Customer Data .Rmd

---
title: "Dashboard"
output: 
  flexdashboard::flex_dashboard:
    vertical_layout: scroll
    theme: cosmo
---
```{r setup, include=FALSE}
library(dplyr)
library(flexdashboard)
library(ggplot2)
library(readr)
library(highcharter)
library(plotly)
library(ggpubr)
library(ggthemes)
library(tidyr)
library(lubridate)
data <- read.csv(file="Exercise data.csv ",na.strings=c("£-",NULL))
data=data.frame(data)
data <-na.omit(data)
data$Revenue <- sub(',','',data$Revenue)
data$Revenue <- sub('£','',data$Revenue)
data$Revenue <- gsub("-",NA,data$Revenue)
#data$Revenue <- gsub(NaN,NULL,data$Revenue)
data$Revenue <- sub('\\..*','',data$Revenue)
data$Bounce.Rate <- as.numeric(sub("%", "",data$Bounce.Rate,fixed=TRUE))
data <-data[order(as.Date(data$Date, format="%d/%m/%Y")),]
data1 <-data[order(as.Date(data$Date, format="%d/%m/%Y")),]
data$Date <- as.Date(data$Date, format = "%d/%m/%Y")
data1$Date <- as.Date(data1$Date, format = "%d/%m/%Y")
data1$Medium<-tolower(data$Medium)
data1$Revenue[is.na(data1$Revenue)] <- 0
data1$Revenue<-as.numeric(data1$Revenue)
data1 <- data1 %>% mutate(day_of_the_week = strftime(data1$Date,'%A'))
dd4<-data1%>%
  group_by(Medium)%>%
summarise(
  trans=sum(Transactions),
  sess=sum(Sessions),
  rev=sum(as.numeric(Revenue)),
  user=sum(Users)
)
Year=year(as.Date(data1$Date,"%d/%m/%Y"))
Month=month(as.Date(data1$Date,"%d/%m/%Y"))
data1$Year=Year
data1$Month=Month
dd<-data1%>%
  group_by(Month,Year)%>%
summarise(
  rev=sum(as.numeric(Revenue)),
  sess=sum(Sessions),
  trans=sum(Transactions)
)
dd6<-data1%>%
  group_by(day_of_the_week)%>%
  summarise(
  meantrans=mean(Transactions),
  meanrev=sum(Revenue),
  meansess=mean(Sessions),
  meanuser=mean(Users),
  n=n()


)
dd1<-data1%>%
  group_by(Month,Year,Medium)%>%
summarise(
  rev=sum(as.numeric(Revenue)),
  sess=sum(Sessions),
  trans=sum(Transactions)
)
L<-c("cpc","organic","(none)","email")
dd2<-subset(dd1,Medium %in% L)
dd7<-data1%>%
  group_by(Year)%>%
  summarise(
    sess=mean(Sessions),
    rev=mean(Revenue),
    sess1=sum(Sessions),
    rev1=sum(Revenue)
    
    
  )
dd10<-data1%>%
  group_by(Month)%>%
summarise(
  trans=sum(Transactions),
  sess=sum(Sessions),
  rev=sum(as.numeric(Revenue)),
  user=sum(Users)
)




```
 Medium Layout 1 {data-orientation=rows data-icon="fa-bar-chart"}
=====================================
## Row 1 {data-height=110}

### Average Website Users

```{r}
valueBox(999, icon = "fa fa-user", color="rgb(100,100,100)")
```

### Average Website Revenue

```{r}
valueBox("13784.32", icon = "fa fa-money", color="rgb(200,100,100)")
```

### Average Website Transaction

```{r}
valueBox("5.760153", icon = "fa fa-exchange",color="rgb(26,110,204)")
```

## Row 2 
### Relation of Sessions to Transactions


```{r,warning=FALSE}
gg1<- ggplot(data1,
    aes(                  
      x = Sessions,
      y = Transactions,
      color = Medium
    )
  ) +
  geom_point(alpha = 5/7)+  theme_minimal(base_family = "Helvetica Neue")+scale_x_continuous(name = " sessions", limits = c(0, NA)) +
  scale_y_continuous(name = "Sales", limits = c(0, NA))
ggplotly(gg1)
```
## Row 3  

### Relation of Sessions to Revenue

```{r}
       
 gg2<- ggplot(data1,
    aes(                 
      x = Sessions,
      y = Revenue,
      color = Medium
    )
  ) +
  geom_point(alpha = 5/7)+  theme_minimal(base_family = "Helvetica Neue")+scale_x_continuous(name = " Sessions", limits = c(0, NA)) +
  scale_y_continuous(name = "Revenue", limits = c(0, NA))
ggplotly(gg2)

```


## Row 3 

### Count Of The Various Mediums

```{r}
gg3<-ggplot(data1, aes(Medium)) +
  geom_bar(fill = "#0073C2FF") +
  theme_pubclean()+theme(axis.text.x = element_text(angle=65, vjust=0.6))+scale_y_continuous(limits = c(0, NA))
ggplotly(gg3)
```


Medium Layout 2 {data-orientation=rows data-icon="fa-bar-chart"}
=====================================
## Row 1 



