數(shù)據(jù)分析的圖形可視化是了解數(shù)據(jù)分布彪标、波動和相關(guān)性等屬性必不可少的手段德频。數(shù)據(jù)分布可視化圖形主要有：小提琴圖、核密度曲線圖、柱狀圖、箱線圖和山脊圖等泉哈。
小提琴圖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
核密度圖 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])
柱狀圖 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])
箱線圖 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="")
)
山脊圖 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])
