你睡眠情況怎麼樣？Jawbone Up 數據，R 實現

身邊很多朋友都對 Jawbone Up 對睡眠監控的情況很感興趣，一般會順帶問問：

“你睡眠情況怎麼樣？”。

“好吧，俺計算一下再告訴你。”

下面的文中涉及到如何用 R 畫基於日曆的熱度圖，以及如何將多個圖進行合併的內容。

數據獲取

需要使用的數據是每日的睡眠情況數據。

要獲取這個數據有兩個辦法，最簡單的辦法是從 Jawbone.com 網站下載個人的數據文件。詳情參見個人數據官方下載 。

還有一種辦法是利用 非官方API 中的 user/%userXid%/trends 方法獲取按日統計數據。

下面的代碼採用 Python 實現，用於從服務器端獲取 Trends 數據。

account = { "email" : "your@email" , "passwd" : "yourpassword" } device = DeviceJawboneUp () # login device.get_users_login(account["email"], account["passwd"]) # Trends daytrends = device.get_users_trends( endDate = None , inRange = "d" , bucketSize = "d" ) device.saveJsonData( filename = "/users_trends-day.json" , data = daytrends)

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account = { "email" : "your@email" , "passwd" : "yourpassword" }   device = DeviceJawboneUp ()   # login device.get_users_login(account["email"], account["passwd"])   # Trends daytrends = device.get_users_trends( endDate =  None , inRange = "d" , bucketSize = "d"  ) device.saveJsonData( filename = "/users_trends-day.json" , data = daytrends)

返回的JSON數據示例如下：

{ "meta": { "code": 200, "message": "OK", "user_xid": "RGaCBFg9CsDYVvm2xxxxxx", "time": 1374161411 }, "data": { "earliest": 20130605, "data": [ [ 20130605, { "e_protein": null, "weight": 57.0, "m_distance": 270.0, "s_awakenings": null, "height": 1.58, "m_lcat": 88.0, "n_awakenings": null, "s_quality": null, "n_deep": null, "m_steps": 367.0, "e_cholesterol": null, "s_light": null, "s_deep": null, "s_bedtime": null, "n_bedtime": null, "m_workout_time": 0.0, "e_calcium": null, "e_sat_fat": null, "n_duration": null, "n_light": null, "e_calories": null, "pal": null, "m_lcit": 0.0, "s_awake_time": null, "e_unsat_fat": null, "m_calories": 12.3110000491, "bmr": 1319.38485068, "m_total_calories": 1331.69585073, "e_sugar": null, "e_sodium": null, "s_awake": null, "s_asleep_time": null, "s_duration": null, "gender": 1.0, "age": 37.0136986301, "e_carbs": null, "e_fiber": null, "n_quality": null, "n_awake_time": null, "n_awake": null, "n_asleep_time": null, "m_active_time": 201.0 } ] ], "links": { "next": "/nudge/api/v.1.34/users/RGaCBFg9CsDYVvm2kchbcw/trends/?end_date=20110724&range=d&range_duration=730&bucket_size=d" } } }

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{     "meta": {         "code": 200,         "message": "OK",         "user_xid": "RGaCBFg9CsDYVvm2xxxxxx",         "time": 1374161411     },     "data": {         "earliest": 20130605,         "data": [             [                 20130605,                 {                     "e_protein": null,                     "weight": 57.0,                     "m_distance": 270.0,                     "s_awakenings": null,                     "height": 1.58,                     "m_lcat": 88.0,                     "n_awakenings": null,                     "s_quality": null,                     "n_deep": null,                     "m_steps": 367.0,                     "e_cholesterol": null,                     "s_light": null,                     "s_deep": null,                     "s_bedtime": null,                     "n_bedtime": null,                     "m_workout_time": 0.0,                     "e_calcium": null,                     "e_sat_fat": null,                     "n_duration": null,                     "n_light": null,                     "e_calories": null,                     "pal": null,                     "m_lcit": 0.0,                     "s_awake_time": null,                     "e_unsat_fat": null,                     "m_calories": 12.3110000491,                     "bmr": 1319.38485068,                     "m_total_calories": 1331.69585073,                     "e_sugar": null,                     "e_sodium": null,                     "s_awake": null,                     "s_asleep_time": null,                     "s_duration": null,                     "gender": 1.0,                     "age": 37.0136986301,                     "e_carbs": null,                     "e_fiber": null,                     "n_quality": null,                     "n_awake_time": null,                     "n_awake": null,                     "n_asleep_time": null,                     "m_active_time": 201.0                 }             ]         ],         "links": {             "next": "/nudge/api/v.1.34/users/RGaCBFg9CsDYVvm2kchbcw/trends/?end_date=20110724&range=d&range_duration=730&bucket_size=d"         }     } }

