rpy2 ggplot2

In general, this book covers a lot of r language knowledge, but the content of the relatively trivial, it is difficult to string the whole book chapters, if it as a knowledge dictionary of the R language is very good. This book mainly covers the following, but it does not carry out in-depth discussion of these contents.the next thing to doThe output of the R detection function needs to be thoroughly understoodCombining real-life case practice with the contents of this bookR Language Programming

This function is a function under the Stringr package, it is useful to do data cleaning, presumably use to extract a string under a certain content, according to some of the rules you want, the specific use of the following: x Str_extract_all (x, "[F0-9]")[[1]][1] "F" "1" "2" > Str_extract_all (x, "[f0-9]{1,3}")[[1]][1] "F12" > Str_extract_all (x, "[f0-9]{1,2}")[[1]][1] "F1" "2" Attach some code written in peacetime Library (GGPLOT2) library (rmysql)

In the recent internship need R language analysis, for the final result needs to be shown below the diagram to show (sample image in the paper interception) Because of contact with R language soon, just start is a variety of Meng, think directly with R is not drawing such pictures, and then asked the classmate said with R language Ggplot2 expansion package, so found some tutorials on the internet to find this kind of picture can be painted, but its

packages covered in this article are: In [3]: Set.seed (1680) # Set a random seed, making the results of this article a reproducible library (DPLYR) library (ISLR) library ( cluster) library (Rtsne ) Library (GGPLOT2) Attaching package: ' Dplyr ' The following objects is masked from ' package:stats ': filter, lag the following O Bjects is masked from ' package:base ': intersect, Setdiff, setequal, union Before building a clustering mod

(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