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在分享完即可统计又可可视化绘制的优秀可视化包后(具体内容可看 统计绘图 | 既能统计分析又能可视化绘制的技能 。就有小伙伴私信问我需要绘制出版级别的可视化图表有什么快速的方法？“。鉴于我是一个比较宠粉的小编，几天就给大家推荐一个技巧，让你快速绘制出符合出版要求绘图技能。主要内容如下：
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R-ggpubr包主要类型函数介绍
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R-ggpubr包主要案列展示
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更多详细的数据可视化教程，可订阅我们的店铺课程：
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R-ggpubr包主要类型函数介绍
虽然在Python中我们也可以通过使用Matplotlib定制化出符合出版要求的图表，但这毕竟对使用者的绘图技能要求较高，当然也是还有部分轮子可以用的，详细请参考这篇：因为配图，SCI多次返修！？因为你还没发现这个Python科学绘图宝藏工具包。而我们今天则介绍一个高性能的R包-ggpubr，从名字就可以看出这个包的主要用途了。
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官网: https://rpkgs.datanovia.com/ggpubr/index.html
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几大绘图函数类型
这个包对于绘图类型分的较为详细，主要按照变量个数进行划分，详细介绍如下
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「绘制一个变量-X，连续」
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ggdensity(): 密度图
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stat_overlay_normal_density(): 覆盖法线密度图
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gghistogram(): 直方图
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ggecdf(): 经验累积密度函数
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ggqqplot(): QQ图
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「绘制两个变量-X和Y，离散X和连续Y」
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ggboxplot(): 箱形图
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ggviolin(): 小提琴图
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ggdotplot(): 点图
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ggstripchart(): 条形图
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ggbarplot(): 条形图
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ggline(): 线图
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ggerrorplot(): 错误图
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ggpie(): 饼图
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ggdonutchart(): 甜甜圈图
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ggdotchart()、theme_cleveland(): 克利夫兰的点图
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ggsummarytable()、ggsummarystats():添加摘要统计信息表
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「绘制两个连续变量」
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ggscatter(): 散点图
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stat_cor(): 将具有P值的相关系数添加到散点图中
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stat_stars(): 将星星添加到散点图中
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ggscatterhist(): 具有边际直方图的散点图
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「比较均值并添加p值」
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compare_means(): 均值比较
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stat_compare_means(): 将均值比较P值添加到ggplot
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stat_pvalue_manual():手动将P值添加到ggplot
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stat_bracket()、geom_bracket(): 将带有标签的括号添加到GGPlot
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其他更多优秀函数，小伙伴们可自行查阅官网进行探索。
R-ggpubr包主要案列展示
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Density plot
set.seed(1234)
wdata = data.frame(sex = factor(rep(c("F", "M"), each=200)),weight = c(rnorm(200, 55), rnorm(200, 58)))
ggdensity <- ggdensity(wdata, x = "weight", fill = "lightgray",add = "mean", rug = TRUE) +labs(title = "Example of <span style='color:#D20F26'>ggpubr::ggdensity function</span>",subtitle = "processed charts with <span style='color:#1A73E8'>ggdensity()</span>",caption = "Visualization by <span style='color:#DD6449'>DataCharm</span>") +hrbrthemes::theme_ipsum(base_family = "Roboto Condensed") +theme( plot.title = element_markdown(hjust = 0.5,vjust = .5,color = "black",size = 20, margin = margin(t = 1, b = 12)),plot.subtitle = element_markdown(hjust = 0,vjust = .5,size=15),plot.caption = element_markdown(face = 'bold',size = 12),)
Density plot
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Histogram plot
set.seed(1234)
