是否可以擴展R中x軸的間隔？

Question

我有兩個圖：一個barplot和一個ggplot（geom_jitter bubbleplot）。 最終，我使用照片編輯應用程序將這兩幅圖對齊。 如您所見，這兩個圖的底部的間隔不匹配，這是我的問題。 我想這樣做，所以我可以將兩個圖的底部x軸更改為400（覆蓋兩個圖的x軸的最小公共間隔）。 我不想更改數據值，僅更改軸值。

Barplot Code

GYPCdomain <- read.csv(file.choose(), header=TRUE)

GYPCbarplot <- barplot(as.matrix(GYPCdomain), horiz=TRUE, xlab = "Length (Protein Domains Shown)",
                         col=c("azure", "plum1", "skyblue"), 
                         legend = c("Cytoplasmic", "Helical Membrane", "Extracellular"))

sample data: 

structure(list(GYPC = c(0L, 0L, 171L, 0L, 72L, 0L, 141L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L)), class = "data.frame", row.names = c(NA, -42L))

Bubbleplot Code

library(ggplot2)
library(scales)
data(GYPC, package="ggplot2")

GYPC <- read.csv(file.choose(), header = TRUE)

GYPCggplot <- ggplot(GYPC, aes(Position, log10(Frequency)))+
  geom_jitter(aes(col=Geographical.Location, size =(p.value)))+
  labs(subtitle="Frequency of Various Polymorphisms", title="GYPC Gene") +
  labs(color = "Geographical Location") +
  labs(size = "p-value") + labs(x = "Position of Polymorphism on GYPC Gene") + 
  scale_size_continuous(range=c(1,4.5), trans = "reverse") +
  guides(size = guide_legend(reverse = TRUE))


sample data:
structure(list(Variant = structure(c(4L, 4L, 4L, 4L, 4L, 8L, 
8L, 8L, 8L, 8L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 12L, 
12L, 12L, 12L, 12L, 14L, 14L, 14L, 14L, 14L, 2L, 2L, 2L, 2L, 
2L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 
9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 
11L, 13L, 13L, 13L, 13L, 13L, 15L, 15L, 15L, 15L, 15L), .Label = c("rs111631066", 
"rs114199197", "rs115178969", "rs115201071", "rs139780142", "rs139816143", 
"rs143080607", "rs143216051", "rs199797395", "rs531807314", "rs545780841", 
"rs551011574", "rs560942282", "rs567759380", "rs571586275"), class = "factor"), 
    Position = c(213L, 213L, 213L, 213L, 213L, 60L, 60L, 60L, 
    60L, 60L, 249L, 249L, 249L, 249L, 249L, 183L, 183L, 183L, 
    183L, 183L, 282L, 282L, 282L, 282L, 282L, 294L, 294L, 294L, 
    294L, 294L, 150L, 150L, 150L, 150L, 150L, 135L, 135L, 135L, 
    135L, 135L, 258L, 258L, 258L, 258L, 258L, 255L, 255L, 255L, 
    255L, 255L, 138L, 138L, 138L, 138L, 138L, 159L, 159L, 159L, 
    159L, 159L, 141L, 141L, 141L, 141L, 141L, 198L, 198L, 198L, 
    198L, 198L, 258L, 258L, 258L, 258L, 258L), Geographical.Location = structure(c(1L, 
    2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 
    2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 
    2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 
    2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 
    2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L), .Label = c("AFR", 
    "AMR", "EAS", "EUR", "SAS"), class = "factor"), Frequency = c(0.023, 
    0.001, 0, 0, 0, 0.017, 0.001, 0, 0, 0, 0.012, 0, 0, 0, 0, 
    0.002, 0.003, 0.002, 0.023, 0.016, 0.001, 0, 0, 0, 0, 0, 
    0, 0, 0, 0.004, 0, 0, 0, 0.001, 0, 0, 0, 0, 0, 0.001, 0, 
    0, 0.001, 0, 0, 0.001, 0, 0, 0, 0, 0, 0.001, 0, 0, 0, 0, 
    0, 0, 0, 0.002, 0, 0, 0.001, 0, 0, 0, 0, 0, 0, 0.001, 0, 
    0, 0.001, 0, 0), pre.p.value = c(6.32e-17, 0.113, 0.00126, 
    0.00126, 0.00211, 2.51e-12, 0.356, 0.00806, 0.00809, 0.0139, 
    4.86e-10, 0.15, 0.0542, 0.0542, 0.0537, 0.000376, 0.0778, 
    0.0068, 7.4e-06, 0.0109, 0.264, 1, 1, 1, 1, 0.579, 1, 0.589, 
    0.59, 0.00144, 1, 1, 1, 0.201, 1, 1, 1, 1, 1, 0.195, 1, 1, 
    0.201, 1, 1, 1, 1, 0.201, 1, 1, 1, 0.139, 1, 1, 1, 1, 1, 
    1, 1, 0.0381, 1, 1, 0.201, 1, 1, 1, 1, 1, 1, 0.195, 1, 1, 
    0.201, 1, 1), p.value = c(0, 0.75, 0.5, 0.5, 0.5, 0, 0.75, 
    0.5, 0.5, 0.75, 0, 0.75, 0.75, 0.75, 0.75, 0.5, 0.75, 0.5, 
    0.25, 0.75, 0.75, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 1, 1, 1, 0.75, 
    1, 1, 1, 1, 1, 0.75, 1, 1, 0.75, 1, 1, 1, 1, 0.75, 1, 1, 
    1, 0.75, 1, 1, 1, 1, 1, 1, 1, 0.75, 1, 1, 0.75, 1, 1, 1, 
    1, 1, 1, 0.75, 1, 1, 0.75, 1, 1), log.p.value. = c(-16.19928292, 
    -0.947, -2.899629455, -2.899629455, -2.675717545, -11.60032628, 
    -0.449, -2.093664958, -2.092051478, -1.8569852, -9.313363731, 
    -0.824, -1.266000713, -1.266000713, -1.270025714, -3.424812155, 
    -1.11, -2.167491087, -5.13076828, -1.962573502, -0.5783960731, 
    0, 0, 0, 0, -0.2373214363, 0, -0.2298847052, -0.2291479884, 
    -2.841637508, 0, 0, 0, -0.6968039426, 0, 0, 0, 0, 0, -0.7099653886, 
    0, 0, -0.6968039426, 0, 0, 0, 0, -0.6968039426, 0, 0, 0, 
    -0.857, 0, 0, 0, 0, 0, 0, 0, -1.419075024, 0, 0, -0.6968039426, 
    0, 0, 0, 0, 0, 0, -0.7099653886, 0, 0, -0.6968039426, 0, 
    0), X = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA), range = structure(c(2L, 6L, 5L, 4L, 3L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("", "0 < p-value < 1E-9", 
    "1E-2 < p-value < 1", "1E-4 < p-value < 1E-2", "1E-6 < p-value < 1E-4", 
    "1E-9 < p-value < 1E-6"), class = "factor")), class = "data.frame", row.names = c(NA, 
-75L))

