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<!DOCTYPE html>
<html>
<head>
<title>R 语言中级培训</title>
<meta charset="utf-8">
<meta name="description" content="R 语言中级培训">
<meta name="author" content="Copyright @Transwarp Inc. | All Rights Reserved">
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src="./libraries/frameworks/io2012/js/require-1.0.8.min.js">
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</head>
<body style="opacity: 0">
<slides class="layout-widescreen">
<!-- LOGO SLIDE -->
<slide class="title-slide segue nobackground">
<hgroup class="auto-fadein">
<h1>R 语言中级培训</h1>
<h2>星环数据挖掘组_daitao.xing</h2>
<p>Copyright @Transwarp Inc. | All Rights Reserved<br/></p>
</hgroup>
<article></article>
</slide>
<!-- SLIDES -->
<slide class="" id="slide-1" style="background:;">
<article data-timings="">
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-2" style="background:;">
<hgroup>
<h2>目录</h2>
</hgroup>
<article data-timings="">
<ul>
<li>R的核心</li>
<li>替代For的Apply家族函数</li>
<li>管道操作</li>
<li>数据获取</li>
<li>R高效数据处理</li>
<li>其他有关数据科学的topic </li>
<li>深入了解数据:ggplot2</li>
</ul>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="segue dark" id="slide-3" style="background:;">
<hgroup>
<h2>R的核心:函数式编程思想</h2>
</hgroup>
<article data-timings="">
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-4" style="background:;">
<hgroup>
<h2>FP</h2>
</hgroup>
<article data-timings="">
<pre><code class="r">1+2
</code></pre>
<pre><code>## [1] 3
</code></pre>
<pre><code class="r">'+'(1,2)
</code></pre>
<pre><code>## [1] 3
</code></pre>
<pre><code class="r">funs <- c(lm, median, sd, mad, IQR)
</code></pre>
<ul>
<li>Anonymous functions </li>
<li>Closures</li>
<li>List of functions</li>
</ul>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-5" style="background:;">
<article data-timings="">
<pre><code class="r">summary <- function(x) {
c(mean(x, na.rm = TRUE),
median(x, na.rm = TRUE),
sd(x, na.rm = TRUE),
mad(x, na.rm = TRUE),
IQR(x, na.rm = TRUE))
}
summary <- function(x) {
funs <- c(mean, median, sd, mad, IQR)
lapply(funs, function(f) f(x, na.rm = TRUE))
}
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-6" style="background:;">
<hgroup>
<h2>闭包</h2>
</hgroup>
<article data-timings="">
<pre><code class="r">power <- function(exponent) {
function(x) {
x ^ exponent
}
}
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-7" style="background:;">
<hgroup>
<h2>Lists of functions</h2>
</hgroup>
<article data-timings="">
<pre><code class="r">compute_mean <- list(
base = function(x) mean(x),
sum = function(x) sum(x) / length(x),
manual = function(x) {
total <- 0
n <- length(x)
for (i in seq_along(x)) {
total <- total + x[i] / n
}
total
}
)
compute_mean$sum()
compute_mean[["sum"]]()
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-8" style="background:;">
<hgroup>
<h2>FP的效率</h2>
</hgroup>
<article data-timings="">
<pre><code class="r">x <- c()
system.time({
for(i in 1:100000){
if(i %% 2 ==0)
x <- c(x,i)
}
})
</code></pre>
<pre><code>## user system elapsed
## 4.475 0.608 5.089
</code></pre>
<pre><code class="r">system.time({
x <- 1:100000
x[x %% 2 == 0]
})
</code></pre>
<pre><code>## user system elapsed
## 0.002 0.001 0.003
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="segue dark" id="slide-9" style="background:;">
<hgroup>
<h2>R中的管道操作</h2>
</hgroup>
<article data-timings="">
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-10" style="background:;">
<hgroup>
<h2>管道操作</h2>
</hgroup>
<article data-timings="">
<ul>
<li>shell |</li>
<li>magrittr 和 pipeR(renkun)</li>
<li>上一步输出为下一步的输入</li>
<li>比管道更加灵活（主动判断应该填入的位置）</li>
<li>%>% 将结果输送到函数的第一个参数</li>
<li>%>>% 将结果输送到表达式中.的位置</li>
<li>recharts</li>
</ul>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-11" style="background:;">
<hgroup>
<h2>自定义的管道</h2>
</hgroup>
<article data-timings="">
<pre><code class="r">`%^_^%` <- function(from,to) {
cat(paste(from,"smiles to",to))
}
"Ken" %^_^% "Jenny"
</code></pre>
<pre><code>## Ken smiles to Jenny
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-12" style="background:;">
<article data-timings="">
<pre><code class="r">library(magrittr)
rnorm(10000,mean=10,sd=1) %>>%
sample(.,size=length(.)*0.1,replace=FALSE) %>%
log %>%
diff %>>%
plot(.,col="red",type="l",
main=sprintf("length: %d",length(.)))
