datacamp ggplot

"""This example demonstrates the ' Ggplot ' style, which adjusts the style toemulate ggplot_ (a popular plotting package fo R r_). These settings were shamelessly stolen from [1]_ (with permission) ... [1] http://www.huyng.com/posts/sane-color-scheme-for-matplotlib/. _ggplot:http://had.co.nz/ggplot/. _r:http://www.r-project.org/"""ImportNumPy as NPImportMatplotlib.pyplot as Pltplt.style.use ('

/PREDBLOG.RMD# #载入需要的包require (XML) require (DPLYR) require (Tidyr) require (READR) require (mosaic) require (Rcurl) require (GGPLOT2) Require (lubridate) require (Rjsonio) # #数据拉取url = "http://projects.fivethirtyeight.com/2016-election-forecast/ national-polls/"Doc View all selection data: AllolldataFast VisualizationIt is necessary to simply look at the data before figuring out the proportion of the projected votes for the 2016 U.S. presidential campaign. The data set has been collated and vis

Php Chinese network (www.php.cn) provides the most comprehensive basic tutorial on programming technology, introducing HTML, CSS, Javascript, Python, Java, Ruby, C, PHP, basic knowledge of MySQL and other programming languages. At the same time, this site also provides a large number of online instances, through which you can better learn programming... Reply: many good-looking PYTHON libraries are developed and encapsulated based on matplotlib! I have used seaborn, bokeh, and

format only show hours ggplot(message,aes(x=time,y=size)) + geom_line() + labs(title="时间-流量图",y=‘size(KB)‘) + scale_x_datetime(breaks=date_breaks("1 hour"),labels= date_format("%H")) Save picture ggsave(filename=‘e:/R/时间-流量图-分.jpg‘,width=15,height=8) Statistics URL Access times graph ggplot(message)+ geom_bar(aes(x=url)) + coord_flip() + labs(x=‘url‘,y=‘count‘) Pie chart

0 reply: many good-looking PYTHON image libraries are developed and encapsulated based on matplotlib! I have used seaborn, bokeh, and ggplot databases! Seaborn is biased towards statistical plot, especially linear plot, which is easy to use and simple. The entire syntax layer of seaborn will also be much simpler, and it looks nice to draw a picture without any modification. However, the drawing method is limited and not flexible enough. Bokeh uses js

). Learning pandas you have to master is: Summarize and calculate descriptive statistics, process missing data, hierarchical indexes Clean, transform, merge, reshape, GroupBy technology Date and time data types and tools (date processing easy to fly) See blog: http://www.cnblogs.com/chaosimple/p/4153083.htmlthree. Matplotlib:The most famous drawing system in Python, many other drawings such as Seaborn (for pandas drawings) are also encapsulated by them. John Hunter, a found

The first academic paper in my life was submitted yesterday. Funny is the last change of the result also forgot to cut the picture. The boss said that my carelessness will certainly make me pour a lot of mold, I thought. But there is really no motivation when writing a paper, there is no meaning to change. I want to program every day, but I didn't do it at all.See how many books and courses you would like to see but not read (can you read them on a laptop?) ）： Java Data structure Prince

Original linkSummary: 1. Data Science Quick Start Guide for Python If you're just getting started with Python, this little meter is perfect for you. Check out this small meter and you'll get guidance on how to learn python in a progressive manner. It provides the necessary packages for Python learning and some useful learning techniques and other resources.1. Python's Data Science Quick Start GuideIf you're just getting started with Python, then this little meter is perfect for you. Check out th

Label: HTTP color OS AR for SP data 2014 ad Lm function, you can use a thread relationship to establish the development trend between two data, that is, thread fitting, and use the predict function to use the results of this development trend for data prediction. fr=lm(Height~Weight,data=hw)coef(fr) Establish a thread fitting method based on weight to predict the height. A straight line defined by intercept and slope. Visualized display: library("ggplot2")

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:SCAL

The R language draws maps, which are often used in data analysis and can achieve very good results, and this section provides examples of how to use the R language tools to draw the ideal map.Examples of this section run smoothly under the R version 2.15.3 release, and other versions are pending.The code is as follows: The first small example# load the appropriate package, read the data, and then draw. Library (maptools), library (Ggplot2), China_map"d://map//bou2_ 4p.shp", Proj4string=crs ("+p

that the average rice yield is not 150000. Apply this test to all variables, and assume that the mean value is 15000. we have: print ss.ttest_1samp(a = df, popmean = 15000) # OUTPUT(array([ -1.12817385, 1.07053437, -65.81425599, -4.564575 , 6.17156198]), array([ 2.62704721e-01, 2.87680340e-01, 4.15643528e-70, 1.83764399e-05, 2.82461897e-08])) The first array is the t statistic, and the second array is the corresponding p value. Visualization Python has many visualization modules, and the mo

