生信绘图与配色

#install.packages(c('tidyverse','ggsci'))
library(tidyverse)
library(ggsci)

ggplot(mtcars,aes(mpg,qsec,color = factor(cyl)))+
 size = 3, alpha = .6)+
  theme_bw()+
  scale_color_lancet()+
  scale_x_continuous()+
  scale_y_continuous()

mtcars <- mtcars %>% as_tibble() %>% mutate(cyl = factor(cyl),
                                            carb = factor(carb))
set.seed(2019)
dsamp <- diamonds[sample(nrow(diamonds), 1000), ]
p1 <- ggplot(mtcars,aes(mpg,qsec,fill = cyl))+
  geom_point(size = 3,shape = 21, color = 'black')+
  theme_classic()+
  theme(legend.position = c(.1,.8),#图标的位置
        legend.background = element_rect(color = 'black'))#图标的颜色

p2 <- ggplot(mtcars,aes(mpg,qsec,fill = carb))+
  geom_point(size = 3,shape = 21, color = 'black')+
  theme_classic()+
  theme(legend.position = c(.1,.7),
        legend.background = element_rect(color = 'black'))
#plot_grid()函数用于多个 ggplot 对象（即图表）排列组合,align参数设置对齐方式 ，v表示垂直对齐，h表示水平对齐。
plot_grid(p1,p2,ncol=2,labels = c('A','B'),align = c('v','h'))

dsamp <- dsamp %>% mutate(depth = depth - median(depth))
p1 <- ggplot(dsamp, aes(carat, price)) +
  geom_point(aes(fill = depth),shape=21, color = 'black',size = 2)+
  scale_fill_gradient2(low = '#41ab5d',mid = 'white',high = '#e31a1c',
                        midpoint = 0)+
  theme(legend.position = c(.1,.8))

p2 <- ggplot(dsamp, aes(carat, price)) +
  geom_point(aes(fill = depth),shape=21, color = 'black',size = 2)+
  scale_fill_gsea()+
  theme(legend.position = c(.1,.8))

plot_grid(p1,p2,ncol=2,labels = c('A','B'),align = c('v','h'))

set.seed(2019)
dsamp <- diamonds[sample(nrow(diamonds), 500), ]
dsamp <-  dsamp %>% mutate(x = exp(x))

p1 <- ggplot(dsamp, aes(carat, price)) +
  geom_point(aes(size = x,fill = cut),shape=21, 
             color = 'black')+
  scale_fill_brewer(palette = 'Set2')

p2 <- ggplot(dsamp, aes(carat, price)) +
  geom_point(aes(size = log(x),fill = cut),shape=21, 
             color = 'black')+
  scale_fill_brewer(palette = 'Set2')
#RColorBrewer配色包--颜色区分较大，适合分类变量，如 Set1, Set2, Set3, Accent, Paired, Pastel1, Pastel2 等
plot_grid(p1,p2,ncol=2,labels = c('A','B'),align = c('v','h'))

a <- data.frame( x=rnorm(20000, 10, 1.9), y=rnorm(20000, 10, 1.2) )
b <- data.frame( x=rnorm(20000, 14.5, 1.9), y=rnorm(20000, 14.5, 1.9) )
c <- data.frame( x=rnorm(20000, 9.5, 1.9), y=rnorm(20000, 15.5, 1.9) )
data <- rbind(a,b,c)
p2 <- ggplot(data, aes(x=x, y=y) ) +
  stat_density_2d(aes(fill = ..level..), geom = "polygon")+
  theme_bw()+
  scale_fill_gradient(low = '#edf8e9',high = '#238b45')+
  theme(panel.grid = element_blank())

install.packages("ggExtra")
library(ggExtra)

piris <- ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
  geom_point(shape=21, size = 3,stroke = 1.2)+
  scale_color_npg()+
  theme_bw()+
  theme(legend.position = c(.1,.86))
ggMarginal(piris, type= 'density',
           groupFill = TRUE)

install.packages('ggridges')
library(ggridges)

