gpplot2 examples

라벨: Informatics
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URL

• http://docs.ggplot2.org/0.9.3/theme.html

Color

• http://stackoverflow.com/questions/8197559/emulate-ggplot2-default-color-palette
• http://www.r-bloggers.com/r-using-rcolorbrewer-to-colour-your-figures-in-r/
• http://www.cookbook-r.com/Graphs/Colors_%28ggplot2%29/
• http://stackoverflow.com/questions/21322819/generating-a-consistent-dynamic-color-palette-in-ggplot-within-a-loop

Layout

• http://stackoverflow.com/questions/13081310/combining-multiple-complex-plots-as-panels-in-a-single-figure
• https://kohske.wordpress.com/2010/12/29/faq-how-to-order-the-factor-variables-in-ggplot2/

Type

• http://rgraphgallery.blogspot.com/search/label/boxplot

Multiple data series

• http://www.sixhat.net/how-to-plot-multpile-data-series-with-ggplot.html
•  http://stackoverflow.com/questions/10357768/plotting-lines-and-the-group-aesthetic-in-ggplot2
• http://www.cookbook-r.com/Graphs/Colors_%28ggplot2%29/

Bubble Chart


[e.g.#1]

#updated for ggplot 0.9.1

crime <-read.csv("http://datasets.flowingdata.com/crimeRatesByState2005.tsv", header=TRUE, sep="\t")

ggplot(crime, aes(x=murder, y=burglary, size=population, label=state),guide=FALSE)+

geom_point(colour="white", fill="red", shape=21)+ scale_area(range=c(1,25))+

scale_x_continuous(name="Murders per 1,000 population", limits=c(0,12))+

scale_y_continuous(name="Burglaries per 1,000 population", limits=c(0,1250))+

geom_text(size=4)+

theme_bw()







[e.g. #1-2]


#crime <-read.csv("http://datasets.flowingdata.com/crimeRatesByState2005.tsv", header=TRUE, sep="\t")
 

crime <- read.csv("http://datasets.flowingdata.com/crimeRatesByState2008.csv", header=TRUE, sep="\t")

p = ggplot(crime, aes(murder,burglary,size=population, label=state)) p = p+geom_point(colour="red") +scale_area(to=c(1,20))+geom_text(size=3)
p + xlab("Murders per 1,000 population") + ylab("Burglaries per 1,000")
 

[e.g.3]

crime <- read.csv("http://datasets.flowingdata.com/crimeRatesByState2005.tsv", header=TRUE, sep="\t" )

symbols(crime$murder, crime$burglary, circles=crime$population)

radius <- sqrt( crime$population/ pi )

symbols(crime$murder, crime$burglary, circles=radius)

symbols(crime$murder, crime$burglary, circles=radius, inches=0.35, fg="white", bg="red", xlab="Murder Rate", ylab="Burglary Rate")

symbols(crime$murder, crime$burglary, squares=sqrt(crime$population), inches=0.5)

text(crime$murder, crime$burglary, crime$state, cex=0.5)

[e.g. #2]    ????

ggplot(asd_data, aes(x=prev2003, y=asd_diff, weight=denom2003, colour=octile, size=denom2003)) +

  geom_point( alpha=0.8, guide="none") +

  scale_size_area(breaks=c(250, 500, 1000, 10000, 50000), "2002 District\nElementary School\nPopulation", max_size=20) +

  stat_smooth(method="rlm", size=0.5, colour="black", alpha=0.4, level=0.95)+

  scale_colour_brewer(palette="Spectral", type="qual",name="2002 Autism\nPrevalence Octile") +

  coord_equal(ratio=1/2)+

  guides(colour = guide_legend(override.aes = list(alpha = 1)))+

  ggtitle("Figure 4. Change in Autism Prevalence between 2002 and 2008 vs Baseline (2002) Prevalence,\n

       Wisconsin Elementary School Districts (with weighted linear best-fit line and 95% confidence band)") +

  scale_x_continuous("2002 Autism Prevalence (per 1,000)") +

  scale_y_continuous("Change in Autism Prevalence (per 1,000) between 2002 and 2008")

[e.g.3]

library(googleVis)

bubbleChart <- gvisBubbleChart(Fruits, idvar="Fruit", xvar="Sales", 

yvar="Expenses", sizevar="Profit",

options=list(hAxis='{minValue:75,

   maxValue:125}', 

width=500, height=300, 

colorAxis="{colors: ['lightblue', 'blue']}"),

chartid="Bubble_Chart_colour_Axis"