</code></pre>
<p><img src="assets/fig/unnamed-chunk-8-1.png" alt="plot of chunk unnamed-chunk-8"> </p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="segue dark" id="slide-13" style="background:;">
<hgroup>
<h2>R apply家族</h2>
</hgroup>
<article data-timings="">
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-14" style="background:;">
<hgroup>
<h2>替代for的高效函数</h2>
</hgroup>
<article data-timings="">
<ul>
<li>apply</li>
<li>mapply</li>
<li>lapply</li>
<li>sapply</li>
</ul>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-15" style="background:;">
<hgroup>
<h2>apply(X,margin,FUN)</h2>
</hgroup>
<article data-timings="">
<p>对对象的每一个部分施加函数</p>
<pre><code class="r">apply(iris[,1:3],2,max)
</code></pre>
<pre><code>## Sepal.Length Sepal.Width Petal.Length
## 7.9 4.4 6.9
</code></pre>
<p>也可以对高维数组操作</p>
<pre><code class="r">x <- 1:27
dim(x) <- c(3,3,3)
apply(x, c(1,2), FUN = paste,collapse =",")
</code></pre>
<pre><code>## [,1] [,2] [,3]
## [1,] "1,10,19" "4,13,22" "7,16,25"
## [2,] "2,11,20" "5,14,23" "8,17,26"
## [3,] "3,12,21" "6,15,24" "9,18,27"
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-16" style="background:;">
<hgroup>
<h2>lapply(X,FUN)和sapply(X,FUN)</h2>
</hgroup>
<article data-timings="">
<p>对对象的每一个元素进行操作(当对象是DF时,逐列进行)</p>
<pre><code class="r">temp <- iris[,1:3]
head(sapply(temp,as.character))
</code></pre>
<pre><code>## Sepal.Length Sepal.Width Petal.Length
## [1,] "5.1" "3.5" "1.4"
## [2,] "4.9" "3" "1.4"
## [3,] "4.7" "3.2" "1.3"
## [4,] "4.6" "3.1" "1.5"
## [5,] "5" "3.6" "1.4"
## [6,] "5.4" "3.9" "1.7"
</code></pre>
<p>借助高效的管道函数，我们可以构造出更灵活的用法</p>
<pre><code class="r">sapply(1:3, . %>% seq_len %>% sum)
</code></pre>
<pre><code>## [1] 1 3 6
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-17" style="background:;">
<article data-timings="">
<pre><code class="r">funs2 <- list(
sum = function(x, ...) sum(x, ..., na.rm = TRUE),
mean = function(x, ...) mean(x, ..., na.rm = TRUE),
median = function(x, ...) median(x, ..., na.rm = TRUE)
)
lapply(funs2, function(f) f(x))
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-18" style="background:;">
<hgroup>
<h2>Mapply(FUN,....,MoreArg = ,....)</h2>
</hgroup>
<article data-timings="">
<ul>
<li>对多个对象逐个元素进行操作(比如对DF中的多列同时操作)</li>
</ul>
<pre><code class="r">mapply(paste,
1:5,letters[1:5],LETTERS[1:5],
MoreArgs = list(sep='-'))
</code></pre>
<pre><code>## [1] "1-a-A" "2-b-B" "3-c-C" "4-d-D" "5-e-E"
</code></pre>
<ul>
<li>当函数FUN需要多个参数输入时</li>
</ul>
<pre><code class="r">test <- c("0","01","002")
res <- mapply(function(x, y) paste0(rep(x, y), collapse = ""), 0, 3- nchar(test))
paste0(res,test)
</code></pre>
<pre><code>## [1] "000" "001" "002"
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-19" style="background:;">
<hgroup>
<h2>rollapply</h2>
</hgroup>
<article data-timings="">
<pre><code class="r">library(zoo)
</code></pre>
<pre><code>##
## Attaching package: 'zoo'
##
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
</code></pre>
<pre><code class="r">z <- rnorm(6)
rollapply(z, 2, sum)
</code></pre>
<pre><code>## [1] 0.8225873 2.0785559 -0.6813391 -1.9719372 -0.4199782
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-20" style="background:;">
<hgroup>
<h2>案例1：bootstrap 抽样</h2>
</hgroup>
<article data-timings="">
<pre><code class="r">boot_lm <- function(formula,data,...){
function(){
lm(formula = formula,
data = data[sample(nrow(data),replace = T),],...)
