ggplot2

1.A little demonstration of pipe function

   library(tidyr)
   data(iris)
   iris %>% summary()

##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
## 

   plot(diff(log(sample(rnorm(n = 10000,mean = 10,sd = 1),size = 100,replace = FALSE))),type = "b",col="red")

   library(ggplot2)
   qplot(x = cty, y = hwy, color = cyl, data = mpg, geom = "point",main = "mpg data")

  ggplot(data = mpg,mapping=aes(x = cty,y = hwy))+
    geom_point(aes(color=cyl))+
    coord_cartesian()+
    scale_color_gradient()+
    theme_bw()

single variable

  a<-ggplot(data = mpg,aes(hwy))
  a+geom_area(stat = "bin",binwidth=10)

  a+geom_density(kernel="gaussian")

  a+geom_dotplot()

## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.

  a+geom_freqpoly(binwidth=10)

  a+geom_bar(color="red",fill="green",size=0.1)

2 variable

  b<-ggplot(data = mpg,mapping=aes(x = cty,y = hwy))
  b+geom_jitter(color="blue2",size=4,shape="S")+geom_jitter(mapping=aes(cty,displ),color="red3")#new layer

  b+geom_point(color="brown2")

  b+geom_quantile(color="blue2",size=0.3)

## Loading required package: SparseM

## 
## Attaching package: 'SparseM'

## The following object is masked from 'package:base':
## 
##     backsolve

## Smoothing formula not specified. Using: y ~ x

  b+geom_smooth(method = loess)

  b+geom_rug(sides = "rb")#right left for y; bottom top for x

  b+geom_text(mapping=aes(label=cty))

3 other layer, mutiple plots

    c <- ggplot(data = mpg,aes(cty,hwy))
    c+geom_bin2d(binwidth=c(0.5,1))#heat map

    c+geom_density2d(color="red3")

    c+geom_point(color="green3")+facet_grid(.~fl)#divide by fl into column

    c+geom_point(color="green3")+facet_grid(fl~.)#divide by fl into row

    c+geom_point(color="green3")+facet_grid(year~fl)#divide by year in row and divide by fl in column

    c+geom_point(color="green3")+facet_wrap(~fl)

density and plots

    require(ggplot2)
    data(diamonds)
    set.seed(42)
    small <- diamonds[sample(nrow(diamonds), 1000), ]
    head(small)

## Source: local data frame [6 x 10]
## 
##   carat       cut  color clarity depth table price     x     y     z
##   (dbl)    (fctr) (fctr)  (fctr) (dbl) (dbl) (int) (dbl) (dbl) (dbl)
## 1  0.71 Very Good      H     SI1  62.5    60  2096  5.68  5.75  3.57
## 2  0.79   Premium      H     SI1  61.8    59  2275  5.97  5.91  3.67
## 3  1.03     Ideal      F     SI1  62.4    57  6178  6.48  6.44  4.03
## 4  0.50     Ideal      E     VS2  62.2    54  1624  5.08  5.11  3.17
## 5  0.27     Ideal      E     VS1  61.6    56   470  4.14  4.17  2.56
## 6  0.30   Premium      E     VS2  61.7    58   658  4.32  4.34  2.67

    ggplot(data = small,mapping = aes(x = carat,y = price,color="darkblue",shape=cut))+geom_point()

    ggplot(data = small)+geom_point(mapping = aes(x = carat,y=price,color=color,shape=cut))

    ggplot(data=small)+geom_histogram(mapping = aes(x = price,fill=cut))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

    ggplot(data=small)+geom_histogram(mapping = aes(x = price,fill=cut),position = "dodge")#dodge prevents overlap

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

    ggplot(small)+geom_bar(mapping = aes(clarity,fill=cut))

    ggplot(small)+geom_density(mapping = aes(x = price,color=cut))

    ggplot(small)+geom_density(mapping = aes(x = price,fill=cut))

    ggplot(small)+geom_boxplot(aes(x = cut,y = price,fill=color))

    ggplot(small)+
      geom_point(aes(x=carat, y=price, shape=cut, color=color))+
      scale_y_log10()+
      scale_color_discrete(h=c(150,350), c=100, l=60)#sedu,qiangdu,mingdu

   ggplot(small,mapping = aes(x = carat,y = price,color=cut))+ 
      geom_point() + 
      scale_color_manual(values=c('blue','cyan', 'yellow', 'orange', 'red'))+
      theme_light()

  ggplot(small, aes(x=carat, y=price))+
    geom_point(color=alpha("darkblue",0.2))+
    scale_y_log10()+
    stat_smooth()#common reflection to ggplot

changing coordinates

    ggplot(small)+geom_bar(aes(x=cut, fill=cut))+coord_flip()# flip the coordinate

    #basically a pie chart
    ggplot(small)+geom_bar(aes(x=factor(1), fill=cut))+coord_polar(theta = "y")#angle theta is mapped to the y(cut size)  #x is the radias which is constant as one

    ggplot(small)+geom_bar(aes(x=clarity, fill=cut))+coord_polar()