R_basic3

package:ggplot2

documentation

• http://docs.ggplot2.org/current/

cheat sheet

• https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf

why ggplot2?

• fancy by default, good for demo and report
• consistent across all kinds of plot in syntax and behavior
• strong support community(the mostly download package on CRAN)

#basic syntax
#ggplot(data,aes(x,y,group,...))+geom_object(...)
#install.packages('ggplot2')

setwd('~/lecture/riii')
load('Statistics/cdc.Rdata')

library('ggplot2')

g <- ggplot(cdc,aes(x=height,y=weight))
g+geom_point(aes(col=exerany))

g <- ggplot(cdc,aes(x=genhlth))
g+geom_bar()

g+geom_bar() + ylab('Count') + ggtitle('cdc')

#fill => 填滿顏色; color => 邊線顏色
g+geom_bar(fill='snow',color='black')

g <- ggplot(cdc,aes(x=genhlth,fill=gender))
g+geom_bar()

#g <- ggplot(cdc,aes(x=genhlth))
#g+geom_bar(aes(fill=gender))

g_bygrp <- ggplot(cdc,aes(x=exerany,fill=genhlth))
g_bygrp + geom_bar()

g_bygrp + geom_bar(position='stack')

g_bygrp + geom_bar(position='dodge')

g_bygrp + geom_bar(position='identity')

precounted = as.data.frame(table(cdc$genhlth,dnn = c('genhlth')))
precounted

##     genhlth Freq
## 1 excellent 4657
## 2 very good 6972
## 3      good 5675
## 4      fair 2019
## 5      poor  677

ggplot(precounted,aes(x=genhlth,y=Freq))+ geom_bar(stat='identity')

g <- ggplot(cdc,aes(x=genhlth,y=height))
g + geom_boxplot()

#facet
g <- ggplot(cdc,aes(x=weight))
g2 = g+ geom_histogram()+facet_wrap(~genhlth)
g2

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggsave(filename='your_file_name.png',plot = g2)

## Saving 7 x 5 in image
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

資料預處理

把dt轉換成日期型態

#getwd()
setwd('~/lecture/riii')

# 以as.POSIXct()轉換
load('Statistics/appledaily.RData')
str(appledaily)

## 'data.frame':    1500 obs. of  5 variables:
##  $ content : chr  "\n                                        (更新:新增影片)想要透過刮刮樂彩券一夕致富，但他卻用錯方法！台中市一名"| __truncated__ "\n                                        澳洲一名就讀雪梨大學的華裔博士生，日前公開一段燒毀中國護照的影片，還"| __truncated__ "\n                                        【行銷專題企劃】房價高高在上，沒錢買房沒關係，但你認為自己是聰明的租"| __truncated__ "\n                                        本內容由中央廣播電臺提供        美國國防部長卡特(Ash Carter)今天(15日"| __truncated__ ...
##  $ title   : chr  "【更新】搶2.2萬彩券刮中1.4萬　沒發財還得入獄" "拿到澳洲護照後　他放火燒中國護照" "【特企】房市大追擊- 租屋這些事情要小心" "【央廣RTI】美菲軍演  美防長南海登艦" ...
##  $ dt      : chr  "2016年04月15日14:32" "2016年04月15日14:32" "2016年04月15日14:31" "2016年04月15日14:30" ...
##  $ category: chr  "社會" "國際" "地產" "國際" ...
##  $ clicked : chr  "人氣(1754)" "人氣(0)" "人氣(0)" "人氣(0)" ...

head(appledaily$dt)

## [1] "2016年04月15日14:32" "2016年04月15日14:32" "2016年04月15日14:31"
## [4] "2016年04月15日14:30" "2016年04月15日14:28" "2016年04月15日14:28"

dt = as.POSIXct(appledaily$dt,format = '%Y年%m月%d日%H:%M')
appledaily$dt = dt
head(appledaily$dt)

## [1] "2016-04-15 14:32:00 CST" "2016-04-15 14:32:00 CST"
## [3] "2016-04-15 14:31:00 CST" "2016-04-15 14:30:00 CST"
## [5] "2016-04-15 14:28:00 CST" "2016-04-15 14:28:00 CST"

# Date 和 POSIX 差別
# Date類別表示 "日期",  表示距離1970/1/1多少天, 單位為天
# POSIX類別表示 "時間", 表示距離1970/1/1多少秒, 單位為秒

now = Sys.time()
class(now)

## [1] "POSIXct" "POSIXt"

unclass(now)

## [1] 1525855772

nowDate = as.Date(now)
class(nowDate)

## [1] "Date"

unclass(nowDate)

## [1] 17660

#比較as.POSIXct() 和 as.POSIXlt
t1 = as.POSIXct(appledaily$dt,format = '%Y年%m月%d日%H:%M')
class(t1)

## [1] "POSIXct" "POSIXt"

head(unclass(t1))

## [1] 1460701920 1460701920 1460701860 1460701800 1460701680 1460701680

t2 = as.POSIXlt(appledaily$dt,format = '%Y年%m月%d日%H:%M')
class(t2)

## [1] "POSIXlt" "POSIXt"

unclass(t2)

