learn-R/tasks/top50ggplot.Rmd

---
title: "ggplot相关的50个图欣赏"
author: "jmzeng1314@163.com"
date: "May 29, 2017"
output: 
  html_document: 
    toc: yes
---
> * [我的博客](http://www.bio-info-trainee.com/)
 * [我们的论坛](http://www.biotrainee.com/forum.php)
 * [捐赠我](http://www.bio-info-trainee.com/donate)

> 总共50个经典ggplot2画出的figures，分成8类，可以快速定位，或者查询细节。
> 如果你还没有安装，就运行下面的代码安装：
```{r,eval=FALSE}
install.packages('ggplot2')
install.packages("ggdendro")
install.packages('ggalt')
install.packages('htmltools')
install.packages('ggthemes')
install.packages("quantmod")
install.packages("ggfortify")
install.packages('gapminder')
install.packages("lubridate")
install.packages('ggExtra')
install.packages('stringi')
devtools::install_github("kassambara/ggcorrplot")
devtools::install_github("dgrtwo/gganimate")
devtools::install_github("wilkox/ggfittext")
devtools::install_github("wilkox/treemapify")

library(ggplot2)
```

> 如果你安装好了，就直接加载它们即可
```{r,warning=FALSE,message=FALSE}
library(ggplot2)
library(ggalt)
```


# 1. Correlation
## Scatterplot
> 最普通的散点图，把数据映射好即可。

```{r}
options(scipen=999)  # turn-off scientific notation like 1e+48
library(ggplot2)
theme_set(theme_bw())  # pre-set the bw theme.
data("midwest", package = "ggplot2")
# midwest <- read.csv("http://goo.gl/G1K41K")  # bkup data source
head(midwest)
# Scatterplot
gg <- ggplot(midwest, aes(x=area, y=poptotal)) + 
  geom_point(aes(col=state, size=popdensity)) + 
  geom_smooth(method="loess", se=F) + 
  xlim(c(0, 0.1)) + 
  ylim(c(0, 500000)) + 
  labs(subtitle="Area Vs Population", 
       y="Population", 
       x="Area", 
       title="Scatterplot", 
       caption = "Source: midwest")

plot(gg)
```

## Scatterplot With Encircling

> 用geom_encircle函数把散点图里面的一部分点圈出来

```{r}
options(scipen = 999)
library(ggplot2)
library(ggalt)
midwest_select <- midwest[midwest$poptotal > 350000 & 
                            midwest$poptotal <= 500000 & 
                            midwest$area > 0.01 & 
                            midwest$area < 0.1, ]

# Plot
ggplot(midwest, aes(x=area, y=poptotal)) + 
  geom_point(aes(col=state, size=popdensity)) +   # draw points
  geom_smooth(method="loess", se=F) + 
  xlim(c(0, 0.1)) + 
  ylim(c(0, 500000)) +   # draw smoothing line
  geom_encircle(aes(x=area, y=poptotal), 
                data=midwest_select, 
                color="red", 
                size=2, 
                expand=0.08) +   # encircle
  labs(subtitle="Area Vs Population", 
       y="Population", 
       x="Area", 
       title="Scatterplot + Encircle", 
       caption="Source: midwest")
```

## Jitter Plot

> 这次在散点图上面添加线性回归的趋势性，lm 代替 loess 

```{r}
library(ggplot2)
data(mpg, package="ggplot2") # alternate source: "http://goo.gl/uEeRGu")
theme_set(theme_bw())  # pre-set the bw theme.
head(mpg)
dim(mpg)
g <- ggplot(mpg, aes(cty, hwy))

# Scatterplot
g + geom_point() + 
  geom_smooth(method="lm", se=F) +
  labs(subtitle="mpg: city vs highway mileage", 
       y="hwy", 
       x="cty", 
       title="Scatterplot with overlapping points", 
       caption="Source: midwest")
```

> 上图里面的点太少了，因为mpg这个数据集其实有234个数据，但是因为我们画的是cty和 hwy的关系，经常在图里面重合了。

加上**geom_jitter**就能解决这个问题啦：

```{r}
library(ggplot2)
data(mpg, package="ggplot2")
# mpg <- read.csv("http://goo.gl/uEeRGu")

# Scatterplot
theme_set(theme_bw())  # pre-set the bw theme.
g <- ggplot(mpg, aes(cty, hwy))
g + geom_jitter(width = .5, size=1) +
  labs(subtitle="mpg: city vs highway mileage", 
       y="hwy", 
       x="cty", 
       title="Jittered Points")
```

