Sales_Report.Rmd

---
title: "Sales Performance Analysis 2010-2011"
output: 
  flexdashboard::flex_dashboard:
#    orientation: rows
    vertical_layout: fill
runtime: shiny
---

```{r global, include=FALSE}

library(flexdashboard)
library(ggplot2)
library(tidyverse)
library(plotly)
library(ggmap)
library(knitr)
library(leaflet)
library(DT)
library(sp)
library(rnaturalearth)
library(highcharter)
library(shiny)
library(factoextra)
library(plotly)
library(sf)
library(tmap)


# import the data

# import the data
dashboard_df<-read_csv("Data/Main_Dashboard_Data.csv")
df_KMeans<-read_csv("Data/KMeans_Data.csv")

                
# amend the data types

dashboard_df$Price<- round(dashboard_df$Price)

# Map set up
map <- ne_countries()
#names(map)[names(map) == "iso_a3"] <- "ISO3"
#names(map)[names(map) == "name"] <- "NAME"
CountryData<- aggregate(Price ~ Country, dashboard_df, sum)
map$Revenue <- CountryData$Price[match(map$name, CountryData$Country)]

# scale the K-means data

KM_df <- 
  df_KMeans %>%
  subset(select = - CustomerId) %>%
  scale()


```


Product Performance {data-icon="fa-chart-line"} 
==========================================================

Column {data-width= 200}
--------------------------------------

### Number of Transactions

```{r}
AnnualSales <- dashboard_df %>% 
  distinct(Invoice) %>% 
  nrow()
valueBox(value= AnnualSales,icon = "fa-usd", caption ="Number of Transactions",color = "Coral")
```

### Gross Annual Revenue

```{r}
AnnualRevenue <- scales::dollar(sum(dashboard_df$Price)-sum((filter(dashboard_df,dashboard_df$Quantity < 0)$Price)))
valueBox(value = AnnualRevenue,icon = "fa-usd",caption = "Gross Annual Revenue", color = 'CornflowerBlue')
```

### Refunds

```{r}
refund <- round((sum((filter(dashboard_df,dashboard_df$Quantity < 0)$Price))/sum(dashboard_df$Price))*100,1)
gauge(refund, min = 0, max = 100, symbol = '%', gaugeSectors(
  success = c(80, 100), warning = c(40, 79), danger = c(0, 39)
))
```

### Global Product Performance
    
```{r}
revenue<- aggregate(cbind(Quantity,Price) ~ Description, dashboard_df, sum)%>%
  filter(Quantity>0 & Price>0)
renderDT ({
DT::datatable(revenue,
  colnames=c('Product Description' = 'Description', 'Revenue' = 'Price'),
  fillContainer=TRUE,
  rownames = FALSE, options = list(
  pageLength = 20,
  lengthMenu = c(10,20,50)
))
})
  
```




Column {.sidebar data-width=100}
-------------------------------------

```{r}
minvalue <- floor(min(map$Revenue, na.rm = TRUE))
maxvalue <- ceiling(max(map$Revenue, na.rm = TRUE))
sliderInput("rangevalues",
  label = "Revenue values:",
  min = minvalue, max = maxvalue,
  value = c(minvalue, maxvalue)
)
           
```


Row {data-height=650}
-------------------------------------
    
### Map
    
```{r}
pal <- colorBin(
  palette = "viridis", domain = map$Revenue ,
  #bins = seq(0, max(map$Revenue, na.rm = TRUE) + 10, by = 10)
)
map$labels <- paste0(
  "<strong> Country: </strong> ",
  map$name, "<br/> ",
  "<strong> Revenue: </strong> ",
  map$Revenue , "<br/> "
) %>%
  lapply(htmltools::HTML)

mapFiltered <- reactive({
  rowsinrangeslider <- which(map$Revenue >= input$rangevalues[1] &
    map$Revenue <= input$rangevalues[2])
  map[rowsinrangeslider, ]
})
renderLeaflet({
  if (nrow(mapFiltered()) == 0) {
    return(NULL)
  }
leaflet(mapFiltered()) %>%
  addTiles() %>%
  setView(lng = 0, lat = 30, zoom = 2) %>%
  addPolygons(
    fillColor = ~ pal(Revenue),
    color = "white",
    fillOpacity = 0.7,
    label = ~labels,
    highlight = highlightOptions(
      color = "black",
      bringToFront = TRUE
    )
  ) %>%
  leaflet::addLegend(
    pal = pal, values = ~Revenue,
    opacity = 0.7, title = "Revenue"
  )
})
  
