在模仿中精进数据可视化_使用R语言绘制双Y轴
❝希望明天顺利且成功！
❝
在模仿中精进数据可视化该系列推文中，我们将从各大顶级学术期刊的Figure入手，
解读文章的绘图思路，
模仿文章的作图风格，
构建适宜的绘图数据，
并且将代码应用到自己的实际论文中。
绘图缘由：小伙伴们总会展示出一些非常好看且精美的图片。我大概率会去学习和复现一下。其实每个人的时间和精力都非常有限和异常宝贵的。之所以我会去做，主要有以下原因：
图片非常好看，我自己看着也手痒痒 图片我自己在Paper也用的上，储备着留着用 保持了持续学习的状态
论文来源：
论文图片：
图片复现：
❝今天的可视化依旧是细节满满！
依旧是稳稳拿捏！
直接上代码：
加载R包
rm(list = ls())
####----load R Package----####
library(tidyverse)
library(readxl)
library(grid)
加载数据
####----load Data----####
data <- read_xlsx(path = "data.xlsx") %>%
dplyr::mutate(Gene = factor(Gene, levels = Gene, ordered = T)) %>%
tidyr::pivot_longer(cols = c(Treatment, Control),
names_to = "Condition",
values_to = "Mean") %>%
tidyr::pivot_longer(cols = c(Treatment_se, Control_se),
names_to = "Condition_se",
values_to = "se") %>%
dplyr::filter((Condition == "Treatment" & Condition_se == "Treatment_se") |
(Condition == "Control" & Condition_se == "Control_se")) %>%
dplyr::select(-Condition_se)
可视化
####----Plot----####
p <- data %>%
ggplot() +
geom_bar(aes(x = Gene, y = Mean, fill = Condition, color = Condition),
stat = "identity",
position = position_dodge(0.9),
width = 0.7,
alpha = 0.7,
linewidth = 1) +
geom_errorbar(aes(x = Gene, y = Mean, ymin = Mean - se, ymax = Mean + se, group = Condition),
position = position_dodge(0.9),
width = 0.3) +
geom_text(data = data %>%
dplyr::filter(Condition == "Treatment") %>%
dplyr::filter(Mean > 0),
aes(x = Gene, y = Mean + 10, label = Label),
hjust = 1.25) +
geom_text(data = data %>%
dplyr::filter(Condition == "Treatment") %>%
dplyr::filter(Mean < 0),
aes(x = Gene, y = Mean - 10, label = Label),
hjust = 1.25) +
scale_y_continuous(
name = expression("Change of cumulative \n SOC stock (kg m"^-2 *")"),
limits = c(-40, 20),
breaks = c(-40, -20, 0, 20),
labels = c(-40, -20, 0, 20),
expand = expansion(mult = c(0.1, 0.1)),
sec.axis = sec_axis(~./5,
name = expression("Change of cumulative \n bound OC stock (kg m"^-2 *")"),
breaks = c(-8, -4, 0, 4),
labels = c(-8, -4, 0, 4))
) +
geom_hline(yintercept = 0, linetype = 2, linewidth = 0.75) +
scale_color_manual(
values = c("Treatment" = "#969696",
"Control" = "#fe9929")) +
scale_fill_manual(
values = c("Treatment" = "#969696",
"Control" = "#fe9929")) +
labs(x = "") +
theme_bw() +
theme(
panel.grid = element_blank(),
panel.border = element_rect(linewidth = 0.75),
axis.text = element_text(color = "#000000", size = 15),
axis.title = element_text(color = "#000000", size = 20),
axis.title.y.right = element_text(color = "#fe9929", size = 20, angle = 90, vjust = -3),
axis.text.y.right = element_text(color = "#fe9929", size = 15),
axis.ticks.length.y.right = unit(6, "pt"),
axis.ticks.y.right = element_line(color = "#fe9929"),
axis.ticks.length.y.left = unit(6, "pt"),
axis.ticks.y.left = element_line(color = "#000000"),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
plot.margin = margin(t = 1, r = 1, b = 1, l = 1, unit = "cm")
) +
coord_cartesian(clip = "off") +
annotation_custom(grob = rectGrob(gp = gpar(col = "#74c476", lwd = 20)),
xmin = unit(0.5, "native"),
xmax = unit(7, "native"),
ymin = unit(-49, "native"),
ymax = unit(-50, "native")) +
annotation_custom(grob = rectGrob(gp = gpar(col = "#fe9929", lwd = 20)),
xmin = unit(6, "native"),
xmax = unit(11.5, "native"),
ymin = unit(-49, "native"),
ymax = unit(-50, "native"))
date_v <- data %>% dplyr::distinct(Gene, .keep_all = T) %>% dplyr::pull(Group)
for (i in 1:11) {
p <- p +
annotation_custom(grob = textGrob(label = unique(data$Gene)[i],
gp = gpar(col = "#ffffff", cex = 0.75)),
xmin = unit(i, "native"),
xmax = unit(i, "native"),
ymin = unit(-49, "native"),
ymax = unit(-50, "native")) +
annotation_custom(grob = textGrob(label = date_v[i],
gp = gpar(col = "#000000", cex = 0.8)),
xmin = unit(i, "native"),
xmax = unit(i, "native"),
ymin = unit(28, "native"),
ymax = unit(30, "native"))
}
p <- p +
annotation_custom(grob = textGrob(label = "Non-Sphagnum wetland",
gp = gpar(col = "#238443", cex = 1,
fontface = "italic")),
xmin = unit(3, "native"),
xmax = unit(3, "native"),
ymin = unit(-53, "native"),
ymax = unit(-55, "native")) +
annotation_custom(grob = textGrob(label = "Sphagnum wetland",
gp = gpar(col = "#ec7014", cex = 1,
fontface = "italic")),
xmin = unit(9, "native"),
xmax = unit(9, "native"),
ymin = unit(-53, "native"),
ymax = unit(-55, "native"))
p
ggsave(filename = "out.pdf",
plot = p,
height = 6,
width = 12)
版本信息
####----sessionInfo----####
sessionInfo()
R version 4.3.0 (2023-04-21)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS 14.6.1
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Asia/Shanghai
tzcode source: internal
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] readxl_1.4.3 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1
[5] dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1
[9] tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] gtable_0.3.5 compiler_4.3.0 tidyselect_1.2.1
[4] textshaping_0.3.7 systemfonts_1.0.5 scales_1.3.0
[7] R6_2.5.1 generics_0.1.3 munsell_0.5.1
[10] pillar_1.9.0 tzdb_0.4.0 rlang_1.1.4
[13] utf8_1.2.4 stringi_1.8.3 timechange_0.2.0
[16] cli_3.6.3 withr_3.0.1 magrittr_2.0.3
[19] rstudioapi_0.15.0 hms_1.1.3 lifecycle_1.0.4
[22] vctrs_0.6.5 glue_1.7.0 farver_2.1.2
[25] cellranger_1.1.0 ragg_1.2.6 fansi_1.0.6
[28] colorspace_2.1-1 tools_4.3.0 pkgconfig_2.0.3
历史绘图合集
进化树合集
环状图
散点图
基因家族合集
换一个排布方式：
首先查看基础版热图:
然后再看进阶版热图:
基因组共线性
WGCNA ggplot2版本
其他科研绘图
合作、联系和交流
有很多小伙伴在后台私信作者，非常抱歉，我经常看不到导致错过，请添加下面的微信联系作者，一起交流数据分析和可视化。
