ggplot tutorial r

will use LDA. Package, so we need to install it before we can use the evaluation function that is specific to the package we start by importing the features we need: import Matplotlib.pyplot as plt # for plotting the results Plt.style.use (' Ggplot ') # for loading the data: From tmtoolkit.utils import unpickle_file # for model evaluation with the LDA package: From tmtoolkit.lda_utils import Tm_lda # for constructing

) time.sleep (1) pbar.finish () # 60% |################################################ ######## | 9) Colorama When you use ProgressBar to print the log, why not add color to them! In fact, when there is a major error, it will be able to 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 in programming: hash, key-value pair stora

First import the related module Import pandas as PD import pandas_datareader import datetime import Matplotlib.pylab as PLT import Seaborn as SNS from Matplotlib.pylab import style to Statsmodels.tsa.arima_model import Arima from Statsmodels.graphics.tsaplots Import PLOT_ACF, PLOT_PACF Set style Style.use (' Ggplot ') plt.rcparams[' font.sans-serif '] = [' Simhei '] plt.rcparams[' axes.unicode_minus '] = False Read into data Stockfile = ' dat

and less wind; a person in Suzhou, Shanghai, Hangzhou, or Xiamen, Hong Kong, Guangzhou, the public, muddy chaos chaos past, can only feel a little cool, autumn flavor, autumn color, autumn mood and posture, total look not full, taste , not a full-on. Autumn in Beijing: Cold, cool fast?Autumn in Shanghai: cool, cool and slow? in[11]:bj=pd.read_csv ( " beijing2004 , Parse_dates=true,index_col= CST " 12]:matplotlib.style.use ( " ggplot " ) in

source JavaScript Chart library.There are two ways to install:Install.packages ("plotly")OrDevtools::install_github ("ropensci/plotly")The plotly package uses the function Plot_ly function to draw the interaction graph.If you plot a scatter plot relative to the iris data set, you need to set the mode parameter to "markers".Library (plotly) P If you want to draw an interactive box line diagram, you need to set the type parameter to box.Library (plotly) Plot_ly (midwest, x = percollege, color = s

not very reassuring. Like what The shiny in R has a partial problem. In the field of data science, R Studio is the best IDE, and even Microsoft's VS has to mimic a data sciences model, which shows that the model of the Editor + console + data panel + plot panel is the most suitable for the analysis. Python is not easy to use Ide,spyder package management and console is not good, Anaconda just put the Spyder into a bag. Rodeo is closest to R Studio, but it doesn't work with Node's front-end ca

First, the questionThere was a problem that bothered me for a long time.I have the following data, the first column is the Hour + minute, the second column is the value:0000 1120001 1230002 122...0059 1230100 120...2359 156How can I draw this into a time sequence diagram of minute granularity? The horizontal axis that is drawn directly using the Ggplot function is a number, not a time.Second, the answerThe essence of the problem is actually to unify t

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)

data accuracy = Clf.score (X_test, y_test) # make predictions Forecast_set = Clf.predict (x_lately ) print (Forecast_set, accuracy)The preceding lines of code are scikit-learn the training and forecasting process using linear regression. We can calculate the accuracy of the model by testing the data accuracy and provide the prediction results by providing the model X_lately forecast_set .I run the resulting results as follows:This accuracy that needs to be noted accuracy does not indicate that

In Ggplot, the future of better data visualization, we may sometimes need to use some coordinate transformation operations, such as to draw a horizontal bar chart or spider chart.Coord_cartesian (Xlim = null, Ylim = NULL)Cartesian coordinates: From the point of view Coord_cartesian parameters are relatively simple, x and y data limitsCoord_flip (...)Horizontal transition coordinates: Swap x and y axes without special parametersCoord_trans (x = "Identi

logarithmic transformation of data, and then plot the density and scatter plots.Ggplot (Top.1000.sites, AES (x = log (pageviews))) + geom_density () Ggplot (Top.1000.sites, AES (x = log (pageviews), y = log (Un iquevisitors)) + Geom_point () #也可以用ggplot2内置的scale_x_log10 () and SCALE_Y_LOG10 () direct conversion scale, same effect　　Perform a linear regression and interpret the results:Lm.fit 　　Call: Calling functionRisiduals: The number of bits of the

