qplot

The dataset used in this article is the GGPLOT2 packet Diamonds dataset, which contains information about the price and quality of about 54000 diamonds. This set of data covers four "C"-carat weight (carat), which reflects the quality of diamonds, cut, color and clarity (clarity), and five physical indicators-depth (depth), diamond width (table), x, Y, Z. The following figure: Another dataset used in this article is a random sample with a capacity of 100 for the original data Set.seed (1410) #

Detailed usage of the Qplot () function:Library (GGPLOT2)# test Data set, Ggplot2 built-in diamond dataQplot (carat, price, data = diamonds)Dsmall #1. Visualize by basic classification of Color,size,shape#1.1 Simple scatter plot (using color classification, diamonds of different colors are represented by dots of different colors)Qplot (carat, price, data = dsmall, colour = color)

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 visualization analysis. Its usage is similar to the plot

Qplot means quick plot, which is part of the GGPLOT2 package, which needs to be loaded before the package is used.Qplot Parameters:Qplot (x, y = NULL, ..., data, facets = NULL, margins = FALSE, geom = "Auto", Xlim = C (Na, NA), Ylim = C (Na, na), log = "", main = null, Xlab = Deparse (Substitute (x)), Ylab = Deparse (substitute (y)), ASP = NA, stat = null, Position = NULL)Where: X and y represent the x-axis and y-axisFacets for paging:

)> Detach (Mtcars)> #par函数可实现一页多图, Color. thickness, etc...In Ggplot2:> Library (GGPLOT2)> Attach (Mtcars)> Qplot (WT,MPG)> Detach (Mtcars)Equivalent to:> Qplot (wt,mpg,data=mtcars)Equivalent to:> Ggplot (Mtcars,aes (X=wt,y=mpg)) +geom_point ()> PlotView HelpUsagePlot (x, Y, ...)ArgumentsXThe coordinates of points in the plot. Alternatively, a single plotting structure, the function or any R object with a p

uses a Photoshop-like layer design when drawing, allowing the user to build graphics step-by-step and make it easier for layers to be modified.4.6.1 Quick Draw Qplot (x, y = NULL, ..., data, facets =null, margins = False,geom = "Auto", stat = list (null), position =list (null), Xlim = C (na,na), Ylim = C (Na, na), log = "", main = null,xlab= deparse (substitute (x)), Ylab = Deparse (substitute (y)), ASP = NA) Take the Diamonds data set

Ggplot2R's Graphing toolkit, you can use very simple statements to achieve very complex and beautiful results.QplotLoad Qplot=#1. Visualize by basic classification of Color,size,shape#1.1 Simple scatter plot (using color classification, diamonds of different colors are represented by dots of different colors)#1.2. Simple scatter plots (using shape classification, different cutting methods are represented by different shapes of points)#2. Drawing diffe

The limitation of Qplot () is that it can only use one dataset and a set of graphical property mappings, and the solution to this problem is to use layers. Each layer can have its own data collection graphical property map, and additional data elements can be added to the layer through layers.A layer consists mainly of 5 parts: data, a set of graphical attribute mappings, geometric objects, statistical transformations, and position adjustments. 1. Cre

factor as an example:> Levels (Singer$voice.part) [1] "Bass 2" "Bass 1" "Tenor 2" "Tenor 1" "Alto 2" "Alto 1" " Soprano 2 "[8]" Soprano 1 "16.3 Ggplot2 BagThe Ggplot2 package provides a drawing system based on a comprehensive and coherent syntax. It makes up for the lack of consistency in creating graphics in RThe ability to create innovative and novel graphic types. The simplest way to draw in Ggplot2 is to use the Qplot () function,

Objective:Learn two variable analysis flow by exploring file PSEUDO_FACEBOOK.TSV dataKnowledge Points:1.ggplot syntax2. How to make a scatter chart3. How to optimize scatter plots4. Condition mean value5. Correlation of variables6. Sub-hubs diagram7. SmoothingBrief introduction:If you are exploring a single variable using a histogram to represent the relationship between the value and the whole, then using a scatter plot is more appropriate to explore the relationship between the two variables w

It may be because of the working environment, and both of them are quite good. Reply: There are still a few people who are proficient in both Python and R. For pythoners, the first step is to read plot(xx.pdf and obtain a csv1_read.csv file to show the data View (xx). Then, how can the R speed be very slow! However, these R newcomers do not know that the original plot can be qplot (xx), or plotly, etc.; the reading of csv should use fread, and the dis

language Features: Case studies and drawings and input and output in R (save graphics as pictures and files)5.R Language Integration development tool Rstutio installation use and R language package installation loading instructions6. Case: Installing the GGPLOT2 package and plotting scatter plots and histogram usageobject and vector vectors in 7.R8.R Central Plains Sub-vector detail and vector propertiesWhat is a function in 9.R and the definition of a function is used10. Project Combat One: Ca

Q-q plot is quantile-quantile plot. It is often used in various types of research, mainly to visually indicate the difference between observed and predicted values. In the SPSS is very tolerant to do, analysis-descriptive Statistics-q-qplot. Q-q plot is mainly used to estimate the difference between the observed and predicted values of quantitative traits. In general, the quantitative trait data we obtained are normal distribution data. In the GWAS st

A: R itself is single-threaded, how to let its multi-threaded run up, improve the speed of computing? Playing parallel computing with parallel and foreach packetsAfter reading the above article will be. Plainly, to load the parallel package, and then rewrite your own code is OK.#-----With a strength to demonstrate R how multithreaded computingFunc n = 1Raw while (x > 1) {X n = n + 1}Return (c (raw,n))}#----Library (parallel)# Use System.time to return the time required to calculateSystem.time ({

I shared a script in the r language and recently learned the script in the r language. I wonder if you have read the book "Fantastic Life". After reading it in high school, in the third year of high school, he copied the method of the great god of Liu bixifu and recorded the time overhead. I personally think it is quite useful. The script imports the following Excel file and automatically converts it into a pie chart to output the image to the local device. The code is here: Record Lib

language Features: Case studies and drawings and input and output in R (save graphics as pictures and files)5.R Language Integration development tool Rstutio installation use and R language package installation loading instructions6. Case: Installing the GGPLOT2 package and plotting scatter plots and histogram usageobject and vector vectors in 7.R8.R Central Plains Sub-vector detail and vector propertiesWhat is a function in 9.R and the definition of a function is used10. Project Combat One: Ca

Install R-base on Ubuntu:sudo apt-get install-y r-baseDownload Rstudio:Https://download1.rstudio.org/rstudio-1.0.153-amd64.debInstall GUI tool (Rstudio) for R Language:sudo Dpkg-i Rstudio-***-amd64.debSome examples for R language:***# Library (Plotrix)# slices # cc # Pie3d (slices, labels = cc, explode = 0.1)# Library (Scatterplot3d)# Scatterplot3d (Iris[,1:2])# Library (GGPLOT2)# Qplot (1:1000, Rnorm (1000))***Install R on Ubuntu

quantile-quantile plot. It is often used in various types of research, mainly to visually indicate the difference between observed and predicted values. In the SPSS is very tolerant to do, analysis-descriptive Statistics-q-qplot. Q-q plot is mainly used to estimate the difference between the observed and predicted values of quantitative traits. In general, the quantitative trait data we obtained are normal distribution data. In the GWAS study, the x

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