analyze_last_fm: What the package does (short line)

# Plotting

# Data
#x <- c(15, 9, 75, 90, 1, 1, 11, 5, 9, 8, 33, 11, 11, 20, 14, 13, 10, 28, 33, 21, 24, 25, 11, 33)

# Clock plot function
#' Taken from \url{http://www.r-graph-gallery.com/49-clock-plot/}
#' 
# clock.plot <- function (self, col = col=viridisLite::viridis(24), ...) {
#   
#   x <- self$data_table %>% select(datetext) %>%
#     rowwise() %>%
#     mutate(hour=gsub(":","",
#                      regmatches(datetext,regexpr("([0-9]{2})\\:",datetext)))) %>%
#     group_by(hour) %>%
#     summarize(count=n()) %>% select(count)
#   
#   if( min(x)<0 ) x <- x - min(x)
#   if( max(x)>1 ) x <- x/max(x)
#   n <- length(x)
#   if(is.null(names(x))) names(x) <- 0:(n-1)
#   m <- 1.05
#   plot(0, type = 'n', xlim = c(-m,m), ylim = c(-m,m), axes = F, xlab = '', ylab = '', ...)
#   a <- pi/2 - 2*pi/200*0:200
#   polygon( cos(a), sin(a) )
#   v <- .02
#   a <- pi/2 - 2*pi/n*0:n
#   segments( (1+v)*cos(a), (1+v)*sin(a), (1-v)*cos(a), (1-v)*sin(a) )
#   segments( cos(a), sin(a),0, 0, col = 'light grey', lty = 3) 
#   ca <- -2*pi/n*(0:50)/50
#   for (i in 1:n) {
#     a <- pi/2 - 2*pi/n*(i-1)
#     b <- pi/2 - 2*pi/n*i
#     polygon( c(0, x[i]*cos(a+ca), 0), c(0, x[i]*sin(a+ca), 0), col=col[i] )
#     v <- .1
#     text((1+v)*cos(a), (1+v)*sin(a), names(x)[i])
#   }
# }

# Use the function on the created data
#clock.plot(x$count, main = "Number of visitors to a web site for each hour of the day",)
#' Clock plot function
#' Taken from \url{http://www.r-graph-gallery.com/49-clock-plot/}
#' @name clock.plot
#' @examples 
#' 
#' test_data <- UserData$new("zappingseb","b25b959554ed76058ac220b7b2e0a026","test")
#' 
#' test_data$clock.plot()
#' 
UserData$set("public", "clock.plot", function(...) {
  
  # Derive the play time per hour by a regular expression match for the two digits before
  # the ":" and grouping by them
  x <- self$data_table %>% select(datetext) %>%
    rowwise() %>%
    mutate(hour=as.numeric(gsub(":","",
                     regmatches(datetext,regexpr("([0-9]{2})\\:",datetext))))+self$timezone) %>%
    group_by(hour) %>%
    summarize(count=n())
  col=viridisLite::viridis(length(x$hour))
  
  x <- 
    x %>% rowwise() %>% mutate(hour=ifelse(hour>=24,hour-24,hour))
  #print(x)
  time_vec <- c()
  for(time in 0:23){
    
    
    
    index <- which(as.numeric(x$hour)==time)
    #browser()
    if(length(index)>0){
      time_vec <- c(time_vec,x[index,"count"])
    }else{
      time_vec <- c(time_vec,0)
    }
    
  }
  x <- unlist(time_vec)
  #browser()
  names(x)<-0:23
  
  if( min(x)<0 ) x <- x - min(x)
  if( max(x)>1 ) x <- x/max(x)
  n <- length(x)
  if(is.null(names(x))) names(x) <- 0:(n-1)
  m <- 1.05
  plot(0, type = 'n', xlim = c(-m,m), ylim = c(-m,m), axes = F, xlab = '', ylab = '', ...)
  a <- pi/2 - 2*pi/200*0:200
  polygon( cos(a), sin(a) )
  v <- .02
  a <- pi/2 - 2*pi/n*0:n
  segments( (1+v)*cos(a), (1+v)*sin(a), (1-v)*cos(a), (1-v)*sin(a) )
  segments( cos(a), sin(a),0, 0, col = 'light grey', lty = 3) 
  ca <- -2*pi/n*(0:50)/50
  for (i in 1:n) {
    a <- pi/2 - 2*pi/n*(i-1)
    b <- pi/2 - 2*pi/n*i
    polygon( c(0, x[i]*cos(a+ca), 0), c(0, x[i]*sin(a+ca), 0), col=col[i] )
    v <- .1
    text((1+v)*cos(a), (1+v)*sin(a), names(x)[i])
  }
  
},overwrite=TRUE)

