大家好，今天我们分享一个神奇的网站，提供各种可视化图片的代码和详解，生信小博士公众号内回复冒号后面的关键词领取网站地址：神奇网站

1.一R代码添加显著性

1#1----set.seed(123)library(ggplot2)library(ggstatsplot)ggbetweenstats(  data  = iris,  x     = Species,  y     = Sepal.Length,  title = "Distribution of sepal length across Iris species")

还有详细参数的解释：

2.还可以换一种方式展示

2#2------library(ggpubr)# Load datadata("ToothGrowth")df <- ToothGrowthhead(df, 4)# Add p-values comparing groups# Specify the comparisons you wantmy_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") )ggboxplot(df, x = "dose", y = "len",          color = "dose", palette =c("#00AFBB", "#E7B800", "#FC4E07"),          add = "jitter", shape = "dose")+ stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value  stat_compare_means(label.y = 50)                   # Add global p-value# Violin plots with box plots inside# :::::::::::::::::::::::::::::::::::::::::::::::::::# Change fill color by groups: dose# add boxplot with white fill colorggviolin(df, x = "dose", y = "len", fill = "dose",         palette = c("#00AFBB", "#E7B800", "#FC4E07"),         add = "boxplot", add.params = list(fill = "white"))+  stat_compare_means(comparisons = my_comparisons, label = "p.signif")+ # Add significance levels  stat_compare_means(label.y = 50)                                      # Add global the p-value

3. 单细胞的密度图

3# 3 单细胞密度图----pbmc=readRDS("~/gzh/pbmc3k_final.rds")library(Seurat)
p=DimPlot(pbmc,label = TRUE)dat=p$datahead(dat)#install.packages("ggpointdensity")library(ggpointdensity)
ggplot(data = dat, mapping = aes(x = UMAP_1, y = UMAP_2)) +  geom_pointdensity(adjust = 4) +viridis::  scale_color_viridis()

4 生存曲线

4 #4 生存曲线------#install.packages('ggsurvfit')library(ggsurvfit)#> Loading required package: ggplot2
p <- survfit2(Surv(time, status) ~ surg, data = df_colon) |>  ggsurvfit(linewidth = 1) +  add_confidence_interval() +  add_risktable() +  add_quantile(y_value = 0.6, color = "gray50", linewidth = 0.75) +  scale_ggsurvfit()pp +  # limit plot to show 8 years and less  coord_cartesian(xlim = c(0, 8)) +  # update figure labels/titles  labs(    y = "Percentage Survival",    title = "Recurrence by Time From Surgery to Randomization",  )