ggplot tutorial r

two kinds and the population obeys multivariate normal distribution. code example: NBSP; > if (Require (MASS) == FALSE) + " mass " ) +} > > Model1=lda (Species~.,data=iris) > table class ) > table Setosa versicolor virginica setosa 50 0 0 versicolor 0 2 virginica 0 1 49> sum (diag (prop.table (table))) ## #判对率 [1] 0.98 as a result, only three of the samples were observed to be judged incorrectly. After the discriminant function is established, the discriminant score can be plott

Related parameters in the code The main object is to use the Add_axes method, note that the coordinates of the three graphs correspond, draw the scatter chart, in the other two sub graphs to draw the corresponding column chart #!/usr/bin/python #coding: Utf-8 import numpy as NP import Matplotlib.pyplot as Plt plt.style.use ("ggplot") FIG = Plt.f Igure () # Draw the main figure bottom_x_1 = 0.15 Bottom_y_1 = 0.15 width = 0.5 height = 0.5 Rect1 = [Bot

1. You could be the same test_prediction = svc.predict (hog_features.reshape) modified before modification 2. This possibility Import NumPy as NP import Matplotlib.pyplot as plt from matplotlib import style style.use ("Ggplot") From Sklearn import SVM x = [1, 5, 1.5, 8, 1, 9] y = [2, 8, 1.8, 8, 0.6, one] plt.scatter (x,y) plt.show () C11/>x = Np.array ([[[1,2], [5,8], [1.5,1.8], [8,8],

(y~x1*x2), y = a*x1+b*x2+c*x1*x2+d Lm (Y~X1*X2*X3) Y =a*x1+b*x2+c*x3+d*x1*x2+e*x1*x2+f*x2*x3+g*x1*x2*x3+h Lm (Y~X1+X2+X3+X1:X2:X3) y = a*x1+b*x2+c*x3+d*x1*x2*x3+e SETP stepwise regression, you can remove the meaningless variable backwards, you can add a new variable to the forward regression Lm (y~x1, subset=1:100) selects only the first 100 data for regression Lm (Y~i (X1+X2)) to (X1+X2) regression Lm (Y~ploy (x,3,raw=true)) Y is the three-quadratic polynomial regression of x Lm (log (y) ~ x1

The two months have not written any code. Also did not do any big project, basically is to write in the previous project that uses the Ggplot2 visualization data to make some supplementary supplement, most technical difficulty all in Ggplot2 and the R language Excel processing here solves and summarizes. Then the amateur help people to modify a rvest written in the Amazon primitive reptile, spent two weekend time.Just remember the recent function block.function block partitioning, and customizin

similar to the original data after filling, and the overall characteristics of the data are basically maintained during the filling process.Second, the abnormal valueOutliers are also very hated a kind of dirty data, outliers tend to pull up or pull down the overall situation of the data, in order to overcome the impact of outliers, we need to deal with outliers. First, we need to identify which values are outliers or outliers, and then how to handle these outliers. The following is still in th

package. Numba-Python's low-level Virtual Machine JIT compiler, compiled by cython and numpy developers for scientific computing Networkx-efficient software for complex networks. Pandas-This database provides high-performance, easy-to-use data structures and data analysis tools. Open Mining-the pandas web interface in Python ). Pymc-MCMC sampling toolkit. Zipline-Python algorithm trading library. Pydy-Full name: Python dynamics, which assists in Dynamic Modeling workflows Based on numpy,

database provides high-performance, easy-to-use data structures and data analysis tools. Open Mining-the pandas web interface in Python ). Pymc-MCMC sampling toolkit. Zipline-Python algorithm trading library. Pydy-Full name: Python dynamics, which assists in Dynamic Modeling workflows Based on numpy, scipy, ipython, and matplotlib. Sympy-Python library for symbolic mathematics. Statsmodels-Python statistical modeling and library of metered economics. Astropy-Python astronomy library, com

= ProgressBar (maxval=10) for I in range (1, one): Pbar.update (i) Time.slee P (1) pbar.finish () # 60% |######################################################## | 9) Colorama When you print the log with ProgressBar, why not add color to them! In fact, when there is a big mistake, it can give you a quick reminder. Colorama is easy to use. Just write it in your script and add it to the text you want to print: Colorama-red) UUID For me, there are only a few tools that are really needed