ggplot(dsamp,aes(x = carat,y = clarity, fill = clarity))+
  geom_density_ridges(alpha = .5)+
  scale_fill_brewer(palette = 'Set3')+
  theme_classic()

install.packages("ggcorrplot")
library(ggcorrplot)
mtcars <- mtcars %>% select(c('mpg','disp','hp','drat','wt','qsec'))
corr <- round(cor(mtcars), 1)
p1 <- ggcorrplot(
  corr,
  type = "lower",
  outline.color = "white",
  colors = c("#6D9EC1", "white", "#E46726")
)

p2 <- ggcorrplot(
  corr,
  type = "upper",
  outline.color = "white",
  colors = c("#084594", "white", "#ef3b2c")
)

p3 <- ggcorrplot(corr,
           type = "lower",
           lab = TRUE)

p.mat <- cor_pmat(mtcars)

p4 <- ggcorrplot(corr,
           type = "lower",
           p.mat = p.mat)

plot_grid(p1,p2,p3,p4,ncol=2,labels = LETTERS[1:4],align = c('v','h'))

set.seed(2020)
id <- 1:8
dat <- tibble(x = rep(id,2),
              y = c(2*id+rnorm(8,0,1),
                    6*id+rnorm(8,0,2)),
              group = rep(c('Group1','Group2'),each = 8))

p1 <- ggplot(dat,aes(x, y, color = group))+
  geom_line(size = .8)+
  geom_point(shape = 21,color = 'black',size=3,fill = 'white')+
  scale_color_d3()+
  theme_classic()+
  theme(legend.position = c(.15,.85))

p2 <- ggplot(dat,aes(x, log(y), color = group))+
  geom_line(size = .8)+
  geom_point(shape = 21,color = 'black',size=3,fill = 'white')+
  scale_color_d3()+
  theme_classic()+
  theme(legend.position = c(.15,.85))

p3 <- ggplot(dat,aes(x, y))+
  geom_line(size = .8,color = '#2b8cbe')+
  geom_point(shape = 21,color = 'black',size=3,fill = 'white')+
  theme_classic()+
  facet_wrap(~group,ncol = 2,scales = 'free_y')+
  theme(legend.position = c(.15,.85),
        strip.background = element_blank(),
        strip.text = element_text(size = 12))

p4 <- plot_grid(p1,p2,ncol=2,labels = LETTERS[1:2],align = c('v','h'))
plot_grid(p4,p3,ncol=1,labels = c('','C'),align = c('v','h'))

install.packages('ggtern')
library(ggtern)
set.seed(2019)
a <- tibble(x=rnorm(20000, 80, 30), 
                 y=rnorm(20000, 100, 30), 
                 z = rnorm(20000, 60, 20))

ggtern(data=a,aes(x,y,z))+
  stat_density_tern(aes(fill = ..level..,alpha = ..level..),
                    geom = 'polygon')+
  scale_fill_gradient(low = '#fcbba1',high = '#ef3b2c')+
  theme_showarrows()+
  guides(color = "none", fill = "none", alpha = "none")  

injuries <- tibble(type = c('Road injury','Self-harm','CVD',
                            'Cancers','Infectious Diseases'),
                   counts = c(214,123,69,24,17),
                   share = counts/sum(counts)*100)

p1 <- ggplot(injuries,aes('', y = counts, fill = type))+
  geom_bar(width = 1, size = 1, color = 'white',stat = 'identity')+
  coord_polar(theta = 'y')+
  geom_text(aes(label = paste0(round(share,1),'%')),
            position = position_stack(vjust = .5))+
  labs(x = NULL, y = NULL, fill = NULL, 
       title = "Injury proportion in young adults")+
  scale_fill_npg()+
  theme_classic() +
  theme(axis.line = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(hjust = 0.5, color = "#666666"))