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數據預處理

下面的代碼，採用 Python 實現了將 Trends 數據轉換成平面CSV文件。

def list2string(list ): ret = "" for l in list: ret = "%s,%s" % (ret , l) return "%s\n" % ret[1:] def parseJawboneTrends(self , path , filename ): fn = "%s%s.json" % ( path , filename ) ofn = "%s.csv" % ( filename ) input = open( fn , 'r' ) str = input.read() v = json.loads(str) output = open( ofn , 'w' ) datas = v["data"]["data"] tmps = datas[0][1] headerKeys = sorted(tmps , key=lambda tmps:tmps[0] ) header = "datestr,%s" % self.list2string(headerKeys) print header output.write( header ) for d in datas: tmps = d[1] stmp = "" for key in headerKeys: stmp = "%s,%s" % ( stmp , tmps[ key ]) str = "%s,%s\n" % (d[0] , stmp[1:]) print str output.write( str ) input.close() output.close() if __name__ == "__main__": datapath = "../data/jawboneup/" parseJawboneTrends( path = datapath , filename = "users_trends-day" )

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def list2string(list ):     ret = ""     for l in list:         ret = "%s,%s" % (ret , l)     return "%s\n" % ret[1:]   def parseJawboneTrends(self , path , filename ):     fn = "%s%s.json" % ( path , filename )     ofn = "%s.csv" % ( filename )       input = open( fn , 'r' )     str = input.read()     v = json.loads(str)       output = open( ofn , 'w' )     datas = v["data"]["data"]       tmps = datas[0][1]     headerKeys = sorted(tmps , key=lambda tmps:tmps[0] )     header = "datestr,%s" % self.list2string(headerKeys)     print header     output.write( header )       for d in datas:         tmps = d[1]         stmp = ""         for key in headerKeys:             stmp = "%s,%s" % ( stmp , tmps[ key ])           str = "%s,%s\n" % (d[0] ,  stmp[1:])         print str         output.write( str )       input.close()     output.close()   if __name__ == "__main__":     datapath = "../data/jawboneup/"     parseJawboneTrends( path = datapath , filename = "users_trends-day" )

處理後的數據文件樣本爲：

datestr,age,bmr,e_protein,e_calories,e_cholesterol,e_sat_fat,e_calcium,e_unsat_fat,e_sugar,e_sodium,e_carbs,e_fiber,gender,height,m_distance,m_lcat,m_workout_time,m_lcit,m_active_time,m_calories,m_total_calories,m_steps,n_awakenings,n_deep,n_bedtime,n_duration,n_light,n_quality,n_awake_time,n_awake,n_asleep_time,pal,s_awakenings,s_quality,s_light,s_deep,s_bedtime,s_awake_time,s_awake,s_asleep_time,s_duration,weight 20130605,37.0136986301,1319.38485068,None,None,None,None,None,None,None,None,None,None,1.0,1.58,270.0,88.0,0.0,0.0,201.0,12.3110000491,1331.69585073,367.0,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,57.0 20130606,37.0164383562,1319.37204082,None,None,None,None,None,None,None,None,None,None,1.0,1.58,8561.0,1129.0,0.0,3480.0,6412.0,336.044000283,1655.4160411,12339.0,None,None,None,None,None,None,None,None,None,None,0.0,71.0,8742.0,11409.0,14.0,374.0,2082.0,41.0,20151.0,57.0 20130607,37.0191780822,1319.35923096,None,None,None,None,None,None,None,None,None,None,1.0,1.58,8818.0,1450.0,0.0,3660.0,7040.0,346.185998712,1665.54522967,13030.0,0.0,2580.0,1317.0,5342.0,2762.0,18.0,1422.0,1052.0,1333.0,None,2.0,64.0,14160.0,7225.0,1418.0,378.0,3297.0,1429.0,21385.0,57.0 20130608,37.0219178082,1319.3464211,None,None,None,None,None,None,None,None,None,None,1.0,1.58,4644.0,645.0,0.0,5640.0,3472.0,183.414000019,1502.76042111,6693.0,None,None,None,None,None,None,None,None,None,None,0.0,0.0,0.0,0.0,1410.0,390.0,0.0,1410.0,25200.0,57.0 20130609,37.0246575342,1319.33361123,15.3100000619,232.0,460.0,4.5640001595,70.0,9.69599956274,0.939999982715,663.0,4.76000010967,0.300000011921,1.0,1.58,7965.0,1662.0,1800.0,7260.0,7917.0,375.598999478,1694.93261071,11611.0,None,None,None,None,None,None,None,None,None,None,2.0,99.0,12406.0,16136.0,1317.0,372.0,2189.0,1321.0,28542.0,57.0

