R 代码积累不定期更新
1.阶乘、递归、reduce、sprintf
#NO.1
# 阶乘函数
fact <- function(n){
if(n==0) return(1) #基例在这
else return(n*fact(n-1))
} #1+2!+3!+...+20!的和
#测试 Reduce('+', lapply(1:3,fact)) 结果是9
Reduce('+', lapply(1:20,fact)) #NO.2
#判断101-200之间有多少个素数，并输出所有素数
is.prime <- function(num) {
if (num == 2) {
TRUE
} else if (any(num %% 2:(num-1) == 0)) {
FALSE
} else {
TRUE
}
}
#101共有多少个素数
Reduce('+', lapply(101:200,is.prime))
#打印出每一个素数
for(i in 101:200){
if(!is.prime(i)) next
print(sprintf('%d 是素数 %s',i,is.prime(i)))
}
2.MD5加密卡号
library(magrittr)
library(digest)
library(data.table)
library(readxl)
library(lubridate) dat=read_excel('data_union.xlsx',sheet=4)
dim(dat)
get_len <- function(a){
return(paste0(nchar(a),a))
}
dat$card_len=lapply(dat$bank_card, get_len)%>%unlist()
dat$card_md5 <- lapply(dat$card_len, digest, algo="md5",serialize=F)%>%unlist()
dat$card_md5_upper <- toupper(dat$card_md5)
write.table(
dat[,c('card_md5_upper')]
,'yqb_card_md5.txt',row.names = F
,col.names=F
,quote = F) #ym_apply
dat$ym_apply=dat$time %m-% months(1)%>%
format("%Y%m")%>%as.numeric() #write out card_md5 and ym_apply
write.table(
dat[c('card_md5_upper','ym_apply')]
,sep=','
,'yqb_card_ym_md5.txt',row.names = F
,col.names=F
,quote = F) unique(dat$ym_apply)
3.时间函数
https://cran.r-project.org/web/packages/lubridate/vignettes/lubridate.html
4.随机森林可视化
library(randomForest)
library(kernlab) # spam数据集 来源于这个 包
library(magrittr)
library(caret)
#setwd('...')
#data <- read.csv('...') data(spam) #加载数据集
# spam 是一个数据集，一条记录代表一封邮件包含哪些特殊词
# ，这封邮件是否是垃圾邮件（y变量）等等一些变量
View(spam)
table(spam$type) #看一下Y变量的分布，是否存在不均衡（imbalanced data）
#type 是y变量 也可以是0,1，但注意，如果你的数据集是 df, 要预先 将y变量转换为factor ,df$y <- as.factor(df$y)
set.seed(2016) #为使模型可再现，这里预先设定随机种子，因为随机森林的 每棵树对行是进行随机有放回的抽样的 rf <- randomForest(type~.
,data=spam
,importance=TRUE #保留变量的重要性 TURE的时候,可通过rf$importance 查看
,proximity=FALSE #在n棵树种，一个矩阵任意两条记录 落在同一个叶子节点的概率，可以表示两条记录的相似程度
,ntree=500 #种多少棵树
,do.trace=20 #每20条显示 误分率
,na.action=na.omit#一行中只要存在一个NA,这条记录就删掉
,strata=spam$type #按照Y变量中（0,1的比例，进行上下采样，对占比少的用oversampling，对占比多的用downsampling）
,sampsize=c(1500,1500) #对0,1采样的个数分别是多少，都是有放回的
,mtry=7 #每一棵决策树，选取几个feature?，对于classification 一般是feature总数的开平方（这个是default）
,keep.forest=FALSE) #保留每一棵数 rf$confusion #混淆矩阵 ##变量重要性,如果你的数据是有上千个变量，可以根据变量的重要性对数据进行降维
par(mfrow = c(2,2))
for(i in 1:4){
plot(sort(rf$importance[,i],decreasing =TRUE)%>%head(20)
,type='h'
,main=paste0(colnames(rf$importance)[i])
,xlab='variable'
,ylab='importance') text(sort(rf$importance[,i],decreasing =TRUE)
,labels=names(sort(rf$importance[,i]))%>%head(20)
,pos=1
,cex=0.9)
} ## 下面画ROC 曲线，计算AUC
library(ROCR)
predictions=as.vector(rf$votes[,2])
pred=prediction(predictions,spam$type) perf_AUC=performance(pred,"auc") #Calculate the AUC value
[email protected][[1]] perf_ROC=performance(pred,"tpr","fpr") #plot the actual ROC curve
plot(perf_ROC, main="ROC plot")