## $sec
##    [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##   [35] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##   [69] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [103] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [137] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [171] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [205] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [239] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [273] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [307] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [341] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [375] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [409] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [443] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [477] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [511] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [545] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [579] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [613] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [647] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [681] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [715] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [749] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [783] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [817] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [851] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [885] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [919] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [953] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [987] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1021] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1055] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1089] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1123] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1157] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1191] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1225] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1259] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1293] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1327] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1361] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1395] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1429] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1463] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1497] 0 0 0 0
## 
## $min
##    [1] 32 32 31 30 28 28 25 24 24 23 22 21 19 19 18 17 15 15 12 10  0 59 59
##   [24] 54 54 52 51 51 50 50 50 49 48 47 45 45 45 44 42 40 40 39 38 34 34 33
##   [47] 30 30 26 23 18 10  8  8  8  7  6  5  5  2  1  0  0 59 56 52 50 49 49
##   [70] 48 47 42 42 41 41 41 39 38 37 36 36 35 34 34 33 33 33 33 29 27 27 26
##   [93] 21 21 20 20 20 19 16 16 15 13 12 11 10 10 10 10  9  9  8  7  7  6  5
##  [116]  5  5  4  3  2  0  0  0  0  0 59 59 58 58 58 56 55 54 54 54 54 53 53
##  [139] 52 52 50 49 48 47 47 47 47 47 46 45 47 47 46 45 45 43 43 42 40 40 39
##  [162] 38 38 37 37 37 35 35 34 32 28 27 27 26 25 25 25 23 23 23 12 11 10 10
##  [185]  9  9  8  7  7  6  5  3  2  0  0  0  0  0  0 57 56 53 49 48 47 47 46
##  [208] 46 44 44 42 42 41 40 39 39 35 34 34 33 31 30 30 30 27 27 25 25 24 22
##  [231] 20 20 18 17 16 15 14 10  5  5 14 10  5  5  4  1  0  0 57 57 55 49 49
##  [254] 47 46 46 45 45 44 44 42 40 38 36 34 34 30 28 26 25 12  8  8  4  4  3
##  [277]  0 57 50 49 44 41 38 28 20 16 15  6  5  4  4  3  2 57 49 45 45 36 35
##  [300] 34 45 36 35 34 32 28 25 25 24 19 17 14 10  8  1 59 56 49 49 46 45 41
##  [323] 37 34 30 30 29 28 20 16 16 14 13 59 56 55 35 21  0 57 47 40 27 25 40
##  [346] 40 29 15 46 29 12 59 55 40 21 10 51 48 38 36 30 29 24 24 24 23 23 18
##  [369] 17 17 17 16 15 15 14 14 13 13 12 12 12 11 10 10 10 10  9  9  8  8  8
##  [392]  8  7  7  6  6  6  5  5  5  5  5  4  4  4  3  3  3  3  3  3  2  1  1
##  [415]  1  1  0  0  0  0  0  0  0  0  0 51 51 46 40 40 26 21 18  8  4  4  0
##  [438] 59 55 54 53 50 47 45 43 42 42 39 37 37 37 36 34 33 33 32 31 30 28 26
##  [461] 26 22 20 20 15 14 12  8  7  6  5  4  4  2  2  2  1  0 59 57 56 53 52
##  [484] 50 49 48 47 46 44 42 42 40 38 30 25 18 18 17 13 13 11  9  8  5  1  1
##  [507] 51 45 45 44 42 42 40 39 39 39 38 38 30 30 29 25 25 24 20 20 18 17 13
##  [530] 12 10  5  4  3  3  1  1  0  0  0 58 56 55 53 52 51 50 49 46 40 40 38
##  [553] 36 36 35 34 33 32 31 31 30 30 27 25 20 20 20 19 15 11 11  8  7  5  1
##  [576]  0  0  0 58 56 56 55 54 53 52 50 47 45 45 44 42 42 40 40 40 39 37 36
##  [599] 34 33 32 30 29 28 28 28 27 27 24 23 22 21 20 20 16 15 14 13 11 10  8
##  [622]  7  4  0 59 58 54 54 52 51 50 49 47 45 44 41 41 41 39 39 39 38 37 36
##  [645] 36 36 35 34 33 33 30 29 29 28 28 24 21 20 18 18 17 16 16 15 15 13 12
##  [668] 10  9  6  6  5  5  5  3  1  0  0  0  0  0 59 59 58 57 57 56 55 54 52
##  [691] 50 50 50 49 47 46 46 45 45 43 40 40 40 39 36 34 33 33 31 30 30 30 30
##  [714] 30 28 23 23 22 22 22 21 20 19 18 17 16 14 12 11 10 10  8  7  6  4  1
##  [737]  0  0  0  0 59 57 52 50 49 49 48 47 43 41 43 41 40 39 39 35 35 32 30
##  [760] 29 28 28 26 25 24 24 22 21 20 20 19 19 13 13 12 12 12 11 11  9  9  6
##  [783]  5  5  2  2  1  0  0  0  0 56 55 53 52 51 50 50 47 45 45 44 43 43 43
##  [806] 41 39 37 35 34 33 32 31 30 30 30 29 27 27 25 25 24 23 23 22 22 20 18
##  [829] 18 17 14 11 10 10  8  6  5  5  5  1  0  0  0  0 59 56 55 55 54 54 53
##  [852] 52 51 49 45 41 40 40 40 39 38 36 34 32 30 24 23 20 19 19 19 15 14  8
##  [875]  6  5  5  4  4  1  1 59 52 49 47 47 46 46 40 36 35 34 31 30 29 29 29
##  [898] 27 26 26 25 25 25 25 24 23 21 19 19 18 17 17 16 16 16 16 16 16 15 13
##  [921] 12 11 11 10  9  9  9  7  7  6  5  5  5  3  3  2  0  0  0 59 59 58 56
##  [944] 55 55 55 54 53 50 50 47 47 46 46 45 45 45 42 41 41 40 40 40 40 34 33
##  [967] 33 32 31 30 28 25 24 20 19 18 15 12 11 10  7  6  5  4  4  1  0  0  0
##  [990]  0  0 59 56 53 52 51 50 48 44 42 41 40 40 40 39 37 37 34 33 32 32 29
## [1013] 29 28 24 24 19 19 19 17 15 11 10 10 10 10 10 10  9  9  9  5  3  3 58
## [1036] 54 54 53 53 53 51 50 46 46 46 41 36 32 30 29 26 23 12 11  6  4  3  2
## [1059]  0  0 56 47 44 40 38 36 30 29 25 24 23  8  1  0  0 58 52 50 48 45 45
## [1082] 44 39 39 38 36 36 36 34 34 33 30 29 21 20 18 18 16 12  7  6  4  0 57
## [1105] 52 50 45 35 35 30 28 27 24 24 19 17  4 55 49 38 38 28 27  7 52 42 35
## [1128] 28 23 51 40 40 37 22 11  5 53 36 32 22 15  0 47 41 12 38 35 35 35 34
## [1151] 34 34 34 34 34 34 34 30 30 30 30 30 30 30 30 28 28 26 25 24 24 24 24
## [1174] 22 18 18 14 13 12 12 10 10  9  9  8  8  6  5  5  4  3  3  2  2  2  1
## [1197]  1  1  0  0  0 59 58 57 50 48 43 41 40 38 35 34 30 30 26 24 23 20 20
## [1220] 13 12 11 10  6  0 57 50 42 40 39 34 33 32 30 28 28 23 23 20 18 17 15
## [1243] 14 13 10  4  2  0  0 59 55 52 51 49 49 47 46 45 45 42 39 38 38 36 30
## [1266] 28 23 22 20 17 16 15 11 10 10 10  8  5  5  2  0  0 58 58 57 56 55 55
## [1289] 53 52 51 50 50 48 48 44 44 40 35 30 28 27 26 24 23 23 22 18 16 12 10
## [1312]  8  8  6  6  5  2  0  0  0 57 56 54 54 53 53 51 50 50 48 45 44 44 42
## [1335] 40 40 40 37 36 32 32 30 30 29 28 27 26 25 20 20 19 18 18 17 17 10 10
## [1358]  8  6  6  3  3  2  1  0  0  0 59 58 58 56 56 54 50 50 48 47 46 46 44
## [1381] 44 44 42 42 40 40 40 39 39 38 37 37 34 33 31 28 27 24 22 21 20 19 15
## [1404] 15 14 11 11 10 10 10 10 10 10  8  7  3  3  3  3  0 59 58 57 56 55 55
## [1427] 55 54 53 50 45 43 43 42 41 41 40 40 40 40 39 39 36 33 33 32 30 30 29
## [1450] 28 25 22 19 18 15 12 11  9  8  5  4  4  3  2 56 55 54 53 53 52 51 51
## [1473] 49 46 41 40 40 38 38 37 35 35 35 35 33 32 31 30 29 27 26 25 23 23 20
## [1496] 19 18 17 14 13
## 
## $hour
##    [1] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 13 13
##   [24] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
##   [47] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 12 12 12 12 12 12
##   [70] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
##   [93] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
##  [116] 12 12 12 12 12 12 12 12 12 12 11 11 11 11 11 11 11 11 11 11 11 11 11
##  [139] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
##  [162] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
##  [185] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 10 10 10 10 10 10 10 10
##  [208] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [231] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10  9  9  9  9  9
##  [254]  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
##  [277]  9  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  7  7  7  7  7  7
##  [300]  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  6  6  6  6  6  6  6
##  [323]  6  6  6  6  6  6  6  6  6  6  6  5  5  5  5  5  5  4  4  4  4  4  3
##  [346]  3  3  3  2  2  2  1  1  1  1  1  0  0  0  0  0  0  0  0  0  0  0  0
##  [369]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [392]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [415]  0  0  0  0  0  0  0  0  0  0  0 23 23 23 23 23 23 23 23 23 23 23 23
##  [438] 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22
##  [461] 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 21 21 21 21 21
##  [484] 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21
##  [507] 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
##  [530] 20 20 20 20 20 20 20 20 20 20 20 19 19 19 19 19 19 19 19 19 19 19 19
##  [553] 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19
##  [576] 19 19 19 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18
##  [599] 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18
##  [622] 18 18 18 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17
##  [645] 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17
##  [668] 17 17 17 17 17 17 17 17 17 17 17 17 17 17 16 16 16 16 16 16 16 16 16
##  [691] 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16
##  [714] 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16
##  [737] 16 16 16 16 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
##  [760] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
##  [783] 15 15 15 15 15 15 15 15 15 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [806] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [829] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 13 13 13 13 13 13 13
##  [852] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
##  [875] 13 13 13 13 13 13 13 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
##  [898] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
##  [921] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 11 11 11 11
##  [944] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
##  [967] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
##  [990] 11 11 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [1013] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10  9
## [1036]  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
## [1059]  9  9  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  7  7  7  7  7  7
## [1082]  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  6
## [1105]  6  6  6  6  6  6  6  6  6  6  6  6  6  5  5  5  5  5  5  5  4  4  4
## [1128]  4  4  3  3  3  3  3  3  3  2  2  2  2  2  2  1  1  1  0  0  0  0  0
## [1151]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1174]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1197]  0  0  0  0  0 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23
## [1220] 23 23 23 23 23 23 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22
## [1243] 22 22 22 22 22 22 22 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21
## [1266] 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 20 20 20 20 20 20
## [1289] 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
## [1312] 20 20 20 20 20 20 20 20 20 19 19 19 19 19 19 19 19 19 19 19 19 19 19
## [1335] 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19
## [1358] 19 19 19 19 19 19 19 19 19 19 18 18 18 18 18 18 18 18 18 18 18 18 18
## [1381] 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18
## [1404] 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 17 17 17 17 17 17
## [1427] 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17
## [1450] 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 16 16 16 16 16 16 16 16
## [1473] 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16
## [1496] 16 16 16 16 16
## 
## $mday
##    [1] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
##   [24] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
##   [47] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
##   [70] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
##   [93] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
##  [116] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
##  [139] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
##  [162] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
##  [185] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
##  [208] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
##  [231] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
##  [254] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
##  [277] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
##  [300] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
##  [323] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
##  [346] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
##  [369] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
##  [392] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
##  [415] 15 15 15 15 15 15 15 15 15 15 15 14 14 14 14 14 14 14 14 14 14 14 14
##  [438] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [461] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [484] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [507] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [530] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [553] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [576] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [599] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [622] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [645] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [668] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [691] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [714] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [737] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [760] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [783] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [806] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [829] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [852] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [875] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [898] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [921] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [944] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [967] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
##  [990] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
## [1013] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
## [1036] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
## [1059] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
## [1082] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
## [1105] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
## [1128] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
## [1151] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
## [1174] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
## [1197] 14 14 14 14 14 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
## [1220] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
## [1243] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
## [1266] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
## [1289] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
## [1312] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
## [1335] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
## [1358] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
## [1381] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
## [1404] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
## [1427] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
## [1450] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
## [1473] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
## [1496] 13 13 13 13 13
## 
## $mon
##    [1] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##   [35] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##   [69] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [103] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [137] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [171] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [205] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [239] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [273] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [307] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [341] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [375] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [409] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [443] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [477] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [511] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [545] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [579] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [613] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [647] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [681] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [715] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [749] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [783] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [817] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [851] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [885] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [919] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [953] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [987] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1021] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1055] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1089] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1123] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1157] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1191] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1225] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1259] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1293] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1327] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1361] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1395] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1429] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1463] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1497] 3 3 3 3
## 
## $year
##    [1] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##   [18] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##   [35] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##   [52] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##   [69] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##   [86] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [103] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [120] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [137] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [154] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [171] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [188] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [205] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [222] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [239] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [256] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [273] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [290] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [307] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [324] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [341] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [358] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [375] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [392] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [409] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [426] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [443] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [460] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [477] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [494] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [511] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [528] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [545] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [562] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [579] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [596] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [613] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [630] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [647] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [664] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [681] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [698] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [715] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [732] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [749] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [766] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [783] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [800] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [817] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [834] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [851] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [868] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [885] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [902] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [919] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [936] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [953] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [970] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
##  [987] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1004] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1021] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1038] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1055] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1072] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1089] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1106] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1123] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1140] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1157] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1174] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1191] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1208] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1225] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1242] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1259] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1276] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1293] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1310] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1327] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1344] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1361] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1378] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1395] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1412] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1429] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1446] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1463] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1480] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1497] 116 116 116 116
## 
## $wday
##    [1] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
##   [35] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
##   [69] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
##  [103] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
##  [137] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
##  [171] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
##  [205] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
##  [239] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
##  [273] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
##  [307] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
##  [341] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
##  [375] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
##  [409] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
##  [443] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
##  [477] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
##  [511] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
##  [545] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
##  [579] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
##  [613] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
##  [647] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
##  [681] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
##  [715] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
##  [749] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
##  [783] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
##  [817] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
##  [851] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
##  [885] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
##  [919] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
##  [953] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
##  [987] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [1021] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [1055] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [1089] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [1123] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [1157] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [1191] 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1225] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1259] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1293] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1327] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1361] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1395] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1429] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1463] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1497] 3 3 3 3
## 
## $yday
##    [1] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105
##   [18] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105
##   [35] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105
##   [52] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105
##   [69] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105
##   [86] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105
##  [103] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105
##  [120] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105
##  [137] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105
##  [154] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105
##  [171] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105
##  [188] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105
##  [205] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105
##  [222] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105
##  [239] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105
##  [256] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105
##  [273] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105
##  [290] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105
##  [307] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105
##  [324] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105
##  [341] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105
##  [358] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105
##  [375] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105
##  [392] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105
##  [409] 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105
##  [426] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [443] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [460] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [477] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [494] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [511] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [528] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [545] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [562] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [579] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [596] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [613] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [630] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [647] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [664] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [681] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [698] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [715] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [732] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [749] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [766] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [783] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [800] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [817] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [834] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [851] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [868] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [885] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [902] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [919] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [936] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [953] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [970] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
##  [987] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
## [1004] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
## [1021] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
## [1038] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
## [1055] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
## [1072] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
## [1089] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
## [1106] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
## [1123] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
## [1140] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
## [1157] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
## [1174] 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
## [1191] 104 104 104 104 104 104 104 104 104 104 104 103 103 103 103 103 103
## [1208] 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103
## [1225] 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103
## [1242] 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103
## [1259] 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103
## [1276] 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103
## [1293] 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103
## [1310] 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103
## [1327] 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103
## [1344] 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103
## [1361] 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103
## [1378] 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103
## [1395] 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103
## [1412] 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103
## [1429] 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103
## [1446] 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103
## [1463] 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103
## [1480] 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103
## [1497] 103 103 103 103
## 
## $isdst
##    [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##   [35] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##   [69] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [103] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [137] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [171] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [205] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [239] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [273] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [307] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [341] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [375] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [409] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [443] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [477] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [511] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [545] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [579] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [613] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [647] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [681] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [715] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [749] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [783] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [817] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [851] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [885] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [919] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [953] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [987] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1021] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1055] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1089] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1123] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1157] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1191] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1225] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1259] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1293] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1327] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1361] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1395] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1429] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1463] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1497] 0 0 0 0
## 
## $zone
##    [1] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##   [12] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##   [23] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##   [34] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##   [45] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##   [56] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##   [67] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##   [78] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##   [89] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [100] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [111] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [122] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [133] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [144] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [155] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [166] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [177] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [188] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [199] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [210] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [221] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [232] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [243] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [254] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [265] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [276] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [287] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [298] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [309] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [320] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [331] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [342] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [353] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [364] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [375] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [386] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [397] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [408] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [419] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [430] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [441] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [452] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [463] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [474] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [485] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [496] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [507] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [518] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [529] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [540] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [551] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [562] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [573] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [584] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [595] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [606] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [617] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [628] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [639] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [650] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [661] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [672] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [683] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [694] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [705] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [716] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [727] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [738] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [749] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [760] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [771] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [782] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [793] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [804] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [815] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [826] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [837] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [848] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [859] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [870] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [881] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [892] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [903] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [914] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [925] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [936] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [947] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [958] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [969] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [980] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
##  [991] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1002] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1013] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1024] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1035] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1046] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1057] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1068] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1079] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1090] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1101] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1112] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1123] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1134] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1145] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1156] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1167] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1178] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1189] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1200] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1211] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1222] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1233] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1244] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1255] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1266] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1277] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1288] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1299] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1310] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1321] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1332] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1343] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1354] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1365] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1376] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1387] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1398] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1409] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1420] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1431] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1442] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1453] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1464] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1475] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1486] "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST" "CST"
## [1497] "CST" "CST" "CST" "CST"
## 
## $gmtoff
##    [1] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##   [12] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##   [23] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##   [34] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##   [45] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##   [56] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##   [67] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##   [78] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##   [89] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [100] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [111] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [122] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [133] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [144] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [155] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [166] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [177] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [188] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [199] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [210] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [221] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [232] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [243] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [254] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [265] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [276] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [287] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [298] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [309] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [320] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [331] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [342] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [353] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [364] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [375] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [386] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [397] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [408] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [419] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [430] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [441] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [452] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [463] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [474] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [485] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [496] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [507] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [518] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [529] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [540] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [551] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [562] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [573] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [584] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [595] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [606] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [617] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [628] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [639] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [650] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [661] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [672] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [683] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [694] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [705] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [716] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [727] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [738] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [749] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [760] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [771] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [782] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [793] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [804] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [815] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [826] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [837] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [848] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [859] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [870] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [881] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [892] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [903] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [914] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [925] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [936] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [947] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [958] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [969] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [980] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
##  [991] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1002] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1013] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1024] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1035] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1046] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1057] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1068] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1079] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1090] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1101] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1112] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1123] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1134] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1145] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1156] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1167] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1178] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1189] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1200] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1211] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1222] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1233] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1244] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1255] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1266] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1277] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1288] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1299] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1310] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1321] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1332] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1343] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1354] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1365] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1376] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1387] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1398] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1409] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1420] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1431] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1442] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1453] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1464] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1475] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1486] 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800 28800
## [1497] 28800 28800 28800 28800
## 
## attr(,"tzone")
## [1] ""    "CST" "CDT"