}
}
iris_boot <- boot_lm(Sepal.Length ~ Petal.Length,iris)
bstrap <- sapply(X= 1:1000,
FUN = function(x) iris_boot()$coef)
apply(bstrap,MARGIN = 1,FUN = quantile,prob =c(0.025,0.5,0.975))
</code></pre>
<pre><code>## (Intercept) Petal.Length
## 2.5% 4.163022 0.3718770
## 50% 4.305461 0.4095615
## 97.5% 4.446607 0.4439611
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-21" style="background:;">
<hgroup>
<h2>案例2：担保链分析中的递归</h2>
</hgroup>
<article data-timings="">
<pre><code class="r">findCon = function(uid,friends.connected,friends.whole){
tmp=c()
for(i in friends.connected){
for(j in 1:length(i)){
tmp1 = unique(rbind(friends.whole[friends.whole$to == i[j],],friends.whole[friends.whole$from == i[j],]))
tmp = unique(rbind(tmp,tmp1))
}
}
if(dim(unique(rbind(tmp,friends.connected)))[1] == dim(friends.connected)[1]){
return(friends.connected)
}else {
friends.connected=unique(rbind(tmp,friends.connected))
findCon(uid, friends.connected,friends.whole)
}
}
uid="20111214000138"
system.time({
friends.connected = unique(rbind(friends.whole[friends.whole$to ==uid,],friends.whole[friends.whole$from == uid,]))
friends.1=findCon(uid,friends.connected,friends.whole)
friends.1=friends.1[complete.cases(friends.1),]
})
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-22" style="background:;">
<article data-timings="">
<pre><code class="r">friends.whole<-read.table("data.csv",header=T,sep=",",col.names=c("from","to"))
findCon1 = function(friends.connected,friends.whole){
index <- 1:nrow(friends.whole)
len1 <- nrow(as.data.frame(friends.connected))
index_target <- unique(unlist(as.list((friends.connected))))
tmp <- sapply(friends.whole,function(x,y) y[x %in% index_target],index) %>%
unlist() %>%
unique()
friends.connected <- friends.whole[tmp,]
if(nrow(friends.connected) == len1){
return(friends.connected)
}else {
findCon1(friends.connected,friends.whole)
}
}
uid="20111214000138"
system.time({friends.1=findCon1(uid,friends.whole) %>%
unique()
})
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-23" style="background:;">
<article data-timings="">
<p><img src="./assets/fig/xiaoguo.png" alt="效果对比"></p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="segue dark" id="slide-24" style="background:;">
<hgroup>
<h2>R的数据获取，web scraping</h2>
</hgroup>
<article data-timings="">
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-25" style="background:;">
<article data-timings="">
<ul>
<li>XML package 结构化网页数据抓取</li>
<li>rvest package(Hadley god) （非结构化网页数据抓取）</li>
</ul>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-26" style="background:;">
<hgroup>
<h2>结构化网页数据抓取:XML package</h2>
</hgroup>
<article data-timings="">
<pre><code class="r">library(XML)
url <- 'http://www.basketball-reference.com/teams/NYK/2015_games.html'
tables <- readHTMLTable(url,
stringsAsFactors = FALSE,
header=F)
data <- tables[[1]]
head(data,2)
</code></pre>
<pre><code>## V1 V2 V3 V4 V5 V6 V7 V8 V9
## 1 1 Wed, Oct 29, 2014 8:00p EST Box Score Chicago Bulls L
## 2 2 Thu, Oct 30, 2014 8:00p EST Box Score @ Cleveland Cavaliers W
## V10 V11 V12 V13 V14 V15
## 1 80 104 0 1 L 1
## 2 95 90 1 1 W 1
</code></pre>
<p>查看网页上的超链接</p>
<pre><code class="r">getHTMLLinks(url)
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-27" style="background:;">
<hgroup>
<h2>非结构化数据的获取:rvest package</h2>
</hgroup>
<article data-timings="">
<p><a href="http://www.w3schools.com/cssref/css_selectors.asp">css协议</a></p>
