Presentation 2

Library

library(tidyverse)

## -- Attaching packages ---------------------------------- tidyverse 1.2.1 --

## v ggplot2 2.2.1     v purrr   0.2.4
## v tibble  1.3.4     v dplyr   0.7.4
## v tidyr   0.7.2     v stringr 1.2.0
## v readr   1.1.1     v forcats 0.2.0

## -- Conflicts ------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

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

Importing Data

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

1. Rename variables

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

2. Create a table that describes total sales by country and by year and their proportion of the total revenue for each year. The resulting table should have four variables: country, year, revenue, proportion_of_revenue_year.

2.1 Total Sales each year

x<- Sales12_14 %>% 
    group_by(Year) %>% 
  summarise(Revenue = sum(Revenue))%>% 
     spread(Year, Revenue) 

2.2 Total sales and propotion of sales of each country by year

Sales_Prop <- Sales12_14 %>% 
  select(Year, Country, Revenue) %>% 
  group_by(Country, Year) %>% 
  summarise(Revenue = sum(Revenue)) %>% 
  arrange(Year) %>% 
    mutate(proportion_of_revenue_year = ifelse(Year == 2012, Revenue/x$`2012`,
                                             ifelse(Year == 2013, Revenue/x$`2013`,Revenue/x$`2014`)))
kable(Sales_Prop)

Country	Year	Revenue	proportion_of_revenue_year
Australia	2012	25607044	0.0220904
Austria	2012	34568090	0.0298208
Belgium	2012	30396297	0.0262219
Brazil	2012	34234876	0.0295333
Canada	2012	75428386	0.0650696
China	2012	79080488	0.0682201
Denmark	2012	15893267	0.0137106
Finland	2012	47826647	0.0412585
France	2012	72237825	0.0623172
Germany	2012	65238045	0.0562787
Italy	2012	46135925	0.0397999
Japan	2012	86340230	0.0744829
Korea	2012	50844973	0.0438623
Mexico	2012	42363572	0.0365457
Netherlands	2012	44421660	0.0383211
Singapore	2012	48204223	0.0415842
Spain	2012	39237620	0.0338490
Sweden	2012	23966997	0.0206755
Switzerland	2012	26286562	0.0226766
United Kingdom	2012	69420604	0.0598869
United States	2012	201462260	0.1737949
Australia	2013	47799737	0.0319540
Austria	2013	44996737	0.0300802
Belgium	2013	38375433	0.0256539
Brazil	2013	43389954	0.0290061
Canada	2013	98134069	0.0656024
China	2013	99109352	0.0662544
Denmark	2013	20050466	0.0134037
Finland	2013	59648620	0.0398750
France	2013	83075921	0.0555361
Germany	2013	78113787	0.0522189
Italy	2013	61869049	0.0413593
Japan	2013	112137015	0.0749634
Korea	2013	60714213	0.0405873
Mexico	2013	58604265	0.0391768
Netherlands	2013	58301768	0.0389746
Singapore	2013	60942903	0.0407402
Spain	2013	55846233	0.0373331
Sweden	2013	30360527	0.0202959
Switzerland	2013	41463606	0.0277183
United Kingdom	2013	84920826	0.0567694
United States	2013	258036621	0.1724969
Australia	2014	35893189	0.0321239
Austria	2014	33801789	0.0302521
Belgium	2014	28186840	0.0252268
Brazil	2014	31440841	0.0281391
Canada	2014	73325209	0.0656250
China	2014	70633377	0.0632159
Denmark	2014	11954800	0.0106994
Finland	2014	44746716	0.0400477
France	2014	64209896	0.0574669
Germany	2014	57421832	0.0513917
Italy	2014	46625799	0.0417294
Japan	2014	83188254	0.0744523
Korea	2014	47014946	0.0420777
Mexico	2014	38401768	0.0343690
Netherlands	2014	46212968	0.0413599
Singapore	2014	47114688	0.0421670
Spain	2014	41679916	0.0373029
Sweden	2014	26308001	0.0235453
Switzerland	2014	32981707	0.0295182
United Kingdom	2014	64881658	0.0580682
United States	2014	191312080	0.1712216

3. Create two tables, using the results from question 2:

3.1 Transpose (spread) the data so you end up with four variables: country, revenue_2012, rev-enue_y2013, and revenue_y2014.