#difftime
Sys.time() - appledaily$dt[1]

## Time difference of 754.0955 days

擷取點擊數中數值部分

#方法一：利用sub函數取代
clicked = sub('\\)','',sub('人氣\\(','',appledaily$clicked))
clicked = as.integer(clicked)
head(clicked)

## [1] 1754    0    0    0  311   24

#方法二：使用stringr套件的str_match()
library(stringr)

## Warning: package 'stringr' was built under R version 3.4.3

?str_match
clicked = as.integer(str_match(appledaily$clicked,"人氣\\((\\d+)\\)")[,2])

appledaily$clicked = clicked
head(clicked)

## [1] 1754    0    0    0  311   24

其他常見字串處理函式

#grep()  ==> return index
test_str = c('abcd','bcd','cde')
grep('a',test_str)

## [1] 1

test_str[grep('a',test_str)]

## [1] "abcd"

#grepl() ==> return boolean 
grepl('a',test_str)

## [1]  TRUE FALSE FALSE

test_str[grepl('a',test_str)]

## [1] "abcd"

#strsplit() ==> 字串分割
splited = strsplit('abc-def','-')
splited

## [[1]]
## [1] "abc" "def"

class(splited)

## [1] "list"

unlist(splited)

## [1] "abc" "def"

#substring() ==> 取得部份字串
test_s = 'abcdef'
nchar(test_s)

## [1] 6

substring(test_s,2,nchar('abcdef')-1)

## [1] "bcde"

替換類別名稱

names(table(appledaily$category))

##  [1] "3C"                               "財經"                            
##  [3] "地產"                             "動物"                            
##  [5] "國際"                             "國際\",\"LA\",\"SF\",\"NY\",\"US"
##  [7] "國際\",\"SF\",\"US"               "論壇"                            
##  [9] "社會"                             "生活"                            
## [11] "時尚"                             "搜奇"                            
## [13] "體育"                             "娛樂"                            
## [15] "正妹"                             "政治"

appledaily$category[appledaily$category == "國際\",\"LA\",\"SF\",\"NY\",\"US"] = '國際'
appledaily$category[appledaily$category == "國際\",\"SF\",\"US"] = '國際'
names(table(appledaily$category))

##  [1] "3C"   "財經" "地產" "動物" "國際" "論壇" "社會" "生活" "時尚" "搜奇"
## [11] "體育" "娛樂" "正妹" "政治"

儲存處理過的檔案

applenews = appledaily
save(applenews,file = 'Statistics/applenews.RData')

遺失值處理(missing value)

applenews = appledaily
na_list = sample(1:nrow(applenews),30)
applenews[na_list,'clicked'] = NA

#找尋遺失值
is.na(applenews)

##      content title    dt category clicked
## 1      FALSE FALSE FALSE    FALSE   FALSE
## 2      FALSE FALSE FALSE    FALSE   FALSE
## 3      FALSE FALSE FALSE    FALSE   FALSE
## 4      FALSE FALSE FALSE    FALSE   FALSE
## 5      FALSE FALSE FALSE    FALSE   FALSE
## 6      FALSE FALSE FALSE    FALSE   FALSE
## 7      FALSE FALSE FALSE    FALSE   FALSE
## 8      FALSE FALSE FALSE    FALSE   FALSE
## 9      FALSE FALSE FALSE    FALSE   FALSE
## 10     FALSE FALSE FALSE    FALSE   FALSE
## 11     FALSE FALSE FALSE    FALSE   FALSE
## 12     FALSE FALSE FALSE    FALSE   FALSE
## 13     FALSE FALSE FALSE    FALSE   FALSE
## 14     FALSE FALSE FALSE    FALSE   FALSE
## 15     FALSE FALSE FALSE    FALSE   FALSE
## 16     FALSE FALSE FALSE    FALSE   FALSE
## 17     FALSE FALSE FALSE    FALSE   FALSE
## 18     FALSE FALSE FALSE    FALSE   FALSE
## 19     FALSE FALSE FALSE    FALSE   FALSE
## 20     FALSE FALSE FALSE    FALSE   FALSE
## 21     FALSE FALSE FALSE    FALSE   FALSE
## 22     FALSE FALSE FALSE    FALSE   FALSE
## 23     FALSE FALSE FALSE    FALSE   FALSE
## 24     FALSE FALSE FALSE    FALSE   FALSE
## 25     FALSE FALSE FALSE    FALSE   FALSE
## 26     FALSE FALSE FALSE    FALSE   FALSE
## 27     FALSE FALSE FALSE    FALSE   FALSE
## 28     FALSE FALSE FALSE    FALSE   FALSE
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## 1330   FALSE FALSE FALSE    FALSE   FALSE
## 1331   FALSE FALSE FALSE    FALSE   FALSE
## 1332   FALSE FALSE FALSE    FALSE   FALSE
## 1333   FALSE FALSE FALSE    FALSE   FALSE
## 1334   FALSE FALSE FALSE    FALSE   FALSE
## 1335   FALSE FALSE FALSE    FALSE   FALSE
## 1336   FALSE FALSE FALSE    FALSE   FALSE
## 1337   FALSE FALSE FALSE    FALSE   FALSE
## 1338   FALSE FALSE FALSE    FALSE   FALSE
## 1339   FALSE FALSE FALSE    FALSE   FALSE
## 1340   FALSE FALSE FALSE    FALSE   FALSE
## 1341   FALSE FALSE FALSE    FALSE   FALSE
## 1342   FALSE FALSE FALSE    FALSE    TRUE
## 1343   FALSE FALSE FALSE    FALSE   FALSE
## 1344   FALSE FALSE FALSE    FALSE   FALSE
## 1345   FALSE FALSE FALSE    FALSE   FALSE
## 1346   FALSE FALSE FALSE    FALSE   FALSE
## 1347   FALSE FALSE FALSE    FALSE   FALSE
## 1348   FALSE FALSE FALSE    FALSE   FALSE
## 1349   FALSE FALSE FALSE    FALSE   FALSE
## 1350   FALSE FALSE FALSE    FALSE   FALSE
## 1351   FALSE FALSE FALSE    FALSE   FALSE
## 1352   FALSE FALSE FALSE    FALSE   FALSE
## 1353   FALSE FALSE FALSE    FALSE   FALSE
## 1354   FALSE FALSE FALSE    FALSE   FALSE
## 1355   FALSE FALSE FALSE    FALSE   FALSE
## 1356   FALSE FALSE FALSE    FALSE   FALSE
## 1357   FALSE FALSE FALSE    FALSE   FALSE
## 1358   FALSE FALSE FALSE    FALSE   FALSE
## 1359   FALSE FALSE FALSE    FALSE   FALSE
## 1360   FALSE FALSE FALSE    FALSE   FALSE
## 1361   FALSE FALSE FALSE    FALSE   FALSE
## 1362   FALSE FALSE FALSE    FALSE   FALSE
## 1363   FALSE FALSE FALSE    FALSE   FALSE
## 1364   FALSE FALSE FALSE    FALSE   FALSE
## 1365   FALSE FALSE FALSE    FALSE   FALSE
## 1366   FALSE FALSE FALSE    FALSE   FALSE
## 1367   FALSE FALSE FALSE    FALSE   FALSE
## 1368   FALSE FALSE FALSE    FALSE   FALSE
## 1369   FALSE FALSE FALSE    FALSE   FALSE
## 1370   FALSE FALSE FALSE    FALSE   FALSE
## 1371   FALSE FALSE FALSE    FALSE   FALSE
## 1372   FALSE FALSE FALSE    FALSE   FALSE
## 1373   FALSE FALSE FALSE    FALSE   FALSE
## 1374   FALSE FALSE FALSE    FALSE   FALSE
## 1375   FALSE FALSE FALSE    FALSE   FALSE
## 1376   FALSE FALSE FALSE    FALSE   FALSE
## 1377   FALSE FALSE FALSE    FALSE   FALSE
## 1378   FALSE FALSE FALSE    FALSE    TRUE
## 1379   FALSE FALSE FALSE    FALSE    TRUE
## 1380   FALSE FALSE FALSE    FALSE   FALSE
## 1381   FALSE FALSE FALSE    FALSE   FALSE
## 1382   FALSE FALSE FALSE    FALSE   FALSE
## 1383   FALSE FALSE FALSE    FALSE   FALSE
## 1384   FALSE FALSE FALSE    FALSE   FALSE
## 1385   FALSE FALSE FALSE    FALSE   FALSE
## 1386   FALSE FALSE FALSE    FALSE   FALSE
## 1387   FALSE FALSE FALSE    FALSE   FALSE
## 1388   FALSE FALSE FALSE    FALSE   FALSE
## 1389   FALSE FALSE FALSE    FALSE   FALSE
## 1390   FALSE FALSE FALSE    FALSE   FALSE
## 1391   FALSE FALSE FALSE    FALSE   FALSE
## 1392   FALSE FALSE FALSE    FALSE   FALSE
## 1393   FALSE FALSE FALSE    FALSE   FALSE
## 1394   FALSE FALSE FALSE    FALSE   FALSE
## 1395   FALSE FALSE FALSE    FALSE   FALSE
## 1396   FALSE FALSE FALSE    FALSE   FALSE
## 1397   FALSE FALSE FALSE    FALSE   FALSE
## 1398   FALSE FALSE FALSE    FALSE   FALSE
## 1399   FALSE FALSE FALSE    FALSE   FALSE
## 1400   FALSE FALSE FALSE    FALSE   FALSE
## 1401   FALSE FALSE FALSE    FALSE   FALSE
## 1402   FALSE FALSE FALSE    FALSE    TRUE
## 1403   FALSE FALSE FALSE    FALSE   FALSE
## 1404   FALSE FALSE FALSE    FALSE   FALSE
## 1405   FALSE FALSE FALSE    FALSE   FALSE
## 1406   FALSE FALSE FALSE    FALSE   FALSE
## 1407   FALSE FALSE FALSE    FALSE   FALSE
## 1408   FALSE FALSE FALSE    FALSE   FALSE
## 1409   FALSE FALSE FALSE    FALSE   FALSE
## 1410   FALSE FALSE FALSE    FALSE   FALSE
## 1411   FALSE FALSE FALSE    FALSE   FALSE
## 1412   FALSE FALSE FALSE    FALSE   FALSE
## 1413   FALSE FALSE FALSE    FALSE   FALSE
## 1414   FALSE FALSE FALSE    FALSE   FALSE
## 1415   FALSE FALSE FALSE    FALSE   FALSE
## 1416   FALSE FALSE FALSE    FALSE   FALSE
## 1417   FALSE FALSE FALSE    FALSE   FALSE
## 1418   FALSE FALSE FALSE    FALSE   FALSE
## 1419   FALSE FALSE FALSE    FALSE   FALSE
## 1420   FALSE FALSE FALSE    FALSE   FALSE
## 1421   FALSE FALSE FALSE    FALSE   FALSE
## 1422   FALSE FALSE FALSE    FALSE   FALSE
## 1423   FALSE FALSE FALSE    FALSE   FALSE
## 1424   FALSE FALSE FALSE    FALSE   FALSE
## 1425   FALSE FALSE FALSE    FALSE   FALSE
## 1426   FALSE FALSE FALSE    FALSE   FALSE
## 1427   FALSE FALSE FALSE    FALSE   FALSE
## 1428   FALSE FALSE FALSE    FALSE   FALSE
## 1429   FALSE FALSE FALSE    FALSE   FALSE
## 1430   FALSE FALSE FALSE    FALSE   FALSE
## 1431   FALSE FALSE FALSE    FALSE   FALSE
## 1432   FALSE FALSE FALSE    FALSE   FALSE
## 1433   FALSE FALSE FALSE    FALSE   FALSE
## 1434   FALSE FALSE FALSE    FALSE    TRUE
## 1435   FALSE FALSE FALSE    FALSE    TRUE
## 1436   FALSE FALSE FALSE    FALSE   FALSE
## 1437   FALSE FALSE FALSE    FALSE   FALSE
## 1438   FALSE FALSE FALSE    FALSE   FALSE
## 1439   FALSE FALSE FALSE    FALSE   FALSE
## 1440   FALSE FALSE FALSE    FALSE   FALSE
## 1441   FALSE FALSE FALSE    FALSE   FALSE
## 1442   FALSE FALSE FALSE    FALSE   FALSE
## 1443   FALSE FALSE FALSE    FALSE   FALSE
## 1444   FALSE FALSE FALSE    FALSE   FALSE
## 1445   FALSE FALSE FALSE    FALSE   FALSE
## 1446   FALSE FALSE FALSE    FALSE   FALSE
## 1447   FALSE FALSE FALSE    FALSE   FALSE
## 1448   FALSE FALSE FALSE    FALSE   FALSE
## 1449   FALSE FALSE FALSE    FALSE   FALSE
## 1450   FALSE FALSE FALSE    FALSE   FALSE
## 1451   FALSE FALSE FALSE    FALSE   FALSE
## 1452   FALSE FALSE FALSE    FALSE   FALSE
## 1453   FALSE FALSE FALSE    FALSE   FALSE
## 1454   FALSE FALSE FALSE    FALSE   FALSE
## 1455   FALSE FALSE FALSE    FALSE   FALSE
## 1456   FALSE FALSE FALSE    FALSE   FALSE
## 1457   FALSE FALSE FALSE    FALSE   FALSE
## 1458   FALSE FALSE FALSE    FALSE   FALSE
## 1459   FALSE FALSE FALSE    FALSE   FALSE
## 1460   FALSE FALSE FALSE    FALSE   FALSE
## 1461   FALSE FALSE FALSE    FALSE   FALSE
## 1462   FALSE FALSE FALSE    FALSE   FALSE
## 1463   FALSE FALSE FALSE    FALSE   FALSE
## 1464   FALSE FALSE FALSE    FALSE   FALSE
## 1465   FALSE FALSE FALSE    FALSE   FALSE
## 1466   FALSE FALSE FALSE    FALSE   FALSE
## 1467   FALSE FALSE FALSE    FALSE   FALSE
## 1468   FALSE FALSE FALSE    FALSE   FALSE
## 1469   FALSE FALSE FALSE    FALSE   FALSE
## 1470   FALSE FALSE FALSE    FALSE   FALSE
## 1471   FALSE FALSE FALSE    FALSE   FALSE
## 1472   FALSE FALSE FALSE    FALSE   FALSE
## 1473   FALSE FALSE FALSE    FALSE   FALSE
## 1474   FALSE FALSE FALSE    FALSE   FALSE
## 1475   FALSE FALSE FALSE    FALSE   FALSE
## 1476   FALSE FALSE FALSE    FALSE   FALSE
## 1477   FALSE FALSE FALSE    FALSE   FALSE
## 1478   FALSE FALSE FALSE    FALSE   FALSE
## 1479   FALSE FALSE FALSE    FALSE   FALSE
## 1480   FALSE FALSE FALSE    FALSE   FALSE
## 1481   FALSE FALSE FALSE    FALSE   FALSE
## 1482   FALSE FALSE FALSE    FALSE   FALSE
## 1483   FALSE FALSE FALSE    FALSE   FALSE
## 1484   FALSE FALSE FALSE    FALSE   FALSE
## 1485   FALSE FALSE FALSE    FALSE   FALSE
## 1486   FALSE FALSE FALSE    FALSE   FALSE
## 1487   FALSE FALSE FALSE    FALSE   FALSE
## 1488   FALSE FALSE FALSE    FALSE   FALSE
## 1489   FALSE FALSE FALSE    FALSE   FALSE
## 1490   FALSE FALSE FALSE    FALSE   FALSE
## 1491   FALSE FALSE FALSE    FALSE   FALSE
## 1492   FALSE FALSE FALSE    FALSE   FALSE
## 1493   FALSE FALSE FALSE    FALSE    TRUE
## 1494   FALSE FALSE FALSE    FALSE   FALSE
## 1495   FALSE FALSE FALSE    FALSE   FALSE
## 1496   FALSE FALSE FALSE    FALSE   FALSE
## 1497   FALSE FALSE FALSE    FALSE   FALSE
## 1498   FALSE FALSE FALSE    FALSE   FALSE
## 1499   FALSE FALSE FALSE    FALSE   FALSE
## 1500   FALSE FALSE FALSE    FALSE   FALSE