<pre><code class="r">library(rvest)
freak <- html_session("http://torrentfreak.com/top-10-most-pirated-movies-of-the-week-130304/")
freak %>%
html_nodes(".widg-topcomments-post-title") %>%
html_text() %>% .[1:2]
</code></pre>
<pre><code>## [1] "Transmission Releases Long-Awaited BitTorrent Client For Windows"
## [2] "Pirated ‘Star Wars: The Force Awakens’ Blu-Ray Leaks Online"
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="segue dark" id="slide-28" style="background:;">
<hgroup>
<h2>特征工程和数据预处理</h2>
</hgroup>
<article data-timings="">
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-29" style="background:;">
<hgroup>
<h2>reshape2</h2>
</hgroup>
<article data-timings="">
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-30" style="background:;">
<hgroup>
<h2>数据的两种形状</h2>
</hgroup>
<article data-timings="">
<p>统计中待分析的数据框通常有两种形式</p>
<ul>
<li>长型数据（堆叠数据），长型数据是各变量取值在一列中，而对应的变量名在另一列。</li>
<li>宽型数据（非堆叠数据），宽型数据一般是各变量取值类型一致，而变量以不同列的形式构成。</li>
</ul>
<p>例如iris的前四列子集即是一个典型的宽型数据。例如下面将宽型数据转为长型数据：</p>
<pre><code class="r">data_w <- iris[,1:4]
data_l <- stack(data_w)
data_w <- unstack(data_l)
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-31" style="background:;">
<hgroup>
<h2>数据的两种形状</h2>
</hgroup>
<article data-timings="">
<p>只要在一列中存在分类变量，都可以将其看作是长型数据。在上例中iris的前四列可以看作是宽型数据，但最后两列可以看作是一个长型数据。可以根据Species变量将数据转为宽型。并得到各花种类的平均值。</p>
<pre><code class="r">subdata <- iris[,4:5]
data_w <- unstack(subdata)
colMeans(data_w)
</code></pre>
<pre><code>## setosa versicolor virginica
## 0.246 1.326 2.026
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-32" style="background:;">
<hgroup>
<h2>数据重塑计算</h2>
</hgroup>
<article data-timings="">
<p>在实践中这种单纯的长宽格式互转并不多见，因为我们并不是需要不同的数据格式，而需要不同格式下的分析结果。在上例中我们先转换数据格式再计算分析结果，而更常见的是一步直接得到分析结果。此时我们需要的是更为强大的reshape2包。</p>
<pre><code class="r">library(reshape2)
dcast(data=subdata, # 分析对象
formula=Species~., # 数据分组的方式
value.var='Petal.Width', # 要计算的数值对象
fun=mean) # 计算用函数名
</code></pre>
<pre><code>## Species .
## 1 setosa 0.246
## 2 versicolor 1.326
## 3 virginica 2.026
</code></pre>
<p>dcast的思路和aggregate很相似，都是根据变量切分数据，再对分组后的数据进行计算，但dcast的输出格式和功能在多维情况下要方便很多。</p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-33" style="background:;">
<hgroup>
<h2>数据重塑计算</h2>
</hgroup>
<article data-timings="">
<p>即melt函数，将一个宽型数据融合成一个长型数据。例如我们将iris数据集进行融合。</p>
<pre><code class="r">iris_long <- melt(data=iris, # 要融合的对象
id='Species') # 哪些变量不参与到融合中
</code></pre>
<p>一个纯粹的长型数据，只包含一个数值变量，其它均为分类变量。而一个纯粹的宽型数据，则不包含分类变量，均为数值变量。而现实中你遇到要处理的数据，则多半是二者的混杂，正如iris数据集那样。</p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-34" style="background:;">
<hgroup>
<h2>数据重塑计算</h2>
</hgroup>
<article data-timings="">
<p>melt和dcast正如同是铁匠的两种得力工具，melt可以看作是炼炉，负责融合数据，成为一个纯粹的长型。而dcast则可以看作是铁锤，负责重铸数据，使之成为需要的格式，同时加以分析。下面的例子就是将之前生成的数据进行汇总计算</p>
<pre><code class="r">dcast(data=iris_long,
formula=Species~variable,
value.var='value',fun=mean)
</code></pre>
<pre><code>## Species Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 setosa 5.006 3.428 1.462 0.246
## 2 versicolor 5.936 2.770 4.260 1.326
## 3 virginica 6.588 2.974 5.552 2.026
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-35" style="background:;">
<hgroup>
<h2>小练习</h2>
</hgroup>
<article data-timings="">
<p>tips数据集练习，它是一个餐厅侍者收集的关于小费的数据，其中包含了七个变量，包括总费用、付小费的金额、付款者性别、是否吸烟、日期、日间、顾客人数。计算不同性别顾客是否会支付不同的小费比例。则可以按sex变量汇集数据。</p>
<pre><code class="r">dcast(tips,sex~.,value.var='tip',fun=mean)
</code></pre>
<pre><code>## sex .