text(0.5,0.5,paste("AUC = ",format(AUC, digits=5, scientific=FALSE))) #cutoff accuracy
perf <- performance(pred, measure="acc", x.measure="cutoff")
# Get the cutoff for the best accuracy
bestAccInd <- which.max(perf@"y.values"[[1]])
bestMsg <- paste("best accuracy=", perf@"y.values"[[1]][bestAccInd],
" at cutoff=", round(perf@"x.values"[[1]][bestAccInd], 4))
plot(perf, sub=bestMsg)
abline(v=round(perf@"x.values"[[1]][bestAccInd], 4)) # calculate the confusion matrix and plot
cm <- confusionMatrix(rf$predicted, reference = spam$type)
draw_confusion_matrix(cm) #confusion matrix visualization
draw_confusion_matrix <- function(cm) { layout(matrix(c(1,1,2)))
par(mar=c(2,2,2,2))
plot(c(100, 345), c(300, 450), type = "n", xlab="", ylab="", xaxt='n', yaxt='n')
title('CONFUSION MATRIX', cex.main=2) # create the matrix
rect(150, 430, 240, 370, col='#3F97D0')
text(195, 435, rf$classes[1], cex=1.2)
rect(250, 430, 340, 370, col='#F7AD50')
text(295, 435, rf$classes[2], cex=1.2)
text(125, 370, 'Predicted', cex=1.3, srt=90, font=2)
text(245, 450, 'Actual', cex=1.3, font=2)
rect(150, 305, 240, 365, col='#F7AD50')
rect(250, 305, 340, 365, col='#3F97D0')
text(140, 400, rf$classes[1], cex=1.2, srt=90)
text(140, 335, rf$classes[2], cex=1.2, srt=90) # add in the cm results
res <- as.numeric(cm$table)
text(195, 400, res[1], cex=1.6, font=2, col='white')
text(195, 335, res[2], cex=1.6, font=2, col='white')
text(295, 400, res[3], cex=1.6, font=2, col='white')
text(295, 335, res[4], cex=1.6, font=2, col='white') # add in the specifics
plot(c(100, 0), c(100, 0), type = "n", xlab="", ylab="", main = "DETAILS", xaxt='n', yaxt='n')
text(10, 85, names(cm$byClass[1]), cex=1.2, font=2)
text(10, 70, round(as.numeric(cm$byClass[1]), 3), cex=1.2)
text(30, 85, names(cm$byClass[2]), cex=1.2, font=2)
text(30, 70, round(as.numeric(cm$byClass[2]), 3), cex=1.2)
text(50, 85, names(cm$byClass[5]), cex=1.2, font=2)
text(50, 70, round(as.numeric(cm$byClass[5]), 3), cex=1.2)
text(70, 85, names(cm$byClass[6]), cex=1.2, font=2)
text(70, 70, round(as.numeric(cm$byClass[6]), 3), cex=1.2)
text(90, 85, names(cm$byClass[7]), cex=1.2, font=2)
text(90, 70, round(as.numeric(cm$byClass[7]), 3), cex=1.2) # add in the accuracy information
text(30, 35, names(cm$overall[1]), cex=1.5, font=2)
text(30, 20, round(as.numeric(cm$overall[1]), 3), cex=1.4)
text(70, 35, names(cm$overall[2]), cex=1.5, font=2)
text(70, 20, round(as.numeric(cm$overall[2]), 3), cex=1.4)
}
5.一个多线程 爬虫
## 99健康网 医院抓取 ## #step1:by city
url_0='http://yyk.99.com.cn/city.html'
content_0=url_0%>%read_html()
city_nm=c(
content_0%>%html_nodes('.cityarea li a')%>%html_text(trim=T)
,content_0%>%html_nodes('.cityarea .background li a')%>%html_text(trim=T)
)
city_url=c(
content_0%>%html_nodes('.cityarea li a')%>%html_attr('href')%>%paste0('http://yyk.99.com.cn',.)
,content_0%>%html_nodes('.cityarea .background li a')%>%html_attr('href')%>%paste0('http://yyk.99.com.cn',.)