sum(is.na(applenews$clicked))

## [1] 30

#移除missing value
complete.cases(applenews)

##    [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##   [12]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##   [23]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##   [34]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##   [45]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##   [56]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##   [67]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##   [78]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
##   [89]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
##  [100]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [111]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [122]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [133]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE
##  [144]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [155]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [166]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [177]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [188]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [199]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [210]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
##  [221]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [232]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [243]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [254]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [265]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [276]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [287]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [298]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [309]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [320]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [331]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [342]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [353]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
##  [364]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [375]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [386]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [397]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [408]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [419]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [430]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [441]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
##  [452]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [463]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [474]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [485]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [496]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [507]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [518]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [529]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [540]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [551] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [562]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [573]  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
##  [584]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [595]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [606]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [617]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [628]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [639]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [650]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [661]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [672]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [683]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [694]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [705]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [716]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [727]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [738]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [749]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [760]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [771]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [782]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [793]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [804]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
##  [815]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [826]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [837] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [848]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [859]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [870]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
##  [881]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [892]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [903]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [914]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [925]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [936]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [947]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [958]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [969]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [980]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [991]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1002]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1013] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1024]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1035]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1046]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1057]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1068]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1079]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1090]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1101]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1112]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1123]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1134]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1145]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
## [1156]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
## [1167]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1178]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
## [1189]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1200]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1211]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1222]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1233]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1244]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1255]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1266]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1277]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1288]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1299]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1310]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1321]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1332]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
## [1343]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1354]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1365]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1376]  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1387]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1398]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1409]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1420]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1431]  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1442]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1453]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1464]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1475]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [1486]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
## [1497]  TRUE  TRUE  TRUE  TRUE

rm.data <- applenews[complete.cases(applenews), ]
str(rm.data)

## 'data.frame':    1470 obs. of  5 variables:
##  $ content : chr  "\n                                        (更新:新增影片)想要透過刮刮樂彩券一夕致富，但他卻用錯方法！台中市一名"| __truncated__ "\n                                        澳洲一名就讀雪梨大學的華裔博士生，日前公開一段燒毀中國護照的影片，還"| __truncated__ "\n                                        【行銷專題企劃】房價高高在上，沒錢買房沒關係，但你認為自己是聰明的租"| __truncated__ "\n                                        本內容由中央廣播電臺提供        美國國防部長卡特(Ash Carter)今天(15日"| __truncated__ ...
##  $ title   : chr  "【更新】搶2.2萬彩券刮中1.4萬　沒發財還得入獄" "拿到澳洲護照後　他放火燒中國護照" "【特企】房市大追擊- 租屋這些事情要小心" "【央廣RTI】美菲軍演  美防長南海登艦" ...
##  $ dt      : POSIXct, format: "2016-04-15 14:32:00" "2016-04-15 14:32:00" ...
##  $ category: chr  "社會" "國際" "地產" "國際" ...
##  $ clicked : int  1754 0 0 0 311 24 20 314 27 308 ...

#以全體平均填補
applenews = appledaily
na_list = sample(1:nrow(applenews),30)
applenews[na_list,'clicked'] = NA

mean_clicked = as.integer(mean(applenews$clicked,na.rm=T))
applenews$clicked[is.na(applenews$clicked)] = mean_clicked

sum(is.na(applenews$clicked))

## [1] 0

#以類別平均填補
applenews = appledaily
na_list = sample(1:nrow(applenews),30)
applenews[na_list,'clicked'] = NA

cat_means = tapply(applenews$clicked,applenews$category,function(e){as.integer(mean(e,na.rm=T))})
cat_means

##    3C  財經  地產  動物  國際  論壇  社會  生活  時尚  搜奇  體育  娛樂 
##  3954  5199  7015  4741  8963  5683 29867 11604  6855 11386 16989 29618 
##  正妹  政治 
## 84118 11956

for(i in 1:length(names(cat_means))){
  applenews[applenews$category == names(cat_means)[i] & is.na(applenews$clicked),'clicked'] = cat_means[i]
}

sum(is.na(applenews$clicked))

## [1] 0

package dplyr

• 類SQL語法,select,filter,arrange,mutate…
• Chaining %>%, debug方便

cheat sheet

• https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf

load('Statistics/applenews.RData')
str(applenews)

## 'data.frame':    1500 obs. of  5 variables:
##  $ content : chr  "\n                                        (更新:新增影片)想要透過刮刮樂彩券一夕致富，但他卻用錯方法！台中市一名"| __truncated__ "\n                                        澳洲一名就讀雪梨大學的華裔博士生，日前公開一段燒毀中國護照的影片，還"| __truncated__ "\n                                        【行銷專題企劃】房價高高在上，沒錢買房沒關係，但你認為自己是聰明的租"| __truncated__ "\n                                        本內容由中央廣播電臺提供        美國國防部長卡特(Ash Carter)今天(15日"| __truncated__ ...
##  $ title   : chr  "【更新】搶2.2萬彩券刮中1.4萬　沒發財還得入獄" "拿到澳洲護照後　他放火燒中國護照" "【特企】房市大追擊- 租屋這些事情要小心" "【央廣RTI】美菲軍演  美防長南海登艦" ...
##  $ dt      : POSIXct, format: "2016-04-15 14:32:00" "2016-04-15 14:32:00" ...
##  $ category: chr  "社會" "國際" "地產" "國際" ...
##  $ clicked : int  1754 0 0 0 311 24 20 314 27 308 ...

applenews = applenews[,-1]

#install.packages('dplyr')
library(dplyr)

## Warning: package 'dplyr' was built under R version 3.4.2

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

#原先R 提供的過濾功能
head(applenews[applenews$category == "娛樂",])

