library(tidyverse)

## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr

## Conflicts with tidy packages ----------------------------------------------

## filter(): dplyr, stats
## lag():    dplyr, stats

library(readr)
library (knitr)
library(ggrepel)
library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

Sales2012_2014 <- read_csv("C:/Users/ThuyAnh/Desktop/ITKM549/Sales_Prod_2012-14.csv")

## Parsed with column specification:
## cols(
##   `Retailer country` = col_character(),
##   `Order method type` = col_character(),
##   `Retailer type` = col_character(),
##   `Product line` = col_character(),
##   `Product type` = col_character(),
##   Product = col_character(),
##   Year = col_integer(),
##   Quarter = col_character(),
##   Revenue = col_double(),
##   Quantity = col_integer(),
##   `Gross margin` = col_double()
## )

summary(Sales2012_2014)

##  Retailer country   Order method type  Retailer type     
##  Length:88475       Length:88475       Length:88475      
##  Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character  
##                                                          
##                                                          
##                                                          
##                                                          
##  Product line       Product type         Product               Year     
##  Length:88475       Length:88475       Length:88475       Min.   :2012  
##  Class :character   Class :character   Class :character   1st Qu.:2012  
##  Mode  :character   Mode  :character   Mode  :character   Median :2013  
##                                                           Mean   :2013  
##                                                           3rd Qu.:2013  
##                                                           Max.   :2014  
##                                                                         
##    Quarter             Revenue           Quantity        Gross margin     
##  Length:88475       Min.   :      0   Min.   :    1.0   Min.   :-12.8537  
##  Class :character   1st Qu.:   8184   1st Qu.:  131.0   1st Qu.:  0.3699  
##  Mode  :character   Median :  21026   Median :  333.0   Median :  0.4506  
##                     Mean   :  42638   Mean   :  780.6   Mean   :  0.4497  
##                     3rd Qu.:  50391   3rd Qu.:  816.0   3rd Qu.:  0.5201  
##                     Max.   :1635688   Max.   :67875.0   Max.   :  0.7705  
##                                                         NA's   :581

Sales12_14 <- Sales2012_2014 %>% 
  rename(Country = "Retailer country",
         OrderMethod = "Order method type",
         RetailerType = "Retailer type",
         ProductLine = "Product line",
         ProductType = "Product type",
         GrossMargin = "Gross margin")
  summary(Sales12_14)

##    Country          OrderMethod        RetailerType      
##  Length:88475       Length:88475       Length:88475      
##  Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character  
##                                                          
##                                                          
##                                                          
##                                                          
##  ProductLine        ProductType          Product               Year     
##  Length:88475       Length:88475       Length:88475       Min.   :2012  
##  Class :character   Class :character   Class :character   1st Qu.:2012  
##  Mode  :character   Mode  :character   Mode  :character   Median :2013  
##                                                           Mean   :2013  
##                                                           3rd Qu.:2013  
##                                                           Max.   :2014  
##                                                                         
##    Quarter             Revenue           Quantity        GrossMargin      
##  Length:88475       Min.   :      0   Min.   :    1.0   Min.   :-12.8537  
##  Class :character   1st Qu.:   8184   1st Qu.:  131.0   1st Qu.:  0.3699  
##  Mode  :character   Median :  21026   Median :  333.0   Median :  0.4506  
##                     Mean   :  42638   Mean   :  780.6   Mean   :  0.4497  
##                     3rd Qu.:  50391   3rd Qu.:  816.0   3rd Qu.:  0.5201  
##                     Max.   :1635688   Max.   :67875.0   Max.   :  0.7705  
##                                                         NA's   :581

Summary_Sales<- inner_join(Sales_by_Year, Prop_by_Year, by = c("Country")) %>% 
  select(1,2,5,3,6,4,7)

kable(Summary_Sales)

Rev_Growth <- Sales_by_Year %>% 
  select(Country, revenue_2013, revenue_2014) %>% 
  mutate(Growth_Rate = (revenue_2014-revenue_2013)/revenue_2013) %>% 
  arrange(desc(Growth_Rate))
kable(Rev_Growth)

The growth rate of every country does not look good. All of them have a decrease in their revenue comparing between 2014 to 2013. And Sweden is the country with lowest revenue decrease number 13.34%

Prop_Growth <- Prop_by_Year %>% 
  select(Country, prop_2013, prop_2014) %>% 
  mutate(Growth_Rate = (prop_2014-prop_2013)/prop_2013) %>% 
  arrange(desc(Growth_Rate)) %>% 
  head(Growth_Rate, n=1L)
kable(Prop_Growth)

