bpi_plotly.R

bpi_genre_sales_perc <- read.csv("http://myinspirationinformation.com/wp-content/uploads/2014/08/bpi_genre_sales_perc.csv")
bpi_sales_revenue <- read.csv("http://myinspirationinformation.com/wp-content/uploads/2014/08/bpi_sales_revenue.csv")

library(ggplot2)
library(reshape2)
library(ggthemes)
library(plotly)

sales_perc_melt<-melt(bpi_genre_sales_perc)
sales_perc_albums<-sales_perc_melt[sales_perc_melt$Type=="Albums",]
colnames(sales_perc_albums)<-c("Type", "Genre", "Year", "Percent")

sales_perc_singles<-sales_perc_melt[sales_perc_melt$Type=="Singles",]
colnames(sales_perc_singles)<-c("Type", "Genre", "Year", "Percent")


sales_revenue_melt<-melt(bpi_sales_revenue)
colnames(sales_revenue_melt)<-c("Format", "Media", "Type", "Year", "Sales_Million_Pounds")
sales_revenue_melt$label<-paste(sales_revenue_melt$Format, sales_revenue_melt$Type)

digitalimpact<-sales_revenue_melt[sales_revenue_melt$Media %in% c("Physical", "Online"),]

albums<-ggplot(sales_perc_albums, aes(x=Year, y=Percent, group=Genre)) + geom_line(aes(color=Genre)) + theme_calc() + ggtitle("Percentage Album Sales by Genre")
singles<-ggplot(sales_perc_singles, aes(x=Year, y=Percent, group=Genre)) + geom_line(aes(color=Genre)) + theme_calc() + ggtitle("Percentage Single Sales by Genre")
format<-ggplot(digitalimpact, aes(x=Year, y=Sales_Million_Pounds, group=label)) + geom_line(aes(color=label)) + theme_calc() + ggtitle("Digital v Physical Revenue")


API <- plotly()
p1 <- API$ggplotly(albums, kwargs=list(filename="bpi_report/percent_album_sales_by_genre", fileopt="overwrite"))
p2 <- API$ggplotly(singles, kwargs=list(filename="bpi_report/percent_single_sales_by_genre", fileopt="overwrite"))
p3 <- API$ggplotly(format, kwargs=list(filename="bpi_report/revenue_by_format", fileopt="overwrite"))