injuries2 <- injuries %>% mutate(type = factor(type,
                                              levels = type[order(counts)])) 
p2 <- ggplot(injuries2,aes('', y = counts, fill = type))+
  geom_bar(width = 1, size = 1, color = 'white',stat = 'identity')+
  coord_polar(theta = 'y')+
  geom_text(aes(label = paste0(round(share,1),'%')),
            position = position_stack(vjust = .5))+
  labs(x = NULL, y = NULL, fill = NULL, 
       title = "Injury proportion in young adults")+
  scale_fill_npg()+
  theme_classic() +
  theme(axis.line = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(hjust = 0.5, color = "#666666"))+
  guides(fill = guide_legend(reverse = TRUE))

plot_grid(p1,p2,ncol=2,labels = LETTERS[1:2],align = c('v','h'))

ggplot(injuries,aes(x = type, y = counts, fill = type))+
  geom_bar(width = 1,color = 'black',stat = 'identity')+
  coord_polar(theta = 'x')+
  geom_text(aes(label = paste0(round(share,1),'%'),
                y = counts+2))+
  labs(x = NULL, y = NULL, fill = NULL, 
       title = "Injury proportion in young adults")+
  scale_fill_tron()+
  theme_bw() +
  theme(axis.text.x = element_text(size = 13, color='black',
                                   angle = seq(-30,-330,len = 5)),
        plot.title = element_text(hjust = 0.5, color = "#666666"),
        panel.border = element_blank(),
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        legend.position = 'none')

install.packages('ggpubr')
library(ggpubr)

ggdonutchart(injuries,'share',
             label = paste0(round(injuries$share),'%'),
             fill = 'type',color = 'white',
             palette = 'lancet')

install.packages('treemapify')
library(treemapify)
set.seed(2019)
diseases <- data.frame(type = rep(c('CVD','Infections','Cancer',
                            'Metabolic','Digestive','CNS'),
                            times = c(5,5,5,3,4,6)),
                   diseases = c('Heart attack','Stroke','CHD',
                                'Arrhythmia','Heart failure',
                                'Hepatitis','Malaria','HIV',
                                'TB','Influenza','HCC','CRC',
                                'Lung','Gastric','Breast',
                                'Diabetes','Hypertention',
                                'Mucolipidoses','IBD','Celiac Disease',
                                "Crohn's Disease",'Diarrhea',
                                'Autism','ADHD','Depression','Meningitis',
                                'Migraine','GBM'),
                   freqs = sample(20:60,28))

ggplot(diseases,aes(area = freqs,fill = type,
                    label = diseases, subgroup = type))+
  geom_treemap(color = 'gray20')+
  geom_treemap_subgroup_border() +
  geom_treemap_subgroup_text(place = "centre", grow = T, 
                             alpha = 0.5, colour ="black", 
                             fontface = "italic", min.size = 0) +
  geom_treemap_text(colour = "white", place = "topleft", reflow = T)+
  scale_fill_brewer(palette = 'Set2')

injuries <- tibble(age = rep(c('Young adults',
                               'Middle-aged people',
                               'the elders'),each=5),
                   type = rep(c('Road injury','Self-harm','CVD',
                                'Cancers','Infectious Diseases'),3),
                   counts = c(214,123,69,24,17,
                              129,110,201,101,45,
                              56,32,212,189,78))
injuries <- injuries %>% mutate(age = factor(age, 
                                             levels = c('Young adults',
                                                        'Middle-aged people',
                                                        'the elders')))
injuries <- injuries %>%
  mutate(type = factor(type,levels = c('CVD','Road injury','Cancers',
                                       'Self-harm','Infectious Diseases')))
p1 <- ggplot(injuries,aes(type,weight = counts,fill = age))+
  geom_hline(yintercept = seq(100,400,100),color = 'gray')+
  geom_bar(color = 'black',width = .7,position = 'stack')+
  scale_fill_brewer(palette = 'Accent')+
  scale_y_continuous(expand = c(0,0))+
  theme_classic()+
  theme(axis.text.x = element_text(angle = 45,hjust=1))