Sales_by_Year <- Sales_Prop %>% 
  select(Year, Country, Revenue) %>% 
  spread(Year, Revenue) %>% 
  rename(revenue_2012 = `2012`,
         revenue_2013 = `2013`,
         revenue_2014 = `2014`)
kable(Sales_by_Year)

Country	revenue_2012	revenue_2013	revenue_2014
Australia	25607044	47799737	35893189
Austria	34568090	44996737	33801789
Belgium	30396297	38375433	28186840
Brazil	34234876	43389954	31440841
Canada	75428386	98134069	73325209
China	79080488	99109352	70633377
Denmark	15893267	20050466	11954800
Finland	47826647	59648620	44746716
France	72237825	83075921	64209896
Germany	65238045	78113787	57421832
Italy	46135925	61869049	46625799
Japan	86340230	112137015	83188254
Korea	50844973	60714213	47014946
Mexico	42363572	58604265	38401768
Netherlands	44421660	58301768	46212968
Singapore	48204223	60942903	47114688
Spain	39237620	55846233	41679916
Sweden	23966997	30360527	26308001
Switzerland	26286562	41463606	32981707
United Kingdom	69420604	84920826	64881658
United States	201462260	258036621	191312080

3.2 Transpose (spread) the data so you end up with four variables: country, prop_2012, prop_y2013,and prop_y2014.

Prop_by_Year<- Sales_Prop %>% 
  select(Year, Country, proportion_of_revenue_year) %>% 
  spread(Year, proportion_of_revenue_year) %>% 
  rename(prop_2012 = `2012`,
         prop_2013 = `2013`,
         prop_2014 = `2014`)
kable (Prop_by_Year)

Country	prop_2012	prop_2013	prop_2014
Australia	0.0220904	0.0319540	0.0321239
Austria	0.0298208	0.0300802	0.0302521
Belgium	0.0262219	0.0256539	0.0252268
Brazil	0.0295333	0.0290061	0.0281391
Canada	0.0650696	0.0656024	0.0656250
China	0.0682201	0.0662544	0.0632159
Denmark	0.0137106	0.0134037	0.0106994
Finland	0.0412585	0.0398750	0.0400477
France	0.0623172	0.0555361	0.0574669
Germany	0.0562787	0.0522189	0.0513917
Italy	0.0397999	0.0413593	0.0417294
Japan	0.0744829	0.0749634	0.0744523
Korea	0.0438623	0.0405873	0.0420777
Mexico	0.0365457	0.0391768	0.0343690
Netherlands	0.0383211	0.0389746	0.0413599
Singapore	0.0415842	0.0407402	0.0421670
Spain	0.0338490	0.0373331	0.0373029
Sweden	0.0206755	0.0202959	0.0235453
Switzerland	0.0226766	0.0277183	0.0295182
United Kingdom	0.0598869	0.0567694	0.0580682
United States	0.1737949	0.1724969	0.1712216

4 Merge the results from both tables created in question 3.

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

kable(Summary_Sales)

Country	revenue_2012	prop_2012	revenue_2013	prop_2013	revenue_2014	prop_2014
Australia	25607044	0.0220904	47799737	0.0319540	35893189	0.0321239
Austria	34568090	0.0298208	44996737	0.0300802	33801789	0.0302521
Belgium	30396297	0.0262219	38375433	0.0256539	28186840	0.0252268
Brazil	34234876	0.0295333	43389954	0.0290061	31440841	0.0281391
Canada	75428386	0.0650696	98134069	0.0656024	73325209	0.0656250
China	79080488	0.0682201	99109352	0.0662544	70633377	0.0632159
Denmark	15893267	0.0137106	20050466	0.0134037	11954800	0.0106994
Finland	47826647	0.0412585	59648620	0.0398750	44746716	0.0400477
France	72237825	0.0623172	83075921	0.0555361	64209896	0.0574669
Germany	65238045	0.0562787	78113787	0.0522189	57421832	0.0513917
Italy	46135925	0.0397999	61869049	0.0413593	46625799	0.0417294
Japan	86340230	0.0744829	112137015	0.0749634	83188254	0.0744523
Korea	50844973	0.0438623	60714213	0.0405873	47014946	0.0420777
Mexico	42363572	0.0365457	58604265	0.0391768	38401768	0.0343690
Netherlands	44421660	0.0383211	58301768	0.0389746	46212968	0.0413599
Singapore	48204223	0.0415842	60942903	0.0407402	47114688	0.0421670
Spain	39237620	0.0338490	55846233	0.0373331	41679916	0.0373029
Sweden	23966997	0.0206755	30360527	0.0202959	26308001	0.0235453
Switzerland	26286562	0.0226766	41463606	0.0277183	32981707	0.0295182
United Kingdom	69420604	0.0598869	84920826	0.0567694	64881658	0.0580682
United States	201462260	0.1737949	258036621	0.1724969	191312080	0.1712216

