ggplot2 r tutorial

The following plots take the Weibull distribution (Weber distribution, Weibull distribution) as an exampleFor Weibull distribution (Weber distribution, Weibull distribution), please refer to my blog http://www.cnblogs.com/wwxbi/p/6141501.htmlLibrary (GGPLOT2)# both D and Y here are in order of sizedYDfGgplot (Df,aes (x=d,y)) +Geom_line (colour= "green") +Ggtitle ("Weibull distribution \ n probability density map")# Here's H no size orderH Ggplot (Null

1. color and shape control Data features can be expressed not only by coordinates, but also by different colors or shapes. Taking the MPG dataset as an example, the variables used include cty (driving distance in the city), hwy (highway driving distance), displ (displacement size), and year (production year) 1 library(ggplot2)2 p We will use a red circle to represent the models produced in 1999, and a blue triangle to represent the displacement in

diagram. Facets can be compared by placing different subclasses in different graphs:Qplot (carat, data = diamonds, facets = Color ~., Geom = "Histogram", Binwidth = 0.1, Xlim = C (0, 3))Qplot (carat, data = diamonds, facets = Color ~., Geom = "Histogram", Binwidth = 0.1, Xlim = C (0, 3))The following graphic adds new elements based on the beginning: faceted, multiple layers, and statistics. Facets and layers extend the data structures mentioned above: each layer of each facet has its own datase

Ggplot2 Scale Related SettingsScale settings: Mainly used to adjust settings for each layer after Ggplot drawing.1. Related attribute scale settingIncludes Scale_size (), Scale_alpha (), Scale_shape ()As you can see from the name above, these three settings are primarily related to the Ggplot layer properties, including size, transparency, and shape.The main parameters for this setting are listed below:SCALE_XXX (name = Waiver (), breaks = waiver (),

Distribution in R should be regarded as a relatively important content, and by drawing to show the distribution of data, you can more intuitively let us understand the distribution of dataHistogramGeom_histogram (mapping = NULL, data = NULL, stat = "bin", Position = "stack", ..., binwidth = null, bins = NULL, NA.RM = F Alse, show.legend = NA, Inherit.aes = TRUE)Density mapgeom_density (mapping = NULL, data = NULL, stat = "density", Position = "identity", ..., na.rm = FALSE, show.legend = na, I N

1. There are currently four types of themesTheme_gray (), THEME_BW (), Theme_minimal (), Theme_classic ()2. x-Axis Setting scaleScale_x_continuous (Limits=c (1950,2000), Breaks=seq (1950,2000,5))3. Bar LineGgplot2 () +geom_bar (Aes (Y=x,fill=factor (GROUP.2)), stat="identity", position=' Dodge') +scale_x_continuous (Limits=c (38,50), Breaks=seq (38,50,1)) + geom_line (position= "identity", AES (Y=X))4. Ggplot-pieGgplot (Wm,aes (x="", Fill=type) + Geom_bar (width=1) + coord_polar (theta='y') )

#生成数据, used to demonstrate DAT Drawing a mirrored bar chart with Ggplot2

Data sampleFlanking mean SD CNV03651595510036715101820036915.1102530037015.1102740037215.2103150037315.3103260037515.3103370037715.3103480037815.4103490038015.41037A=read.table ("SDandCNV.txt", header=T) lilbrary ("ggplot2 " 1) layer1=ggplot (A,aes (Flanking,mean)) +geom_point (position=pd,size=3) +geom_errorbar ( AES (YMIN=MEAN-SD,YMAX=MEAN+SD), width=0.1, POSITION=PD) +geom_line (position=PD) Layer2=geom_ Line (Aes (y=CNV)) Layer3=geom_point (Aes

Introduction A line chart is generally used to describe a variable of one-dimensional variables as a continuous variable, which is usually time. In other words, a line chart is best suited to describe changes in time series data. Of course, with the change of discrete variables is also possible, but this discrete variable must be ordered. Draw a line chart A basic line chart is relatively simple, as long as the AES in the Ggplot x,y data and Geom designated as line. If x is a continuous variable

Ggplot2 () function Ggplot2 is a powerful mapping tool that allows you to create any graphics that will help you solve your problems without being limited by existing graphics types. Qplot () Qplot () belongs to Ggplot2 () and can be understood as a simplified version of it.Qplot is "rapid mapping" (quick plot), as the name suggests, can quickly data visualizatio

---restore content starts---Install the package and load> install.packages (' Gcookbook ')>install.packages (' Ggplot2 ')> Library (GGPLOT2)Or: Install.packages (' Ggplot2 ', ' Gcokbook ')> Library (Gcookbook)To set the working path:SETWD ("D:/data")GETWD ()[1] "D:/data"Read file> Data > DataX1 X3.8.1941 X1.1.20071 2 1/24/1972 1/1/20072 3 6/1/1932 1/1/20073 4 5/1

second plot ()function to do the same, but place the graph in the first column and the second row. Because the plot () function starts a new page by default, you need to disable the operation, so set the option NewPage = FALSEUse the position = option to have more control over size and placement:> Plot (graph1,position=c (0,.3,1,1)) > Plot (graph2,position=c (0,0,1,.3), Newpage=false)Position = C (xmin, ymin, Xmax, ymax), the page's X-y coordinate system is a rectangle, the x-axis and y-axis di

to market) Now more and more people are starting to use Python to do data analysis, IPython notebook is particularly popular, its real-time interaction has the advantage of scripting language to maximize. So how can you use Ggplot in Ipython notebook? I'm here to share three different ways for you to choose.RPy2 The first approach is to use Rpy2, Rpy2 is a rewrite and redesign of Rpy, designed to provide an API for Python users to use R in Python. RPY2 provides a basic encapsulation of objec

, province, city, county, 4 administrative division, the letter P end of the expression polygon data, used to draw the area, BOU2 is the boundary of Provincial administrative division. This data is not the latest data, the latest data in the National Basic Geographic Information Center can not be found, probably to buy to get, but for the general drawing, this data has been able to meet the requirements. This article draws a map with 3 packages: Sp,maptools,