p2 <- ggplot(injuries,aes(type,weight = counts,fill = age))+
  geom_bar(color = 'black',width = .7,position = 'fill')+
  scale_fill_brewer(palette = 'Accent')+
  scale_y_continuous(expand = c(0,0))+
  theme_classic()+
  theme(axis.text.x = element_text(angle = 45,hjust=1))

plot_grid(p1,p2,ncol=1,labels = c('A','B'))

set.seed(2019)
injuries <- tibble(
                   type = c('Heart attack','Stroke','CHD',
                                'Arrhythmia','Heart failure',
                                'Hepatitis','Malaria','HIV',
                                'TB','Influenza','HCC','CRC',
                                'Lung','Gastric','Breast',
                                'Diabetes','Hypertention',
                                'Mucolipidoses','IBD','Celiac Disease',
                                "Crohn's Disease",'Diarrhea',
                                'Autism','ADHD','Depression','Meningitis',
                                'Migraine','GBM'),
                   counts = sample(20:100,28))
injuries <- injuries %>% mutate(
  type = factor(type,levels = type[order(counts)]))
p1 <- ggplot(injuries,aes(type,weight = counts))+
  geom_hline(yintercept = seq(20,90,10),color = 'gray')+
  geom_bar(color = 'black',width = .7,fill = '#00bdaa',size = .3)+
  scale_y_continuous(expand = c(0,0))+
  theme_classic()+
  theme(axis.text.x = element_text(angle = 45,hjust=1))
injuries2 <- injuries %>% mutate(
  type = factor(type,levels = rev(type[order(counts)])))
# or:
#injuries2 <- injuries %>% mutate(
# type = factor(type,levels = type[order(counts,decreasing = T)]))
p2 <- ggplot(injuries2,aes(type,weight = counts))+
  geom_hline(yintercept = seq(20,90,10),color = 'gray')+
  geom_bar(color = 'black',width = .7,fill = '#a1dd70',size = .3)+
  scale_y_continuous(expand = c(0,0))+
  theme_classic()+
  theme(axis.text.x = element_text(angle = 45,hjust=1))
plot_grid(p1, p2, ncol = 1,labels = c('A','B'),align = c('v','h'))

set.seed(2019)
df <- tibble(diseases = c('Heart attack','Stroke','CHD',
                          'Arrhythmia','Heart failure',
                          'Hepatitis','Malaria','HIV',
                          'TB','Influenza','HCC','CRC',
                          'Lung','Gastric','Breast',
                          'Diabetes','Hypertention',
                          'Mucolipidoses','IBD','Celiac Disease',
                          "Crohn's Disease",'Diarrhea',
                          'Autism','ADHD','Depression','Meningitis',
                          'Migraine','GBM'),
             trends = rnorm(28,0,2))
df <- df %>% mutate(
  diseases = factor(diseases,levels = rev(diseases[order(trends)])))

ggplot(df,aes(diseases,weight = trends))+
  geom_hline(yintercept = seq(-4,3,1),color = 'gray')+
  geom_bar(color = 'black',width = .6,fill = '#5bd1d7',size = .3)+
  scale_y_continuous(limits = c(-5,3.5))+
  theme_classic()+
  ylab('Temporal trends of diseases')+
  theme(axis.text.x = element_text(angle = 45,hjust=1))

ggplot(injuries,aes(y = type,x = counts))+
  geom_segment(aes(y = type, yend = type,x = 0,xend = counts),
               color = 'black')+
  geom_point(color = 'black',fill = '#28c3d4',
             size = 3,shape = 21)+
  scale_y_discrete(expand = c(0.02,0.02))+
  scale_x_continuous(expand = c(0,0.8),
                     breaks = seq(20,100,20),
                     labels = seq(20,100,20))+
  theme_light()+
  ylab('')+
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank()) 