> 但是要注意，这个时候的X,Y坐标就并不是完全映射cty和 hwy，而是有些许扰动了。

## Counts Chart

> 当然，解决数据重合的办法不止一个，还可以用**geom_count**

```{r}
library(ggplot2)
data(mpg, package="ggplot2")
# mpg <- read.csv("http://goo.gl/uEeRGu")

# Scatterplot
theme_set(theme_bw())  # pre-set the bw theme.
g <- ggplot(mpg, aes(cty, hwy))
g + geom_count(col="tomato3", show.legend=F) +
  labs(subtitle="mpg: city vs highway mileage", 
       y="hwy", 
       x="cty", 
       title="Counts Plot")
```

## Bubble plot

> 散点图只能看到两个变量的关系，用气泡图就可以多看一个变量啦。

```{r}
library(ggplot2)
data(mpg, package="ggplot2")
# mpg <- read.csv("http://goo.gl/uEeRGu")

mpg_select <- mpg[mpg$manufacturer %in% c("audi", "ford", "honda", "hyundai"), ]

# Scatterplot
theme_set(theme_bw())  # pre-set the bw theme.
g <- ggplot(mpg_select, aes(displ, cty)) + 
  labs(subtitle="mpg: Displacement vs City Mileage",
       title="Bubble chart")

g + geom_jitter(aes(col=manufacturer, size=hwy)) + 
  geom_smooth(aes(col=manufacturer), method="lm", se=F)
```

> 其实画图函数并没有变化，只是多了一个映射。

## Animated Bubble chart
> 气泡图还可以变成动态的，不过这个动态图依赖于ImageMagick，这里就不秀结果了。

```{r,eval=F}
# Source: https://github.com/dgrtwo/gganimate
# install.packages("cowplot")  # a gganimate dependency
# devtools::install_github("dgrtwo/gganimate")
library(ggplot2)
library(gganimate)
library(gapminder)
head(gapminder)
theme_set(theme_bw())  # pre-set the bw theme.

g <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, frame = year)) +
  geom_point() +
  geom_smooth(aes(group = year), 
              method = "lm", 
              show.legend = FALSE) +
  facet_wrap(~continent, scales = "free") +
  scale_x_log10()  # convert to log scale

gganimate(g, interval=0.2)
```

## Marginal Histogram / Boxplot

> 两个变量在主图是散点图关系，但是可以针对每个变量单独来一个附图。

```{r,warning=FALSE}
# load package and data
library(ggplot2)
library(ggExtra)
data(mpg, package="ggplot2")
# mpg <- read.csv("http://goo.gl/uEeRGu")

# Scatterplot
theme_set(theme_bw())  # pre-set the bw theme.
mpg_select <- mpg[mpg$hwy >= 35 & mpg$cty > 27, ]
g <- ggplot(mpg, aes(cty, hwy)) + 
  geom_count() + 
  geom_smooth(method="lm", se=F)

ggMarginal(g, type = "histogram", fill="transparent")
ggMarginal(g, type = "boxplot", fill="transparent")
# ggMarginal(g, type = "density", fill="transparent")
```

## Correlogram

> 展示多个变量的相关性就不能两两画散点图了，可以直接计算两两的相关系数，再plot到一张图里面。

```{r}
library(ggplot2)
library(ggcorrplot)

# Correlation matrix
data(mtcars)
corr <- round(cor(mtcars), 1)
head(corr)
# Plot
ggcorrplot(corr, hc.order = TRUE, 
           type = "lower", 
           lab = TRUE, 
           lab_size = 3, 
           method="circle", 
           colors = c("tomato2", "white", "springgreen3"), 
           title="Correlogram of mtcars", 
           ggtheme=theme_bw)
```

