R可视化：基础图形可视化之Distribution（三）

数据分析的图形可视化是了解数据分布、波动和相关性等属性必不可少的手段。数据分布可视化图形主要有：小提琴图、核密度曲线图、柱状图、箱线图和山脊图等。更多知识分享请到 https://zouhua.top/。

小提琴图Violin

# Libraries
library(ggplot2)
library(dplyr)
library(hrbrthemes)
library(viridis)

# create a dataset
data <- data.frame(
  name=c( rep("A",500), rep("B",500), rep("B",500), rep("C",20), rep('D', 100)  ),
  value=c( rnorm(500, 10, 5), rnorm(500, 13, 1), rnorm(500, 18, 1), rnorm(20, 25, 4), rnorm(100, 12, 1) )
)

# sample size
sample_size = data %>% group_by(name) %>% summarize(num=n())

# Plot
data %>%
  left_join(sample_size) %>%
  mutate(myaxis = paste0(name, "\n", "n=", num)) %>%
  ggplot( aes(x=myaxis, y=value, fill=name)) +
    geom_violin(width=1.4) +
    geom_boxplot(width=0.1, color="grey", alpha=0.2) +
    scale_fill_viridis(discrete = TRUE) +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=11)
    ) +
    ggtitle("A Violin wrapping a boxplot") +
    xlab("")

# Libraries
library(ggplot2)
library(dplyr)
library(tidyr)
library(forcats)
library(hrbrthemes)
library(viridis)

# Load dataset from github
data <- read.table("dataset/viz/probly.csv", header=TRUE, sep=",")

# Data is at wide format, we need to make it 'tidy' or 'long'
data <- data %>% 
  gather(key="text", value="value") %>%
  mutate(text = gsub("\\.", " ",text)) %>%
  mutate(value = round(as.numeric(value),0)) %>%
  filter(text %in% c("Almost Certainly","Very Good Chance","We Believe","Likely","About Even", "Little Chance", "Chances Are Slight", "Almost No Chance"))

# Plot
p <- data %>%
  mutate(text = fct_reorder(text, value)) %>% # Reorder data
  ggplot( aes(x=text, y=value, fill=text, color=text)) +
    geom_violin(width=2.1, size=0.2) +
    scale_fill_viridis(discrete=TRUE) +
    scale_color_viridis(discrete=TRUE) +
    theme_ipsum() +
    theme(
      legend.position="none"
    ) +
    coord_flip() + # This switch X and Y axis and allows to get the horizontal version
    xlab("") +
    ylab("Assigned Probability (%)")

p

核密度图 density chart

library(ggplot2)
library(hrbrthemes)
library(dplyr)
library(tidyr)
library(viridis)

data <- read.table("dataset/viz/probly.csv", header=TRUE, sep=",")
data <- data %>%
  gather(key="text", value="value") %>%
  mutate(text = gsub("\\.", " ",text)) %>%
  mutate(value = round(as.numeric(value),0))

# A dataframe for annotations
annot <- data.frame(
  text = c("Almost No Chance", "About Even", "Probable", "Almost Certainly"),
  x = c(5, 53, 65, 79),
  y = c(0.15, 0.4, 0.06, 0.1)
)

# Plot
data %>%
  filter(text %in% c("Almost No Chance", "About Even", "Probable", "Almost Certainly")) %>%
  ggplot( aes(x=value, color=text, fill=text)) +
    geom_density(alpha=0.6) +
    scale_fill_viridis(discrete=TRUE) +
    scale_color_viridis(discrete=TRUE) +
    geom_text( data=annot, aes(x=x, y=y, label=text, color=text), hjust=0, size=4.5) +
    theme_ipsum() +
    theme(
      legend.position="none"
    ) +
    ylab("") +
    xlab("Assigned Probability (%)")

# library
library(ggplot2)
library(ggExtra)
 
# classic plot :
p <- ggplot(mtcars, aes(x=wt, y=mpg, color=cyl, size=cyl)) +
      geom_point() +
      theme(legend.position="none")
 