Today's challenge is the density map:The appearance of the myriad, we always need a graph can be a glance to show the characteristics of the data. Data distribution maps are undoubtedly very reflective of data characteristics (user symptoms). It works better with median and 9-bit.For example, because of the secrecy, I hide what each line represents, and what the data output is. From the lines alone, you can see how many people in each channel are accustomed to buying things before, and presumabl

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) + G

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

0MississippiNA 0MissouriNA 0MontanaNA 0NebraskaNA 0NevadaNA 0 ' New Hampshire 'NA 0 ' New Jersey 'NA 0 ' New Mexico 'NA 0 ' North Dakota 'NA 0 ' Rhode Island 'NA 0 ' South Carolina 'NA 0 ' South Dakota 'NA 0VermontNA 0VirginiaNA 0 ' West Virginia 'NA 0WyomingUse the software r3.2.5+rstudio-0.99.893 to analyze the code as follows:Deifen. RLibrary (GGPLOT2) LBJ Read. Table ("G:/myproject/rdoc/unit2/rchap6/lbj.txt", Header = T,quote ="'") Attach (LBJ)# #查看数据前5行Head

Matplotlib Function Points graph for learning matplotlib # Coding: utf-8import numpy as npfrom matplotlib import pyplot as pltfrom matplotlib. patches import Polygon ''' evaluate function credits ''' def func (x): return-(x-2) * (x-8) + 40 print (plt. style. available) plt. style. use ("ggplot") # plot the curve x = np. linspace (0, 10) y = func (x) fig, ax = plt. subplots () plt. plot (x, y, linewidth = 2) # Set axis a = 2b = 9ax. set_xticks ([a, B]

Matplotlib, Seaborn, and Ggplot) · Bokeh has the flexibility to use interactive applications, layouts, and different styling options for visualization Combining the advantages of bokeh and its challenges, bokeh is the ideal tool for the rapid development of prototype products. However, if you want to make something new in the context of the product, D3.js may still be your best bet.　　The challenges facing bokeh: As with any upcoming

in range(1, 11): pbar.update(i) time.sleep(1)pbar.finish()# 60% |######################################################## | 9) colorama When you use progressbar to print logs, why don't you add colors to them! In fact, when a major error occurs, it will give you a quick reminder. Colorama is easy to use. Just write it into your script and add it to the text you want to print: Colorama-red10) uuid For me, there are only a few tools really needed in programming: hash, key-Value Pair stor

: pip install Python modules Tutorial.If you ' re still have trouble, feel free-to-contact us, using the "contact" in the footer of this Website. import numpy asimport Matplotlib.as Pltfrom matplotlib import Stylestyle. ( "ggplot" ) from.import kmeans here, we ' re just doing our basic imports. We ' re importing NumPy which is a useful numbers crunching module, then matplotlib for graphing, and then Kmeans from Sklea Rn.Confused about imports an

Plotting data(1) Bar chart (hist)1 ImportMatplotlib.pyplot as Plt2Plt.style.use ('Ggplot')3 #%matplotlib Inline4 ImportNumPy as NP5 6Fig,ax = Plt.subplots (figsize= (6,4))7Ax.hist (df['Petal Width'], color='Black')8Ax.set_ylabel ('Count', fontsize=12)9Ax.set_xlabel ('Width', fontsize=12)TenPlt.title ('Iris Petal Width', Fontsize=14, y=1.01) OnePlt.show ()Note:%matplotlib inline is ipython in the statement, first temporarily sealed off, figsize= (6,4)

package. Watch data wrangling with R via Rstudio. (https://www.rstudio.com/resources/webinars/data-wrangling-with-r-and-rstudio/) Read and practice how to use the Dplyr, Tidyr, and data.table packages. Step five: Effective data visualizationIt is a matter of pride to create your own data visualization work. However, data visualization is both a skill and an art. Many scholars read Edward Tufte's "visual quantitative data" principle, or Stephenfew's "Pitfalls on dashboard Design". Y

moved closer to the higher-density areas. The algorithm stops at the stage where the centroid is no longer moving. With the help of the following code, the mean shift clustering algorithm is implemented in Python. Use the Scikit-learn module.Import the necessary packages-Import NumPy as NP from Import Meanshift Import Matplotlib.pyplot as Plt from Import Stylestyle.use ("ggplot")The following code generates a sklearn.dataset two-dimensiona