Tips in R Language Applications

Plotting Graphic

##Layout

par(mfrow=c(1,2)) 

plot( thickness, torsion_disp, xlab= "Steel Plate Thickness (mm)", ylab="Torsion Stiffness Scenario Displacement (mm)")  
lines(lowess(thickness, torsion_disp), col = "red" )

plot( thickness, mass, xlab= "Steel Plate Thickness (mm)", ylab="BIW Mass (Kg)")  
lines(lowess(thickness, mass), col = "red" )

##Make a pdf graphic[3]

pdf("my_plot.pdf", width=6, height=4)
plot(1:10)
dev.off()

Organizing the plotting commands in a function

fig.myplot <- function() {
  plot(1:10)
  points(runif(100, 1, 10), runif(100, 1, 10), col=rainbow(100))
  abline(h=5)
  title(main="My lovely plot")
}
pdf("my_plot.pdf", width=6, height=4)
fig.myplot()
dev.off()

##Save graphic as a jpg file
filename <- paste( “D:\scatter”, “.jpg” )
jpeg( filename );
plot( mass, disp, xlab= “Mass (Kg)”, ylab=“Displacement (mm)”)
lines(lowess(mass, disp), col = “blue” )
abline( lm(disp~mass), col = “red”)

##Title and Axis Label
An example from [3]

	plot(1:10, xlab="", ylab="")
	xlab <- expression(paste("Photosynthetic rate (", mu, " mol ", m^-2, s^-1, ")"))
	ylab <- "Photosynthetic rate (u mol m^-2 s^-1)"
	mtext(xlab, 1, 3)
	mtext(ylab, 2, 3)
	title(main="Doesn't the x axis label look nice?")

##Draw points

 points(  x, y,pch=19,col="red");

#直方图
在R语言环境中绘制直方图可以使用 hist, plot和ggplot2等

##hist

###语法

hist(x, breaks = "Sturges",
     freq = NULL, probability = !freq,
     include.lowest = TRUE, right = TRUE,
     density = NULL, angle = 45, col = NULL, border = NULL,
     main = paste("Histogram of" , xname),
     xlim = range(breaks), ylim = NULL,
     xlab = xname, ylab,
     axes = TRUE, plot = TRUE, labels = FALSE,
     nclass = NULL, warn.unused = TRUE, …)

###参数

• x – 数组， 包含histogram所要展示的数据(a vector of values for which the histogram is desired.)

• breaks, 可为以下几种类型：

• 数组 – 包含histogram单元分隔点(a vector giving the breakpoints between histogram cells)

• 函数 – 用于计算分割点数组(a function to compute the vector of breakpoints)

• 数 – 设定histogram中单元数量(a single number giving the number of cells for the histogram)

• 字符串 – 指定计算histogram中单元数量的算法 (a character string naming an algorithm to compute the number of cells)

• 函数 – 计算histogram单元数量(a function to compute the number of cells)

• freq – 逻辑(布尔型)变量.

• True – the histogram graphic is a representation of frequencies, the counts component of the result

• False – probability densities, component density, are plotted (so that the histogram has a total area of one)

• probability
  an alias for !freq, for S compatibility.

• include.lowest
  logical; if TRUE, an x[i] equal to the breaks value will be included in the first (or last, for right = FALSE) bar. This will be ignored (with a warning) unless breaks is a vector.

• right
  logical; if TRUE, the histogram cells are right-closed (left open) intervals.

• density
  the density of shading lines, in lines per inch. The default value of NULL means that no shading lines are drawn. Non-positive values of density also inhibit the drawing of shading lines.

• angle
  the slope of shading lines, given as an angle in degrees (counter-clockwise).

• col
  a colour to be used to fill the bars. The default of NULL yields unfilled bars.

• border
  the color of the border around the bars. The default is to use the standard foreground color.

• main, xlab, ylab
  these arguments to title have useful defaults here.

• xlim, ylim
  the range of x and y values with sensible defaults. Note that xlim is not used to define the histogram (breaks), but only for plotting (when plot = TRUE).

• axes
  logical. If TRUE (default), axes are draw if the plot is drawn.

• plot
  logical. If TRUE (default), a histogram is plotted, otherwise a list of breaks and counts is returned. In the latter case, a warning is used if (typically graphical) arguments are specified that only apply to the plot = TRUE case.

