---
title: "ANLY 512 The Quantified Self"
author: "235208"
output: 
  flexdashboard::flex_dashboard:
    storyboard: true
    social: menu
    source: embed
    orientation: columns
    vertical_layout: fill
---

```{r setup, include=FALSE}
if (!require("readxl")) {
install.packages("readxl")
library(readxl)
}
rawData <- read_excel("raw processed data.xlsx", sheet = "Sheet2", col_types = c("date", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric","numeric", "numeric", "numeric","numeric"))
rawDataF = data.frame(rawData)
if (!require("flexdashboard")) {
install.packages("flexdashboard")
library(flexdashboard)
}
if (!require("knitr")) {
install.packages("knitr")
library(knitr)
}

if (!require("dplyr")) {
install.packages("dplyr")
library(dplyr)
}
if (!require("tidyr")) {
install.packages("tidyr")
library(tidyr)
}


```


###Q1: Visualizing the trend of how I watch animations
```{r}
rawDataT = rawDataF
rawDataT$date = as.POSIXct(rawDataF$date)

if (!require("ggplot2")) {
install.packages("ggplot2")
library(ggplot2)
}

plotA = ggplot(rawDataT, aes(x = rawDataT$date, y = rawDataF$Animation, fill=rawDataT$Animation)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red") + 
  labs(title = "Trend on how I watch animations", x= "Date", y= "Animation Series Counts", fill = "quantity")
plotA

```

***
The result is a very good straight line, showing that I watch animations with time increasing, nearly 120 animations per years.

In the past I could get bored with bad art works, screwed stories, too much knew plots and many other reasons made me want to stop watch animations. And beyond that I then calm down and watch the details such as how the screenwriter organize a scene, how the director rearrange the charpters, based on the story is slow or fast I could tell this season is based on 2 or 4 books of light novels, and sometimes I even proofreading the subtitle.
This is my life style now, and I may continue doing this until my life changes.


###Q2: Am I doing something else? 

```{r}
if (!require("plotrix")) {
install.packages("plotrix")
library(plotrix)
}

lastRecord = rawDataT[1,]
pieval<-c(lastRecord$Animation+lastRecord$Satoban+lastRecord$movie+lastRecord$live.action.animation.movies,lastRecord$manga+lastRecord$eromanga+lastRecord$light.novel,lastRecord$galgame+lastRecord$game)
pielabels<-c("Watch on Screen","Read from Print Media","Play a Game Software")

pie3D(pieval,radius=1.5,labels=pielabels,explode=0.5,shade=0.8, theta = 0.7,main="Cross-media Analysis")

```

***
It's a pity the record doesn't contain time spent on each wroks.

From the pie chart, I could conclude that I over the half of the series are watched on screen. And less than 20% are games, this is also very natural because games require you spent a lot more time into it and sometimes you cannot paused in the flow.

###Q2.5: Over time cross-media series Analysis: 

```{r}
if (!require("dygraphs")) {
install.packages("dygraphs")
library(dygraphs)
}
#Use valid data
rawDataS = rawDataT[1:36,1:10]

Animation = ts(rawDataS$Animation, frequency = 1)
Animation[] <- rev(Animation)
Satoban = ts(rawDataS$Satoban, frequency = 1)
Satoban[] <- rev(Satoban)
movie = ts(rawDataS$movie, frequency = 1)
movie[] <- rev(movie)
Animovie = ts(rawDataS$live.action.animation.movies, frequency = 1)
Animovie[] <- rev(Animovie)
Multimedia = Animation+Satoban+movie+Animovie

manga = ts(rawDataS$manga, frequency = 1)
manga[] <- rev(manga)
eromanga = ts(rawDataS$eromanga, frequency = 1)
eromanga[] <- rev(eromanga)
LN = ts(rawDataS$light.novel, frequency = 1)
LN[] <- rev(LN)
Printed = manga+eromanga+LN
  
galgame = ts(rawDataS$galgame, frequency = 1)
galgame[] <- rev(galgame)
game = ts(rawDataS$game, frequency = 1)
game[] <- rev(game)
Games = galgame+game
Total = Multimedia+Printed+Games
PlotCombine <- cbind(Multimedia,Printed,Games,Total)

dygraph(PlotCombine, main = "Over time cross-media Counts", xlab = "Record Points", ylab="Series Counts") %>%
dyRangeSelector() %>%
dyLegend(width = 500, show = "onmouseover") %>%
dyOptions(drawGrid = FALSE) %>%
dyOptions(colors = RColorBrewer::brewer.pal(4, "Set2"))

```

***
This plot is showing the trend by media during the last two years, the trend is stable over time, which means my time spent on these series are fixed. There maybe a change earlier, but I do not have enough categorized data.

