在模仿中精进数据可视化_使用R语言绘制时序图
❝
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
❝今天提供素材的依旧是叶大师兄！
不过天知道，他咋总看预印版的论文。
可能就这种大佬才会如此跟进文献吧。
论文原图
图片复现：
❝细节自己调整吧，绘图基本上全是细节。模仿一个大概。
直接上代码：
加载R包
rm(list = ls())
####----load R Package----####
library(tidyverse)
library(grid)
加载数据
####----load Data----####
data <- read_delim(file = "Input/AirPassengers2.csv", col_names = T, delim = ",") %>%
tidyr::pivot_longer(cols = -Month, names_to = "Type", values_to = "Value")
data %>%
dplyr::group_by(Month) %>%
summarise(mean = mean(Value),
sd = sd(Value),
se = sd / sqrt(n()),
ci_h = qt(0.975, df=n()-1) * se,
ci_l = qt(0.025, df=n()-1) * se
) %>%
dplyr::ungroup() %>%
dplyr::mutate(Month = as_date(Month, format = "%Y-%m")) -> data2
# qt(0.975, df=n()-1) * se 是t分布的上分位数，经常被用来计算置信区间。
# 在这里，0.975是指在双尾测试中，上尾部分占总体5%（也就是2.5%在上尾）的情况下的t值。
# 然后乘以标准误差(se)就得到了95%的置信区间的边界。
绘图
####----Plot----####
ggplot(data = data2, aes(x = Month, y = mean)) +
geom_line(group = 1) +
geom_ribbon(aes(ymin = mean + ci_l, ymax = mean + ci_h), fill = "lightblue", alpha = 0.5) +
scale_x_date(
date_breaks = "1 year",
date_labels = "%Y",
limits = c(as.Date("1949-01-01"), as.Date("1960-12-01")),
expand = c(0, 0)
) +
labs(x = "", y = "Passengers") +
ggtitle(label = "Example") +
theme_bw() +
theme(
panel.grid = element_blank(),
panel.border = element_rect(linewidth = 0.75),
axis.text.x = element_text(size = 12.5, color = "#000000"),
axis.text.y = element_text(size = 12.5, color = "#000000"),
axis.title = element_text(size = 15, color = "#000000"),
plot.margin = margin(t = 1, r = 1, b = 1, l = 1, unit = "cm"),
plot.title = element_text(size = 15, color = "#000000", hjust = 0.5)
) +
coord_cartesian(clip = "off") +
annotation_custom(
grob = rectGrob(x = unit(0.5, "native"), y = unit(0.5, "native"),
width = unit(1, "native"), height = unit(0.5, "native"),
gp = gpar(col = "#000000", fill = "#d6604d", lwd = 2, alpha = 1)),
xmin = as.Date("1949-01-01"), xmax = as.Date("1953-01-01"), ymin = 25, ymax = 80
) +
annotation_custom(
grob = textGrob(label = "Confident",
gp = gpar(col = "#FFFFFFFF")),
as.Date("1949-01-01"), xmax = as.Date("1953-01-01"), ymin = 25, ymax = 80
) +
annotation_custom(
grob = rectGrob(x = unit(0.5, "native"), y = unit(0.5, "native"),
width = unit(1, "native"), height = unit(0.5, "native"),
gp = gpar(col = "#000000", fill = "#74a9cf", lwd = 2, alpha = 1)),
xmin = as.Date("1953-01-01"), xmax = as.Date("1957-01-01"), ymin = 25, ymax = 80
) +
annotation_custom(
grob = textGrob(label = "Confident2",
gp = gpar(col = "#FFFFFFFF")),
as.Date("1953-01-01"), xmax = as.Date("1957-01-01"), ymin = 25, ymax = 80
) +
annotation_custom(
grob = rectGrob(x = unit(0.5, "native"), y = unit(0.5, "native"),
width = unit(1, "native"), height = unit(0.5, "native"),
gp = gpar(col = "#000000", fill = "#78c679", lwd = 2, alpha = 1)),
xmin = as.Date("1957-01-01"), xmax = as.Date("1960-12-01"), ymin = 25, ymax = 80
) +
annotation_custom(
grob = textGrob(label = "Confident3",
gp = gpar(col = "#FFFFFFFF")),
as.Date("1957-01-01"), xmax = as.Date("1960-12-01"), ymin = 25, ymax = 80
)
ggsave(filename = "Output/Example.pdf",
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 15.0.1
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 base
other attached packages:
[1] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 readr_2.1.5
[7] tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] bit_4.0.5 gtable_0.3.5 compiler_4.3.0 crayon_1.5.2 tidyselect_1.2.1
[6] parallel_4.3.0 textshaping_0.3.7 systemfonts_1.1.0 scales_1.3.0 R6_2.5.1
[11] labeling_0.4.3 generics_0.1.3 munsell_0.5.1 pillar_1.9.0 tzdb_0.4.0
[16] rlang_1.1.4 utf8_1.2.4 stringi_1.8.3 bit64_4.0.5 timechange_0.2.0
[21] cli_3.6.3 withr_3.0.1 magrittr_2.0.3 vroom_1.6.4 rstudioapi_0.15.0
[26] hms_1.1.3 lifecycle_1.0.4 vctrs_0.6.5 glue_1.8.0 farver_2.1.2
[31] ragg_1.2.6 fansi_1.0.6 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
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