这就是相关系数矩阵图

# Deviation

## Diverging bars(KEGG通路富集)

> 条形图和直方图都是**geom_bar()**主要通过**stat=identity**来区分，而且条形图需要Y变量。

```{r}
library(ggplot2)
theme_set(theme_bw())  

# Data Prep
data("mtcars")  # load data
mtcars$`car name` <- rownames(mtcars)  # create new column for car names
mtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), 2)  # compute normalized mpg
mtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, "below", "above")  # above / below avg flag
mtcars <- mtcars[order(mtcars$mpg_z), ]  # sort
mtcars$`car name` <- factor(mtcars$`car name`, levels = mtcars$`car name`)  # convert to factor to retain sorted order in plot.

head(mtcars)

# Diverging Barcharts
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + 
  geom_bar(stat='identity', aes(fill=mpg_type), width=.5)  +
  scale_fill_manual(name="Mileage", 
                    labels = c("Above Average", "Below Average"), 
                    values = c("above"="#00ba38", "below"="#f8766d")) + 
  labs(subtitle="Normalised mileage from 'mtcars'", 
       title= "Diverging Bars") + 
  coord_flip()
```

> 上面这个可以用来画上调下调的KEGG通路富集结果。

PS;上面的**mtcars**已经被改变了，后面有些绘图用的是这个被改变后的mtcars数据

## Diverging Lollipop Chart

> 用**geom_point** 和 **geom_segment**来模拟**geom_bar()**的效果。

```{r}
library(ggplot2)
theme_set(theme_bw())

ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + 
  geom_point(stat='identity', fill="black", size=6)  +
  geom_segment(aes(y = 0, 
                   x = `car name`, 
                   yend = mpg_z, 
                   xend = `car name`), 
               color = "black") +
  geom_text(color="white", size=2) +
  labs(title="Diverging Lollipop Chart", 
       subtitle="Normalized mileage from 'mtcars': Lollipop") + 
  ylim(-2.5, 2.5) +
  coord_flip()
```

## Diverging Dot Plot

> 当然，那个线条可以去掉，退化成纯粹的点图

```{r}
library(ggplot2)
theme_set(theme_bw())

# Plot
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + 
  geom_point(stat='identity', aes(col=mpg_type), size=6)  +
  scale_color_manual(name="Mileage", 
                     labels = c("Above Average", "Below Average"), 
                     values = c("above"="#00ba38", "below"="#f8766d")) + 
  geom_text(color="white", size=2) +
  labs(title="Diverging Dot Plot", 
       subtitle="Normalized mileage from 'mtcars': Dotplot") + 
  ylim(-2.5, 2.5) +
  coord_flip()
```

## Area Chart

> 主要应用于时间序列相关的数据

```{r}
library(ggplot2)
library(quantmod)
data("economics", package = "ggplot2")

# Compute % Returns
economics$returns_perc <- c(0, diff(economics$psavert)/economics$psavert[-length(economics$psavert)])
head(economics)

# Create break points and labels for axis ticks
brks <- economics$date[seq(1, length(economics$date), 12)]
lbls <- lubridate::year(economics$date[seq(1, length(economics$date), 12)])

# Plot
ggplot(economics[1:100, ], aes(date, returns_perc)) + 
  geom_area() + 
  scale_x_date(breaks=brks, labels=lbls) + 
  theme(axis.text.x = element_text(angle=90)) + 
  labs(title="Area Chart", 
       subtitle = "Perc Returns for Personal Savings", 
       y="% Returns for Personal savings", 
       caption="Source: economics")
```

# Ranking

## Ordered Bar Chart

> 可以在绘图数据预处理阶段自己把数据排序好

```{r}
# Prepare data: group mean city mileage by manufacturer.
cty_mpg <- aggregate(mpg$cty, by=list(mpg$manufacturer), FUN=mean)  # aggregate
colnames(cty_mpg) <- c("make", "mileage")  # change column names
cty_mpg <- cty_mpg[order(cty_mpg$mileage), ]  # sort
cty_mpg$make <- factor(cty_mpg$make, levels = cty_mpg$make)  # to retain the order in plot.
head(cty_mpg, 4)
library(ggplot2)
theme_set(theme_bw())

# Draw plot
ggplot(cty_mpg, aes(x=make, y=mileage)) + 
  geom_bar(stat="identity", width=.5, fill="tomato3") + 
  labs(title="Ordered Bar Chart", 
       subtitle="Make Vs Avg. Mileage", 
       caption="source: mpg") + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6))