```   
 


Row {data-height=350}
-------------------------------------

### Revenue monthly performance

```{r}
time_revenue<-aggregate(Price ~ InvoiceDate, dashboard_df, sum)
  
  
renderHighchart({
time_revenue%>% 
 
  hchart('line', hcaes(x = 'InvoiceDate', y = 'Price'))
  
})
```


### Product demand monthly performance

```{r}
time_demand<-aggregate(Quantity ~ InvoiceDate, dashboard_df, sum)
  
renderHighchart({
time_demand%>% 
 
  hchart('line', hcaes(x = 'InvoiceDate', y = 'Quantity'))
  
})
```
   
RFM Customer Segmentation {data-icon="fas fa-users"} 
===================================================     

Column {.sidebar data-width=100}
-------------------------------------

```{r}
numericInput('clusters', 'Number of Segments', 3,
              min = 2, max = 8)
           
```
    
    

Row 
-------------------------------------    
    
### Segments

```{r}

palette(c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3",
  "#FF7F00", "#FFFF33", "#A65628", "#F781BF", "#999999"))

clusters <- reactive({
  kmeans(KM_df, input$clusters, nstart = 25)
})

renderPlot({
  #par(mar = c(5.1, 4.1, 0, 1))
fviz_cluster(clusters(), geom = "point", data = KM_df)
})


```

Row 
------------------------------------- 

### Segments Summary

```{r}
# exclude the customer id column

summary_K2 <- reactive({ df_KMeans%>%
  subset(select = -CustomerId)%>%
  mutate(Segment = clusters()$cluster) %>%
  group_by(Segment) %>%
  summarise_all(list(n="length", mean="mean"))%>%
  subset(select = -c(recency_n, frequency_n))%>%
  rename(segment_size = total_value_n )
})


# transpose the summary table to facilitate the visualisation
summary_K_transposed2 <- reactive({ gather(summary_K2(),
                                   key = "feature",
                                   value = "value",
                                   -Segment)

})

renderTable({
 head(summary_K_transposed2(), n=32) 
})
```

### Segments Visualisation

```{r}

 renderPlot({
    
  ggplot(summary_K_transposed2(),aes(x=reorder(Segment,-value),y=value, fill=as.factor(Segment)))+
  geom_col()+
  facet_grid(.~feature)+
  labs(x="Segments", subtitle = paste("Total N of customers: ", sum(summary_K2()$segment_size)))
})

```



Market Analysis {data-icon="fas fa-globe"} 
===================================================     
  
Column {data-width= 600}
-------------------------------------
### Non UK Market segment performance
  
```{r}

segment_K <- reactive({ df_KMeans%>%
  mutate(Segment = clusters()$cluster) %>%
  group_by(Segment)%>%
  inner_join(y = dashboard_df )%>%
  distinct(CustomerId, .keep_all = TRUE)%>%
  select(Segment, Country)
})



# Split the segment int o2 for UK and non-UK countries


segmented_noUK<-reactive({segment_K()})


renderPlot(segmented_noUK()%>%
  filter(Country != "United Kingdom")%>%
  ggplot(aes(Country, Segment, fill = as.factor(Segment)))+
  geom_col()+
  labs(title="Segments performance for the rest of the world", ylab = "Segment"))
```

### UK Market segment performance

```{r}

segmented_UK<-reactive({segment_K()})

renderPlot(segmented_UK()%>%
  filter(Country == "United Kingdom")%>%
  ggplot(aes(Segment,fill = as.factor(Segment)))+
  geom_bar()+
  labs(title="Segments performance for UK"))
  
```   
 
Column {data-width=350}
-------------------------------------
### Product performance per segment
  
```{r}

segment_product <- reactive({ df_KMeans%>%
  mutate(Segment = clusters()$cluster) %>%
  group_by(Segment)%>%
  inner_join(y = dashboard_df )%>%
  filter(Quantity>0 & Price>0)%>%
  select(Segment,Description, Price, Quantity )%>%
  group_by(Segment,Description) %>% 
    summarise_each(funs(sum))
})



renderDT ({
DT::datatable(segment_product(),
  colnames=c('Product Description' = 'Description', 'Revenue' = 'Price'),
  fillContainer=TRUE,
  rownames = FALSE, options = list(
  pageLength = 20,
  lengthMenu = c(10, 15, 20,50,100)
))
})

```