set.seed(2019)
diseases <- data.frame(type = rep(c('CVD','Infections','Cancer',
                                    'Metabolic','Digestive','CNS'),
                                  times = c(5,5,5,3,4,6)),
                       disease = c('Heart attack','Stroke','CHD',
                                    'Arrhythmia','Heart failure',
                                    'Hepatitis','Malaria','HIV',
                                    'TB','Influenza','HCC','CRC',
                                    'Lung','Gastric','Breast',
                                    'Diabetes','Hypertention',
                                    'Mucolipidoses','IBD','Celiac Disease',
                                    "Crohn's Disease",'Diarrhea',
                                    'Autism','ADHD','Depression','Meningitis',
                                    'Migraine','GBM'),
                       freqs = sample(20:60,28))
diseases2 <- diseases %>% 
  mutate(disease = 
           factor(disease,levels = disease[order(freqs,decreasing = TRUE)]))
sums <- tapply(diseases$freqs,type,sum)
diseases3 <- diseases2 %>% 
  mutate(type = factor(type,levels = 
                         names(sums)[order(sums,decreasing = T)]),
         disease = interaction(disease,type,drop = TRUE))          
ggplot(diseases3,aes(disease, weight = freqs, fill = type))+
  geom_bar(color = 'black',width = .6,size = .3)+
  scale_y_continuous(expand = c(0,0))+
  scale_x_discrete(labels = gsub('\\.[a-zA-Z]{0,20}','',
                                 levels(diseases3$disease)))+
  scale_fill_lancet()+
  theme_classic()+
  theme(axis.text.x = element_text(angle = 45,hjust=1))

df <- tibble(Age = rep(c('0-19','20-34','35-49','50-64','65+'),2),
             Sex = rep(c('Male','Female'),each = 5),
             Values = c(18,21,20,26,29,15,18,15,22,23))
df2 <- df %>% tidyr::spread(key = Sex, value = Values)
p2 <- ggplot()+
  geom_segment(data = df2,aes(x = Female, xend = Male, 
                              y = Age, yend = Age),
               size = 1.5,color = 'gray')+
  geom_point(data = df, aes(Values, Age, fill = Sex),
             shape = 21,size = 3)+
  labs(x = 'Values')+
  scale_fill_jco()+
  theme_light()+
  theme(legend.position = 'top',
        panel.grid.minor=element_blank(),
        panel.grid.major.y=element_blank(),
        panel.grid.major.x=element_line(),
        axis.ticks=element_blank(),
        panel.border=element_blank())
plot_grid(p1, p2, ncol = 2,labels = c('A','B'),align = c('v','h'))

data <- data.frame(
  individual=paste( "Mister ", seq(1,60), sep=""),
  group=c( rep('A', 10), rep('B', 30), rep('C', 14), rep('D', 6)) ,
  value=sample( seq(10,100), 60, replace=T)
)

empty_bar <- 4
to_add <- data.frame( matrix(NA, empty_bar*nlevels(data$group), ncol(data)) )
colnames(to_add) <- colnames(data)
to_add$group <- rep(levels(data$group), each=empty_bar)
data <- rbind(data, to_add)
data <- data %>% arrange(group)
data$id <- seq(1, nrow(data))

label_data <- data
number_of_bar <- nrow(label_data)
angle <- 90 - 360 * (label_data$id-0.5) /number_of_bar     
label_data$hjust <- ifelse( angle < -90, 1, 0)
label_data$angle <- ifelse(angle < -90, angle+180, angle)

ggplot(data, aes(x=as.factor(id), y=value, fill=group)) +     
  geom_bar(stat="identity", alpha=0.5) +
  ylim(-100,120) +
  theme_minimal() +
  theme(
    legend.position = "none",
    axis.text = element_blank(),
    axis.title = element_blank(),
    panel.grid = element_blank(),
    plot.margin = unit(rep(-1,4), "cm")) +
  coord_polar() + 
  geom_text(data=label_data, aes(x=id, y=value+10, label=individual, 
                                 hjust=hjust), color="black", 
            fontface="bold",alpha=0.6, size=2.5,
            angle= label_data$angle, inherit.aes = FALSE ) 