• labels
  logical or character string. Additionally draw labels on top of bars, if not FALSE; see plot.histogram.

• nclass
  numeric (integer). For S(-PLUS) compatibility only, nclass is equivalent to breaks for a scalar or character argument.

• warn.unused
  logical. If plot = FALSE and warn.unused = TRUE, a warning will be issued when graphical parameters are passed to hist.default().

###样例

####样例 1 – 使用hist

# 准备数据
data<-rnorm(n=1000, m=24.2, sd=2.2)
# 绘制直方图
hist(data)

####样例 2 – 使用hist - 调整数据间隔数量

# 准备数据
data<-rnorm(n=1000, m=24.2, sd=2.2)
# 绘制直方图
hist(data, breaks=30)

####样例 3 – 使用hist - 分布密度直方图

# 准备数据
data<-rnorm(n=1000, m=24.2, sd=2.2)
# 绘制直方图
hist(data, freq=FALSE) 

####样例 4 – 使用hist - 分布密度直方图 + 密度分布曲线

# 准备数据
data<-rnorm(n=1000, m=24.2, sd=2.2)
# 绘制直方图
hist( data, freq = FALSE, ylim = c(0, 0.2))
curve(dnorm(x, mean=mean(data), sd=sd(data)), col = 2, lty = 2, lwd = 2, add = TRUE)

####样例 5 – 使用hist - 分布频度直方图 + 色彩

# 准备数据
data<-rnorm(n=1000, m=24.2, sd=2.2)
# 绘制直方图
colors = c("red", "yellow", "green", "violet", "orange",  "blue", "pink", "cyan") 
hist(data,  right=FALSE, col=colors, main="Data Distrubution",  xlab="x data")   

####样例 6 – 使用hist - 分布频度直方图 + label

# 准备数据
data<-rnorm(n=1000, m=24.2, sd=2.2)
# 绘制直方图
colors = c("red", "yellow", "green", "violet", "orange",  "blue", "pink", "cyan") 
h <-hist(data,  right=FALSE, col=colors, main="Data Distrubution",  xlab="x data")   
text(h$mids,h$counts,labels=h$counts, adj=c(0.5, -0.5))

####样例 7 – 使用hist - 两个分布频度直方图

# 准备数据
data1<-rnorm(n=1000, m=24.2, sd=2.2) 
data2<-rnorm(n=1000, m=34.2, sd=2.2)
# 绘制直方图
hist( data1, freq = FALSE, ylim = c(0, 0.20), xlim=c(15, 45), col='skyblue', main="Histogram of Data",  xlab="data")
hist( data2, freq = FALSE, ylim = c(0, 0.20), add=T, col='red')

####样例 8 – 使用hist - 两个分布频度直方图 + 透视色

# 准备数据
data1<-rnorm(n=1000, m=24.2, sd=2.2) 
data2<-rnorm(n=1000, m=34.2, sd=2.2)
# 绘制直方图
hist( data1, freq = FALSE, ylim = c(0, 0.20), xlim=c(15, 45), border=T, col='skyblue', main="Histogram of Data",  xlab="data")
hist( data2, freq = FALSE, ylim = c(0, 0.20), add=T, border=T, col=rgb(0, 1, 0, 0.5))

##plot

###样例

####样例 9 – 使用plot - 两个分布频度直方图

# 准备数据
data1<-rnorm(n=1000, m=24.2, sd=2.2) 
data2<-rnorm(n=1000, m=34.2, sd=2.2)

p1 <- hist(data1,  plot=FALSE)
p2 <- hist(data2,  plot=FALSE)
# 绘制直方图
plot(0,0,type="n",xlim=c(15,45),ylim=c(0,200),xlab="x",ylab="freq",main="Two histograms")
plot(p1,col="green",density=10,angle=135,add=TRUE)
plot(p2,col="blue",density=10,angle=45,add=TRUE)

