R语言绘图速查手册(58图)

# p 是point
# rnorm(n, mean = 0, sd = 1) 生成随机正态分布的序列
plot(rnorm(200), rnorm(200), type="p")

# type = "n" 没有对角线的意思
plot(-4:4, -4:4, type = "n")
points(rnorm(200), rnorm(200), col = "red")

library(ggplot2)
df <- data.frame(x=rnorm(200), y=rnorm(200)) 
ggplot(df, aes(x, y))+geom_point()

# l 是line的意思
plot(1:10, rnorm(10), type="l")

plot(cars, main = "Stopping Distance versus Speed") 
# 
lines(stats::lowess(cars))

ggplot(economics, aes(date, unemploy)) + geom_line()

# 生成 100 个服从泊松分布 λ = 5 的随机数，并对随机数做列联表统计，条形图展示了列联表统计的结果。
tN <- table(Ni <- stats::rpois(100, lambda = 5)) 
barplot(tN, col = rainbow(20))

barplot(VADeaths)

barplot(VADeaths, beside = TRUE,
        col = c("lightblue", "mistyrose", "lightcyan",
                "lavender", "cornsilk"),
        legend = rownames(VADeaths), ylim = c(0, 110))
title(main = "Death Rates in Virginia", font.main = 4)

library(ggplot2)
ggplot(mpg, aes(class))+geom_bar()

library(ggplot2)
library(reshape)
ggplot(data=melt(VADeaths), aes(x=X2, y=value, fill=X1)) + geom_bar(stat="identity")

ggplot(data=melt(VADeaths), aes(x=X2, y=value, fill=X1)) + geom_bar(stat="identity", position="dodge")

ggplot(data=melt(VADeaths), aes(x=X2, y=value, fill=X1)) + geom_bar(stat="identity", position="fill")

d <- density(diamonds[diamonds$cut=="Ideal",]$price) 
plot(d,main="",xlab = "Price")
polygon(d, col="red",border = "red")
d <- density(diamonds[diamonds$cut=="Premium",]$price) 
polygon(d, col="orange",border = "orange")
d <- density(diamonds[diamonds$cut=="Good",]$price) 
polygon(d, col="black",border = "black")
d <- density(diamonds[diamonds$cut=="Very Good",]$price) 
polygon(d, col="green",border = "green")
d <- density(diamonds[diamonds$cut=="Fair",]$price) 
polygon(d, col="yellow",border ="yellow")

stackedPlot <- function(data, time=NULL, col=1:length(data), ...){ 
  if (is.null(time))
    time <- 1:length(data[[1]]);
  plot(0, 0, xlim = range(time), ylim = c(0,max(rowSums(data))), t="n", ...); 
  for (i in length(data):1) {
  # Die Summe bis zu aktuellen Spalte
  prep.data <- rowSums(data[1:i]);
  # Das Polygon muss seinen ersten und letzten Punkt auf der Nulllinie haben 
  prep.y <- c(0, prep.data, 0)
  prep.x <- c(time[1], time, time[length(time)])
  polygon(prep.x, prep.y, col=col[i], border = NA);
  }
}
diamonds.data <- as.data.frame.matrix(t(table(diamonds$cut,diamonds$price))) 
stackedPlot(diamonds.data)

ggplot(diamonds, aes(x = price, fill = cut))+ geom_area(stat = "bin")

set.seed(1234) 
rating <- rnorm(200) 
plot(density(rating))

set.seed(1234)
rating <- rnorm(200)
rating2 <- rnorm(200, mean=.8)
plot(density(rating)) 
lines(density(rating2),col="red")

ggplot(diamonds, aes(depth, colour = cut)) + geom_density()

myhist <- hist(mtcars$mpg,plot = FALSE) 
multiplier <- myhist$counts / myhist$density 
mydensity <- density(mtcars$mpg) 
mydensity$y <- mydensity$y * multiplier[1]
plot(mydensity)

ggplot(diamonds, aes(price, colour = cut)) + geom_freqpoly(binwidth = 500)

hist(sqrt(islands), breaks = 12, col = "lightblue", border = "pink")

ggplot(as.data.frame(islands), aes(sqrt(islands))) + geom_histogram()

boxplot(count ~ spray, data = InsectSprays, col = "lightgray")

ggplot(InsectSprays, aes(spray, count))+geom_boxplot()

ggplot(InsectSprays, aes(spray, count))+ geom_boxplot()+
  stat_boxplot(geom ='errorbar',width=0.5)

ggplot(InsectSprays, aes(spray, count))+ stat_boxplot(geom ='errorbar',width=0.5)+ geom_boxplot()