##                                         title                  dt category
## 16           澎恰恰收女弟子　拱當台灣第一名伶 2016-04-15 14:17:00     娛樂
## 21 【唱新聞】詐騙嗎？R.O.C.有CHINA但不是CHINA 2016-04-15 14:00:00     娛樂
## 32         白曉燕命案19年了　白冰冰「不能忘」 2016-04-15 13:49:00     娛樂
## 40           好萊塢男神好威　女友再當高齡產婦 2016-04-15 13:40:00     娛樂
## 47         隋棠帶兒遠征南台灣　吃成膨皮母子檔 2016-04-15 13:30:00     娛樂
## 50   伊勢谷友介掰了長澤雅美　半同居小16歲辣模 2016-04-15 13:23:00     娛樂
##    clicked
## 16    1749
## 21   11696
## 32    3329
## 40    4307
## 47    4651
## 50    5141

#dplyr 的過濾功能
head(filter(applenews, category == "娛樂"))

## Warning: package 'bindrcpp' was built under R version 3.4.4

##                                        title                  dt category
## 1           澎恰恰收女弟子　拱當台灣第一名伶 2016-04-15 14:17:00     娛樂
## 2 【唱新聞】詐騙嗎？R.O.C.有CHINA但不是CHINA 2016-04-15 14:00:00     娛樂
## 3         白曉燕命案19年了　白冰冰「不能忘」 2016-04-15 13:49:00     娛樂
## 4           好萊塢男神好威　女友再當高齡產婦 2016-04-15 13:40:00     娛樂
## 5         隋棠帶兒遠征南台灣　吃成膨皮母子檔 2016-04-15 13:30:00     娛樂
## 6   伊勢谷友介掰了長澤雅美　半同居小16歲辣模 2016-04-15 13:23:00     娛樂
##   clicked
## 1    1749
## 2   11696
## 3    3329
## 4    4307
## 5    4651
## 6    5141

#and/or 
head(filter(applenews, category == "娛樂" & clicked > 1000))

##                                        title                  dt category
## 1           澎恰恰收女弟子　拱當台灣第一名伶 2016-04-15 14:17:00     娛樂
## 2 【唱新聞】詐騙嗎？R.O.C.有CHINA但不是CHINA 2016-04-15 14:00:00     娛樂
## 3         白曉燕命案19年了　白冰冰「不能忘」 2016-04-15 13:49:00     娛樂
## 4           好萊塢男神好威　女友再當高齡產婦 2016-04-15 13:40:00     娛樂
## 5         隋棠帶兒遠征南台灣　吃成膨皮母子檔 2016-04-15 13:30:00     娛樂
## 6   伊勢谷友介掰了長澤雅美　半同居小16歲辣模 2016-04-15 13:23:00     娛樂
##   clicked
## 1    1749
## 2   11696
## 3    3329
## 4    4307
## 5    4651
## 6    5141

head(filter(applenews, category == "娛樂" | clicked > 1000))

##                                          title                  dt
## 1 【更新】搶2.2萬彩券刮中1.4萬　沒發財還得入獄 2016-04-15 14:32:00
## 2                   同居人女兒熟睡　淫男伸狼爪 2016-04-15 14:22:00
## 3              又要下雨了　中南部6縣市大雨特報 2016-04-15 14:19:00
## 4   韓留學生超羨慕　「台灣人失業可以賣雞排」　 2016-04-15 14:18:00
## 5             澎恰恰收女弟子　拱當台灣第一名伶 2016-04-15 14:17:00
## 6           手機截圖的極限在哪？　鄉民接力完成 2016-04-15 14:15:00
##   category clicked
## 1     社會    1754
## 2     社會    1076
## 3     生活   12347
## 4     生活    1312
## 5     娛樂    1749
## 6     搜奇    1005

#篩選多個類別
head(filter(applenews, category %in% c("娛樂", "社會")))

##                                          title                  dt
## 1 【更新】搶2.2萬彩券刮中1.4萬　沒發財還得入獄 2016-04-15 14:32:00
## 2                   同居人女兒熟睡　淫男伸狼爪 2016-04-15 14:22:00
## 3             澎恰恰收女弟子　拱當台灣第一名伶 2016-04-15 14:17:00
## 4    【驚險有片】BMW撞翻撞公車　後方機車神穿越 2016-04-15 14:12:00
## 5   【唱新聞】詐騙嗎？R.O.C.有CHINA但不是CHINA 2016-04-15 14:00:00
## 6               九巡翁霸坐展售車？　原因好心酸 2016-04-15 13:52:00
##   category clicked
## 1     社會    1754
## 2     社會    1076
## 3     娛樂    1749
## 4     社會   11886
## 5     娛樂   11696
## 6     社會    4582

#原先R的欄位選取
head(applenews[, c("category","clicked")])

##   category clicked
## 1     社會    1754
## 2     國際       0
## 3     地產       0
## 4     國際       0
## 5     時尚     311
## 6     財經      24

#dplyr 的欄位選取

#選擇列舉出的欄位
head(select(applenews,category,clicked))

##   category clicked
## 1     社會    1754
## 2     國際       0
## 3     地產       0
## 4     國際       0
## 5     時尚     311
## 6     財經      24

#選擇從category~clicked欄位
head(select(applenews,category:clicked))

##   category clicked
## 1     社會    1754
## 2     國際       0
## 3     地產       0
## 4     國際       0
## 5     時尚     311
## 6     財經      24

#選擇欄位名稱含有click字串的欄位
head(select(applenews,contains('click')))

##   clicked
## 1    1754
## 2       0
## 3       0
## 4       0
## 5     311
## 6      24

?matches

#想同時filter 和 select
head(filter(select(applenews,category:clicked),category == '娛樂'))

##   category clicked
## 1     娛樂    1749
## 2     娛樂   11696
## 3     娛樂    3329
## 4     娛樂    4307
## 5     娛樂    4651
## 6     娛樂    5141

#使用Chaining
select(applenews,category:clicked) %>%
  filter(category == '娛樂') %>%
  head()

##   category clicked
## 1     娛樂    1749
## 2     娛樂   11696
## 3     娛樂    3329
## 4     娛樂    4307
## 5     娛樂    4651
## 6     娛樂    5141

applenews %>% 
  select(category:clicked) %>%
  filter(category == '娛樂') %>%
  head()

##   category clicked
## 1     娛樂    1749
## 2     娛樂   11696
## 3     娛樂    3329
## 4     娛樂    4307
## 5     娛樂    4651
## 6     娛樂    5141

#使用Arrange將資料做排序
applenews %>%
  select(category,clicked) %>% 
  filter(category == "社會") %>% 
  arrange(.,desc(clicked)) %>%
  head()

##   category clicked
## 1     社會  241842
## 2     社會  228203
## 3     社會  217096
## 4     社會  214796
## 5     社會  172024
## 6     社會  171408

# 總點擊數
freqsum = applenews %>%
  select(clicked) %>% 
  sum()

#使用mutate產生新欄位
applenews %>%
  select(title, category,clicked) %>% 
  mutate(portion= clicked / freqsum) %>%
  arrange(desc(portion)) %>%
  head()

##                                          title category clicked    portion
## 1     泰國超正變性人徵兵處報到　網友：這我可以     正妹  344733 0.01597908
## 2     本土劇女神整形前照出土？！黑皮塌鼻引戰火     娛樂  299235 0.01387016
## 3     【狗仔偷拍】好大的派頭　李㼈違停霸氣外露     娛樂  265355 0.01229975
## 4  【更新】正晶揭露新詐騙案　7百萬存款不翼而飛     社會  241842 0.01120987
## 5 有內情？辣模女友控MP廷廷　「對我做可怕的事」     娛樂  239697 0.01111045
## 6         貴婦人妻太閒了　她只好和一些網友嘿咻     社會  228203 0.01057768

#新增portion欄位
applenews = applenews %>%
  mutate(portion= clicked / freqsum)

#group_by & summarise
applenews %>%
  group_by(category) %>%
  summarise(clicked_sum = sum(clicked, na.rm=TRUE)) %>%
  arrange(desc(clicked_sum))

## # A tibble: 14 x 2
##    category clicked_sum
##    <chr>          <int>
##  1 社會         5721750
##  2 娛樂         3571005
##  3 生活         3417804
##  4 國際         2540411
##  5 政治         1701980
##  6 體育         1598067
##  7 正妹          672949
##  8 搜奇          668307
##  9 財經          618243
## 10 論壇          312592
## 11 時尚          260499
## 12 地產          220812
## 13 3C            146308
## 14 動物          123287

#多個欄位計算
applenews %>%
  group_by(category) %>% 
  summarise_at(.vars=vars(clicked,portion),.funs=funs(sum))

## # A tibble: 14 x 3
##    category clicked portion
##    <chr>      <int>   <dbl>
##  1 3C        146308 0.00678
##  2 財經      618243 0.0287 
##  3 地產      220812 0.0102 
##  4 動物      123287 0.00571
##  5 國際     2540411 0.118  
##  6 論壇      312592 0.0145 
##  7 社會     5721750 0.265  
##  8 生活     3417804 0.158  
##  9 時尚      260499 0.0121 
## 10 搜奇      668307 0.0310 
## 11 體育     1598067 0.0741 
## 12 娛樂     3571005 0.166  
## 13 正妹      672949 0.0312 
## 14 政治     1701980 0.0789

applenews %>%
  group_by(category) %>% 
  summarise_at(.vars=vars(clicked),.funs=funs(sum,mean))

## # A tibble: 14 x 3
##    category     sum   mean
##    <chr>      <int>  <dbl>
##  1 3C        146308  3954.
##  2 財經      618243  5109.
##  3 地產      220812  6900.
##  4 動物      123287  4742.
##  5 國際     2540411  8914.
##  6 論壇      312592  5683.
##  7 社會     5721750 29494.
##  8 生活     3417804 11469.
##  9 時尚      260499  6855.
## 10 搜奇      668307 12151.
## 11 體育     1598067 16822.
## 12 娛樂     3571005 31602.
## 13 正妹      672949 84119.
## 14 政治     1701980 11902.

applenews %>%
  group_by(category) %>%
  summarise_at(.funs=funs(min,max), .vars=vars(matches('clicked')), na.rm=T)

## # A tibble: 14 x 3
##    category   min    max
##    <chr>    <dbl>  <dbl>
##  1 3C         267  20509
##  2 財經        24  54886
##  3 地產         0  80691
##  4 動物      1211  11753
##  5 國際         0 150825
##  6 論壇       275  68208
##  7 社會       918 241842
##  8 生活        20 132880
##  9 時尚       311  67086
## 10 搜奇       199  83036
## 11 體育       523 162907
## 12 娛樂      1631 299235
## 13 正妹      7999 344733
## 14 政治       221  83059

#一般計數
applenews %>%
  summarise(n())

##    n()
## 1 1500

#不重複計數
applenews %>%
  summarise(n_distinct(category))

##   n_distinct(category)
## 1                   14

cat_stat = applenews %>%
  group_by(category) %>%
  summarise(clicked_sum = sum(clicked)) 

cat_stat

## # A tibble: 14 x 2
##    category clicked_sum
##    <chr>          <int>
##  1 3C            146308
##  2 財經          618243
##  3 地產          220812
##  4 動物          123287
##  5 國際         2540411
##  6 論壇          312592
##  7 社會         5721750
##  8 生活         3417804
##  9 時尚          260499
## 10 搜奇          668307
## 11 體育         1598067
## 12 娛樂         3571005
## 13 正妹          672949
## 14 政治         1701980

#繪製長條圖
barplot(cat_stat$clicked_sum, names.arg=cat_stat$category, col=rainbow(length(cat_stat$category)),family="Songti SC")

#繪製圓餅圖
pie(cat_stat$clicked_sum, label = cat_stat$category,family="Songti SC")

補充：連接資料庫範例(以sqlite3為例)

# sqlite3 download page: https://www.sqlite.org/download.html
#install.packages('dbplyr')
#install.packages('RSQLite')
library('dbplyr')

## Warning: package 'dbplyr' was built under R version 3.4.3

## 
## Attaching package: 'dbplyr'

## The following objects are masked from 'package:dplyr':
## 
##     ident, sql

library('RSQLite')

my_database = src_sqlite('./mydatabase',create=T)
copy_to(my_database,applenews,temporary = F)
tbl(my_database,"applenews")