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datestr,age,bmr,e_protein,e_calories,e_cholesterol,e_sat_fat,e_calcium,e_unsat_fat,e_sugar,e_sodium,e_carbs,e_fiber,gender,height,m_distance,m_lcat,m_workout_time,m_lcit,m_active_time,m_calories,m_total_calories,m_steps,n_awakenings,n_deep,n_bedtime,n_duration,n_light,n_quality,n_awake_time,n_awake,n_asleep_time,pal,s_awakenings,s_quality,s_light,s_deep,s_bedtime,s_awake_time,s_awake,s_asleep_time,s_duration,weight 20130605,37.0136986301,1319.38485068,None,None,None,None,None,None,None,None,None,None,1.0,1.58,270.0,88.0,0.0,0.0,201.0,12.3110000491,1331.69585073,367.0,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,57.0 20130606,37.0164383562,1319.37204082,None,None,None,None,None,None,None,None,None,None,1.0,1.58,8561.0,1129.0,0.0,3480.0,6412.0,336.044000283,1655.4160411,12339.0,None,None,None,None,None,None,None,None,None,None,0.0,71.0,8742.0,11409.0,14.0,374.0,2082.0,41.0,20151.0,57.0 20130607,37.0191780822,1319.35923096,None,None,None,None,None,None,None,None,None,None,1.0,1.58,8818.0,1450.0,0.0,3660.0,7040.0,346.185998712,1665.54522967,13030.0,0.0,2580.0,1317.0,5342.0,2762.0,18.0,1422.0,1052.0,1333.0,None,2.0,64.0,14160.0,7225.0,1418.0,378.0,3297.0,1429.0,21385.0,57.0 20130608,37.0219178082,1319.3464211,None,None,None,None,None,None,None,None,None,None,1.0,1.58,4644.0,645.0,0.0,5640.0,3472.0,183.414000019,1502.76042111,6693.0,None,None,None,None,None,None,None,None,None,None,0.0,0.0,0.0,0.0,1410.0,390.0,0.0,1410.0,25200.0,57.0 20130609,37.0246575342,1319.33361123,15.3100000619,232.0,460.0,4.5640001595,70.0,9.69599956274,0.939999982715,663.0,4.76000010967,0.300000011921,1.0,1.58,7965.0,1662.0,1800.0,7260.0,7917.0,375.598999478,1694.93261071,11611.0,None,None,None,None,None,None,None,None,None,None,2.0,99.0,12406.0,16136.0,1317.0,372.0,2189.0,1321.0,28542.0,57.0