#' Get a simple barplot by week
#' 
#' @name barplots
#' @param choice_of_aggregation \code{character} How you would like to aggregate the listened tracks
UserData$set("public", "barplots", function(choice_of_aggregation=c("day","month","week","weekdays")) {
  
  
  
  barplot(self$bar_data(choice_of_aggregation))
  
},overwrite=TRUE)

#' Function to generate aggregated data per time
#' @name bar_data
#' @param choice_of_aggregation \code{character} How you would like to aggregate the listened tracks
UserData$set("public", "bar_data", function(choice_of_aggregation=c("day","month","week","weekdays")) {
  data_with_month <- self$data_table %>% rowwise() %>% mutate(real_date = 
                                                                as.POSIXct(as.numeric(uts),origin="1970-01-01",tz="GMT")) %>%
                     mutate(year=substring(as.character(real_date),1,4)) %>% 
                     filter(as.numeric(year)==self$year)
  
  if(choice_of_aggregation=="weekdays"){
    data_by_month <- data_with_month %>% group_by(month=format(as.Date(real_date), "%a")) %>%
      summarize(amount=n())
    
    if("Di" %in% data_by_month$month){
      data_by_month$month <-  factor(data_by_month$month, levels= c("Mo", "Di", 
                                                                    "Mi", "Do", "Fr", "Sa", "So"))
    }else{
      data_by_month$month <- factor(data_by_month$month, levels= c("Su", "Mo", 
                                                                   "Tu", "We", "Th", "Fr", "Sa"))
      
      
    }
    data_by_month <- data_by_month[order(data_by_month$month),]
    naming <- data_by_month %>% .$month
  }else{
    
    data_by_month <- data_with_month %>% group_by(month=lubridate::floor_date(real_date, choice_of_aggregation)) %>%
      summarize(amount=n())
    naming <- data_by_month %>% .$month
  }
  
  if(choice_of_aggregation=="month"){
    naming <- data_by_month %>% mutate(month=months(month)) %>% select(month) %>% .$month
  }else if(choice_of_aggregation=="week"){
    naming <- data_by_month %>% mutate(month=strftime(month,"%V")) %>% select(month) %>% .$month
  }
  my_vector <- data_by_month %>% select(amount) %>% flatten() %>% .$amount
  
  names(my_vector) <- naming
  
  return(my_vector)
},overwrite=TRUE)

UserData$set("public", "daily_month_plot", function() {
  
  monthly_data <- self$bar_data("month")
  
  daily_data <- self$bar_data("day")
  
  # Number of Days function from stackoverflow
  numberOfDays <- function(date) {
    m <- format(date, format="%m")
    
    while (format(date, format="%m") == m) {
      date <- date + 1
    }
    
    return(as.integer(format(date - 1, format="%d")))
  }
  
  # Create a vector where each day contains the average value of each month
  
  year_of_analysis <- year(as.POSIXct(as.numeric(self$data_table$uts[1]),origin="1970-01-01",tz="GMT"))
  
  dates = as.Date(paste0(year_of_analysis,"-",1:12,"-23"), "%Y-%m-%d")
  
  nr_of_days <- sapply(dates, numberOfDays)

  avg_per_month <- monthly_data/nr_of_days
  
  month_data <- c()
  
  
  
  for(month_index in 1:length(avg_per_month)){
    
    month_data <-c(month_data,
                   rep(avg_per_month[month_index],nr_of_days[month_index]))
    
  }
  # Get the index of the 15th day of each month
  day15_index <- sapply(1:12,function(x)sum(nr_of_days[1:x])+15-nr_of_days[x])
  
  # Rename the month to have just the 15th of each month labelled
  names(month_data) <- NA
  names(month_data)[day15_index] <- names(avg_per_month)
  
  dates_of_year <- seq( as.Date(paste0(self$year,"-01-01")), as.Date(paste0(self$year,"-12-31")), by="+1 day")
  
  dates_of_year_data <- daily_data[
    match(as.character(dates_of_year),names(daily_data))]
  
  # Plot the monthly data in the background
  # Including labels for the x-axis
  barplot(month_data,
          border=NA,
          col = "#3F4788FF",
          beside=TRUE,
          space = c(0, .2),
          ylim=c(0,max(dates_of_year_data,na.rm=T)),
          las=2)
  par(new=TRUE)
  # Plot the daily data in the front
  barplot(dates_of_year_data,
          border=NA,
          col = "#74D055FF",
          beside=TRUE,
          space = c(0, .2),
          ylim=c(0,max(dates_of_year_data,na.rm=T)),
          axes=FALSE,
          axisnames = FALSE)
  legend("topright",fill=c("#3F4788FF","#74D055FF"),c("Average per month","Daily"),border=NA,bty = "n")
},overwrite=TRUE)