## # Source:   table<applenews> [?? x 5]
## # Database: sqlite 3.22.0 [./mydatabase]
##    title                                     dt category clicked   portion
##    <chr>                                  <dbl> <chr>      <int>     <dbl>
##  1 【更新】搶2.2萬彩券刮中1.4萬　沒發財還得入獄…    1.46e9 社會        1754   8.13e-5
##  2 拿到澳洲護照後　他放火燒中國護照      1.46e9 國際           0   0.     
##  3 【特企】房市大追擊- 租屋這些事情要小心…    1.46e9 地產           0   0.     
##  4 【央廣RTI】美菲軍演  美防長南海登艦…    1.46e9 國際           0   0.     
##  5 全球最閃牽手夫妻　絕美禮服出自台灣……    1.46e9 時尚         311   1.44e-5
##  6 公司遭搜索　浩鼎籲檢調勿公開商業機密…    1.46e9 財經          24   1.11e-6
##  7 【央廣RTI】每318秒就有1人罹癌  大腸癌名列第一…    1.46e9 生活          20   9.27e-7
##  8 垃圾掉滿地　村民請神明幫忙            1.46e9 生活         314   1.46e-5
##  9 【熊本強震】取消去九州　華航5月8日前退改票免手續費…    1.46e9 生活          27   1.25e-6
## 10 麵龜摻非工業色素　千顆不良品早下肚    1.46e9 生活         308   1.43e-5
## # ... with more rows

tbl(my_database,"applenews") %>% collect()

## # A tibble: 1,500 x 5
##    title                                     dt category clicked   portion
##    <chr>                                  <dbl> <chr>      <int>     <dbl>
##  1 【更新】搶2.2萬彩券刮中1.4萬　沒發財還得入獄…    1.46e9 社會        1754   8.13e-5
##  2 拿到澳洲護照後　他放火燒中國護照      1.46e9 國際           0   0.     
##  3 【特企】房市大追擊- 租屋這些事情要小心…    1.46e9 地產           0   0.     
##  4 【央廣RTI】美菲軍演  美防長南海登艦…    1.46e9 國際           0   0.     
##  5 全球最閃牽手夫妻　絕美禮服出自台灣……    1.46e9 時尚         311   1.44e-5
##  6 公司遭搜索　浩鼎籲檢調勿公開商業機密…    1.46e9 財經          24   1.11e-6
##  7 【央廣RTI】每318秒就有1人罹癌  大腸癌名列第一…    1.46e9 生活          20   9.27e-7
##  8 垃圾掉滿地　村民請神明幫忙            1.46e9 生活         314   1.46e-5
##  9 【熊本強震】取消去九州　華航5月8日前退改票免手續費…    1.46e9 生活          27   1.25e-6
## 10 麵龜摻非工業色素　千顆不良品早下肚    1.46e9 生活         308   1.43e-5
## # ... with 1,490 more rows

category_stat = tbl(my_database,"applenews") %>% 
  group_by(category) %>%
  summarise_at(.funs=funs(min,max,mean), .vars=vars(matches('clicked'))) %>%
  arrange(desc(mean)) %>%
  collect()

## Warning: Missing values are always removed in SQL.
## Use `MIN(x, na.rm = TRUE)` to silence this warning

## Warning: Missing values are always removed in SQL.
## Use `MAX(x, na.rm = TRUE)` to silence this warning

## Warning: Missing values are always removed in SQL.
## Use `AVG(x, na.rm = TRUE)` to silence this warning

library('ggplot2')
g <- ggplot(category_stat,aes(x=category,y=mean))
g + geom_bar(stat='identity') + theme(text=element_text(size=16,  family="Songti SC")) + scale_x_discrete(limits=category_stat$category)

Learning map

• http://scikit-learn.org/stable/_static/ml_map.png

• http://www.r-bloggers.com/whats-the-difference-between-machine-learning-statistics-and-data-mining/

• http://mp.weixin.qq.com/s?__biz=MjM5ODczNTkwMA==&mid=2650107069&idx=1&sn=44a2eab6c4858c56af236749fdd1d784#rd

Classification

Decision Tree - using churn data in C50 package

#install.packages("C50")
library(C50)

data(churn)
str(churnTrain)

## 'data.frame':    3333 obs. of  20 variables:
##  $ state                        : Factor w/ 51 levels "AK","AL","AR",..: 17 36 32 36 37 2 20 25 19 50 ...
##  $ account_length               : int  128 107 137 84 75 118 121 147 117 141 ...
##  $ area_code                    : Factor w/ 3 levels "area_code_408",..: 2 2 2 1 2 3 3 2 1 2 ...
##  $ international_plan           : Factor w/ 2 levels "no","yes": 1 1 1 2 2 2 1 2 1 2 ...
##  $ voice_mail_plan              : Factor w/ 2 levels "no","yes": 2 2 1 1 1 1 2 1 1 2 ...
##  $ number_vmail_messages        : int  25 26 0 0 0 0 24 0 0 37 ...
##  $ total_day_minutes            : num  265 162 243 299 167 ...
##  $ total_day_calls              : int  110 123 114 71 113 98 88 79 97 84 ...
##  $ total_day_charge             : num  45.1 27.5 41.4 50.9 28.3 ...
##  $ total_eve_minutes            : num  197.4 195.5 121.2 61.9 148.3 ...
##  $ total_eve_calls              : int  99 103 110 88 122 101 108 94 80 111 ...
##  $ total_eve_charge             : num  16.78 16.62 10.3 5.26 12.61 ...
##  $ total_night_minutes          : num  245 254 163 197 187 ...
##  $ total_night_calls            : int  91 103 104 89 121 118 118 96 90 97 ...
##  $ total_night_charge           : num  11.01 11.45 7.32 8.86 8.41 ...
##  $ total_intl_minutes           : num  10 13.7 12.2 6.6 10.1 6.3 7.5 7.1 8.7 11.2 ...
##  $ total_intl_calls             : int  3 3 5 7 3 6 7 6 4 5 ...
##  $ total_intl_charge            : num  2.7 3.7 3.29 1.78 2.73 1.7 2.03 1.92 2.35 3.02 ...
##  $ number_customer_service_calls: int  1 1 0 2 3 0 3 0 1 0 ...
##  $ churn                        : Factor w/ 2 levels "yes","no": 2 2 2 2 2 2 2 2 2 2 ...

names(churnTrain) %in% c("state", "area_code", "account_length")

##  [1]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

!names(churnTrain) %in% c("state", "area_code", "account_length")

##  [1] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [12]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE

#選擇建模變數
variable.list = !names(churnTrain) %in% c('state','area_code','account_length')
churnTrain=churnTrain[,variable.list]

str(churnTrain)

## 'data.frame':    3333 obs. of  17 variables:
##  $ international_plan           : Factor w/ 2 levels "no","yes": 1 1 1 2 2 2 1 2 1 2 ...
##  $ voice_mail_plan              : Factor w/ 2 levels "no","yes": 2 2 1 1 1 1 2 1 1 2 ...
##  $ number_vmail_messages        : int  25 26 0 0 0 0 24 0 0 37 ...
##  $ total_day_minutes            : num  265 162 243 299 167 ...
##  $ total_day_calls              : int  110 123 114 71 113 98 88 79 97 84 ...
##  $ total_day_charge             : num  45.1 27.5 41.4 50.9 28.3 ...
##  $ total_eve_minutes            : num  197.4 195.5 121.2 61.9 148.3 ...
##  $ total_eve_calls              : int  99 103 110 88 122 101 108 94 80 111 ...
##  $ total_eve_charge             : num  16.78 16.62 10.3 5.26 12.61 ...
##  $ total_night_minutes          : num  245 254 163 197 187 ...
##  $ total_night_calls            : int  91 103 104 89 121 118 118 96 90 97 ...
##  $ total_night_charge           : num  11.01 11.45 7.32 8.86 8.41 ...
##  $ total_intl_minutes           : num  10 13.7 12.2 6.6 10.1 6.3 7.5 7.1 8.7 11.2 ...
##  $ total_intl_calls             : int  3 3 5 7 3 6 7 6 4 5 ...
##  $ total_intl_charge            : num  2.7 3.7 3.29 1.78 2.73 1.7 2.03 1.92 2.35 3.02 ...
##  $ number_customer_service_calls: int  1 1 0 2 3 0 3 0 1 0 ...
##  $ churn                        : Factor w/ 2 levels "yes","no": 2 2 2 2 2 2 2 2 2 2 ...

#sample
?sample

## Help on topic 'sample' was found in the following packages:
## 
##   Package               Library
##   dplyr                 /Library/Frameworks/R.framework/Versions/3.4/Resources/library
##   base                  /Library/Frameworks/R.framework/Resources/library
## 
## 
## Using the first match ...

sample(1:10)

##  [1]  2  7  3 10  1  4  6  5  8  9

sample(1:10, size = 5)

## [1]  8  7 10  3  4

sample(c(0,1), size= 10, replace = T)

##  [1] 0 0 1 0 1 1 0 0 0 1

sample.int(20, 12) # 兩個參數都要放整數，此例為取1:20中的12個不重複樣本

##  [1] 19 20 16 10 11 12  5  9  7  4  1 14

set.seed(2)
#把資料分成training data 和 testing data
ind<-sample(1:2, size=nrow(churnTrain), replace=T, prob=c(0.7, 0.3))
trainset=churnTrain[ind==1,]
testset=churnTrain[ind==2,]

rpart

#install.packages('rpart')
library('rpart')
#使用rpart(CART)建立決策樹模型
?rpart
con = rpart.control(cp=0.01)
?rpart.control
churn.rp<-rpart(churn ~., data=trainset,control = con)
#churn.rp<-rpart(churn ~ total_day_charge + international_plan, data=trainset)

churn.rp

## n= 2315 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
##  1) root 2315 342 no (0.14773218 0.85226782)  
##    2) total_day_minutes>=265.45 144  59 yes (0.59027778 0.40972222)  
##      4) voice_mail_plan=no 110  29 yes (0.73636364 0.26363636)  
##        8) total_eve_minutes>=188.5 67   3 yes (0.95522388 0.04477612) *
##        9) total_eve_minutes< 188.5 43  17 no (0.39534884 0.60465116)  
##         18) total_day_minutes>=282.7 19   6 yes (0.68421053 0.31578947) *
##         19) total_day_minutes< 282.7 24   4 no (0.16666667 0.83333333) *
##      5) voice_mail_plan=yes 34   4 no (0.11764706 0.88235294) *
##    3) total_day_minutes< 265.45 2171 257 no (0.11837863 0.88162137)  
##      6) number_customer_service_calls>=3.5 168  82 yes (0.51190476 0.48809524)  
##       12) total_day_minutes< 160.2 71  10 yes (0.85915493 0.14084507) *
##       13) total_day_minutes>=160.2 97  25 no (0.25773196 0.74226804)  
##         26) total_eve_minutes< 155.5 20   7 yes (0.65000000 0.35000000) *
##         27) total_eve_minutes>=155.5 77  12 no (0.15584416 0.84415584) *
##      7) number_customer_service_calls< 3.5 2003 171 no (0.08537194 0.91462806)  
##       14) international_plan=yes 188  76 no (0.40425532 0.59574468)  
##         28) total_intl_calls< 2.5 38   0 yes (1.00000000 0.00000000) *
##         29) total_intl_calls>=2.5 150  38 no (0.25333333 0.74666667)  
##           58) total_intl_minutes>=13.1 32   0 yes (1.00000000 0.00000000) *
##           59) total_intl_minutes< 13.1 118   6 no (0.05084746 0.94915254) *
##       15) international_plan=no 1815  95 no (0.05234160 0.94765840)  
##         30) total_day_minutes>=224.15 251  50 no (0.19920319 0.80079681)  
##           60) total_eve_minutes>=259.8 36  10 yes (0.72222222 0.27777778) *
##           61) total_eve_minutes< 259.8 215  24 no (0.11162791 0.88837209) *
##         31) total_day_minutes< 224.15 1564  45 no (0.02877238 0.97122762) *

summary(churn.rp)