### Q3: Did my focus shift from time to time?

```{r}
if (!require("plotly")) {
install.packages("plotly")
library(plotly)
}
recordPoint = rawDataT$date
#There is a clear trend in plot 1
#Animation = rawDataT$Animation
Satoban = rawDataT$Satoban
movie = rawDataT$movie
Animovie = rawDataT$live.action.animation.movies
manga = rawDataT$manga
eromanga = rawDataT$eromanga
comics = rawDataT$comics.old.ver
LN = rawDataT$light.novel
galgame = rawDataT$galgame
game = rawDataT$game

p <- plot_ly(y = ~Satoban, x = ~recordPoint, type = 'scatter', mode = 'lines', name = 'Adult Animation', fill = 'tozeroy') %>%
  add_trace(y = ~movie, x = ~recordPoint, name = 'Movie', fill = 'Animation Movie', fill = 'tozeroy') %>%
  add_trace(y = ~Animovie, x = ~recordPoint, name = 'Live Action Animation Movie', fill = 'tozeroy') %>%
  add_trace(y = ~galgame, x = ~recordPoint, name = 'Bishojo game', fill = 'tozeroy') %>%
  add_trace(y = ~game, x = ~recordPoint, name = 'Game', fill = 'tozeroy') %>%
  add_trace(y = ~eromanga, x = ~recordPoint, name = 'Adult Comics', fill = 'tozeroy') %>%
  add_trace(y = ~comics, x = ~recordPoint, name = 'Comics(Before seperate)', fill = 'tozeroy') %>%
  add_trace(y = ~LN, x = ~recordPoint, name = 'Light Novel', fill = 'tozeroy') %>%
  add_trace(y = ~manga, x = ~recordPoint, name = 'Comics', fill = 'tozeroy') %>%

  layout(xaxis = list(title = 'Year'),
         yaxis = list(title = 'Quantity of Series'),
         hovermode = 'compare')
p

```

***
From the stacked line chart, it is clear that comics at the early stage, and the eromanga in the later is at an increasing trend.

In fact I noticed this change earlier, today there are some website you can read these comics online, and in the past people have to download many and use a comic view software to read and turn the page. The convenience made I choose more comics.


###Q4: Is my rating system biased?

```{r}

Anime_List <- read_excel("C:/Users/alan/Downloads/Alan/Anime List.xlsx", col_names = FALSE, col_types = c("numeric","numeric", "numeric"))
Anime_List = Anime_List[,3] * 10
Anime_List =  as.numeric(unlist(Anime_List + 20))


hist(Anime_List, freq=F, breaks=20, main = "Rating Scores Distribution")
lines(density(Anime_List, adjust=1.5), col="red")
lines(seq(0, 70, by=.5), dnorm(seq(0, 70, by=.5),
      mean(Anime_List), sd(Anime_List)), col="blue")

```

***
My rating system is quite unusually, because I build my own system on reviewing those works in many different respects, my reviews are based on what I watched in the past and I used to keep a distance from the public rating. I do not rate high because people say it is good, the voice may come from a promotion. I do not rate low because some people strongly condemn it, maybe it is just because of a controversy in the story.

The normal curve overlapped on density line are close, hence I could say that my rating system is not much biased. After all, I have more than 3000 stories in my mind.


###Q5: I have a concern that I prefer more adult contents, is that true?

```{r}
rawDataA.Adult = rawDataS$Satoban+rawDataS$eromanga+rawDataS$galgame
rawDataA.General = rawDataS$Animation+rawDataS$movie+rawDataS$live.action.animation.movies+rawDataS$manga+rawDataS$light.novel+rawDataS$game
rawDataA.Adult[] <- rev(rawDataA.Adult)
rawDataA.General[] <- rev(rawDataA.General)
rawDataA.Time = rawDataS$date
rawDataA.Time[] <- rev(rawDataA.Time)
rawDataA.Total = rawDataA.Adult + rawDataA.General
rawDataA.AdultPercent = rawDataA.Adult/rawDataA.Total
rawDataA.GeneralPercent = rawDataA.General/rawDataA.Total

versus_plot = ggplot() +
  geom_area(aes(rawDataA.Time, rawDataA.GeneralPercent), fill = 'green') +
  geom_area(aes(rawDataA.Time, rawDataA.AdultPercent), fill = 'grey') + 
  
labs(title = "adult content versus all ages admitted", x= "Date", y= "Counts")
ggplotly(versus_plot)
```

***
Based on the area plot, I could conclude that the assumption that I am watching more and more adult contents than all age admitted works is true.

So be it.