5. Which country has highest growth rate in terms of revenue in 2013, 2014

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)

Country	revenue_2013	revenue_2014	Growth_Rate
Sweden	30360527	26308001	-0.1334801
Switzerland	41463606	32981707	-0.2045625
Netherlands	58301768	46212968	-0.2073488
Korea	60714213	47014946	-0.2256353
Singapore	60942903	47114688	-0.2269044
France	83075921	64209896	-0.2270938
United Kingdom	84920826	64881658	-0.2359747
Italy	61869049	46625799	-0.2463793
Austria	44996737	33801789	-0.2487947
Australia	47799737	35893189	-0.2490923
Finland	59648620	44746716	-0.2498281
Canada	98134069	73325209	-0.2528058
Spain	55846233	41679916	-0.2536665
Japan	112137015	83188254	-0.2581553
United States	258036621	191312080	-0.2585855
Germany	78113787	57421832	-0.2648950
Belgium	38375433	28186840	-0.2654978
Brazil	43389954	31440841	-0.2753889
China	99109352	70633377	-0.2873187
Mexico	58604265	38401768	-0.3447274
Denmark	20050466	11954800	-0.4037645

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%

6. Which country has highest growth rate in terms of proportion of total revenue, taking into account of 2013 - 2014

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)

Country	prop_2013	prop_2014	Growth_Rate
Sweden	0.0202959	0.0235453	0.1600979

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

7. Bar plot

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())

8. Scatterplot

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')`

9. Most profitable country

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)

Country	Year	Revenue	GrossProfit	Margin
Australia	2012	25607044	10384588	0.4055364
Austria	2012	34568090	14175416	0.4100723
Belgium	2012	30396297	12339880	0.4059666
Brazil	2012	34234876	13854339	0.4046849
Canada	2012	75428386	30504205	0.4044128
China	2012	79080488	31963420	0.4041884
Denmark	2012	15893267	6438462	0.4051063
Finland	2012	47826647	19278203	0.4030850
France	2012	72237825	29313542	0.4057922
Germany	2012	65238045	26505188	0.4062842
Italy	2012	46135925	18851632	0.4086107
Japan	2012	86340230	35044742	0.4058912
Korea	2012	50844973	20542348	0.4040193
Mexico	2012	42363572	17109565	0.4038745
Netherlands	2012	44421660	17974572	0.4046353
Singapore	2012	48204223	19323204	0.4008612
Spain	2012	39237620	16061212	0.4093320
Sweden	2012	23966997	9672219	0.4035641
Switzerland	2012	26286562	10694524	0.4068438
United Kingdom	2012	69420604	28013289	0.4035299
United States	2012	201462260	81547970	0.4047804
Australia	2013	47799737	19807230	0.4143795
Austria	2013	44996737	18886350	0.4197271
Belgium	2013	38375433	16025043	0.4175860
Brazil	2013	43389954	18078456	0.4166507
Canada	2013	98134069	40958729	0.4173752
China	2013	99109352	41126760	0.4149635
Denmark	2013	20050466	8241322	0.4110289
Finland	2013	59648620	24909923	0.4176111
France	2013	83075921	34596692	0.4164467
Germany	2013	78113787	32560564	0.4168350
Italy	2013	61869049	25862271	0.4180163
Japan	2013	112137015	46843843	0.4177376
Korea	2013	60714213	25151197	0.4142555
Mexico	2013	58604265	24335576	0.4152526
Netherlands	2013	58301768	24408939	0.4186655
Singapore	2013	60942903	25312852	0.4153536
Spain	2013	55846233	23323546	0.4176387
Sweden	2013	30360527	12776374	0.4208219
Switzerland	2013	41463606	17373916	0.4190160
United Kingdom	2013	84920826	35421323	0.4171100
United States	2013	258036621	107415232	0.4162790
Australia	2014	35893189	14900245	0.4151274
Austria	2014	33801789	14202010	0.4201556
Belgium	2014	28186840	11719669	0.4157851
Brazil	2014	31440841	13128548	0.4175635
Canada	2014	73325209	30479726	0.4156787
China	2014	70633377	29398530	0.4162130
Denmark	2014	11954800	4978965	0.4164826
Finland	2014	44746716	18531797	0.4141488
France	2014	64209896	26816103	0.4176319
Germany	2014	57421832	23918484	0.4165399
Italy	2014	46625799	19497491	0.4181696
Japan	2014	83188254	34877011	0.4192540
Korea	2014	47014946	19484528	0.4144326
Mexico	2014	38401768	15879189	0.4135015
Netherlands	2014	46212968	19133312	0.4140247
Singapore	2014	47114688	19651191	0.4170927
Spain	2014	41679916	17407446	0.4176459
Sweden	2014	26308001	11016468	0.4187497
Switzerland	2014	32981707	13848073	0.4198713
United Kingdom	2014	64881658	27038316	0.4167328
United States	2014	191312080	79853980	0.4174017