Install.packages ("Ggplot2")Library (GGPLOT2)# WINDROSE.R Http://stackoverflow.com/questions/17266780/wind-rose-with-ggplot-rRequire (GGPLOT2)Require (Rcolorbrewer)Plot.windrose SpdDirSpdres = 10,Dirres = 30,Spdmin = 0,Spdmax = 90,Spdseq = NULL,palette = "Ylgnbu",Countmax = NA,debug = 0) {# look-to-see-what data is passed in to the functionif (Is.numeric (SPD) Is.numeric (dir)) {# Assume that we ' ve been given vectors of the speed and direction vect

Regression Model performance evaluation series 1-QQ chart, regression model evaluation 1-qq(Erbqi) the QQ plot is the Quantile-Quantile diagram, that is, the Quantile-Quantile diagram. A simple understanding is to plot the values of the two same Quantile distributions into points (x, y; if the two distributions are very close, the vertex (x, y) will be distributed near the y = x straight line; otherwise, no; the prediction result of the regression model can be evaluated from the QQ plot. There a

coordinates: You can quickly draw, you do not need to do so much work.The code is as follows:#dplyr处理数据data2 3) cluster diagramThe drawing point is that when the data is plotted, adding Geom_bar, position= "Dodge" (separate) if this part is removed, the default is to generate a stacked chart.The code is as follows:Data3If you want to define the corresponding order of colors, you can use the factorFor example, just use this line of code to redefine the color, use levels to change the factor orde

% | ##################################### #################### | 9)Colorama Since you have set a good progress bar for logs, why not make them colorful! You can also remind yourself when a serious error occurs. Colorama is super easy to use. Just pop up your script and add any text you want to change the color: 10)Uuid In my mind, we actually only need a few tools for programming: hashing, key/value storage, and the Globally unique Identifier universally unique ids, uuid ). Uuid is built into t

drop and you want T o Make sure everyone gets their own promo code or ID number?And if you ' re worried on running out of IDs, then fear not! The number of UUIDs you can generate are comparable to the number of atoms in the universe.import uuidprint uuid.uuid4()# e7bafa3d-274e-4b0a-b9cc-d898957b4b61 Well if you were a uuid probably would is. One) BashplotlibShameless self-promotion, is one of bashplotlib my creations. It lets you plot histograms and scatterplots using stdin. So

R Language Data Analysis series nine--by Comaple.zhangIn this section, logical regression and R language implementations, logistic regression (lr,logisticregression) is actually a generalized regression model, according to the types of dependent variables and the distribution can be divided into the common multivariate linear regression model, and logistic regression, the logistic regression is that the dependent variable is discrete and the value range is { 0,1} Two classes, if the discrete var

1) Bar chartBar charts are perhaps the most commonly used graphics, often used to show the relationship between categories (different categories on the x-axis) and values (numeric values on the y-axis). Sometimes the bar heights represent counts of cases in the data set, and sometimes they represent values in the data set (with The bar chart height represents the frequency (count) in the dataset, and sometimes represents the values in the dataset, which should be kept firmly in mind, otherwise c

(), and Dcast (), but they can "knead" the data into various shapes. Melt itself means dissolution, decomposition, and its role in a data set is actually split data, its object can be an array, a data frame or a list. > Library (RESHAPE2) > Data (airquality) > str (airquality) ' Data.frame ': 153obs. of 6 variables: $Ozone: int 41 36 12 Na 8 na ... $ solar.r:int 118 149 313 na Na 299 ... $Wind: num 7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ... $Temp: int .... $Month: int 5 5 5 5 5 5 5 5 5 5 .

scientific computing use Networkx-is an efficient software used for complex networks. pandas-This library provides high-performance, easy-to-use data structures and data analysis tools. The Business Intelligence tool (Pandas Web interface) in Open Mining-python. PYMC-MCMC Sampling Toolkit. Zipline-python's algorithmic Trading library. pydy-full name Python dynamics, assisting with dynamic modeling workflows based on NumPy, SciPy, Ipython, and Matplotlib. sympy-symbol