = "red", Exceptional = "springgreen4",
Poor = "red")) +
scale_shape_manual("", values = c(Tool2 = 17, Tool7 = 16)) +
ylab("Fish IBI (% difference from Poor threshold)") +
xlab("") +
facet_grid(~Watershed, scale = "free", space = "free") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, color = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(fill = "white"),
axis.line.x = element_line(color = "black"),
axis.line.y = element_line(color = "black"),
legend.key = element_blank(),
text = element_text(size=18),
panel.spacing = unit(2, "lines"))
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