9.1 Most profitable country in terms of profit

MostProfitable_Country<- Sales_Profit %>% 
  filter (Year =="2014") %>% 
  select(Country, GrossProfit) %>% 
  arrange(desc(GrossProfit))
kable(MostProfitable_Country)

Country	GrossProfit
United States	79853980
Japan	34877011
Canada	30479726
China	29398530
United Kingdom	27038316
France	26816103
Germany	23918484
Singapore	19651191
Italy	19497491
Korea	19484528
Netherlands	19133312
Finland	18531797
Spain	17407446
Mexico	15879189
Australia	14900245
Austria	14202010
Switzerland	13848073
Brazil	13128548
Belgium	11719669
Sweden	11016468
Denmark	4978965

The U.S is the most profitable country

9.2 Most Profitable contry in terms of margin

HighestProfitability<- Sales_Profit %>% 
  filter (Year =="2014") %>% 
  select(Country, Margin) %>% 
  arrange(desc(Margin))
kable(HighestProfitability)

Country	Margin
Austria	0.4201556
Switzerland	0.4198713
Japan	0.4192540
Sweden	0.4187497
Italy	0.4181696
Spain	0.4176459
France	0.4176319
Brazil	0.4175635
United States	0.4174017
Singapore	0.4170927
United Kingdom	0.4167328
Germany	0.4165399
Denmark	0.4164826
China	0.4162130
Belgium	0.4157851
Canada	0.4156787
Australia	0.4151274
Korea	0.4144326
Finland	0.4141488
Netherlands	0.4140247
Mexico	0.4135015

The country with highest profitability is Austria

10. Scatter plot of Revenue and Profit 2012-2014

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')`

11. Which Country places the most orders online in 2014?

summary(as.factor(Sales12_14$OrderMethod))

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

11.1 Country places the most orders online in terms of Revenue

Online_Orders2014_byRevenue<- Sales12_14 %>% 
  filter(Year == "2014", OrderMethod == "Web") %>% 
  select(Country, Revenue) %>% 
  na.omit() %>% 
  group_by(Country) %>% 
    summarise(Revenue = sum(Revenue)) %>% 
  arrange(desc(Revenue)) %>% 
  head(Revenue, n=1L)
Online_Orders2014_byRevenue$Revenue <- prettyNum(Online_Orders2014_byRevenue$Revenue, 
                                         big.mark = ",")
kable(Online_Orders2014_byRevenue)

Country	Revenue
United States	170,254,299
### The U.S is t	he country with most orders online with $170,254,299

11.1 Country places the most orders online in terms of Quantity

Online_Orders2014_byQuan<- Sales12_14 %>% 
  filter(Year == "2014", OrderMethod == "Web") %>% 
  select(Country, Quantity) %>% 
  na.omit() %>% 
  group_by(Country) %>% 
    summarise(Quantity = sum(Quantity)) %>% 
  arrange(desc(Quantity))%>% 
  head(Quantity, n=1L)
Online_Orders2014_byQuan$Quantity <- prettyNum(Online_Orders2014_byQuan$Quantity, 
                                         big.mark = ",")
kable(Online_Orders2014_byQuan)

Country	Quantity
United States	2,956,446

The U.S is the country with most orders online with 2959043 of numbers items

Overall, the US is the country places the most order online in 2014

12. Which country has the biggest growth in online orders?

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

12.1 Orderonline growth 2013-2014 by Quantity

Online_Orders_Growth_1 <- Online_Orders_Growth %>% 
  filter(Year != "2012") %>% 
  select(Country, Year, Quantity) %>% 
  spread(Year, Quantity) %>% 
  mutate(difference = `2014`-`2013`) %>% 
  arrange(desc(difference))%>% 
  head(difference, n=1L)
kable(Online_Orders_Growth_1)

Country	2013	2014	difference
Sweden	365828	352089	-13739

12.2 Online Order growth 2013 -2014 by Revenue

Online_Orders_Growth_2 <- Online_Orders_Growth %>% 
  filter(Year != "2012") %>% 
  select(Country, Year, Revenue) %>% 
  spread(Year, Revenue) %>% 
  mutate(difference = `2014`-`2013`) %>% 
  arrange(desc(difference))%>% 
  head(difference, n=1L)
kable(Online_Orders_Growth_2)