wdata = data.frame(sex = factor(rep(c("F", "M"), each=200)),weight = c(rnorm(200, 55), rnorm(200, 58)))gghistogram <- gghistogram(wdata, x = "weight", fill = "sex",add = "mean", palette = c("lightgray", "gray50"),add_density = TRUE,rug = TRUE)+labs(title = "Example of <span style='color:#D20F26'>ggpubr::gghistogram function</span>",subtitle = "processed charts with <span style='color:#1A73E8'>gghistogram()</span>",caption = "Visualization by <span style='color:#DD6449'>DataCharm</span>") +hrbrthemes::theme_ipsum(base_family = "Roboto Condensed") +theme( plot.title = element_markdown(hjust = 0.5,vjust = .5,color = "black",size = 20, margin = margin(t = 1, b = 12)),plot.subtitle = element_markdown(hjust = 0,vjust = .5,size=15),plot.caption = element_markdown(face = 'bold',size = 12),)
Histogram plot
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QQ Plots
# Create some data format
set.seed(1234)
wdata = data.frame(sex = factor(rep(c("F", "M"), each=200)),weight = c(rnorm(200, 55), rnorm(200, 58)))# Basic QQ plot
ggqqplot <- ggqqplot(wdata, x = "weight") +labs(title = "Example of <span style='color:#D20F26'>ggpubr::ggqqplot function</span>",subtitle = "processed charts with <span style='color:#1A73E8'>ggqqplot()</span>",caption = "Visualization by <span style='color:#DD6449'>DataCharm</span>") +hrbrthemes::theme_ipsum(base_family = "Roboto Condensed") +theme( plot.title = element_markdown(hjust = 0.5,vjust = .5,color = "black",size = 20, margin = margin(t = 1, b = 12)),plot.subtitle = element_markdown(hjust = 0,vjust = .5,size=15),plot.caption = element_markdown(face = 'bold',size = 12),)
QQ Plots
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Scatter plot
# Load data
data("mtcars")
df <- mtcars
df$cyl <- as.factor(df$cyl)
ggscatter <- ggscatter(df, x = "wt", y = "mpg",add = "loess", conf.int = TRUE,cor.coef = TRUE, cor.coeff.args = list(method = "pearson", label.x = 5,label.y=35, label.size=25,label.sep = "\n"))+labs(title = "Example of <span style='color:#D20F26'>ggpubr::ggscatter function</span>",subtitle = "processed charts with <span style='color:#1A73E8'>ggscatter()</span>",caption = "Visualization by <span style='color:#DD6449'>DataCharm</span>") +hrbrthemes::theme_ipsum(base_family = "Roboto Condensed") +theme( plot.title = element_markdown(hjust = 0.5,vjust = .5,color = "black",size = 20, margin = margin(t = 1, b = 12)),plot.subtitle = element_markdown(hjust = 0,vjust = .5,size=15),plot.caption = element_markdown(face = 'bold',size = 12),)
Scatter plot
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Add Manually P-values to a ggplot
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
# Comparisons against reference
stat.test <- compare_means(len ~ dose, data = ToothGrowth, group.by = "supp",method = "t.test", ref.group = "0.5"
)bp <- ggbarplot(ToothGrowth, x = "supp", y = "len",fill = "dose", palette = "jco",add = "mean_sd", add.params = list(group = "dose"),position = position_dodge(0.8))
bp + stat_pvalue_manual(stat.test, x = "supp", y.position = 33,label = "p.signif",position = position_dodge(0.8)
) + labs(title = "Example of <span style='color:#D20F26'>ggpubr::stat_pvalue_manual function</span>",subtitle = "processed charts with <span style='color:#1A73E8'>stat_pvalue_manual()</span>",caption = "Visualization by <span style='color:#DD6449'>DataCharm</span>") +hrbrthemes::theme_ipsum(base_family = "Roboto Condensed") +theme( plot.title = element_markdown(hjust = 0.5,vjust = .5,color = "black",size = 20, margin = margin(t = 1, b = 12)),plot.subtitle = element_markdown(hjust = 0,vjust = .5,size=15),plot.caption = element_markdown(face = 'bold',size = 12),)
Add Manually P-values to a ggplot
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Draw a Textual Table
# data
df <- head(iris)# Default table
table1 <- ggtexttable(df, rows = NULL)
table2 <- ggtexttable(df, rows = NULL, theme = ttheme("blank")) %>%tab_add_hline(at.row = 1:2, row.side = "top", linewidth = 2)
table1
table2
总结
今天推文我们介绍了「R-ggpubr」实现极少代码绘制出符合期刊要求的可视化图表，极大省去了绘制单独图表元素的时间，为统计分析及可视化探索提供非常便捷的方式，感兴趣的小伙伴可探索更多的绘图函数哦~~