install.packages("vioplot")
library(sm)
library(vioplot)
x1 <- mtcars$mpg[mtcars$cyl==4]
x2 <- mtcars$mpg[mtcars$cyl==6]
x3 <- mtcars$mpg[mtcars$cyl==8]
vioplot(x1, x2, x3, names=c("4 cyl", "6 cyl", "8 cyl"),col="gold")
title("Violin Plots of Miles Per Gallon")

ggplot(mtcars, aes(factor(cyl), mpg))+ geom_violin()

ggplot(mtcars, aes(factor(cyl), mpg))+ geom_violin()+ geom_boxplot(width=.1)

ggplot(mtcars, aes(factor(cyl), mpg))+ geom_violin()+ stat_summary(fun.data = mean_sdl,
                                                                   geom = "pointrange",
                                                                   color = "red",
                                                                   fun.args = list(mult = 1))

dotchart(mtcars$mpg,labels=row.names(mtcars),cex=.7, main="Gas Milage for Car Models",
         xlab="Miles Per Gallon")

ggplot(mtcars,aes(x = mpg,y = row.names(mtcars), fill =row.names(mtcars))) + geom_dotplot(binaxis = "y",
                                                                                          stackgroups = TRUE,
                                                                                          binwidth = 1,
                                                                                          method = "histodot")

x <- as.matrix(mtcars)
rc <- rainbow(nrow(x), start = 0, end = .3) 
cc <- rainbow(ncol(x), start = 0, end = .3) 
hv <- heatmap(x,
                col = cm.colors(256),
                scale = "column",
                RowSideColors = rc, ColSideColors = cc,
                margins = c(5,10),
                xlab = "specification variables", ylab = "Car Models",
                main = "Heatmap of Mtcars data")

library(reshape2)
library(ggplot2)
dat <- matrix(rnorm(100, 3, 1), ncol=10) 
names(dat) <- paste("X", 1:10)
dat2 <- melt(dat, id.var = "X1")
ggplot(dat2, aes(as.factor(X1), X2, group=X2)) + geom_tile(aes(fill = value))+geom_text(aes(fill = dat2$value, label = round(dat2$value, 1))) + scale_fill_gradient(low = "white", high = "red")

library(pheatmap)
test = matrix(rnorm(200), 20, 10)
test[1:10, seq(1, 10, 2)] = test[1:10, seq(1, 10, 2)] + 3 
test[11:20, seq(2, 10, 2)] = test[11:20, seq(2, 10, 2)] + 2 
test[15:20, seq(2, 10, 2)] = test[15:20, seq(2, 10, 2)] + 4 
colnames(test) = paste("Test", 1:10, sep = "") 
rownames(test) = paste("Gene", 1:20, sep = "")
# 设置每一列的注释
annotation_col = data.frame(
  CellType = factor(rep(c("CT1", "CT2"), 5)), Time = 1:5
)
rownames(annotation_col) = paste("Test", 1:10, sep = "") # 设置每一行的注释
annotation_row = data.frame(
  GeneClass = factor(rep(c("Path1", "Path2", "Path3"), c(10, 4, 6)))
)
rownames(annotation_row) = paste("Gene", 1:20, sep = "") # 设置注释的颜色
ann_colors = list(
  Time = c("white", "firebrick"),
  CellType = c(CT1 = "#1B9E77", CT2 = "#D95F02"),
  GeneClass = c(Path1 = "#7570B3", Path2 = "#E7298A", Path3 = "#66A61E")
) 
pheatmap(test,
           annotation_col = annotation_col,
           annotation_row = annotation_row,
           annotation_colors = ann_colors)

library(devtools) 
install_github("vqv/ggbiplot")
library(plyr)
library(ggbiplot)
data("wine")
wine.pca <- prcomp(wine, scale. = TRUE) 
ggbiplot(wine.pca, obs.scale = 1, var.scale = 1,
        groups = wine.class, ellipse = TRUE, circle = TRUE) + scale_color_discrete(name = '')

hc <- hclust(dist(USArrests), "ave") 
plot(hc)

install.packages("ggdendro")
library(ggdendro)
hc <- hclust(dist(USArrests), "ave")
hcdata <- dendro_data(hc)
ggdendrogram(hcdata, rotate=TRUE, size=2) + labs(title="Dendrogram in ggplot2")

install.packages("ape")
hc <- hclust(dist(USArrests), "ave") 
library(ape)
plot(as.phylo(hc), type = "fan")

plot(table(rpois(100, 5)), type = "h", col = "red", lwd = 10, main = "rpois(100, lambda = 5)",sub="this is a sub title", xlab="x axis title",ylab="y axis title"
)

