统计绘图 | 一行代码教你绘制顶级期刊要求配图

DataCharm

发布于 2021-03-11 14:45:12

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发布于 2021-03-11 14:45:12

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：

R-ggpubr包主要类型函数介绍
R-ggpubr包主要案列展示

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。

官网:https://rpkgs.datanovia.com/ggpubr/index.html
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

「绘制一个变量-X，连续」
- ggdensity(): 密度图
- stat_overlay_normal_density(): 覆盖法线密度图
- gghistogram(): 直方图
- ggecdf(): 经验累积密度函数
- ggqqplot(): QQ图
「绘制两个变量-X和Y，离散X和连续Y」
- ggboxplot(): 箱形图
- ggviolin(): 小提琴图
- ggdotplot(): 点图
- ggstripchart(): 条形图
- ggbarplot(): 条形图
- ggline(): 线图
- ggerrorplot(): 错误图
- ggpie(): 饼图
- ggdonutchart(): 甜甜圈图
- ggdotchart()、theme_cleveland(): 克利夫兰点图
- ggsummarytable()、ggsummarystats():添加摘要统计信息表
「绘制两个连续变量」
- ggscatter(): 散点图
- stat_cor(): 将具有P值的相关系数添加到散点图中
- stat_stars(): 将星星添加到散点图中
- ggscatterhist(): 具有边际直方图的散点图
「比较均值并添加p值」
- compare_means(): 均值比较
- stat_compare_means(): 将均值比较P值添加到ggplot
- stat_pvalue_manual():手动将P值添加到ggplot
- stat_bracket()、geom_bracket(): 将带有标签的括号添加到GGPlot

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

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

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

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

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

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

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)