Answer 1

我也隨便用ggplot生成了您的barplot，因為我們可以使用cowplot軟件包的cowplot功能，該功能是為cowplot事情而設計的。 可以使用ylim()或xlim()來設置軸限制，但是由於圖例的寬度不同，我們需要Cowplot軟件包才能真正對齊圖（或者圖例需要位於圖的下方）

#recreating the barplot
library(dplyr) #needed for data wrangling

GYPCbarplot_ggplot=GYPCdomain %>% 
  filter(GYPC>0) %>% 
  mutate(domain=factor(c("Cytoplasmic", "Helical Membrane", "Extracellular"),
                       levels=c("Cytoplasmic", "Helical Membrane", "Extracellular"),
                       ordered = T)) %>% 
ggplot(aes(x=1,y=GYPC,fill=domain))+
  geom_col(position="stack")+
  scale_fill_manual(values=c("Cytoplasmic"="azure", "Helical Membrane"="plum1", "Extracellular"="skyblue"))+
  coord_flip()+
  xlab("GYPC")+
  ylab( "Length (Protein Domains Shown)")+
  ylim(0,400)+ #creates the limit
  theme(panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank())

#the bubbleplot
GYPC_bubbleplot <- ggplot(GYPC_data, aes(Position, log10(Frequency)))+
  geom_jitter(aes(col=Geographical.Location, size =(p.value)))+
  labs(subtitle="Frequency of Various Polymorphisms", title="GYPC Gene") +
  labs(color = "Geographical Location") +
  labs(size = "p-value") + labs(x = "Position of Polymorphism on GYPC Gene") + 
  scale_size_continuous(range=c(1,4.5), trans = "reverse") +
  guides(size = guide_legend(reverse = TRUE))+
  xlim(0,400) #added this limit

library(cowplot) #used to arrange the two plots

plot_grid(GYPCbarplot_ggplot,GYPC_bubbleplot,
    ncol = 1, #both plots in one column (below each other)
    align = "v", #align both bottom axes
    rel_heights = c(1,1.5) #make bottom plot a bit higher    
)

等：

Answer 2

如果我理解正確，OP正在要求同步X軸，以顯示GYPC基因上某個位置所屬的蛋白質結構域。

如果我的假設是正確的，那么有另一種方法可以根據蛋白質結構域填充氣泡圖的背景 ：

library(dplyr)
domain_name <- c("Cytoplasmic", "Helical Membrane", "Extracellular")
domain_fill <- c("azure", "plum1", "skyblue")
names(domain_fill) <- domain_name
GPYCdomain_2 <- GYPCdomain %>%
  filter(GYPC > 0) %>%
  mutate(domain_name = forcats::fct_inorder(rev(domain_name)),
         end_pos = cumsum(GYPC),
         start_pos = lag(end_pos, default = 0L)) 

library(ggplot2)
ggplot(GYPC, aes(Position, log10(Frequency))) +
  geom_rect(aes(xmin = start_pos, xmax = end_pos, ymin = -Inf, ymax = Inf, fill = domain_name),
            data = GPYCdomain_2, inherit.aes = FALSE, alpha = 0.6) +
  scale_fill_manual(values = domain_fill) +
  geom_jitter(aes(color = Geographical.Location, size = (p.value))) +
  labs(subtitle = "Frequency of Various Polymorphisms", title = "GYPC Gene") +
  labs(color = "Geographical Location") +
  labs(size = "p-value") + 
  labs(x = "Position of Polymorphism on GYPC Gene") +
  labs(fill = "Protein Domain") + 
  scale_size_continuous(range = c(1, 4.5), trans = "reverse") +
  guides(size = guide_legend(reverse = TRUE))