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利用 R 進行數據統計和可視化

下面的代碼實現了數據的讀取、預處理、並構建了基於日曆的熱度圖（calendarHeatmap）

# 設置工作路徑 setwd("D:\\PythonWorkspaces\\VisHealth\\R") # 讀取 CSV 文件 trends.daily <- read.csv("users_trends-day.csv" , as.is=TRUE) # 僅選取所需的數據列 names( trends.daily ) sleeps <- trends.daily[ , c( "datestr" , "s_light" , "s_deep" , "s_duration" ) ] # 數據清洗 sleeps.clean <- subset( sleeps , ! (is.na( s_duration ) | is.na( s_deep ) | ( s_duration == 0) | ( s_deep == 0) | ( s_deep == "None" ) | ( s_duration == "None" ) ) ) # 格式轉換 sleeps.clean$s_light <- as.numeric( sleeps.clean$s_light ) sleeps.clean$s_deep <- as.numeric( sleeps.clean$s_deep ) sleeps.clean$s_duration <- as.numeric( sleeps.clean$s_duration ) # 去掉忘記切換睡眠模式的數據 sleeps.clean <- subset( sleeps.clean , s_light > 0.1 ) # 睡眠質量取值 = 深度睡眠時長 / 完整睡眠時長 sleeps.clean$q <- sleeps.clean$s_deep / sleeps.clean$s_duration # 設置做圖所需的日期變量 sleeps.clean$date <- strptime ( sleeps.clean$datestr , format="%Y%m%d" )

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# 設置工作路徑 setwd("D:\\PythonWorkspaces\\VisHealth\\R")   # 讀取 CSV 文件 trends.daily <- read.csv("users_trends-day.csv" , as.is=TRUE)   # 僅選取所需的數據列 names( trends.daily ) sleeps <- trends.daily[ , c( "datestr" , "s_light" , "s_deep" , "s_duration" ) ]   # 數據清洗 sleeps.clean <- subset( sleeps , ! (is.na( s_duration ) | is.na( s_deep ) | ( s_duration == 0)     | ( s_deep == 0) | ( s_deep == "None" ) | ( s_duration == "None" ) ) )   # 格式轉換 sleeps.clean$s_light <- as.numeric( sleeps.clean$s_light ) sleeps.clean$s_deep <- as.numeric( sleeps.clean$s_deep ) sleeps.clean$s_duration <- as.numeric( sleeps.clean$s_duration )   # 去掉忘記切換睡眠模式的數據 sleeps.clean <- subset( sleeps.clean , s_light > 0.1  )   # 睡眠質量取值 = 深度睡眠時長 / 完整睡眠時長 sleeps.clean$q <- sleeps.clean$s_deep / sleeps.clean$s_duration   # 設置做圖所需的日期變量 sleeps.clean$date <- strptime ( sleeps.clean$datestr , format="%Y%m%d" )

數據顯示樣本：

> head(sleeps.clean , 10) datestr s_light s_deep s_duration q date 2 20130606 8742 11409 20151 0.5661754 2013-06-06 3 20130607 14160 7225 21385 0.3378536 2013-06-07 5 20130609 12406 16136 28542 0.5653423 2013-06-09 6 20130610 13966 10080 24046 0.4191965 2013-06-10 7 20130611 11393 4408 15801 0.2789697 2013-06-11 8 20130612 20652 15093 35745 0.4222409 2013-06-12 9 20130613 9480 11118 20598 0.5397611 2013-06-13 10 20130614 8840 10967 19807 0.5536931 2013-06-14 > summary( sleeps.clean[,2:5] ) s_light s_deep s_duration q Min. : 6300 Min. : 3613 Min. :11235 Min. :0.2052 1st Qu.:11331 1st Qu.: 6728 1st Qu.:19720 1st Qu.:0.3420 Median :13070 Median : 8528 Median :21283 Median :0.3982 Mean :13153 Mean : 9268 Mean :22421 Mean :0.4065 3rd Qu.:14430 3rd Qu.:11191 3rd Qu.:24744 3rd Qu.:0.4814 Max. :21180 Max. :16740 Max. :35745 Max. :0.6042

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> head(sleeps.clean , 10)     datestr s_light s_deep s_duration         q       date 2  20130606    8742  11409      20151 0.5661754 2013-06-06 3  20130607   14160   7225      21385 0.3378536 2013-06-07 5  20130609   12406  16136      28542 0.5653423 2013-06-09 6  20130610   13966  10080      24046 0.4191965 2013-06-10 7  20130611   11393   4408      15801 0.2789697 2013-06-11 8  20130612   20652  15093      35745 0.4222409 2013-06-12 9  20130613    9480  11118      20598 0.5397611 2013-06-13 10 20130614    8840  10967      19807 0.5536931 2013-06-14   > summary( sleeps.clean[,2:5] )     s_light          s_deep        s_duration          q         Min.   : 6300   Min.   : 3613   Min.   :11235   Min.   :0.2052   1st Qu.:11331   1st Qu.: 6728   1st Qu.:19720   1st Qu.:0.3420   Median :13070   Median : 8528   Median :21283   Median :0.3982   Mean   :13153   Mean   : 9268   Mean   :22421   Mean   :0.4065   3rd Qu.:14430   3rd Qu.:11191   3rd Qu.:24744   3rd Qu.:0.4814   Max.   :21180   Max.   :16740   Max.   :35745   Max.   :0.6042

