在模仿中精进数据可视化_R语言绘制对称柱形图
❝
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
期刊
原图
复现
直接上代码：
加载R包
rm(list = ls())
####----load R Package----####
library(tidyverse)
library(readxl)
library(ggfun)
加载数据
####----load Data----####
df <- read_xlsx(path = "Input/test.xlsx", col_names = T)
df2 <- df %>%
tidyr::pivot_longer(cols = -c(Kind,pvalue,signif),
names_to = "Name",
values_to = "Value") %>%
dplyr::group_by(Kind) %>%
dplyr::summarise(
mean = mean(Value),
sd = sd(Value),
se = sd/sqrt(n())
)
df_plot <- df2 %>%
dplyr::left_join(df %>% dplyr::select(1,5,6), by = c("Kind" = "Kind")) %>%
dplyr::arrange(desc(mean)) %>%
dplyr::mutate(Kind = factor(Kind, levels = rev(Kind), ordered = T)) %>%
dplyr::mutate(Change = ifelse(mean > 0, "Up", "Down")) %>%
dplyr::mutate(Change = factor(Change, levels = c("Up", "Down"), ordered = T))
绘图
####----Plot----####
p <- ggplot(data = df_plot) +
geom_bar(aes(x = mean, y = Kind, fill = Change), stat = "identity", width = 0.75) +
geom_errorbar(aes(x = mean, y = Kind,
xmin = mean - se,
xmax = mean + se,
color = Change), width = 0.15, linewidth = 1,
show.legend = F) +
geom_text(data = df_plot %>% dplyr::filter(Change == "Up"),
aes(x = -0.01, y = Kind, label = Kind), hjust = "right",
size = 5) +
geom_text(data = df_plot %>% dplyr::filter(Change == "Down"),
aes(x = 0.01, y = Kind, label = Kind), hjust = "left",
size = 5) +
geom_text(data = df_plot %>% dplyr::filter(Change == "Up"),
aes(x = mean + se + 0.03 , y = Kind, label = signif, color = Change),
size = 8,
vjust = 0.7,
show.legend = F
) +
geom_text(data = df_plot %>% dplyr::filter(Change == "Down"),
aes(x = mean - se - 0.03 , y = Kind, label = signif, color = Change),
size = 8,
vjust = 0.7,
show.legend = F
) +
geom_vline(xintercept = 0, linewidth = 1) +
labs(x = "Effect Size", y = "") +
scale_fill_manual(values = c("Up" = "#fb6a4a",
"Down" = "#4292c6")) +
scale_color_manual(values = c("Up" = "#cb181d",
"Down" = "#08519c")) +
scale_x_continuous(
expand = expansion(mult = c(0.2, 0.2)),
limits = c(-0.2, 0.2),
breaks = c(-0.2, -0.1, 0, 0.1, 0.2),
labels = c(-0.2, -0.1, 0, 0.1, 0.2)
) +
theme_bw() +
theme(
axis.text.y = element_blank(),
axis.text.x = element_text(color = "#000000", size = 12.5),
axis.ticks.y = element_blank(),
axis.title.x = element_text(color = "#000000", size = 15),
legend.background = element_roundrect(color = "#525252"),
panel.background = element_rect(fill = "#e7e1ef"),
panel.border = element_rect(color = "#000000", linewidth = 1.5),
panel.grid.major = element_line(color = "#f0f0f0", linewidth = 0.85),
panel.grid.minor = element_line(color = "#f0f0f0", linewidth = 0.85)
)
p
ggsave(filename = "Output/p.pdf",
plot = p,
height = 8.5,
width = 12)
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] ggfun_0.1.5 readxl_1.4.3 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
[7] purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] gtable_0.3.5 crayon_1.5.2 compiler_4.3.0 tidyselect_1.2.1 textshaping_0.3.7
[6] systemfonts_1.1.0 scales_1.3.0 R6_2.5.1 labeling_0.4.3 generics_0.1.3
[11] munsell_0.5.1 pillar_1.9.0 tzdb_0.4.0 rlang_1.1.4 utf8_1.2.4
[16] stringi_1.8.3 timechange_0.2.0 cli_3.6.3 withr_3.0.1 magrittr_2.0.3
[21] grid_4.3.0 rstudioapi_0.15.0 hms_1.1.3 lifecycle_1.0.4 vctrs_0.6.5
[26] writexl_1.4.2 glue_1.8.0 farver_2.1.2 cellranger_1.1.0 ragg_1.2.6
[31] fansi_1.0.6 colorspace_2.1-1 tools_4.3.0 pkgconfig_2.0.3
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
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。