)
length(city_nm)
length(city_url) #step2:get hospital by city
get_hospital_1 <- function(i){
# i=1
content_1=city_url[i]%>%read_html(encoding='gbk')
hospital_nm=content_1%>%html_nodes('.tablist li span')%>%html_text(trim=T)
hospital_url=content_1%>%html_nodes('.tablist li a')%>%html_attr('href')%>%paste0(.,'jianjie.html')
return(data.frame(
stringsAsFactors = F
,city_nm=rep(city_nm[i],length(hospital_nm))
,hospital_nm=hospital_nm
,hospital_url=hospital_url
))
}
system.time({
x <- 301:length(city_url)
res_99jk_3 <- do.call('rbind',lapply(x,get_hospital_1))
})
write.table(res_99jk_1,'res_99jk_1.csv',row.names = F,sep='\t')
write.table(res_99jk_2,'res_99jk_2.csv',row.names = F,sep='\t')
write.table(res_99jk_3,'res_99jk_3.csv',row.names = F,sep='\t')
res_99jk <- rbind(res_99jk_1
,res_99jk_2
,res_99jk_3) #step3 get all fileds level beds_cnt...
res_99jk <- read.table('res_99jk.csv',header = T)
hospital_url_pool=res_99jk$hospital_url%>%unique()
get_detail <- function(hospital_url){
tryCatch({
res=hospital_url%>%read_html(encoding='gbk')%>%html_nodes('.tdr')%>%html_text(trim=T)
df=data.frame(stringsAsFactors = F
,hospital_url=hospital_url
,nm=res[1]
,addr=res[2]
,hp_admin=res[3]
,start_dt=res[4]
,type=res[5]
,level=res[6]
,section_cnt=res[7]
,doc_cnt=res[8]
,bed_cnt=res[9]
,traffic=res[10]
,insurance=res[11]
,web_site=res[12]
,phone=res[13]
,addr=res[14]
,postcode=res[15])
},error = function(e) {
rm(df)
df=data.frame(stringsAsFactors = F
,hospital_url=hospital_url
,nm=NA
,addr=NA
,hp_admin=NA
,start_dt=NA
,type=NA
,level=NA
,section_cnt=NA
,doc_cnt=NA
,bed_cnt=NA
,traffic=NA
,insurance=NA
,web_site=NA
,phone=NA
,addr=NA
,postcode=NA)
})
return(df)
} op <- pboptions(type = "timer")
x <- hospital_url_pool[1:5000]
system.time(rdf_99jk_1 <- do.call(rbind.data.frame,pblapply(x,get_detail)))
pboptions(op)
write.table(rdf_99jk_1,'rdf_99jk_1_5000.csv',row.names = F,sep='\t') ##多线程爬虫
pkg <- c('RMySQL','DBI','xml2','rvest','magrittr','xml2','stringr','httpuv','R2HTML','pbapply','curl')
for(i in pkg){
if(!i %in% installed.packages()[,1]) (install.packages(i))
} library(magrittr)
library(bitops)
library(rvest)
library(stringr)
library(DBI)
library(RCurl)
library(curl)
library(sqldf)
library(gdata)
library(xml2)
library(pbapply)
library(parallel) res_99jk <- read.table('res_99jk.csv',header = T,stringsAsFactors = F)
hospital_url_pool=res_99jk$hospital_url%>%unique()
get_detail <- function(hospital_url){
tryCatch({
res=hospital_url%>%read_html(encoding='gbk')%>%html_nodes('.tdr')%>%html_text(trim=T)
df=data.frame(stringsAsFactors = F
,hospital_url=hospital_url
,nm=res[1]
,addr=res[2]
,hp_admin=res[3]
,start_dt=res[4]
,type=res[5]
,level=res[6]
,section_cnt=res[7]
,doc_cnt=res[8]
,bed_cnt=res[9]
,traffic=res[10]
,insurance=res[11]
,web_site=res[12]
,phone=res[13]
,addr=res[14]
,postcode=res[15])
},error = function(e) {
rm(df)
df=data.frame(stringsAsFactors = F
,hospital_url=hospital_url
,nm=NA
,addr=NA
,hp_admin=NA
,start_dt=NA
,type=NA
,level=NA
,section_cnt=NA
,doc_cnt=NA
,bed_cnt=NA
,traffic=NA
,insurance=NA
,web_site=NA
,phone=NA
,addr=NA
,postcode=NA)
})
return(df)
} # 用system.time来返回计算所需时间
system.time({
x <- hospital_url_pool[5001:length(hospital_url_pool)]
cl <- makeCluster(4) # 初始化四核心集群
clusterEvalQ(cl, library(magrittr))
clusterEvalQ(cl, library(rvest))
clusterEvalQ(cl, library(RCurl))
clusterEvalQ(cl, library(curl))
clusterEvalQ(cl, library(xml2))
results <- parLapply(cl,x,get_detail) # lapply的并行版本
res.df <- do.call('rbind',results) # 整合结果
stopCluster(cl) # 关闭集群
}) # res2=do.call(rbind.data.frame,results)
res2=data.frame(stringsAsFactors = F)
for(i in 1:length(results)){
print(paste0('this is ',i))
if (!identical(dim(results[[i]]),dim(results[[1]]))) next
res2=rbind(res2,results[[i]])
}