```

## Lollipop Chart

> 同理，上面的条形图可以换成线+点来展现。

```{r}
library(ggplot2)
theme_set(theme_bw())

# Plot
ggplot(cty_mpg, aes(x=make, y=mileage)) + 
  geom_point(size=3) + 
  geom_segment(aes(x=make, 
                   xend=make, 
                   y=0, 
                   yend=mileage)) + 
  labs(title="Lollipop Chart", 
       subtitle="Make Vs Avg. Mileage", 
       caption="source: mpg") + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6))
```

## Dot Plot

> 同理，线条可以去掉，就展现点而已

```{r}
library(ggplot2)
library(scales)
theme_set(theme_classic())

# Plot
ggplot(cty_mpg, aes(x=make, y=mileage)) + 
  geom_point(col="tomato2", size=3) +   # Draw points
  geom_segment(aes(x=make, 
                   xend=make, 
                   y=min(mileage), 
                   yend=max(mileage)), 
               linetype="dashed", 
               size=0.1) +   # Draw dashed lines
  labs(title="Dot Plot", 
       subtitle="Make Vs Avg. Mileage", 
       caption="source: mpg") +  
  coord_flip()
```

## Slope Chart

> 主要应用于配对样本的数值比较

```{r}
library(ggplot2)
library(scales)
theme_set(theme_classic())

# prep data
df <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/gdppercap.csv")
colnames(df) <- c("continent", "1952", "1957")
left_label <- paste(df$continent, round(df$`1952`),sep=", ")
right_label <- paste(df$continent, round(df$`1957`),sep=", ")
df$class <- ifelse((df$`1957` - df$`1952`) < 0, "red", "green")

head(df)

# Plot
p <- ggplot(df) + geom_segment(aes(x=1, xend=2, y=`1952`, yend=`1957`, col=class), size=.75, show.legend=F) + 
                  geom_vline(xintercept=1, linetype="dashed", size=.1) + 
                  geom_vline(xintercept=2, linetype="dashed", size=.1) +
                  scale_color_manual(labels = c("Up", "Down"), 
                                     values = c("green"="#00ba38", "red"="#f8766d")) +  # color of lines
                  labs(x="", y="Mean GdpPerCap") +  # Axis labels
                  xlim(.5, 2.5) + ylim(0,(1.1*(max(df$`1952`, df$`1957`))))  # X and Y axis limits

# Add texts
p <- p + geom_text(label=left_label, y=df$`1952`, x=rep(1, NROW(df)), hjust=1.1, size=3.5)
p <- p + geom_text(label=right_label, y=df$`1957`, x=rep(2, NROW(df)), hjust=-0.1, size=3.5)
p <- p + geom_text(label="Time 1", x=1, y=1.1*(max(df$`1952`, df$`1957`)), hjust=1.2, size=5)  # title
p <- p + geom_text(label="Time 2", x=2, y=1.1*(max(df$`1952`, df$`1957`)), hjust=-0.1, size=5)  # title

# Minify theme
p + theme(panel.background = element_blank(), 
           panel.grid = element_blank(),
           axis.ticks = element_blank(),
           axis.text.x = element_blank(),
           panel.border = element_blank(),
           plot.margin = unit(c(1,2,1,2), "cm"))
```

这个图比较有趣的地方是边框的调整，还有绘图边界的微调。

## Dumbbell Plot(meta－analysis图，亦称森林图)

> 这个图看起来有一点像是森林图了,meta－analysis图（亦称森林图）

```{r}
# devtools::install_github("hrbrmstr/ggalt")
library(ggplot2)
library(ggalt)
theme_set(theme_classic())

health <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/health.csv")
health$Area <- factor(health$Area, levels=as.character(health$Area))  # for right ordering of the dumbells

head(health)

# health$Area <- factor(health$Area)
gg <- ggplot(health, aes(x=pct_2013, xend=pct_2014, y=Area, group=Area)) + 
        geom_dumbbell(color="#a3c4dc", 
                      size=0.75, 
                      point.colour.l="#0e668b") + 
        scale_x_continuous(label=percent) + 
        labs(x=NULL, 
             y=NULL, 
             title="Dumbbell Chart", 
             subtitle="Pct Change: 2013 vs 2014", 
             caption="Source: https://github.com/hrbrmstr/ggalt") +
        theme(plot.title = element_text(hjust=0.5, face="bold"),
              plot.background=element_rect(fill="#f7f7f7"),
              panel.background=element_rect(fill="#f7f7f7"),
              panel.grid.minor=element_blank(),
              panel.grid.major.y=element_blank(),
              panel.grid.major.x=element_line(),
              axis.ticks=element_blank(),
              legend.position="top",
              panel.border=element_blank())
plot(gg)
```