####样例 10 – 使用plot - 两个分布密度曲线图

# 准备数据
data1<-rnorm(n=1000, m=24.2, sd=2.2) 
data2<-rnorm(n=1000, m=34.2, sd=2.2)
## 计算分布密度
densdata1 <- density(data1)
densdata2 <- density(data2)
##  
xlim <- range(densdata2$x,densdata1$x)
ylim <- range(0,densdata2$y, densdata1$y)
#pick the colours
data1Col <- rgb(1,0,0,0.2)
data2Col <- rgb(0,0,1,0.2)
##  
plot(densdata1, xlim = xlim, ylim = ylim, xlab = 'data',
     main = 'Distribution of data2 and data2', 
     panel.first = grid())
# 
polygon(densdata1, density = -1, col = data1Col)
polygon(densdata2, density = -1, col = data2Col)
## 标题
legend('topleft',c('data1','data2'),
       fill = c(data1Col, data2Col), bty = 'n',
       border = NA)

##ggplot

###样例

####样例 11 – 使用ggplot2 - 分布密度曲线
安装

install.packages("ggplot2")

library(ggplot2)
# 准备数据
data<-rnorm(n=1000, m=24.2, sd=2.2) 
# 分布密度曲线
ggplot(data=NULL, aes(x=data)) + geom_density()

####样例 12 – 使用ggplot2 - 两个分布密度曲线

library(ggplot2)
# 准备数据
data1 <- data.frame( length = rnorm(n=1000, m=24.2, sd=2.2) )
data2 <- data.frame( length = rnorm(n=1000, m=34.2, sd=2.2) )

data1$veg <- 'A'
data2$veg <- 'B'

vegLengths <- rbind(data1, data2)

ggplot(vegLengths, aes(length, fill = veg)) + geom_density(alpha = 0.2)

#Functions in R Language

##Defining functions

functionName <- function( parameter1, parameter2 )
{
#   code
    return ( results )
}

# Call the function 
print( functionName( 1, 2, 3 ) )

#Array

Iterating an array

As in many other programming languages, you repeat an action for every entry of a vector through a for loop[1].

for(i in values){
  ... do something ...
}

samples <- c(rep(1:10))
baseList <- c(1,2,3,4,5)

XList =  list()
YList =  list()
ZList =  list()

for ( i in baseList)
{
    x = baseList[i]
    tmp = sin(x);
    XList[i] = tmp

    tmp = x*x
    YList[i] = tmp
}

seq_along creates a sequence from 1 up to the length of its input[2]:

pp <- c("Peter", "Piper", "picked", "a", "peck", "of", "pickled", "peppers")
for(i in seq_along(pp)) print(pp[i])

for (id in c(1,2,3,4,5)){
  print(paste("This is", id))
}

for (id in c(1:5)){
  print(paste("This is", id))
}

#调用R程序
在node.js 环境中调用 R程序

//-----------------------------------
//   Modules 
//-----------------------------------
const cp = require('child_process');


// 
//  Variables 
//
var r_script_file_path = './statis.r'
var resultPath = './result.txt';  
const R_Path = "D:\\R\\R-3.4.1\\bin\\x64\\R.exe"


//-----------------------------------
//   Methods
//-----------------------------------

//  Run R jobs
//
function runRcode( thefilename )
{
    console.log( Date() ) 
    console.log( '      --  R statisitc analysis --    ' ) ; 
    var command = R_Path + ' CMD BATCH --vanilla --slave ' + thefilename + ' ' + resultPath ;
    cp.execSync( command );
    console.log(  command ) ;
}


//-----------------------------------
// Main process 
//-----------------------------------

try{
    runRcode( r_script_file_path );

} catch( exception )
{
    console.log( exception );
}

[1] R in Action, http://www.statmethods.net/graphs/
[2] https://www.stat.auckland.ac.nz/~paul/RG2e/
[3] https://nicercode.github.io/guides/plotting/
[4] http://bxhorn.com/r-graphics-plot-parameters/#anatomy-of-a-plot
[5] https://www.rdocumentation.org/packages/graphics/versions/3.4.3/topics/hist
[6] http://www.r-tutor.com/elementary-statistics/quantitative-data/histogram
[7] https://www.r-bloggers.com/basics-of-histograms/
[8] https://stackoverflow.com/questions/3541713/how-to-plot-two-histograms-together-in-r
[9] http://ggplot2.org/
[10] http://www.cookbook-r.com/Graphs/Plotting_distributions_(ggplot2)/
[11] http://www.dummies.com/programming/r/how-to-loop-through-values-in-r/
[12] https://www.safaribooksonline.com/library/view/learning-r/9781449357160/ch04.html