calendarHeatmap 的實現和使用

calendarHeatmap 將按日聚合數據以熱度圖的形式展現在日曆上。用戶可以指定聚合所使用的計算方法。

labelMon <- c("JAN","FEB","MAR","APR","MAY","JUN","JUL", "AUG","SEP","OCT","NOV","DEC") # labelMon <- c("一月","二月","三月","四月","五月","六月","七月", "八月","九月","十月","十一月","十二月") labelWeek <- c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat" , "Sun") # labelWeek <- c("週一","週二","週三","週四","週五","週六","週日") # ================================ # calander heatmap # 參數說明 # data， 所需展現的數據框 # dateCol， 用於標識時間的列名，缺省爲“date”，可以根據您的數據指定。此列的數據格式爲 POSIXlt 格式日期。 # valueCol， 需要進行統計的列名，此列的數據格式需要是數字。 # statics， 統計分析函數方法，用於對 valueCol 進行計算，如：sum、mean、sd 等均可。 # islog2， 是否對數據進行 log2 操作，此參數主要用於更好的可視化符合冪律分佈的數據 # picTitle， 圖標標題，缺省會根據此圖標的 valueCol 等信息自動生成 # isguide， 是否顯示圖例 calendarHeatmap <- function( data , dateCol = "date" , valueCol = "steps" , statics = "sum", islog2 = FALSE , picTitle = NA , isguide = FALSE ){ require("ggplot2") require("reshape2") data["daystr"] <- format ( data[, dateCol] , "%Y-%m-%d") data.mtx <- tapply(data[ , valueCol ] , list( data$daystr ) , statics) data.mtx [is.na(data.mtx )]= 0 if( islog2 ){ data.mtx[ data.mtx == 0 ] = 1 data.mtx <- log(data.mtx , 2) } m <- melt( data.mtx ) names(m) <- c( "daystr" , "values" ) m$date <- strptime ( m$daystr , format="%Y-%m-%d" ) m$mday = m$date$mday m$month <- m$date$mon + 1 m$monthf <- factor(m$month , levels=as.character(1:12), labels=labelMon, ordered=TRUE) m$weekday = m$date$wday m$weekday[ m$weekday == 0 ] = 7 m$weekdayf <- factor( m$weekday ,levels=c(1:7), labels=labelWeek,ordered=TRUE) m$week <- as.numeric(format( m$date ,"%W")) print(head(m)) pic <- ggplot(m, aes( weekdayf, week, fill = values)) + geom_tile(color="white", size= 1 ) + # facet_wrap(~ monthf ,nrow=3) + theme_bw() + xlab("") + ylab("Week of Year") + scale_y_reverse() + geom_text(aes(label = round( mday , 2 ) ) , vjust = 1) if( isguide ) pic <- pic + scale_fill_gradient(limits=c(0, max(m$values)), low='white', high='red') else pic <- pic + scale_fill_gradient(limits=c(0, max(m$values)), low='white', high='red' , guide="none") if( ! is.na(picTitle) ) pic <- pic + ggtitle( picTitle ) pic }