# Set relative size of marginal plots (main plot 10x bigger than marginals)
p1 <- ggMarginal(p, type="histogram", size=10)
 
# Custom marginal plots:
p2 <- ggMarginal(p, type="histogram", fill = "slateblue", xparams = list(  bins=10))
 
# Show only marginal plot for x axis
p3 <- ggMarginal(p, margins = 'x', color="purple", size=4)

cowplot::plot_grid(p, p1, p2, p3, ncol = 2, align = "hv", 
                   labels = LETTERS[1:4])

柱状图 histogram

# library
library(ggplot2)
library(dplyr)
library(hrbrthemes)

# Build dataset with different distributions
data <- data.frame(
  type = c( rep("variable 1", 1000), rep("variable 2", 1000) ),
  value = c( rnorm(1000), rnorm(1000, mean=4) )
)

# Represent it
p <- data %>%
  ggplot( aes(x=value, fill=type)) +
    geom_histogram( color="#e9ecef", alpha=0.6, position = 'identity') +
    scale_fill_manual(values=c("#69b3a2", "#404080")) +
    theme_ipsum() +
    labs(fill="")
p

# Libraries
library(ggplot2)
library(hrbrthemes)

# Dummy data
data <- data.frame(
  var1 = rnorm(1000),
  var2 = rnorm(1000, mean=2)
)

# Chart
p <- ggplot(data, aes(x=x) ) +
  # Top
  geom_density( aes(x = var1, y = ..density..), fill="#69b3a2" ) +
  geom_label( aes(x=4.5, y=0.25, label="variable1"), color="#69b3a2") +
  # Bottom
  geom_density( aes(x = var2, y = -..density..), fill= "#404080") +
  geom_label( aes(x=4.5, y=-0.25, label="variable2"), color="#404080") +
  theme_ipsum() +
  xlab("value of x")

p1 <- ggplot(data, aes(x=x) ) +
  geom_histogram( aes(x = var1, y = ..density..), fill="#69b3a2" ) +
  geom_label( aes(x=4.5, y=0.25, label="variable1"), color="#69b3a2") +
  geom_histogram( aes(x = var2, y = -..density..), fill= "#404080") +
  geom_label( aes(x=4.5, y=-0.25, label="variable2"), color="#404080") +
  theme_ipsum() +
  xlab("value of x")
cowplot::plot_grid(p, p1, ncol = 2, align = "hv", 
                   labels = LETTERS[1:2])

箱线图 boxplot

# Library
library(ggplot2)
library(dplyr)
library(forcats)

# Dataset 1: one value per group
data <- data.frame(
  name=c("north","south","south-east","north-west","south-west","north-east","west","east"),
  val=sample(seq(1,10), 8 )
)


# Reorder following the value of another column:
p1 <- data %>%
  mutate(name = fct_reorder(name, val)) %>%
  ggplot( aes(x=name, y=val)) +
    geom_bar(stat="identity", fill="#f68060", alpha=.6, width=.4) +
    coord_flip() +
    xlab("") +
    theme_bw()
 
# Reverse side
p2 <- data %>%
  mutate(name = fct_reorder(name, desc(val))) %>%
  ggplot( aes(x=name, y=val)) +
    geom_bar(stat="identity", fill="#f68060", alpha=.6, width=.4) +
    coord_flip() +
    xlab("") +
    theme_bw()

# Using median
p3 <- mpg %>%
  mutate(class = fct_reorder(class, hwy, .fun='median')) %>%
  ggplot( aes(x=reorder(class, hwy), y=hwy, fill=class)) + 
    geom_boxplot() +
    geom_jitter(color="black", size=0.4, alpha=0.9) +
    xlab("class") +
    theme(legend.position="none") +
    xlab("")
 