# Distribution

## Histogram

> 直方图用来看连续变量的分布情况，只需要一个变量即可。

```{r}
library(ggplot2)
theme_set(theme_classic())

# Histogram on a Continuous (Numeric) Variable
g <- ggplot(mpg, aes(displ)) + scale_fill_brewer(palette = "Spectral")

g + geom_histogram(aes(fill=class), 
                   binwidth = .1, 
                   col="black", 
                   size=.1) +  # change binwidth
  labs(title="Histogram with Auto Binning", 
       subtitle="Engine Displacement across Vehicle Classes")  

g + geom_histogram(aes(fill=class), 
                   bins=5, 
                   col="black", 
                   size=.1) +   # change number of bins
  labs(title="Histogram with Fixed Bins", 
       subtitle="Engine Displacement across Vehicle Classes") 
```

主要是调整**binwidth**和**bins**来决定连续性变量如何分类进行统计个数或者百分比。

> 对离散性变量的统计类似于table函数，反映每个分类的个数或者百分比。

```{r}
library(ggplot2)
theme_set(theme_classic())

# Histogram on a Categorical variable
g <- ggplot(mpg, aes(manufacturer))
g + geom_bar(aes(fill=class), width = 0.5) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Histogram on Categorical Variable", 
       subtitle="Manufacturer across Vehicle Classes") 
```

值得注意的是直方图里面有一个参数是position可以为fill或者dosage等，效果差别很大的。

## Density plot

> 针对连续性变量，而且也只需要一个变量即可。

```{r}
library(ggplot2)
theme_set(theme_classic())

# Plot
g <- ggplot(mpg, aes(cty))
g + geom_density(aes(fill=factor(cyl)), alpha=0.8) + 
    labs(title="Density plot", 
         subtitle="City Mileage Grouped by Number of cylinders",
         caption="Source: mpg",
         x="City Mileage",
         fill="# Cylinders")
```

## Box Plot

> 用**varwidth=T**可以设置每个box的宽度来反映该box涵盖的数据个数。

```{r}
library(ggplot2)
theme_set(theme_classic())

# Plot
g <- ggplot(mpg, aes(class, cty))
g + geom_boxplot(varwidth=T, fill="plum") + 
    labs(title="Box plot", 
         subtitle="City Mileage grouped by Class of vehicle",
         caption="Source: mpg",
         x="Class of Vehicle",
         y="City Mileage")
```

> 当然，同时还可以进行分类，把变量映射到其它元素即可，比如box的填充。

```{r}
library(ggthemes)
g <- ggplot(mpg, aes(class, cty))
g + geom_boxplot(aes(fill=factor(cyl))) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Box plot", 
       subtitle="City Mileage grouped by Class of vehicle",
       caption="Source: mpg",
       x="Class of Vehicle",
       y="City Mileage")
```

# Dot + Box Plot

> 因为boxplot是统计后的图形，没办法反映原始数据，所以可以通过添加**geom_dotplot**来弥补这一点。

```{r}
library(ggplot2)
theme_set(theme_bw())

# plot
g <- ggplot(mpg, aes(manufacturer, cty))
g + geom_boxplot() + 
  geom_dotplot(binaxis='y', 
               stackdir='center', 
               dotsize = .5, 
               fill="red") +
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Box plot + Dot plot", 
       subtitle="City Mileage vs Class: Each dot represents 1 row in source data",
       caption="Source: mpg",
       x="Class of Vehicle",
       y="City Mileage")
```