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labelMon <- c("JAN","FEB","MAR","APR","MAY","JUN","JUL", "AUG","SEP","OCT","NOV","DEC") # labelMon <- c("一月","二月","三月","四月","五月","六月","七月", "八月","九月","十月","十一月","十二月") labelWeek <- c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat" , "Sun") # labelWeek <- c("週一","週二","週三","週四","週五","週六","週日")   # ================================ # calander heatmap # 參數說明 # data， 所需展現的數據框 # dateCol， 用於標識時間的列名，缺省爲“date”，可以根據您的數據指定。此列的數據格式爲 POSIXlt 格式日期。 # valueCol， 需要進行統計的列名，此列的數據格式需要是數字。 # statics， 統計分析函數方法，用於對 valueCol 進行計算，如：sum、mean、sd 等均可。     # islog2， 是否對數據進行 log2 操作，此參數主要用於更好的可視化符合冪律分佈的數據 # picTitle， 圖標標題，缺省會根據此圖標的 valueCol 等信息自動生成 # isguide， 是否顯示圖例   calendarHeatmap <- function( data , dateCol = "date" , valueCol = "steps" , statics = "sum",     islog2 = FALSE , picTitle = NA , isguide = FALSE ){     require("ggplot2")     require("reshape2")       data["daystr"] <- format ( data[, dateCol] , "%Y-%m-%d")       data.mtx <- tapply(data[ , valueCol ] , list( data$daystr ) , statics)     data.mtx [is.na(data.mtx )]= 0       if( islog2 ){         data.mtx[ data.mtx == 0 ] = 1         data.mtx <- log(data.mtx , 2)     }       m <- melt( data.mtx )     names(m) <- c( "daystr" , "values" )       m$date <- strptime ( m$daystr , format="%Y-%m-%d" )       m$mday = m$date$mday       m$month <- m$date$mon + 1     m$monthf <- factor(m$month , levels=as.character(1:12), labels=labelMon, ordered=TRUE)       m$weekday = m$date$wday     m$weekday[ m$weekday == 0 ] = 7     m$weekdayf <- factor( m$weekday ,levels=c(1:7), labels=labelWeek,ordered=TRUE)       m$week <-  as.numeric(format( m$date ,"%W"))       print(head(m))       pic <- ggplot(m, aes( weekdayf, week, fill = values)) +         geom_tile(color="white", size= 1 ) +         # facet_wrap(~ monthf ,nrow=3) +         theme_bw() +         xlab("") +         ylab("Week of Year") +         scale_y_reverse() +         geom_text(aes(label = round( mday , 2 ) ) , vjust = 1)       if( isguide )         pic <- pic + scale_fill_gradient(limits=c(0, max(m$values)), low='white', high='red')     else         pic <- pic + scale_fill_gradient(limits=c(0, max(m$values)), low='white', high='red' , guide="none")       if( ! is.na(picTitle) )         pic <- pic + ggtitle( picTitle )       pic }

對每日睡眠質量的可視化展現。

pic8 <- calendarHeatmap( sleeps.clean , valueCol = "q", picTitle = "Heatmap" , islog2 = FALSE , isguide = TRUE) ggsave(plot = pic8, filename = "sleep_q_calendarheatmap.png")

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pic8 <- calendarHeatmap(  sleeps.clean , valueCol = "q", picTitle = "Heatmap" , islog2 = FALSE , isguide = TRUE) ggsave(plot = pic8, filename = "sleep_q_calendarheatmap.png")

多圖合併

將睡眠質量的柱狀圖(參見Jawbone Up 數據的按小時統計及可視化)、數據Summary，和基於日曆的熱度圖合併在一張圖上，以更全面的描述睡眠質量：

library(gridExtra) g1 <- tableGrob(summary( sleeps.clean[,2:5], digits = 2 )) pic7 <- bucketBar( sleeps.clean , valueCol = "q" , bucketSize = "mday", statics = "mean" , picTitle = "Days in Month") g2 <- arrangeGrob(g1, pic7, ncol=2) png(file = "sleep_q_all.png", bg = "transparent" , width = 800, height = 800, units = "px") grid.arrange(pic8, g2, ncol=1, main="Sleeps") dev.off()

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library(gridExtra) g1 <- tableGrob(summary( sleeps.clean[,2:5], digits = 2 ))   pic7 <- bucketBar( sleeps.clean , valueCol = "q" , bucketSize = "mday", statics = "mean" , picTitle = "Days in Month") g2 <- arrangeGrob(g1, pic7, ncol=2)   png(file = "sleep_q_all.png", bg = "transparent" , width = 800, height = 800, units = "px")     grid.arrange(pic8, g2,  ncol=1, main="Sleeps") dev.off()

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代碼地址

https://github.com/iascchen/VisHealth/

數據預處理 R/json2csv.py; 可視化展現 R/vischarts.R 。

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玩的開心！

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Author : iascchen(at)gmail(dot)com

Date : 2013-08-05

新浪微博 : @問天鼓

原文鏈接：http://www.wearable.pw/index.php/archives/281