# Using number of observation per group
p4 <- mpg %>%
  mutate(class = fct_reorder(class, hwy, .fun='length' )) %>%
  ggplot( aes(x=class, y=hwy, fill=class)) + 
  stat_summary(fun.y=mean, geom="point", shape=20, size=6, color="red", fill="red") +
    geom_boxplot() +
    xlab("class") +
    theme(legend.position="none") +
    xlab("") +
    xlab("")

p5 <- data %>%
  arrange(val) %>%    # First sort by val. This sort the dataframe but NOT the factor levels
  mutate(name=factor(name, levels=name)) %>%   # This trick update the factor levels
  ggplot( aes(x=name, y=val)) +
    geom_segment( aes(xend=name, yend=0)) +
    geom_point( size=4, color="orange") +
    coord_flip() +
    theme_bw() +
    xlab("")
 
p6 <- data %>%
  arrange(val) %>%
  mutate(name = factor(name, levels=c("north", "north-east", "east", "south-east", "south", "south-west", "west", "north-west"))) %>%
  ggplot( aes(x=name, y=val)) +
    geom_segment( aes(xend=name, yend=0)) +
    geom_point( size=4, color="orange") +
    theme_bw() +
    xlab("")

cowplot::plot_grid(p1, p2, p3, p4, p5, p6, 
                   ncol = 2, align = "hv", 
                   labels = LETTERS[1:6])

library(dplyr)
# Dummy data
names <- c(rep("A", 20) , rep("B", 8) , rep("C", 30), rep("D", 80))
value <- c( sample(2:5, 20 , replace=T) , sample(4:10, 8 , replace=T), 
       sample(1:7, 30 , replace=T), sample(3:8, 80 , replace=T) )
data <- data.frame(names, value) %>%
  mutate(names=factor(names))
 
# Draw the boxplot. Note result is also stored in a object called boundaries
boundaries <- boxplot(data$value ~ data$names , col="#69b3a2" , ylim=c(1,11))
# Now you can type boundaries$stats to get the boundaries of the boxes

# Add sample size on top
nbGroup <- nlevels(data$names)
text( 
  x=c(1:nbGroup), 
  y=boundaries$stats[nrow(boundaries$stats),] + 0.5, 
  paste("n = ",table(data$names),sep="")  
)

山脊图 ridgeline

# library
library(ggridges)
library(ggplot2)
library(dplyr)
library(tidyr)
library(forcats)

# Load dataset from github
data <- read.table("dataset/viz/probly.csv", header=TRUE, sep=",")
data <- data %>% 
  gather(key="text", value="value") %>%
  mutate(text = gsub("\\.", " ",text)) %>%
  mutate(value = round(as.numeric(value),0)) %>%
  filter(text %in% c("Almost Certainly","Very Good Chance","We Believe","Likely","About Even", "Little Chance", "Chances Are Slight", "Almost No Chance"))

# Plot
p1 <- data %>%
  mutate(text = fct_reorder(text, value)) %>%
  ggplot( aes(y=text, x=value,  fill=text)) +
    geom_density_ridges(alpha=0.6, stat="binline", bins=20) +
    theme_ridges() +
    theme(
      legend.position="none",
      panel.spacing = unit(0.1, "lines"),
      strip.text.x = element_text(size = 8)
    ) +
    xlab("") +
    ylab("Assigned Probability (%)")

p2 <- data %>%
  mutate(text = fct_reorder(text, value)) %>%
  ggplot( aes(y=text, x=value,  fill=text)) +
    geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01) +
    theme_ridges() +
    theme(
      legend.position="none",
      panel.spacing = unit(0.1, "lines"),
      strip.text.x = element_text(size = 8)
    ) +
    xlab("") +
    ylab("Assigned Probability (%)")

cowplot::plot_grid(p1, p2, 
                   ncol = 2, align = "hv", 
                   labels = LETTERS[1:2])

参考

1. The R Graph Gallery

参考文章如引起任何侵权问题，可以与我联系，谢谢。