## Call:
## rpart(formula = churn ~ ., data = trainset, control = con)
##   n= 2315 
## 
##           CP nsplit rel error    xerror       xstd
## 1 0.07602339      0 1.0000000 1.0000000 0.04992005
## 2 0.07456140      2 0.8479532 0.9970760 0.04985964
## 3 0.05555556      4 0.6988304 0.7602339 0.04442127
## 4 0.02631579      7 0.4941520 0.5263158 0.03767329
## 5 0.02339181      8 0.4678363 0.5204678 0.03748096
## 6 0.02046784     10 0.4210526 0.5087719 0.03709209
## 7 0.01754386     11 0.4005848 0.4707602 0.03578773
## 8 0.01000000     12 0.3830409 0.4766082 0.03599261
## 
## Variable importance
##             total_day_minutes              total_day_charge 
##                            18                            18 
## number_customer_service_calls            total_intl_minutes 
##                            10                             8 
##             total_intl_charge              total_eve_charge 
##                             8                             8 
##             total_eve_minutes            international_plan 
##                             8                             7 
##              total_intl_calls         number_vmail_messages 
##                             6                             3 
##               voice_mail_plan             total_night_calls 
##                             3                             1 
##               total_eve_calls 
##                             1 
## 
## Node number 1: 2315 observations,    complexity param=0.07602339
##   predicted class=no   expected loss=0.1477322  P(node) =1
##     class counts:   342  1973
##    probabilities: 0.148 0.852 
##   left son=2 (144 obs) right son=3 (2171 obs)
##   Primary splits:
##       total_day_minutes             < 265.45 to the right, improve=60.145020, (0 missing)
##       total_day_charge              < 45.125 to the right, improve=60.145020, (0 missing)
##       number_customer_service_calls < 3.5    to the right, improve=53.641430, (0 missing)
##       international_plan            splits as  RL,         improve=43.729370, (0 missing)
##       voice_mail_plan               splits as  LR,         improve= 6.089388, (0 missing)
##   Surrogate splits:
##       total_day_charge < 45.125 to the right, agree=1, adj=1, (0 split)
## 
## Node number 2: 144 observations,    complexity param=0.07602339
##   predicted class=yes  expected loss=0.4097222  P(node) =0.06220302
##     class counts:    85    59
##    probabilities: 0.590 0.410 
##   left son=4 (110 obs) right son=5 (34 obs)
##   Primary splits:
##       voice_mail_plan       splits as  LR,         improve=19.884860, (0 missing)
##       number_vmail_messages < 9.5    to the left,  improve=19.884860, (0 missing)
##       total_eve_minutes     < 167.05 to the right, improve=14.540020, (0 missing)
##       total_eve_charge      < 14.2   to the right, improve=14.540020, (0 missing)
##       total_day_minutes     < 283.9  to the right, improve= 6.339827, (0 missing)
##   Surrogate splits:
##       number_vmail_messages < 9.5    to the left,  agree=1.000, adj=1.000, (0 split)
##       total_night_minutes   < 110.3  to the right, agree=0.785, adj=0.088, (0 split)
##       total_night_charge    < 4.965  to the right, agree=0.785, adj=0.088, (0 split)
##       total_night_calls     < 50     to the right, agree=0.778, adj=0.059, (0 split)
##       total_intl_minutes    < 15.3   to the left,  agree=0.771, adj=0.029, (0 split)
## 
## Node number 3: 2171 observations,    complexity param=0.0745614
##   predicted class=no   expected loss=0.1183786  P(node) =0.937797
##     class counts:   257  1914
##    probabilities: 0.118 0.882 
##   left son=6 (168 obs) right son=7 (2003 obs)
##   Primary splits:
##       number_customer_service_calls < 3.5    to the right, improve=56.398210, (0 missing)
##       international_plan            splits as  RL,         improve=43.059160, (0 missing)
##       total_day_minutes             < 224.15 to the right, improve=10.847440, (0 missing)
##       total_day_charge              < 38.105 to the right, improve=10.847440, (0 missing)
##       total_intl_minutes            < 13.15  to the right, improve= 6.347319, (0 missing)
## 
## Node number 4: 110 observations,    complexity param=0.02631579
##   predicted class=yes  expected loss=0.2636364  P(node) =0.0475162
##     class counts:    81    29
##    probabilities: 0.736 0.264 
##   left son=8 (67 obs) right son=9 (43 obs)
##   Primary splits:
##       total_eve_minutes   < 188.5  to the right, improve=16.419610, (0 missing)
##       total_eve_charge    < 16.025 to the right, improve=16.419610, (0 missing)
##       total_night_minutes < 206.85 to the right, improve= 5.350500, (0 missing)
##       total_night_charge  < 9.305  to the right, improve= 5.350500, (0 missing)
##       total_day_minutes   < 281.15 to the right, improve= 5.254545, (0 missing)
##   Surrogate splits:
##       total_eve_charge   < 16.025 to the right, agree=1.000, adj=1.000, (0 split)
##       total_night_calls  < 82     to the right, agree=0.655, adj=0.116, (0 split)
##       total_intl_minutes < 3.35   to the right, agree=0.636, adj=0.070, (0 split)
##       total_intl_charge  < 0.905  to the right, agree=0.636, adj=0.070, (0 split)
##       total_day_minutes  < 268.55 to the right, agree=0.627, adj=0.047, (0 split)
## 
## Node number 5: 34 observations
##   predicted class=no   expected loss=0.1176471  P(node) =0.01468683
##     class counts:     4    30
##    probabilities: 0.118 0.882 
## 
## Node number 6: 168 observations,    complexity param=0.0745614
##   predicted class=yes  expected loss=0.4880952  P(node) =0.07257019
##     class counts:    86    82
##    probabilities: 0.512 0.488 
##   left son=12 (71 obs) right son=13 (97 obs)
##   Primary splits:
##       total_day_minutes             < 160.2  to the left,  improve=29.655880, (0 missing)
##       total_day_charge              < 27.235 to the left,  improve=29.655880, (0 missing)
##       total_eve_minutes             < 180.65 to the left,  improve= 8.556953, (0 missing)
##       total_eve_charge              < 15.355 to the left,  improve= 8.556953, (0 missing)
##       number_customer_service_calls < 4.5    to the right, improve= 5.975362, (0 missing)
##   Surrogate splits:
##       total_day_charge              < 27.235 to the left,  agree=1.000, adj=1.000, (0 split)
##       total_night_calls             < 79     to the left,  agree=0.625, adj=0.113, (0 split)
##       total_intl_calls              < 2.5    to the left,  agree=0.619, adj=0.099, (0 split)
##       number_customer_service_calls < 4.5    to the right, agree=0.607, adj=0.070, (0 split)
##       total_eve_calls               < 89.5   to the left,  agree=0.601, adj=0.056, (0 split)
## 
## Node number 7: 2003 observations,    complexity param=0.05555556
##   predicted class=no   expected loss=0.08537194  P(node) =0.8652268
##     class counts:   171  1832
##    probabilities: 0.085 0.915 
##   left son=14 (188 obs) right son=15 (1815 obs)
##   Primary splits:
##       international_plan splits as  RL,         improve=42.194510, (0 missing)
##       total_day_minutes  < 224.15 to the right, improve=16.838410, (0 missing)
##       total_day_charge   < 38.105 to the right, improve=16.838410, (0 missing)
##       total_intl_minutes < 13.15  to the right, improve= 6.210678, (0 missing)
##       total_intl_charge  < 3.55   to the right, improve= 6.210678, (0 missing)
## 
## Node number 8: 67 observations
##   predicted class=yes  expected loss=0.04477612  P(node) =0.02894168
##     class counts:    64     3
##    probabilities: 0.955 0.045 
## 
## Node number 9: 43 observations,    complexity param=0.02046784
##   predicted class=no   expected loss=0.3953488  P(node) =0.01857451
##     class counts:    17    26
##    probabilities: 0.395 0.605 
##   left son=18 (19 obs) right son=19 (24 obs)
##   Primary splits:
##       total_day_minutes   < 282.7  to the right, improve=5.680947, (0 missing)
##       total_day_charge    < 48.06  to the right, improve=5.680947, (0 missing)
##       total_night_minutes < 212.65 to the right, improve=4.558140, (0 missing)
##       total_night_charge  < 9.57   to the right, improve=4.558140, (0 missing)
##       total_eve_minutes   < 145.4  to the right, improve=4.356169, (0 missing)
##   Surrogate splits:
##       total_day_charge   < 48.06  to the right, agree=1.000, adj=1.000, (0 split)
##       total_day_calls    < 103    to the left,  agree=0.674, adj=0.263, (0 split)
##       total_eve_calls    < 104.5  to the left,  agree=0.674, adj=0.263, (0 split)
##       total_intl_minutes < 11.55  to the left,  agree=0.651, adj=0.211, (0 split)
##       total_intl_charge  < 3.12   to the left,  agree=0.651, adj=0.211, (0 split)
## 
## Node number 12: 71 observations
##   predicted class=yes  expected loss=0.1408451  P(node) =0.03066955
##     class counts:    61    10
##    probabilities: 0.859 0.141 
## 
## Node number 13: 97 observations,    complexity param=0.01754386
##   predicted class=no   expected loss=0.257732  P(node) =0.04190065
##     class counts:    25    72
##    probabilities: 0.258 0.742 
##   left son=26 (20 obs) right son=27 (77 obs)
##   Primary splits:
##       total_eve_minutes             < 155.5  to the left,  improve=7.753662, (0 missing)
##       total_eve_charge              < 13.22  to the left,  improve=7.753662, (0 missing)
##       total_intl_minutes            < 13.55  to the right, improve=2.366149, (0 missing)
##       total_intl_charge             < 3.66   to the right, improve=2.366149, (0 missing)
##       number_customer_service_calls < 4.5    to the right, improve=2.297667, (0 missing)
##   Surrogate splits:
##       total_eve_charge  < 13.22  to the left,  agree=1.000, adj=1.00, (0 split)
##       total_night_calls < 143.5  to the right, agree=0.814, adj=0.10, (0 split)
##       total_eve_calls   < 62     to the left,  agree=0.804, adj=0.05, (0 split)
## 
## Node number 14: 188 observations,    complexity param=0.05555556
##   predicted class=no   expected loss=0.4042553  P(node) =0.0812095
##     class counts:    76   112
##    probabilities: 0.404 0.596 
##   left son=28 (38 obs) right son=29 (150 obs)
##   Primary splits:
##       total_intl_calls   < 2.5    to the left,  improve=33.806520, (0 missing)
##       total_intl_minutes < 13.1   to the right, improve=30.527050, (0 missing)
##       total_intl_charge  < 3.535  to the right, improve=30.527050, (0 missing)
##       total_day_minutes  < 221.95 to the right, improve= 3.386095, (0 missing)
##       total_day_charge   < 37.735 to the right, improve= 3.386095, (0 missing)
## 
## Node number 15: 1815 observations,    complexity param=0.02339181
##   predicted class=no   expected loss=0.0523416  P(node) =0.7840173
##     class counts:    95  1720
##    probabilities: 0.052 0.948 
##   left son=30 (251 obs) right son=31 (1564 obs)
##   Primary splits:
##       total_day_minutes   < 224.15 to the right, improve=12.5649300, (0 missing)
##       total_day_charge    < 38.105 to the right, improve=12.5649300, (0 missing)
##       total_eve_minutes   < 244.95 to the right, improve= 4.7875890, (0 missing)
##       total_eve_charge    < 20.825 to the right, improve= 4.7875890, (0 missing)
##       total_night_minutes < 163.85 to the right, improve= 0.9074391, (0 missing)
##   Surrogate splits:
##       total_day_charge < 38.105 to the right, agree=1, adj=1, (0 split)
## 
## Node number 18: 19 observations
##   predicted class=yes  expected loss=0.3157895  P(node) =0.008207343
##     class counts:    13     6
##    probabilities: 0.684 0.316 
## 
## Node number 19: 24 observations
##   predicted class=no   expected loss=0.1666667  P(node) =0.01036717
##     class counts:     4    20
##    probabilities: 0.167 0.833 
## 
## Node number 26: 20 observations
##   predicted class=yes  expected loss=0.35  P(node) =0.008639309
##     class counts:    13     7
##    probabilities: 0.650 0.350 
## 
## Node number 27: 77 observations
##   predicted class=no   expected loss=0.1558442  P(node) =0.03326134
##     class counts:    12    65
##    probabilities: 0.156 0.844 
## 
## Node number 28: 38 observations
##   predicted class=yes  expected loss=0  P(node) =0.01641469
##     class counts:    38     0
##    probabilities: 1.000 0.000 
## 
## Node number 29: 150 observations,    complexity param=0.05555556
##   predicted class=no   expected loss=0.2533333  P(node) =0.06479482
##     class counts:    38   112
##    probabilities: 0.253 0.747 
##   left son=58 (32 obs) right son=59 (118 obs)
##   Primary splits:
##       total_intl_minutes < 13.1   to the right, improve=45.356840, (0 missing)
##       total_intl_charge  < 3.535  to the right, improve=45.356840, (0 missing)
##       total_day_calls    < 95.5   to the left,  improve= 4.036407, (0 missing)
##       total_day_minutes  < 237.75 to the right, improve= 1.879020, (0 missing)
##       total_day_charge   < 40.42  to the right, improve= 1.879020, (0 missing)
##   Surrogate splits:
##       total_intl_charge < 3.535  to the right, agree=1.0, adj=1.000, (0 split)
##       total_day_minutes < 52.45  to the left,  agree=0.8, adj=0.063, (0 split)
##       total_day_charge  < 8.92   to the left,  agree=0.8, adj=0.063, (0 split)
## 
## Node number 30: 251 observations,    complexity param=0.02339181
##   predicted class=no   expected loss=0.1992032  P(node) =0.1084233
##     class counts:    50   201
##    probabilities: 0.199 0.801 
##   left son=60 (36 obs) right son=61 (215 obs)
##   Primary splits:
##       total_eve_minutes     < 259.8  to the right, improve=22.993380, (0 missing)
##       total_eve_charge      < 22.08  to the right, improve=22.993380, (0 missing)
##       voice_mail_plan       splits as  LR,         improve= 4.745664, (0 missing)
##       number_vmail_messages < 7.5    to the left,  improve= 4.745664, (0 missing)
##       total_night_minutes   < 181.15 to the right, improve= 3.509731, (0 missing)
##   Surrogate splits:
##       total_eve_charge < 22.08  to the right, agree=1, adj=1, (0 split)
## 
## Node number 31: 1564 observations
##   predicted class=no   expected loss=0.02877238  P(node) =0.675594
##     class counts:    45  1519
##    probabilities: 0.029 0.971 
## 
## Node number 58: 32 observations
##   predicted class=yes  expected loss=0  P(node) =0.01382289
##     class counts:    32     0
##    probabilities: 1.000 0.000 
## 
## Node number 59: 118 observations
##   predicted class=no   expected loss=0.05084746  P(node) =0.05097192
##     class counts:     6   112
##    probabilities: 0.051 0.949 
## 
## Node number 60: 36 observations
##   predicted class=yes  expected loss=0.2777778  P(node) =0.01555076
##     class counts:    26    10
##    probabilities: 0.722 0.278 
## 
## Node number 61: 215 observations
##   predicted class=no   expected loss=0.1116279  P(node) =0.09287257
##     class counts:    24   191
##    probabilities: 0.112 0.888

