一行代碼教你繪制頂級期刊要求配圖
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R-ggpubr包主要類型函數(shù)介紹 R-ggpubr包主要案列展示
R-ggpubr包主要類型函數(shù)介紹
雖然在Python中我們也可以通過使用Matplotlib定制化出符合出版要求的圖表，但這畢竟對使用者的繪圖技能要求較高，當(dāng)然也是還有部分輪子可以用的。而我們今天則介紹一個高性能的R包-ggpubr，從名字就可以看出這個包的主要用途了。
官網(wǎng):https://rpkgs.datanovia.com/ggpubr/index.html
幾大繪圖函數(shù)類型
這個包對于繪圖類型分的較為詳細(xì)，主要按照變量個數(shù)進(jìn)行劃分，詳細(xì)介紹如下
「繪制一個變量-X，連續(xù)」
ggdensity(): 密度圖 stat_overlay_normal_density(): 覆蓋法線密度圖 gghistogram(): 直方圖 ggecdf(): 經(jīng)驗累積密度函數(shù) ggqqplot(): QQ圖 「繪制兩個變量-X和Y，離散X和連續(xù)Y」
ggboxplot(): 箱形圖 ggviolin(): 小提琴圖 ggdotplot(): 點圖 ggstripchart(): 條形圖 ggbarplot(): 條形圖 ggline(): 線圖 ggerrorplot(): 錯誤圖 ggpie(): 餅圖 ggdonutchart(): 甜甜圈圖 ggdotchart()、theme_cleveland(): 克利夫蘭點圖 ggsummarytable()、ggsummarystats():添加摘要統(tǒng)計信息表 「繪制兩個連續(xù)變量」
ggscatter(): 散點圖 stat_cor(): 將具有P值的相關(guān)系數(shù)添加到散點圖中 stat_stars(): 將星星添加到散點圖中 ggscatterhist(): 具有邊際直方圖的散點圖 「比較均值并添加p值」
compare_means(): 均值比較 stat_compare_means(): 將均值比較P值添加到ggplot stat_pvalue_manual():手動將P值添加到ggplot stat_bracket()、geom_bracket(): 將帶有標(biāo)簽的括號添加到GGPlot
其他更多優(yōu)秀函數(shù)，小伙伴們可自行查閱官網(wǎng)進(jìn)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),
)
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),
)
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),
)
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),
)
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),
)
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)
總結(jié)
今天推文我們介紹了「R-ggpubr」實現(xiàn)極少代碼繪制出符合期刊要求的可視化圖表，極大省去了繪制單獨圖表元素的時間，為統(tǒng)計分析及可視化探索提供非常便捷的方式，感興趣的小伙伴可探索更多的繪圖函數(shù)哦~~
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