Country	2013	2014	difference
Sweden	22681966	20740107	-1941860

Sweden is the country with the smallest decline in online orders

13. Which are the most profitable products?

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

13.1 The most profitable in terms of total profit

Highest_Profit_1 <- Profitable_Product %>% 
  select(Product, Profit) %>% 
  arrange(desc(Profit)) %>% 
head(Profit, n= 10L)
Highest_Profit_1$Profit <- prettyNum(Highest_Profit_1$Profit, big.mark = ",")
kable(Highest_Profit_1)

Product	Profit
Hailstorm Titanium Woods Set	45,209,375
Zone	40,899,394
Infinity	40,621,391
Maximus	40,282,326
Star Lite	39,061,918
Inferno	38,145,555
TX	37,711,940
Hailstorm Titanium Irons	35,374,379
Star Gazer 2	33,674,494
Lady Hailstorm Titanium Woods Set	30,449,255

13.2 The most profitable in terms of profit per unit, and cost per unit

Highest_Profit_2 <- Profitable_Product %>%
  na.omit() %>% 
  mutate(Profit_per_unit=Profit/Quantity) %>% 
  select(Product, Profit_per_unit) %>% 
  arrange(desc(Profit_per_unit)) %>% 
  head(Profit, n= 10L)
Highest_Profit_2$Profit_per_unit <- prettyNum(Highest_Profit_2$Profit_per_unit, 
                                              big.mark = ",")
kable(Highest_Profit_2)

Product	Profit_per_unit
Lady Hailstorm Titanium Woods Set	663.5993
Hailstorm Titanium Woods Set	563.1532
Lady Hailstorm Steel Woods Set	427.658
Lady Hailstorm Titanium Irons	410.6811
Hailstorm Titanium Irons	401.4615
Hailstorm Steel Woods Set	313.556
Star Gazer 6	302.8579
Star Gazer 3	238.291
Lady Hailstorm Steel Irons	226.8703
Star Dome	226.7223

14. Which are the most profitable product lines?

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

14.1 The most profitable product lines in terms of total profit

Highest_Profit_3 <- Profitable_ProductLine%>% 
  select(ProductLine, Profit) %>% 
  arrange(desc(Profit)) 
Highest_Profit_3$Profit <- prettyNum(Highest_Profit_3$Profit, big.mark = ",")
kable(Highest_Profit_3)

ProductLine	Profit
Personal Accessories	618,601,957
Camping Equipment	470,295,189
Golf Equipment	281,893,861
Mountaineering Equipment	163,278,857
Outdoor Protection	24,699,875

14.2 The most profitable product lines in terms of profit per unit

Highest_Profit_4 <- Profitable_ProductLine %>%
  na.omit() %>% 
  mutate(Profit_per_unit=Profit/Quantity) %>% 
  select(ProductLine, Profit_per_unit) %>% 
  arrange(desc(Profit_per_unit))
Highest_Profit_4$Profit_per_unit <- prettyNum(Highest_Profit_4$Profit_per_unit, 
                                              big.mark = ",")
kable(Highest_Profit_4)

ProductLine	Profit_per_unit
Golf Equipment	70.32727
Personal Accessories	22.63765
Camping Equipment	21.99339
Mountaineering Equipment	16.49422
Outdoor Protection	3.880184

15. Which is the best retailer type?

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

15.1 Best retailer type with the highest profit

HighestProfit_RetailerType_1 <- Best_RetailerType %>% 
  select(RetailerType, Profit) %>% 
  arrange(desc(Profit))

15.2 Best retailer type with the highest number of quantity

HighestProfit_RetailerType_2<- Best_RetailerType %>% 
  select(RetailerType, Quantity) %>% 
  arrange(desc(Quantity))

15.3 Best retailer type with the highest revenue

HighestProfit_RetailerType_3<- Best_RetailerType %>% 
  select(RetailerType, Revenue) %>% 
  arrange(desc(Revenue))

join1 <- inner_join(HighestProfit_RetailerType_1, HighestProfit_RetailerType_2,  by = "RetailerType")
  Best_Retailer_Type <- inner_join(join1, HighestProfit_RetailerType_3, by = "RetailerType")
  kable(Best_Retailer_Type )

RetailerType	Profit	Quantity	Revenue
Outdoors Shop	511033204	25905357	1267370732
Sports Store	399994822	16753737	969880760
Department Store	274906813	12878704	662445711
Golf Shop	193519777	4392068	410870169
Eyewear Store	74140318	2701365	171718155
Warehouse Store	64956723	3373039	192336169
Direct Marketing	22685977	1899138	54367078
Equipment Rental Store	17532106	1079436	43434191

The best retailer type is Outdoor Shop.