#畫出決策樹
par(mfrow=c(1,1))
?plot.rpart
plot(churn.rp, uniform=TRUE,branch = 0.6, margin=0.1)
text(churn.rp, all=TRUE, use.n=TRUE, cex=0.7)

printcp(churn.rp)

## 
## Classification tree:
## rpart(formula = churn ~ ., data = trainset, control = con)
## 
## Variables actually used in tree construction:
## [1] international_plan            number_customer_service_calls
## [3] total_day_minutes             total_eve_minutes            
## [5] total_intl_calls              total_intl_minutes           
## [7] voice_mail_plan              
## 
## Root node error: 342/2315 = 0.14773
## 
## n= 2315 
## 
##         CP nsplit rel error  xerror     xstd
## 1 0.076023      0   1.00000 1.00000 0.049920
## 2 0.074561      2   0.84795 0.99708 0.049860
## 3 0.055556      4   0.69883 0.76023 0.044421
## 4 0.026316      7   0.49415 0.52632 0.037673
## 5 0.023392      8   0.46784 0.52047 0.037481
## 6 0.020468     10   0.42105 0.50877 0.037092
## 7 0.017544     11   0.40058 0.47076 0.035788
## 8 0.010000     12   0.38304 0.47661 0.035993

plotcp(churn.rp)

Prune

#找出minimum cross-validation errors
min_row = which.min(churn.rp$cptable[,"xerror"])
churn.cp = churn.rp$cptable[min_row, "CP"]
#將churn.cp設為臨界值來修剪樹
prune.tree=prune(churn.rp, cp=churn.cp)

plot(prune.tree, margin=0.1)
text(prune.tree, all=TRUE, use.n=TRUE, cex=0.7)

predictions <-predict(prune.tree, testset, type='class')
table(predictions,testset$churn)

##            
## predictions yes  no
##         yes  95  14
##         no   46 863

#install.packages('caret')
#install.packages('e1071')
library('caret')

## Loading required package: lattice

library('e1071')
confusionMatrix(table(predictions, testset$churn))

## Confusion Matrix and Statistics
## 
##            
## predictions yes  no
##         yes  95  14
##         no   46 863
##                                           
##                Accuracy : 0.9411          
##                  95% CI : (0.9248, 0.9547)
##     No Information Rate : 0.8615          
##     P-Value [Acc > NIR] : 2.786e-16       
##                                           
##                   Kappa : 0.727           
##  Mcnemar's Test P-Value : 6.279e-05       
##                                           
##             Sensitivity : 0.67376         
##             Specificity : 0.98404         
##          Pos Pred Value : 0.87156         
##          Neg Pred Value : 0.94939         
##              Prevalence : 0.13851         
##          Detection Rate : 0.09332         
##    Detection Prevalence : 0.10707         
##       Balanced Accuracy : 0.82890         
##                                           
##        'Positive' Class : yes             
## 

?confusionMatrix

## Help on topic 'confusionMatrix' was found in the following
## packages:
## 
##   Package               Library
##   caret                 /Library/Frameworks/R.framework/Versions/3.4/Resources/library
##   ModelMetrics          /Library/Frameworks/R.framework/Versions/3.4/Resources/library
## 
## 
## Using the first match ...

ctree

#install.packages("party")
library('party')

## Loading required package: grid

## Loading required package: mvtnorm

## Loading required package: modeltools

## Loading required package: stats4

## Loading required package: strucchange

## Loading required package: zoo

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

## Loading required package: sandwich

## 
## Attaching package: 'strucchange'

## The following object is masked from 'package:stringr':
## 
##     boundary

ctree.model = ctree(churn ~ . , data = trainset)
plot(ctree.model, margin=0.1)

daycharge.model = ctree(churn ~ total_day_charge + international_plan, data = trainset)
plot(daycharge.model)

ctree.predict = predict(ctree.model ,testset)
table(ctree.predict, testset$churn)

##              
## ctree.predict yes  no
##           yes  99  15
##           no   42 862

confusionMatrix(table(ctree.predict, testset$churn))

## Confusion Matrix and Statistics
## 
##              
## ctree.predict yes  no
##           yes  99  15
##           no   42 862
##                                           
##                Accuracy : 0.944           
##                  95% CI : (0.9281, 0.9573)
##     No Information Rate : 0.8615          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.7449          
##  Mcnemar's Test P-Value : 0.0005736       
##                                           
##             Sensitivity : 0.70213         
##             Specificity : 0.98290         
##          Pos Pred Value : 0.86842         
##          Neg Pred Value : 0.95354         
##              Prevalence : 0.13851         
##          Detection Rate : 0.09725         
##    Detection Prevalence : 0.11198         
##       Balanced Accuracy : 0.84251         
##                                           
##        'Positive' Class : yes             
## 

C5.0

#install.packages("C50")
library(C50)
c50.model = C5.0(churn ~., data=trainset)

?C5.0Control

c=C5.0Control(minCases = 20)
c50.model = C5.0(churn ~., data=trainset,control = c)

summary(c50.model)

## 
## Call:
## C5.0.formula(formula = churn ~ ., data = trainset, control = c)
## 
## 
## C5.0 [Release 2.07 GPL Edition]      Wed May  9 16:49:41 2018
## -------------------------------
## 
## Class specified by attribute `outcome'
## 
## Read 2315 cases (17 attributes) from undefined.data
## 
## Decision tree:
## 
## number_customer_service_calls > 3:
## :...total_day_minutes <= 160.1: yes (71/10)
## :   total_day_minutes > 160.1: no (108/32)
## number_customer_service_calls <= 3:
## :...international_plan = yes:
##     :...total_intl_calls <= 2: yes (41)
##     :   total_intl_calls > 2:
##     :   :...total_intl_minutes <= 13.1: no (134/13)
##     :       total_intl_minutes > 13.1: yes (34)
##     international_plan = no:
##     :...total_day_minutes <= 224.1: no (1564/45)
##         total_day_minutes > 224.1:
##         :...voice_mail_plan = yes: no (97/4)
##             voice_mail_plan = no:
##             :...total_eve_charge <= 17.47:
##                 :...total_day_minutes <= 278.4: no (124/10)
##                 :   total_day_minutes > 278.4: yes (20/5)
##                 total_eve_charge > 17.47:
##                 :...total_day_minutes > 264: yes (46)
##                     total_day_minutes <= 264:
##                     :...total_eve_charge > 22.04: yes (29/4)
##                         total_eve_charge <= 22.04:
##                         :...total_night_charge <= 9.04: no (23/1)
##                             total_night_charge > 9.04: yes (24/9)
## 
## 
## Evaluation on training data (2315 cases):
## 
##      Decision Tree   
##    ----------------  
##    Size      Errors  
## 
##      13  133( 5.7%)   <<
## 
## 
##     (a)   (b)    <-classified as
##    ----  ----
##     237   105    (a): class yes
##      28  1945    (b): class no
## 
## 
##  Attribute usage:
## 
##  100.00% number_customer_service_calls
##   92.27% international_plan
##   90.97% total_day_minutes
##   15.68% voice_mail_plan
##   11.49% total_eve_charge
##    9.03% total_intl_calls
##    7.26% total_intl_minutes
##    2.03% total_night_charge
## 
## 
## Time: 0.0 secs

plot(c50.model)

c50.predict = predict(c50.model,testset)
table(c50.predict, testset$churn)

##            
## c50.predict yes  no
##         yes  97  15
##         no   44 862

confusionMatrix(table(c50.predict, testset$churn))

## Confusion Matrix and Statistics
## 
##            
## c50.predict yes  no
##         yes  97  15
##         no   44 862
##                                           
##                Accuracy : 0.942           
##                  95% CI : (0.9259, 0.9556)
##     No Information Rate : 0.8615          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.7342          
##  Mcnemar's Test P-Value : 0.0002671       
##                                           
##             Sensitivity : 0.68794         
##             Specificity : 0.98290         
##          Pos Pred Value : 0.86607         
##          Neg Pred Value : 0.95143         
##              Prevalence : 0.13851         
##          Detection Rate : 0.09528         
##    Detection Prevalence : 0.11002         
##       Balanced Accuracy : 0.83542         
##                                           
##        'Positive' Class : yes             
##