google_analytics_engagement.r

setwd("r-playground/R/ga_engagement")
list.of.packages <- c("rjson", "RCurl","RGoogleAnalytics")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
require(rjson)
require(RCurl)
require(RGoogleAnalytics)
require(ggplot2)
token <- Auth('YOUR API USER ID','YOUR API USER PASSWORD')
save(token,file="./token_file")
profile <- GetProfiles(token) # show all your profile 
#          id                               name
#    XXXXXX                      HELLO (All)
#  YYYYYYYY                      COOL
#   ....

# Your profile number (not the GA ID number)
my_profile    <- profile[profile$name == 'HELLO (All)',1] 

time_start_seq <- as.Date(ISOdate(2014,seq(1,12),1))
#[1] "2014-01-01" "2014-02-01" "2014-03-01" "2014-04-01" "2014-05-01" "2014-06-01"
#[7] "2014-07-01" "2014-08-01" "2014-09-01" "2014-10-01" "2014-11-01" "2014-12-01"
time_end_seq <- seq(as.Date("2014-02-01"), length=12, by="1 month") - 1
# [1] "2014-01-31" "2014-02-28" "2014-03-31" "2014-04-30" "2014-05-31" "2014-06-30"
# [7] "2014-07-31" "2014-08-31" "2014-09-30" "2014-10-31" "2014-11-30" "2014-12-31"

# 為了做資料紀錄
every_month_2014 <- list()
all_data <- data.frame()

for ( i in 1:length(time_start_seq)) {

  query.list <- Init(start.date = as.character(time_start_seq[i]),
                     end.date = as.character(time_end_seq[i]),
                     dimensions = "ga:sessionDurationBucket",
                     metrics = "ga:sessions,ga:pageviews",
                     sort = "ga:sessionDurationBucket",
                     max.results = 10000,
                     table.id = paste("ga:",my_profile,sep="",collapse=",")
                     )
  
  # 建立一個query等一下就是透過這個query與token拿資料！
  ga.query <- QueryBuilder(query.list)
  
  # 向GA抓取資料，存成data frame
  ga.data <- GetReportData(ga.query, token) 
  # ga.data <- GetReportData(ga.query, token,split_daywise = T,paginate_query = TRUE)  另外一種拿法也work


  # 資料處理部分，由於抓回來的 “data$sessionDurationBucket” 是個string，所要把它轉成 數字
  less_than_10_seconds <- ga.data[as.numeric(ga.data$sessionDurationBucket) <= 10,]
  between_11_to_30     <- ga.data[as.numeric(ga.data$sessionDurationBucket) > 10 & as.numeric(ga.data$sessionDurationBucket) <= 30,]
  between_31_to_60     <- ga.data[as.numeric(ga.data$sessionDurationBucket) > 30 & as.numeric(ga.data$sessionDurationBucket) <= 60,]
  between_61_to_180    <- ga.data[as.numeric(ga.data$sessionDurationBucket) > 60 & as.numeric(ga.data$sessionDurationBucket) <= 180,]
  between_181_to_600   <- ga.data[as.numeric(ga.data$sessionDurationBucket) > 180 & as.numeric(ga.data$sessionDurationBucket) <= 600,]
  between_601_to_1800  <- ga.data[as.numeric(ga.data$sessionDurationBucket) > 600 & as.numeric(ga.data$sessionDurationBucket) <= 1800,]
  more_than_1801       <- ga.data[as.numeric(ga.data$sessionDurationBucket) > 1800,]
  
  # 處理後資料長這樣
  #     sessionDurationBucket sessions pageviews
  #                        0   342840    341561
  #                        1     1445      1906
  #                       10     2567      4392
  #                        2     2210      2704
  #                        3     2283      2934
  #                        4     2309      3133
  #                        5     2368      3384

  
  # 資料整併
  every_month_2014[[i]] <- rbind(colSums(less_than_10_seconds[,-1]),colSums(between_11_to_30[,-1]),
                                 colSums(between_31_to_60[,-1]),colSums(between_61_to_180[,-1]),
                                 colSums(between_181_to_600[,-1]),colSums(between_601_to_1800[,-1]),
                                 colSums(more_than_1801[,-1]))
  #       sessions pageviews
  #[1,]   365814    375361
  #[2,]    44857     92466
  #[3,]    48003    133645
  #[4,]    96890    423865
  #[5,]   107373    872529
  #[6,]    78404   1057353
  #[7,]    28183    812783

  
  # 補上一欄時間區間
  month_all_data <- cbind(c(as.character(i)),c("< 10s","11 ~ 30","31~60","61~180","181~600","601~1800",">1801"),every_month_2014[[i]])
  #                     sessions pageviews
  #[1,] "12" "< 10s"    "365814" "375361" 
  #[2,] "12" "11 ~ 30"  "44857"  "92466"  
  #[3,] "12" "31~60"    "48003"  "133645" 
  #[4,] "12" "61~180"   "96890"  "423865" 
  #[5,] "12" "181~600"  "107373" "872529" 
  #[6,] "12" "601~1800" "78404"  "1057353"
  #[7,] "12" ">1801"    "28183"  "812783" 

  # 將每一年的統整資料放在一起，作圖需要
  all_data <- rbind(all_data,month_all_data)
}


# 補上欄位名稱
colnames(all_data) <- c("month","time_interval","sessions","pageviews")


# 先畫pageviews
# 將資料的型態改正，因為上面建立的時候，會將每個欄位的屬性變成"factor"，這會對畫圖趙成莫大影響！
all_data <- transform(all_data, 
    time_interval = factor(time_interval, levels = 
        c('< 10s','11 ~ 30','31~60', '61~180','181~600', '601~1800','>1801')),
    pageviews = as.numeric(as.character(pageviews)))

# 作圖
plot <- ggplot(data = all_data, aes(x = month, y = pageviews, fill = time_interval)) +  
    geom_bar(stat = "identity", position = "dodge", colour = "black")

# 將圖存出
ggsave(plot,file=paste("2014年engagement-pageviews圖.png",sep=""),width=15, height=10)


# 再畫sessions
all_data <- transform(all_data, 
    time_interval = factor(time_interval, levels = 
        c('< 10s','11 ~ 30','31~60', '61~180','181~600', '601~1800','>1801')),
    sessions = as.numeric(as.character(sessions)))

plot <- ggplot(data = all_data, aes(x = month, y = sessions, fill = time_interval)) +  
    geom_bar(stat = "identity", position = "dodge", colour = "black")

ggsave(plot,file=paste("2014年engagement-sessions圖.png",sep=""),width=15, height=10)