当然，如果原始数据太多了，可以添加扰动**geom_jitter*
 
## Tufte Boxplot

> 我本身觉得这个图很无聊

```{r}
library(ggthemes)
library(ggplot2)
theme_set(theme_tufte())  # from ggthemes

# plot
g <- ggplot(mpg, aes(manufacturer, cty))
g + geom_tufteboxplot() + 
      theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
      labs(title="Tufte Styled Boxplot", 
           subtitle="City Mileage grouped by Class of vehicle",
           caption="Source: mpg",
           x="Class of Vehicle",
           y="City Mileage")
```

## Violin Plot

> 越来越多的人开始使用小提琴图啦，可以作为boxplot的替代品

```{r}
library(ggplot2)
theme_set(theme_bw())

# plot
g <- ggplot(mpg, aes(class, cty))
g + geom_violin() + 
  labs(title="Violin plot", 
       subtitle="City Mileage vs Class of vehicle",
       caption="Source: mpg",
       x="Class of Vehicle",
       y="City Mileage")
```

## Population Pyramid

> 好像也很无聊的图，不过我喜欢**ggfortify**包里面的**theme_tufte**主题,其实就是把边框全部剔除

```{r}
library(ggplot2)
library(ggthemes)
options(scipen = 999)  # turns of scientific notations like 1e+40

# Read data
email_campaign_funnel <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/email_campaign_funnel.csv")

# X Axis Breaks and Labels 
brks <- seq(-15000000, 15000000, 5000000)
lbls = paste0(as.character(c(seq(15, 0, -5), seq(5, 15, 5))), "m")

# Plot
ggplot(email_campaign_funnel, aes(x = Stage, y = Users, fill = Gender)) +   # Fill column
                              geom_bar(stat = "identity", width = .6) +   # draw the bars
                              scale_y_continuous(breaks = brks,   # Breaks
                                                 labels = lbls) + # Labels
                              coord_flip() +  # Flip axes
                              labs(title="Email Campaign Funnel") +
                              theme_tufte() +  # Tufte theme from ggfortify
                              theme(plot.title = element_text(hjust = .5), 
                                    axis.ticks = element_blank()) +   # Centre plot title
                              scale_fill_brewer(palette = "Dark2")  # Color palette
```

# Composition

## Waffle Chart

> 意义不大，看看吧,反正也是一种数据展现方式

```{r}
var <- mpg$class  # the categorical data 

## Prep data (nothing to change here)
nrows <- 10
df <- expand.grid(y = 1:nrows, x = 1:nrows)
categ_table <- round(table(var) * ((nrows*nrows)/(length(var))))
categ_table 
df$category <- factor(rep(names(categ_table), categ_table))  
# NOTE: if sum(categ_table) is not 100 (i.e. nrows^2), it will need adjustment to make the sum to 100.

head(df)

## Plot
ggplot(df, aes(x = x, y = y, fill = category)) + 
        geom_tile(color = "black", size = 0.5) +
        scale_x_continuous(expand = c(0, 0)) +
        scale_y_continuous(expand = c(0, 0), trans = 'reverse') +
        scale_fill_brewer(palette = "Set3") +
        labs(title="Waffle Chart", subtitle="'Class' of vehicles",
             caption="Source: mpg") + 
        theme(panel.border = element_rect(size = 2),
              plot.title = element_text(size = rel(1.2)),
              axis.text = element_blank(),
              axis.title = element_blank(),
              axis.ticks = element_blank(),
              legend.title = element_blank(),
              legend.position = "right")
```

## Pie Chart

> ggplot里面并没有专门的饼图函数，其实是条形图加上极坐标系统。

```{r}
library(ggplot2)
theme_set(theme_classic())

# Source: Frequency table
df <- as.data.frame(table(mpg$class))
colnames(df) <- c("class", "freq")
head(df)
pie <- ggplot(df, aes(x = "", y=freq, fill = factor(class))) + 
  geom_bar(width = 1, stat = "identity") +
  theme(axis.line = element_blank(), 
        plot.title = element_text(hjust=0.5)) + 
  labs(fill="class", 
       x=NULL, 
       y=NULL, 
       title="Pie Chart of class", 
       caption="Source: mpg")

pie + coord_polar(theta = "y", start=0)