populations <- tibble(age = rep(c("0-4","5-9","10-14","15-19",
                              "20-24","25-29","30-34",
                              "35-39","40-44","45-49","50-54",
                              "55-59","60-64","65-69","70-74",
                              "75-79","80-84",'85+'),2),
                      sex = rep(c('Male','Female'),each = 18),
                      pops = c(91646,65397,75560,108622,245368,
                               278821,196527,153679,153202,248852,
                               388813,350713,241516,130285,103534,
                               119681,76644,47576,
                               85376,62108,72649,107474,238128,269100,
                               192116,159126,156828,240783,378479,
                               361806,225168,123183,112859,149189,
                               105804,78370
                               ))
populations <- populations %>% mutate(
  age = factor(age, levels = c("0-4","5-9","10-14","15-19",
                               "20-24","25-29","30-34",
                               "35-39","40-44","45-49","50-54",
                               "55-59","60-64","65-69","70-74",
                               "75-79","80-84",'85+')),
  pops = pops/1e3,
  pops = ifelse(sex == 'Female',-pops, pops))
library(ggthemes)
ggplot(populations,aes(x =age,y=pops,fill=sex,width=0.8)) +
  coord_flip() +
  geom_bar(data=subset(populations,sex=="Female"),stat = "identity") +
  geom_bar(data=subset(populations,sex=="Male"), stat = "identity") +
  scale_y_continuous(breaks = seq(-400,400,length=9),
                     labels = paste0(as.character
                                     (c(abs(seq(-400,400,length=9)))), "k"), 
                     limits = c(-450, 450)) +
  theme_economist_white(horizontal = FALSE) +
  scale_fill_economist() + 
  labs(title="2010",
       y="Population",x="Age") + 
  guides(fill=guide_legend(reverse = TRUE))+
  theme(
    legend.position =c(0.8,0.9),
    legend.title = element_blank(),
    plot.title = element_text(size=20),
    plot.caption = element_text(size=12,hjust=0)
  )

install.packages('VennDiagram')
library(VennDiagram)
set.seed(2019)
SNP_pop_1=paste(rep("SNP_" , 200) , sample(c(1:1000) , 
                                           200 , replace=F) , sep="")
SNP_pop_2=paste(rep("SNP_" , 200) , sample(c(1:1000) , 
                                           200 , replace=F) , sep="")
SNP_pop_3=paste(rep("SNP_" , 200) , sample(c(1:1000) , 
                                           200 , replace=F) , sep="")
SNP_pop_4=paste(rep("SNP_" , 200) , sample(c(1:1000) , 
                                           200 , replace=F) , sep="")

venn.diagram(
  x = list(SNP_pop_1 , SNP_pop_2 , SNP_pop_3, SNP_pop_4),
  category.names = c("SNP pop 1" , "SNP pop 2 " , "SNP pop 3","SNP_pop_4"),
  filename = 'Venn.png',
  output = T ,
  lty = 'blank',
  fill = c('#07689f','#a2d5f2','#ff7e67','#10ddc2')
)

install.packages('ggupset')
library(ggupset)

set.seed(2019)
df <- tibble(cancers = rep(c('Bladder','Bone','CNS','Breast','Cervix uteri',
                         'CRC','Lung','Liver','Prostate','Kidney',
                         'Eye','Skin','Thyroid','HLM','GBM'),3),
             regions = rep(c('Region A','Region B','Region C'),each = 15),
             incidence = sample(10:100,45))
#16-rank(incidence)显示肿瘤在不同regions从大到小排列，需要根据自己的数据更改
df <- df %>% group_by(regions) %>% mutate(ranks = 16-rank(incidence))

ggplot(df, aes(cancers, regions,fill = incidence))+
  geom_tile(color = 'gray20',size = .4)+
  theme_gray()+
  scale_x_discrete(expand = c(0,0)) + 
  scale_y_discrete(expand = c(0,0))+
  theme(axis.text.x = element_text(angle = 45,hjust=1))+ 
  labs(x = "", y = "") + 
  geom_text(aes(label = ranks))+
  scale_fill_gradient(low = '#fff5f0',high = '#cb181d')

