在模仿中精进数据可视化_使用R语言绘制柱形体和折线图的组合图
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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
图片
直接上代码：
加载R包
rm(list = ls())
####----load R Package----####
library(tidyverse)
library(readxl)
library(ggpmisc)
加载数据
####----load Data----####
df <- read_xlsx(path = "Input/test.xlsx", col_names = T)
glimpse(df)
开始绘图
####----Plot----####
formula <- y ~ x
p <- ggplot(data = df, aes(x = year, y = number)) +
geom_bar(aes(x = year, y = number), stat = "identity", color = "#000000", fill = "#74c476", width = 0.75) +
geom_smooth(aes(x = year, y = number), method = "loess", formula = formula, se = F, color = "#e7298a") +
geom_text(aes(x = year, y = number+5, label = number), size = 5, color = "#005a32") +
stat_poly_eq(use_label(c("eq", "adj.R2", "P")),formula = formula, size = 6,
label.x = "right",
label.y = "top") +
scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
scale_x_continuous(breaks = c(2014:2024),
labels = c(2014:2024)) +
theme_bw() +
theme(
axis.text = element_text(color = "#000000", size = 12.5),
axis.title = element_text(color = "#000000", size = 15),
panel.border = element_rect(linewidth = 1)
)
p
ggsave(filename = "Output/p.pdf",
plot = p,
height = 5,
width = 7)
版本信息
R version 4.3.0 (2023-04-21)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS 15.1.1
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggpmisc_0.5.5 ggpp_0.5.5 readxl_1.4.3 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1
[7] dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1
[13] tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] utf8_1.2.4 generics_0.1.3 stringi_1.8.3 lattice_0.22-5 hms_1.1.3
[6] magrittr_2.0.3 grid_4.3.0 timechange_0.2.0 confintr_1.0.2 cellranger_1.1.0
[11] Matrix_1.6-5 survival_3.5-7 mgcv_1.9-0 fansi_1.0.6 scales_1.3.0
[16] textshaping_0.3.7 cli_3.6.3 rlang_1.1.4 crayon_1.5.2 munsell_0.5.1
[21] splines_4.3.0 withr_3.0.1 tools_4.3.0 SparseM_1.81 polynom_1.4-1
[26] tzdb_0.4.0 MatrixModels_0.5-3 colorspace_2.1-1 vctrs_0.6.5 R6_2.5.1
[31] lifecycle_1.0.4 MASS_7.3-60 ragg_1.2.6 pkgconfig_2.0.3 pillar_1.9.0
[36] gtable_0.3.5 glue_1.8.0 systemfonts_1.1.0 tidyselect_1.2.1 rstudioapi_0.15.0
[41] farver_2.1.2 nlme_3.1-163 labeling_0.4.3 compiler_4.3.0 quantreg_5.97
历史绘图合集
公众号推文一览
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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