For more information, see: R, Ggplot2, shiny summaryInitial pattern:Library(GGPLOT2)DT= data. Frame(a = c(2, 7, 4, ten, 1), B = C('b', 'A ', 'C', 'D', 'E'))windowsfonts(MyFont = Windowsfont ("in italics") # # Bound FontP= Ggplot (DT, AES (x =B, y =A, fill =B) + Geom_bar (stat ="Identity", alpha =0.7) + Coord_polar ()Pthe rose chart after repair:Library (ggplot2) dt = data. Frame(A = C (2,7,4,Ten,1), B = C (' B ',' A ',' C ',' D ',' E ')) windowsfonts

a true data.frame type. the Ggplot2 package specifically provides a special version of the Fortify function for geographic data to do this workUse this function to cook the X,Geom_polygon is a function of the polygon fill path, and the map is actually a variety of combinations of polygons, so with this function, it is appropriate to draw a map. mymap=ggplot (data = fortify (x)) +geom_polygon (Aes (X=LONG,Y=LAT,GROUP=ID), colour= "Black", Fill=na) +th

, and making their scatter plot plt. scatter (forest_fires ["wind"], forest_fires ["area"]) plt. show () Plt. scatter (forest_fires ['wind'], forest_fires ['region']) plt. title ('wind speed vs fire area') plt. xlabel ('wind speed when fire started') plt. ylabel ('area consumed by fire') plt. show () # Use the list data as the axis age = [5, 10, 15, 20, 25, 30] height = [25, 45, 65, 75, 75] plt. plot (age, height) plt. title ('Age vs height') plt. xlabel ('age') plt. ylabel ('height') plt. show

, self-help sampling, K-fold cross-validation and so on. Next you can use function Evalute () to evaluate the performance of multiple evaluation algorithms using the evaluation plan.2. Example AnalysisLibrary (Recommenderlab)Library (GGPLOT2)# #数据处理与数据探索性分析Data (Movielense)Image (Movielense)# get ratingsRatings.movie Summary (ratings.movie$ratings)# # Min. 1st Qu. Median Mean 3rd Qu. Max.# 1.00 3.00 4.00 3.53 4.00 5.00Ggplot (Ratings.movie, AES (x = ratings)) + Geom_histogram (fill = "Beige", co

Randnorm# #rnorm (3000) produces 3,000 positive too many distributions Randdensity# # # #dnorm (randnorm) to find its density function value Ggplot (Data.frame (x=randnorm,y=randdensity)) +aes (x=x,y=y) +geom_point () +labs (x= "Random Normal varables", y= " Randdensity ") # #将这个你太分分布数以及对应的密度函数值作为x, y-axis values, and draw point graphs P# #变量p作为该段代码的引用 Neg1seq# #生成一段序列, the starting value is min (randnorm), the end is to=-1, step 0.1 Lessthanneg1# #将序

Label:According to the characteristics of various industries, a variety of clustering algorithms are proposed, which are divided into several categories: hierarchy, Division, density, graph theory, grid and model. Among them, the density-based clustering algorithm is the most representative in Dbscan. Assuming a set of data, the R code of the generated data is as follows X1 0, Pi,length. out= -) Y10.1*rnorm ( -) X21.5+ SEQ (0, Pi,length. out= -) Y20.1*rnorm ( -) DataData.frame (C (X1,X2), C (y1

([ -1.12817385, 1.07053437, -65.81425599, -4.564575 , 6.17156198]), array([ 2.62704721e-01, 2.87680340e-01, 4.15643528e-70, 1.83764399e-05, 2.82461897e-08])) The first array is the t statistic, and the second array is the corresponding P value. Visualization Python has many visualization modules, and the most popular one is the matpalotlib library. We can also select the bokeh and seaborn modules. In my previous blog post, I have explained the function of the box map module in the matplotlib l

([2.62704721e-01, 2.87680340e-01, 4.15643528e-70, 1.83764399e-05, 2.82461897e-08])) The first array is the T statistic, and the second array is the corresponding P-value. Visualization of There are many visual modules in Python, the most popular being the Matpalotlib library. With a little mention, we can also choose the bokeh and Seaborn modules. In the previous blog post, I have explained the function of the box Whisker diagram module in the Matplotlib library. # Import the module for Plot