柱状图
library(ggplot2)
#install.packages("ggsignif")
library(ggsignif)
ggplot(data=dd,aes(group,gene))+
stat_summary(geom = "bar",
fun=mean,
aes(fill=group))+
stat_summary(geom="errorbar",
fun.min = min,
fun.max = max,
width=0.2)+
geom_signif(comparisons = list(c("early","mid"),
# c("B","D"),
c("mid","late")),
test = 't.test',
y_position = c(10,17,18),
map_signif_level = T)+
scale_y_continuous(expand = c(0,0),limits = c(0,5))+
theme_bw()+
scale_fill_manual(values = c("steelblue",
"yellowgreen",
"violetred1"))+
theme(legend.title = element_blank())+
labs(x="group",
y="expression",
title = "LAMA4")+
theme(panel.grid = element_blank())
-
山脊图
image.png
#山脊图
##产生数据
set.seed(2019)
dsamp <- diamonds[sample(nrow(diamonds), 1000), ]
ggplot(dsamp,aes(carat,fill = clarity))+
geom_density(alpha = .5,color = NA)+
scale_fill_brewer(palette = 'Set2')+
theme_classic()+
theme(legend.position = c(.85,.65))
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()
- 折线图+散点
set.seed(2019)
x <- 1:8
dat <- tibble(x = rep(x,2),
y = 1.2*x+5+rnorm(16,0,2),
group = rep(c('Group1','Group2'),each = 8))
p1 <- ggplot(dat,aes(x, y, color = group))+
geom_line(size = .8)+
scale_color_d3()+
theme_classic()+
theme(legend.position = c(.85,.15))
##log转换y
p2 <- 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(.85,.15))
plot_grid(p1,p2,ncol=2,labels = LETTERS[1:2],align = c('v','h'))
-
配对坡度图
image.png
ggplot(data = data, aes(x = Date, y = Pct,
group = Party,color = Party)) +
geom_line(size = 2) +
geom_point(size = 4) +
annotate('text',x = 2.1,y = c(37.9, 33.3, 27.3, 5, 8.4),
label = c("Green", "Liberal", "NDP", "Others", "PC"))+
scale_x_discrete(position = "top") +
scale_color_brewer(palette = 'Paired')+
theme_bw() +
theme(legend.position = 'none') +
theme(panel.border = element_blank())
- 散点图加曲线
#平滑曲线
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)+
geom_smooth(se = .8)+
theme(legend.position = c(.1,.8))
##巧用数据叠加
p2 <- ggplot()+
geom_point(data = dsamp,aes(carat, price, fill = cut),
shape = 21, color = 'black', size = 3)+
geom_smooth(data = dsamp,aes(carat, price),se = .8)+
theme(legend.position = c(.1,.8))
plot_grid(p1,p2,ncol=2,labels = LETTERS[1:2],align = c('v','h'))
#ggplot2内置了很多平滑方法
p <- ggplot(mtcars, aes(x = hp, y = mpg)) +
geom_point(size = 3,shape = 21,fill = '#5dc863',color = 'black')
p1 <- p + geom_smooth(method = "lm", formula = y ~ x, size = 1)+
ggtitle(label = 'liner model')
##x的平方
p2 <- p + geom_smooth(method = "lm", formula = y ~ poly(x, 2), size = 1)+
ggtitle(label = 'polynomial regression')
##广义加性模型
p3 <- p + geom_smooth(method = "gam", formula = y ~ x, size = 1)+
ggtitle(label = 'GAM model')
p4 <- p + geom_smooth(method = "gam", formula = y ~ s(x), size = 1)+
ggtitle(label = 'GAM model with spline')
plot_grid(p1,p2,p3,p4,ncol=2,labels = LETTERS[1:4],align = c('v','h'))
- 生存曲线
library(survminer)
library(survival)
###按照sex进行分类，拟合分组信息
fit <- survfit(Surv(time, status) ~ sex, data = lung)
p1 <- ggsurvplot(fit, data = lung,
legend.title = 'Sex',
legend = c(.85,.8))
p2 <- ggsurvplot(
fit,
data = lung,
size = 1,
palette = c("#E7B800", "#2E9FDF"),
conf.int = TRUE,
pval = TRUE,
risk.table = TRUE,
risk.table.col = "strata",
legend.labs =
c("Male", "Female"),
risk.table.height = 0.25,
ggtheme = theme_bw(),
legend.title = 'Sex',
legend = c(.85,.8)
)
-
和弦图
image.png
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)
-
饼图
image.png
##排序后
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))
- 玫瑰图
#画一朵玫瑰
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(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')
- 玫瑰图内有分类
#多层玫瑰图
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')))
ggplot(injuries,aes(x = type, y = counts, fill = age))+
geom_bar(width = 1,color = 'black',stat = 'identity',size = .2)+
coord_polar(theta = 'x')+
labs(x = NULL, y = NULL, fill = NULL,
title = "Injury proportion in young adults")+
scale_fill_brewer(palette = 'Blues')+
theme(plot.title = element_text(hjust = 0.5, color = "#666666"),
panel.border = element_blank(),
panel.background = element_blank())
-
柱状图有组成成分
image.png
#条柱的另外两种排列方式
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))
-
上下分明的条图
image.png
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))
- 好看的箱子图
ggbetweenstats(
data = iris,
plot.type = 'box',
x = Species,
y = Sepal.Length,
mean.plotting = F,
messages = T,
type = 'p',
palette = 'Set1') +
scale_y_continuous(breaks = seq(3, 8, by = 1))+
theme_classic()+
theme(legend.position = c(.13,.8))
带组间比较
#添加统计信息标识符
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))
- seqlogo
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