set.seed(2019)
df <- tibble(gene = paste0('gene',1:5000),
             expressions = c(rnorm(4995,3,0.5),
                             5.5,6,7,8,9),
             position = sample(1:100,5000,replace = T))

df <- df %>% mutate(expressions = ifelse(expressions<0,0.01,expressions),
                    level = ifelse(expressions>5,'High','Low'))

install.packages('ggrepel')
library(ggrepel)

ggplot(df, aes(x = position, y= expressions,
               fill = level,size = level))+
  geom_hline(yintercept = 5,color = 'gray',linetype =2)+
  geom_jitter(shape = 21, color = 'black')+
  scale_fill_manual(values = c('blue','gray'))+
  scale_size_manual(values = c(4,2))+
  geom_text_repel(data = df[df$expressions>=5,],
            aes(x = position, y= expressions,label = gene))+
  theme(legend.position = 'none')

install.packages('gghighlight')
library(gghighlight)

gghighlight_point(df,aes(position, expressions, color = level,
                         size = level),
                  expressions > 5)+
  geom_hline(yintercept = 5,color = 'gray',linetype =2)+
  scale_color_manual(values = c('blue','gray'))+
  scale_size_manual(values = c(4,2))+
  theme(legend.position = 'none')

ggplot(mtcars, aes(x = wt, y = mpg)) + 
  geom_point(shape = 21, color = 'black',fill = '#ff8a5c',size=4)+
  geom_smooth(method = lm)+
  annotate("text", x = 5, y = 32, label = "italic(R) ^ 2 == 0.75",
           parse = TRUE,size = 6, color = 'blue')+
  theme_bw()

install.packages("ggsignif")
library(ggsignif)

set.seed(2019)
df <- tibble(group = rep(paste('Group',LETTERS[1:4]),times = c(10,8,10,13)),
             values = c(rnorm(10,15,2),rnorm(8,14,2),
                        rnorm(10,8,1),rnorm(13,5,2)))

p1 <- ggplot(df,aes(group,values))+
  geom_boxplot()+
  geom_jitter(size = 2,shape = 21,fill = 'gray40')+
  geom_signif(comparisons = list(c('Group A','Group B'),
                                 c('Group A','Group C'),
                                 c('Group A','Group D')),
              map_signif_level = FALSE, textsize=6, #TRUE标记星号
              test = t.test,
              step_increase = 0.2) +
  ylim(NA, 25)+
  theme_classic()
  plot_grid(p1,p2,ncol=2,align = 'h',labels = c('A','B'))

install.packages("ggstatsplot")
library(ggstatsplot)
ggbetweenstats(
  data = iris,
  plot.type = 'box',
  x = Species,
  y = Sepal.Length,
  pairwise.comparisons = T,
  mean.plotting = F,
  messages = T,
  type = 'p',
  palette = 'Set1') + 
  scale_y_continuous(breaks = seq(3, 8, by = 1))+
  theme_classic()+
  theme(legend.position = c(.11,.8))

install.packages('ggseqlogo')
library(ggseqlogo)

data(ggseqlogo_sample)
ggseqlogo( seqs_dna$MA0001.1 )

ggplot()+
  annotate("rect", xmin = 0.5, xmax = 3.5, ymin = -0.05, ymax = 1.9, 
           alpha=0.1, col="black", fill="yellow")+
  geom_logo(seqs_dna$MA0001.1, stack_width = 0.9,
            col_scheme = 'nucleotide2')+
  annotate("segment", x=4, xend = 8, y=1.2, yend = 1.2, size=2)+
  annotate("text", x=6, y=1.3, label="Text annotation")+
  theme_logo()

nwk <- system.file("extdata", "sample.nwk", package="treeio")
tree <- read.tree(nwk)
circ <- ggtree(tree, layout = "circular")
df <- data.frame(first=c("a", "b", "a", "c", "d", "d", 
                         "a", "b", "e", "e", "f", "c", "f"),
                 second= c("z", "z", "z", "z", "y", "y", 
                           "y", "y", "x", "x", "x", "a", "a"))
rownames(df) <- tree$tip.label