ggplot(PlantGrowth, aes(x=group, y=weight)) + geom_boxplot() +
  ggtitle("Plant growth with\ndifferent treatments")+ xlab("this is xlab")+
  ylab("this is ylab")

attach(mtcars)
par(mfrow=c(2,2))
plot(wt,mpg, main="Scatterplot of wt vs. mpg") 
plot(wt,disp, main="Scatterplot of wt vs disp") 
hist(wt, main="Histogram of wt")
boxplot(wt, main="Boxplot of wt")

attach(mtcars)
layout(matrix(c(1,1,2,3), 2, 2, byrow = TRUE),
       widths=c(3,1), heights=c(1,2))
hist(wt) 
hist(mpg) 
hist(disp)

library(cowplot)
sp <- ggplot(mtcars, aes(x = mpg, y = hp, colour = factor(cyl)))+
  geom_point(size=2.5)
bp <- ggplot(diamonds, aes(clarity, fill = cut)) +
  geom_bar() +
  theme(axis.text.x = element_text(angle=90, vjust=0.5)) 
plot.iris <- ggplot(iris, aes(Sepal.Length, Sepal.Width)) +
  geom_point() + facet_grid(. ~ Species) + stat_smooth(method = "lm") + background_grid(major = 'y', minor = "none") +
  panel_border()
  plot_grid(sp, bp, labels=c("A", "B"), ncol = 2, nrow = 1)
ggdraw() +
  draw_plot(plot.iris, 0, .5, 1, .5) +
  draw_plot(sp, 0, 0, .5, .5) +
  draw_plot(bp, .5, 0, .5, .5) +
  draw_plot_label(c("A", "B", "C"), c(0, 0, 0.5), c(1, 0.5, 0.5), size = 15)  

df <- ToothGrowth
df$dose <- as.factor(df$dose) 
## 计量同牙齿长度的箱线图
bp <- ggplot(df, aes(x=dose, y=len, color=dose)) + geom_boxplot() +
  theme(legend.position = "none")
## 计量同牙齿长度的 Cleveland 点图
dp <- ggplot(df, aes(x=dose, y=len, fill=dose)) +
  geom_dotplot(binaxis='y', stackdir='center')+ stat_summary(fun.data=mean_sdl, fun.args = list(mult=1),
                                                             geom="pointrange", color="red")+ theme(legend.position = "none")
## 计量同牙齿长度的提琴图
vp <- ggplot(df, aes(x=dose, y=len)) +
  geom_violin()+
  geom_boxplot(width=0.1)
## 计量同牙齿长度的散点图(jitter 抖动模式)
sc <- ggplot(df, aes(x=dose, y=len, color=dose, shape=dose)) +
  geom_jitter(position=position_jitter(0.2))+ theme(legend.position = "none") + theme_gray()


library(gridExtra)
grid.arrange(bp, dp, vp, sc, ncol=2, nrow =2)
## Warning: Computation failed in `stat_summary()`:
## Hmisc package required for this function

tN <- table(Ni <- stats::rpois(100, lambda = 5)) 
barplot(tN, col = rainbow(20), horiz=TRUE)

ggplot(PlantGrowth, aes(x=group, y=weight))+ geom_boxplot() + coord_flip()

ggplot(PlantGrowth, aes(x=group, y=weight, fill=group)) + geom_boxplot() +
  theme_grey()

ggplot(PlantGrowth, aes(x=group, y=weight, fill=group)) + geom_boxplot() +
  theme_gray()

ggplot(PlantGrowth, aes(x=group, y=weight, fill=group)) + geom_boxplot() +
theme_bw()

ggplot(PlantGrowth, aes(x=group, y=weight, fill=group)) + geom_boxplot() +
  theme_linedraw()

ggplot(PlantGrowth, aes(x=group, y=weight, fill=group)) + geom_boxplot() +
  theme_light()

ggplot(PlantGrowth, aes(x=group, y=weight, fill=group)) + geom_boxplot() +
  theme_minimal()

ggplot(PlantGrowth, aes(x=group, y=weight, fill=group)) + geom_boxplot() +
  theme_classic()

ggplot(PlantGrowth, aes(x=group, y=weight, fill=group)) + geom_boxplot() +
  theme_dark()

ggplot(PlantGrowth, aes(x=group, y=weight, fill=group)) + geom_boxplot() +
  theme_void()

library(ggplot2)
a <- seq(1,20)
b <- a^0.25
df <- as.data.frame(cbind(a,b)) 
ggplot(df, aes(a, b)) +
  geom_point() +
  theme(axis.line.x = element_line(color = "black"),
        axis.line.y = element_line(color = "black"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.border = element_blank(), panel.background = element_blank())