你的柱状图out了！看看Nature是怎么画的——柱状图+散点图+误差线！
Part1你的柱状图out了！看看Nature是怎么画的——柱状图+散点图+误差线！在日常的研究中，可能仅仅绘制柱状图就完事了，让我们看看Nature子刊是如何呈现的吧。柱状图+散点图+误差线！
Nature子刊
主要复现这篇文章中的Fig 7中的柱状图子图：
Nature子刊的柱状图
本文介绍如何使用R语言和ggplot2包将简单的柱状图转化为更为高级和信息丰富的视觉表示形式。通过结合柱状图、散点图和误差线，结果展示出一个全面和精确的数据视图，这种方法在Nature等顶级科学期刊中经常见到。
1设置环境
首先，我们需要加载必要的R包来处理数据和生成图形：
这些包提供了数据处理和图形创建的强大功能，是进行高级数据可视化的基础。
library(ggplot2)
library(dplyr)
2读取绘图数据
接下来，我们从指定的CSV文件中读取数据：
df1包含了柱状图所需的平均值数据，而df2则包含了散点图和误差线所需的原始数据和误差信息。
如果有同学想尝试代码，可以文末获取数据+代码
df1 <- read.csv('data/df1.csv')
df2 <- read.csv('data/test_err.csv')
3绘图
使用ggplot2的绘图代码，我们可以创建一个结合了柱状图、散点图和误差线的复合图形：
ggplot() +
geom_bar(data = df1, aes(x = type, y = mean), fill = "white", size = 2,
color = c("#4169B2", "#B1A4C0", "#479E9B", "#BB2BA0"),
position = position_dodge(width = 0.65), stat = "identity", width = 0.65) +
geom_jitter(data = df2, aes(x = type, y = value, fill = type, colour = type), size = 2,height = 0.02,width = 0.1) +
scale_color_manual(values = c("#4169B2", "#B1A4C0", "#479E9B", "#BB2BA0")) +
geom_errorbar(data = df1, aes(x = type, ymin = mean-se, ymax = mean+se), width = 0.3,
color = c("#4169B2", "#B1A4C0", "#479E9B", "#BB2BA0"), size = 1.5,
position = position_dodge(width = 0.65)) +
labs(y = "Mobile Phone sales", x = "") +
geom_hline(aes(yintercept = 5), linetype = "dashed", linewidth = 1.2, colour = "gray56") +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
text = element_text(family = "serif", size = 15), # This sets the font family and size globally
axis.text.x = element_text(size = 18), # Larger font size for X axis text
axis.text.y = element_text(size = 18), # Larger font size for Y axis text
axis.title.x = element_text(size = 20), # Larger font size for X axis title
axis.title.y = element_text(size = 20)) # Larger font size for Y axis title
代码绘制结果
此代码段创建了一个复杂的图形，不仅展示了每种类型的平均值数据，而且通过散点图展示了个体数据点，同时误差线提供了数据的变异性信息。使用不同颜色来区分不同的类型，增加了图形的清晰度和吸引力。
4结语
通过这种方法，我们不仅能够展示数据的中心趋势，如平均值，还能展示数据的分布和可靠性。这种图形表达方式在科学研究和报告中极为重要，因为它提供了一个全面且准确的数据视图，有助于读者更好地理解和评估研究结果。
学习了本篇后，再也不用烦恼只会绘制简单的柱状图了！
本文转自微信公众号：好奇心Log
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