## 1 Female 2.833448
## 2 Male 3.089618
</code></pre>
<p>又或者，按sex和size变量划分数据，分别计算小费金额，可以观察到用餐人数越多时，小费相应给的越多，而且男性顾客一般会比女性顾客大方一点。</p>
<pre><code class="r">dcast(tips,sex~size,value.var='tip',fun=mean)
</code></pre>
<pre><code>## sex 1 2 3 4 5 6
## 1 Female 1.276667 2.528448 3.250000 4.021111 5.14 4.60
## 2 Male 1.920000 2.614184 3.476667 4.172143 3.75 5.85
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-36" style="background:;">
<hgroup>
<h2>dcast函数的使用前提</h2>
</hgroup>
<article data-timings="">
<ul>
<li>数据中已经存在分类变量，例如sex或者smoke</li>
<li>根据分类变量划分数据</li>
<li>再计算某个数值变量的指标</li>
</ul>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-37" style="background:;">
<hgroup>
<h2>更复杂的需求</h2>
</hgroup>
<article data-timings="">
<p>如果我们想同时计算出不同性别顾客的小费和总费用。但现有的数据集中并没有这种分类变量，怎么处理呢？</p>
<p>一种是笨一点的方法，将前面用过的方法用两次，然后合并这两个结果。但这种方法在多变量情况下并不好。</p>
<pre><code class="r">dcast(tips,sex~.,value.var='tip',fun=mean)
</code></pre>
<pre><code>## sex .
## 1 Female 2.833448
## 2 Male 3.089618
</code></pre>
<pre><code class="r">dcast(tips,sex~.,value.var='total_bill',fun=mean)
</code></pre>
<pre><code>## sex .
## 1 Female 18.05690
## 2 Male 20.74408
</code></pre>
<p>另一种推荐的方法就是使用前面提到的melt函数，先将数据融合成纯粹的长型数据，再用dcast重铸。</p>
<pre><code class="r">tips_melt <- melt(data = tips, id.vars=c('sex','smoker','time','size','day'))
dcast(data = tips_melt, sex ~ variable, value.var='value',fun= mean)
</code></pre>
<pre><code>## sex total_bill tip
## 1 Female 18.05690 2.833448
## 2 Male 20.74408 3.089618
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-38" style="background:;">
<hgroup>
<h2>更复杂的需求</h2>
</hgroup>
<article data-timings="">
<p>要同时考虑不同性别和吸烟习惯的顾客给小费的相对例。</p>
<pre><code class="r">tips_mean <- dcast(data = tips_melt, sex+ smoker~ variable, fun= mean)
tips_mean$rate <- with(tips_mean,tip/total_bill)
tips_mean
</code></pre>
<pre><code>## sex smoker total_bill tip rate
## 1 Female No 18.10519 2.773519 0.1531892
## 2 Female Yes 17.97788 2.931515 0.1630623
## 3 Male No 19.79124 3.113402 0.1573122
## 4 Male Yes 22.28450 3.051167 0.1369188
</code></pre>
<p>在dcast函数中的公式同时考虑到了三个分类变量，在第二步计算了小费相对于总餐费的比率，可以清楚的看到，吸烟的女性顾客相对是最大方的，而吸烟的男性则是最小气的。</p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-39" style="background:;">
<hgroup>
<h2>时间相关数据的类别</h2>
</hgroup>
<article data-timings="">
<ul>
<li>时间类对象，仅包含日期和时间信息的数据</li>
<li>时间序列类对象，在一个普通的数据对象上附加了时间戳的数据</li>
</ul>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-40" style="background:;">
<hgroup>
<h2>时间类对象</h2>
</hgroup>
<article data-timings="">
<ul>
<li>简单的Date类型，只包含日期而不包含时钟信息</li>
<li>复杂的POSIXct类型。不仅包括日期还包括了时钟和时区信息。</li>
</ul>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-41" style="background:;">
<hgroup>
<h2>Date类型</h2>
</hgroup>
<article data-timings="">