```

> 上图是预习制作好绘图数据，所以需要 stat = "identity"来指定不进行统计计算。也可以直接给一个变量给X，ggplot会自动进行统计计算。

```{r}
# Source: Categorical variable.
# mpg$class
pie <- ggplot(mpg, aes(x = "", fill = factor(class))) + 
  geom_bar(width = 1) +
  theme(axis.line = element_blank(), 
        plot.title = element_text(hjust=0.5)) + 
  labs(fill="class", 
       x=NULL, 
       y=NULL, 
       title="Pie Chart of class", 
       caption="Source: mpg")
  
pie + coord_polar(theta = "y", start=0)
```

## Treemap

> 有点类似于云单词的组合展现模式

```{r}
library(ggplot2) 
library(treemapify)
proglangs <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/proglanguages.csv")

# plot
treeMapCoordinates <- treemapify(proglangs,
                                 area = "value",
                                 fill = "parent",
                                 label = "id",
                                 group = "parent")

treeMapPlot <- ggplotify(treeMapCoordinates) + 
                  scale_x_continuous(expand = c(0, 0)) +
                  scale_y_continuous(expand = c(0, 0)) +
                  scale_fill_brewer(palette = "Dark2")

print(treeMapPlot)
```

真的是很酷，但是为了画这一个图，我得安装好几个包。

## Bar Chart

> 条形图和直方图没有本质的区别

如果过绘图数据已经制作好了，不需要进行额外的统计，就指定 stat="identity" 

```{r}
freqtable <- table(mpg$manufacturer)
df <- as.data.frame.table(freqtable)
head(df)
library(ggplot2)
theme_set(theme_classic())

# Plot
g <- ggplot(df, aes(Var1, Freq))
g + geom_bar(stat="identity", width = 0.5, fill="tomato2") + 
      labs(title="Bar Chart", 
           subtitle="Manufacturer of vehicles", 
           caption="Source: Frequency of Manufacturers from 'mpg' dataset") +
      theme(axis.text.x = element_text(angle=65, vjust=0.6))
```

```{r}
# From on a categorical column variable
g <- ggplot(mpg, aes(manufacturer))
g + geom_bar(aes(fill=class), width = 0.5) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) +
  labs(title="Categorywise Bar Chart", 
       subtitle="Manufacturer of vehicles", 
       caption="Source: Manufacturers from 'mpg' dataset")
```


# Groups

## Hierarchical Dendrogram(进化树)

> 看起来就是进化树

```{r}
# install.packages("ggdendro")
library(ggplot2)
library(ggdendro)
theme_set(theme_bw())

hc <- hclust(dist(USArrests), "ave")  # hierarchical clustering

# plot
ggdendrogram(hc, rotate = TRUE, size = 2)
```

## Clusters (主成分分析)

> 看起来明明就是主成分分析的图

```{r}
# devtools::install_github("hrbrmstr/ggalt")
library(ggplot2)
library(ggalt)
library(ggfortify)
theme_set(theme_classic())

# Compute data with principal components ------------------
df <- iris[c(1, 2, 3, 4)]
pca_mod <- prcomp(df)  # compute principal components

# Data frame of principal components ----------------------
df_pc <- data.frame(pca_mod$x, Species=iris$Species)  # dataframe of principal components
df_pc_vir <- df_pc[df_pc$Species == "virginica", ]  # df for 'virginica'
df_pc_set <- df_pc[df_pc$Species == "setosa", ]  # df for 'setosa'
df_pc_ver <- df_pc[df_pc$Species == "versicolor", ]  # df for 'versicolor'
 
# Plot ----------------------------------------------------
ggplot(df_pc, aes(PC1, PC2, col=Species)) + 
  geom_point(aes(shape=Species), size=2) +   # draw points
  labs(title="Iris Clustering", 
       subtitle="With principal components PC1 and PC2 as X and Y axis",
       caption="Source: Iris") + 
  coord_cartesian(xlim = 1.2 * c(min(df_pc$PC1), max(df_pc$PC1)), 
                  ylim = 1.2 * c(min(df_pc$PC2), max(df_pc$PC2))) +   # change axis limits
  geom_encircle(data = df_pc_vir, aes(x=PC1, y=PC2)) +   # draw circles
  geom_encircle(data = df_pc_set, aes(x=PC1, y=PC2)) + 
  geom_encircle(data = df_pc_ver, aes(x=PC1, y=PC2))
```





