)

plot(bubbleChart)


[final]  

library(ggplot2)


# Datasets

prc = read.csv("http://ichart.finance.yahoo.com/table.csv?s=^GSPC&d=0&e=1&f=2013&g=m&a=0&b=1&c=1990&ignore=.csv", as.is=T)
vix = read.csv("http://ichart.finance.yahoo.com/table.csv?s=%5EVIX&a=00&b=2&c=1990&d=0&e=1&f=2013&g=m&ignore=.csv", as.is=T)


# Data processing
 
prc$Date <- as.Date(prc$Date)
prc <- prc[, c(1,7)]
colnames(prc)[2] <-c("Value")


vix$Date <- as.Date(vix$Date)
vix <- vix[, c(1,5)]
colnames(vix)[2] <-c("VIX")


df = merge(prc, vix)
df$year = as.integer(substring(df$Date,1,4))
df$month = as.integer(substring(df$Date,6,7))

# Graphs

par(mfrow=c(2,1))
plot(df$Date, df$Value, type="l",main="S&P500",  xlab="", ylab="")
plot(df$Date, df$VIX, type="l",main="VIX ( VOLATILITY S&P 500) ",  xlab="", ylab="")

# Erase

frame()
par(mfrow=c(1,1)) 


# ggplot2 base layer
p = ggplot(df)


# Line graph

(
p 

+ geom_line( aes(x=df$Date, y=df$Value, colour=VIX ) ) 

+ scale_colour_gradient(low="blue", high="red")

)

# Bubble plots

(

p 

+ geom_point(aes(x = month, y = year, size = Value, colour = VIX),shape=16, alpha=0.80) 

+ scale_colour_gradient(limits = c(10, 60), low="blue", high="red", breaks= seq(10, 60, by = 10)) 

+ scale_x_continuous(breaks = 1:12, labels=c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")) 

+ scale_y_continuous(trans = "reverse")

) 

df= NULL

df$exp = c(1,2,3)

df$cna = c(1,2,3)

df$met = c(1,2,3)

df$name = c("aaa", "bbb","ccc" ) 

df = as.data.frame( df ) 

p = ggplot( df )

( 

p 

+ geom_line( aes( x=cna, y=met, color=exp ) ) 

+ scale_colour_gradient(low="blue", high="red")

)

p = ggplot( df )  

( 

p 

+ geom_point( aes( x=cna, y=met, size=exp, color=exp), shape=16, alpha=0.5) 

+ scale_colour_gradient(limits = c(0, 5), low="blue", high="red", breaks= seq(0, 5, by = 1))   

+ geom_text( aes(x=cna, y=met, label=name), hjust=0, vjust=2, angle=0 )  

) 

#ggplot(nba, aes(x= MIN, y= PTS, colour="green", label=Name))+
#  geom_point() +geom_text(aes(label=Name),hjust=0, vjust=0) 

-----------------

require( ggplot2 )
 
p = ggplot( t )  
( 
p 
#+ geom_point( aes( x=cna, y=met, size=exp, color=exp), shape=exp, alpha=0.) 
#+ geom_jitter(  aes( x=cna, y=met), position = #position_jitter(width = .5))
+ geom_point( aes( x=cna, y=met, fill=exp), color="black", shape=21, size=7, alpha=1, position=position_jitter(w=0, h=0)) 
#+ scale_fill_gradientn(colours = topo.colors(10), breaks=seq(-10,10, by=5) )
+ scale_fill_gradient2(limits = c(-10, 10), low="blue", mid="yellow", high="red", breaks= seq(-10, 10, by=5))   
+ geom_text( aes(x=cna, y=met, label=label2), hjust=-0.3, vjust=0.5, angle=30, size=3.2, position = position_jitter(width=0, height=0))   
+ labs( x="Reccurrent (%) Copy-number Change \n(+:gain | -:loss)", y="Promoter Methylation \n(+:hyper- / -:hypo-methylation)", fill="Expression \nFold Change", size=20)
#+ theme_bw()
+ geom_vline(xintercept = 0,  colour="green", linetype = "longdash")
+ geom_hline(yintercept = 0,  colour="green", linetype = "longdash")
+ theme(axis.text=element_text(size=12),
        axis.title=element_text(size=14,face="bold", color="black"),
        legend.title=element_text( size=14),
        legend.position = c(0.9, 0.9))
)        
 
  

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라벨: Informatics