<p>数据量少的情况下，可以手工输入为字符串格式，然后转为Date类型，数据量多的话应从外部文件输入，再转为Date格式，两种方式都需要as.Date函数。</p>
<pre><code class="r">date1 <- '1989-05-04'
date1 <- as.Date(date1)
class(date1)
</code></pre>
<pre><code>## [1] "Date"
</code></pre>
<pre><code class="r">date1 <- '05/04/1989'
date1 <- as.Date(date1,format='%m/%d/%Y')
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-42" style="background:;">
<hgroup>
<h2>Date类型</h2>
</hgroup>
<article data-timings="">
<p>通常的输入格式是用短横隔开，如果是其它格式，则在as.Date函数内需要有format参数来确定。Date类数据可以进行常规的加减和比较。</p>
<pre><code class="r">date2 <- date1 + 31
date2 - date1
</code></pre>
<pre><code>## Time difference of 31 days
</code></pre>
<pre><code class="r">date2 > date1
</code></pre>
<pre><code>## [1] TRUE
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-43" style="background:;">
<hgroup>
<h2>Date类型</h2>
</hgroup>
<article data-timings="">
<p>时间类数据都是从1970年1月1日作为起始点计算。例如计算从那天开始直到现在的天数。</p>
<pre><code class="r">Sys.Date() - structure(0, class='Date')
</code></pre>
<pre><code>## Time difference of 16899 days
</code></pre>
<p>我们也可以创建一个日期向量，并进行计算。</p>
<pre><code class="r">dates <- seq(date1, length=4, by='day')
format(dates, '%w')
</code></pre>
<pre><code>## [1] "4" "5" "6" "0"
</code></pre>
<pre><code class="r">weekdays(dates)
</code></pre>
<pre><code>## [1] "星期四" "星期五" "星期六" "星期日"
</code></pre>
<p>如果需要了解更多日期的格式转换，可以参见strptime函数的帮助。</p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-44" style="background:;">
<hgroup>
<h2>POSIXct类型</h2>
</hgroup>
<article data-timings="">
<p>POSIXct类型的数据创建和计算是类似的。</p>
<pre><code class="r">time1 <- '1989-05-04'
time1 <- as.POSIXct(time1)
time1 <- "2011-03-1 01:30:00"
time1 <- as.POSIXct(time1,format="%Y-%m-%d %H:%M:%S")
time1 <- as.POSIXct("2011-03-1 01:30:00",tz='GMT')
time2 <- seq(from=time1,to=Sys.time(),by='month')
</code></pre>
<p>POSIXct类型的数据可以不包括时钟信息，或者在日期后加空格以冒号分隔时钟信息，也可以加上时区缩写。如果对输入格式有特别要求，可以使用format参数对输入格式进行设定，再行转换。</p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-45" style="background:;">
<hgroup>
<h2>POSIXct类型</h2>
</hgroup>
<article data-timings="">
<p>之前我们都是输入字符串再转为时间，这种方式有点繁琐，我们也可以直接从数值转为时间</p>
<pre><code class="r">time1 <- ISOdatetime(2011,1,1,0,0,0)
rtimes <- ISOdatetime(2013, rep(4:5,5), sample(30,10), 0, 0, 0)
</code></pre>
<p>ISOdatetime函数能将数值转为POSIXct时间对象，六个输入数值参数分别为年、月、日、时、分、秒。上面第二行代码使用了向量化特性，随机生成了10个时间。</p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="segue dark" id="slide-46" style="background:;">
<hgroup>
<h2>字符串处理</h2>
</hgroup>
<article data-timings="">
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-47" style="background:;">
<hgroup>
<h2>字符串处理概要</h2>
</hgroup>
<article data-timings="">
<p>在文本数据挖掘日趋重要的背景下，在处理字符这种非结构化数据时，你需要能够熟练的操作字符串对象。</p>
<ul>
<li>获取字符串长度：<code>nchar()</code></li>