Sweden is the country has the highest growth rate in term of proportion by total revenue, comparing year of 2014 to 2013

data_for_plot1 <-Sales12_14 %>% 
    select(Year, Country, Revenue) %>% 
  group_by(Country, Year) %>% 
    summarise(Revenue = sum(Revenue)) %>% 
  mutate (Year = as.factor(Year), Revenue = Revenue/1000000)

ggplot(data_for_plot1, aes(x=reorder(Country, -Revenue),
                           y=Revenue, fill=Year )) +   
  geom_bar(position='dodge', stat='identity', color="black") +
    labs (x= 'Country', 
        y = 'Revenue (million)', 
        title = 'Summary of Revenue',
        fill = "Year")+
  theme (axis.text.x = element_text(angle = 90, hjust = 1),
         panel.grid.major = element_blank(), 
         panel.grid.minor = element_blank())

data_for_plot2 <- Rev_Growth %>% 
  select(1,3,4) %>% 
  mutate(revenue_2014 = revenue_2014/1000000)

m <- ggplot(data_for_plot2, aes(x=revenue_2014, 
                           y=Growth_Rate,
                           color=Country)) +
      geom_point(show.legend = F )+
  labs (x= 'Revenue 2014 (million)', 
        y = ' Revenue Growth Rate', 
        title = 'Revenue 2014 - Growth 2013-2014',
        legend = "Country")
  
  ggplotly(m, session = 'knitr')

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

Sales12_14 <- Sales12_14 %>% 
  mutate (GrossProfit = Revenue*GrossMargin) %>% 
  na.omit()

Sales_Profit <- Sales12_14 %>% 
  select(Year, Country, Revenue, GrossMargin, GrossProfit) %>% 
  group_by(Country, Year) %>% 
  summarise(Revenue = sum(Revenue), GrossProfit = sum(GrossProfit)) %>% 
  arrange(Year) %>% 
    mutate(Margin = (GrossProfit/Revenue))
kable(Sales_Profit)

data_for_plot3 <- Sales_Profit %>% 
  filter(Year == "2014") %>% 
  mutate(GrossProfit = GrossProfit/1000000, Revenue = Revenue/1000000)

p <- ggplot(data_for_plot3, aes(x=GrossProfit, y=Revenue, size=Margin,                                  color = Country, alpha = 0.000001)) +
    geom_point(show.legend = F)+
    labs (x= 'Profit 2014 (million)', 
          y = ' Revenue (million)',
          alpha = " ", 
          title = 'Revenue  & Profit 2014')+
  theme(panel.background = element_rect(fill = "#EFEFFB", colour = "#EFEFFB"),
        panel.grid.major = element_line(colour = "white"),
          plot.margin = margin(0, 0, 3, 2, "cm"))
 
ggplotly(p, session = 'knitr')

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

Sliders <- ggplot(Sales_Profit, aes(x=GrossProfit/1000000, 
                                    y=Revenue/1000000, 
                                    color = Country, size = Margin,
                                    alpha = 0.00001)) +
    geom_point(aes(frame = Year)) +
  scale_x_continuous()+
    labs (x= 'Profit (million)', 
          y = ' Revenue (million) ', 
          title = 'Revenue  & Profit 2012-2014',
          legend ="Country",
          size = " ",
          alpha = "")+
  theme(panel.background = element_rect(fill = "white", colour = "grey50"),
        panel.grid.major = element_line(colour = "grey50"),
          plot.margin = margin(0, 0, 3, 2, "cm"))

## Warning: Ignoring unknown aesthetics: frame

ggplotly(Sliders, session = 'knitr') %>% 
animation_opts(1000, easing = "elastic", redraw = FALSE) %>% 
    animation_slider( currentvalue = list(prefix = "YEAR ", font = list(color="violet")))

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

summary(as.factor(Sales12_14$OrderMethod))

##      E-mail         Fax        Mail Sales visit     Special   Telephone 
##        3062        1656        1009        7074         529        5769 
##         Web 
##       68795

Online_Orders_Growth <- Sales12_14 %>% 
  filter(OrderMethod == "Web") %>% 
  select(Country, Year, Quantity, Revenue) %>% 
  group_by(Country, Year) %>% 
  summarise(Quantity = sum(Quantity), Revenue = sum(Revenue))

Profitable_Product <- Sales12_14 %>% 
  select(Product, Quantity, Revenue, GrossProfit) %>% 
  group_by(Product) %>% 
  summarise(Quantity=sum(Quantity), Revenue=sum(Revenue),
            Profit=sum(GrossProfit))

Profitable_ProductLine <- Sales12_14 %>% 
  na.omit() %>% 
    select(ProductLine, Quantity, Revenue, GrossProfit) %>% 
  group_by(ProductLine) %>% 
  summarise(Quantity=sum(Quantity), Revenue=sum(Revenue),
            Profit=sum(GrossProfit))

Best_RetailerType <- Sales12_14 %>% 
  na.omit() %>% 
    select(RetailerType, Quantity, Revenue, GrossProfit) %>% 
  group_by(RetailerType) %>% 
  summarise(Quantity=sum(Quantity), Revenue=sum(Revenue),
            Profit=sum(GrossProfit))