set.seed(2019)
df2 <- as.data.frame(matrix(rnorm(39), ncol=3))
rownames(df2) <- tree$tip.label
colnames(df2) <- LETTERS[1:3]

p1 <- gheatmap(circ, df, offset=.8, width=.2,
               colnames_angle=95, colnames_offset_y = .25) +
  scale_fill_viridis_d(option="D", name="discrete\nvalue")

library(ggnewscale)
p2 <- p1 + new_scale_fill()
p3 <- gheatmap(p2, df2, offset=15, width=.3,
         colnames_angle=90, colnames_offset_y = .25) +
  scale_fill_viridis_c(option = 'C',name="continuous\nvalue")

plot_grid(p1,p3,ncol = 2,labels = c('A','B'))

library(circlize)

set.seed(2019)

numbers <- sample(c(1:1000), 100, replace = T)
data <- matrix( numbers, ncol=5)
rownames(data) <- paste0("Set-", seq(1,20))
colnames(data) <- paste0("Pair-", seq(1,5))

chordDiagram(data, transparency = 0.5)

circos.par("track.height" = 0.4)
set.seed(2019)
data = data.frame(
  factor = sample(letters[1:8], 1000, replace = TRUE),
  x = rnorm(1000),
  y = runif(1000)
)


circos.initialize( factors=data$factor, x=data$x )

circos.trackPlotRegion(factors = data$factor, y = data$y, 
                       panel.fun = function(x, y) {
                         circos.axis()
                       })
circos.trackPoints(data$factor, data$x, data$y, col="deeppink",pch = 16)
circos.trackHist(data$factor, data$x, col="#69b3a2")

devtools::install_github("EmilHvitfeldt/paletteer")
library(paletteer)

p1 <- ggplot(dsamp, aes(carat, price, fill = cut)) +
    geom_point(shape = 21, color = 'black', size = 3) +
    scale_fill_paletteer_d("awtools::mpalette") +
    theme_classic() +
    labs(title = 'awtools-mpalette', x = '', y = '') +
    theme(legend.position = c(.15,.8),
          legend.background = element_rect(fill = NA),
          plot.title = element_text(hjust = 0.5))
p1

set.seed(2019)
dsamp <- diamonds[sample(nrow(diamonds), 1000), ]

P1 <- ggplot(dsamp,aes(carat, price, fill = cut))+
  geom_point(shape = 21, color = 'black', size = 3)+
  scale_fill_manual(name = '',
                    values = c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99'))+
  labs(title = 'default theme',x='',y='')+
  theme(legend.position = c(.15,.8),
        legend.background = element_rect(fill = NA,color = NA),
        plot.title = element_text(hjust = 0.5))

P2 <- ggplot(dsamp,aes(carat, price, fill = cut))+
  geom_point(shape = 21, color = 'black', size = 3)+
  scale_fill_manual(name = '',
                    values = c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99'))+
  labs(title = 'bw theme',x='',y='')+
  theme_bw()+
  theme(legend.position = c(.15,.8),
        legend.background = element_rect(fill = NA,color = NA),
        plot.title = element_text(hjust = 0.5))

set.seed(2019)
id <- 1:10
df <- tibble(x = rep(id,2),
             y = c(id*1.8+rnorm(10,0,1),
                   x=id*3+rnorm(10,0,1)),
             group = rep(c('Group A','Group B'),each = 10))

p1 <- ggplot(df, aes(x,y,fill = group,group = group))+
  geom_line()+
  geom_point(shape = 21, color = 'gray40', size = 3)+
  theme_classic()

p2 <- ggplot(df, aes(x,y,fill = group,group = group))+
  geom_line()+
  geom_point(shape = 21, color = 'gray40', size = 3)+
  theme_classic()+
  theme(legend.position = c(.15,.85))图例位置

CairoPDF('file_name.pdf',height = 6,width = 6)
graph2ppt(object)