### Average Transactions for Cpc

```{r}
valueBox("22.07755", icon = "fa fa-credit-card", color="rgb(100,100,100)")
```

### Average Transactions for Organic

```{r}
valueBox("17.78011", icon = "fa fa-laptop", color="rgb(200,100,100)")
```

### Average Transactions for (none)
```{r}
valueBox("8.951642", icon = "fa fa-globe", color="rgb(200,100,100)")
```



##Row 2

### Various Mediums Bounce Rate

```{r}
q1<-ggplot(data1, aes(x=reorder(Medium,Bounce.Rate), y=Bounce.Rate)) + 
  geom_bar(stat="identity", width=.5, fill="green") + 
  labs(title="Bounce Rate vs Medium",x="Medium") + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6))+ scale_y_continuous(name = "Bounce Rate", limits = c(0,NA))
 ggplotly(q1) 
```



### Medium With Most Transactions 
```{r}
ctd <- ggplot(dd4, aes(x =reorder(Medium,trans), y = trans, colour = Medium)) + 
  geom_point(size = 3) +
  coord_flip() +
  geom_text(aes(y = trans, label = trans, size = 12), hjust = -1) +
  theme_igray() +
  #scale_color_tableau("tableau20") +
  theme(legend.position="none") +
  labs(title="Medium With Most Transactions", x = "Sales", y = "Medium")+scale_y_continuous(name = "Medium", limits = c(0, 35000))
ctd
```



## Row 3 

### Medium Most Popular Among Users 

```{r}
ctd <- ggplot(dd4, aes(x =reorder(Medium,user), y = user, colour = Medium)) + 
  geom_point(size = 3) +
  coord_flip() +
  geom_text(aes(y = user, label = user, size = 12), hjust = -1) +
  theme_igray() +
  #scale_color_tableau("tableau20") +
  theme(legend.position="none") +
  labs(title="  Most Popular Medium Among The Users", x = "Medium", y = "User")+scale_y_continuous(name = "User", limits = c(0, 35000000))
ctd
```   




### Medium With Most Revenue

```{r}
ctr <- ggplot(dd4, aes(x = reorder(Medium, rev), y = rev, fill = Medium)) + 
  geom_bar(stat = "identity") +
  coord_flip() +
  geom_text(aes(y = rev, label = rev, size = 9000000000000000000000000), hjust = -0.1) +
  theme_igray() +
  #scale_color_tableau("tableau20") +
  theme(legend.position="none") +
  labs(title="Medium With Most Revenue", x = "Medium", y = "Revenue")+scale_y_continuous(name = "Revenue", limits = c(0, 70000000))
ctr


```

Time Series Layout 1 {data-orientation=rows data-icon="fa-bar-chart"}
=====================================




Row {data-height=500}
-------------------------------------
    
### Transaction over the 3 years
    
```{r}
t1<-ggplot(data = dd, aes(x = as.numeric(Month), y = trans)) +
    geom_line(stat='identity') + geom_point(size = 3.0,color="red") +
    facet_wrap(~ Year, ncol = 1,scales = "free_x") +
    scale_x_discrete( name="",limits=c(1,2,3,4,5,6,7,8,9,10,11,12))
    #labs(title = "Time-Series years comparison", y="Transaction")+theme_economist()+theme_minimal(base_family = "Helvetica Neue")+theme(panel.spacing = unit(2, "lines"))+scale_y_discrete(name="Transaction")
    ggplotly(t1)

```

Row
-------------------------------------
    
###Days with most Revenue
    
```{r}
ctr <- ggplot(dd6, aes(x = reorder(day_of_the_week, meanrev), y = meanrev, fill = day_of_the_week)) + 
  geom_bar(stat = "identity") +
  coord_flip() +
  geom_text(aes(y = meanrev, label = meanrev, size = 9000000000000000000000000), hjust = -0.1) +
  theme_igray() +
  #scale_color_tableau("tableau20") +
  theme(legend.position="none") +
  labs(title="Day which Gives Most Revenue", x = "Day", y = "Day")+scale_y_continuous(name = "Revenue", limits = c(0, 70000000))
ggplotly(ctr)

```
    
### Session Disribution over the days

```{r,warning=FALSE}

library(ggplot2)
library(RColorBrewer)
library(ggthemes)
library(grid)
fill <- "#4271AE"
line <- "#1F3552"
date <- ggplot(data1, aes(x = data1$day_of_the_week, y = data1$Sessions)) +
        geom_boxplot(fill = fill, colour = line) +
        scale_y_continuous(name = "Sessions",breaks = seq(0, 18000, 2000),
                           limits=c(0, 18000))+
        scale_x_discrete(name = "day of the week") +
        ggtitle("Session Disribution over the days") +
        theme_economist() +
        theme(axis.line.x = element_line(size = 0.5, colour = "black"),
              axis.line.y = element_line(size = 0.5, colour = "black"),
              legend.position = "bottom", legend.direction = "horizontal",
              legend.box = "horizontal",
              legend.key.size = unit(1, "cm"),
              plot.title = element_text(family="Tahoma"),
              text = element_text(family = "Tahoma"),
              axis.title = element_text(size = 12),
              legend.text = element_text(size = 9),
              legend.title=element_text(face = "bold", size = 9))
ggplotly(date)

```

Time Series Layout 2 {data-orientation=rows data-icon="fa-bar-chart"}
=====================================
## Row 1 



### Total Revenue In 2016

```{r}
valueBox("38,709,143", icon = "fa fa-credit-card", color="rgb(100,100,100)")
```

### Total Revenue In 2017

```{r}
valueBox("60,508,624", icon = "fa fa-laptop", color="rgb(200,100,100)")
```

### Total Revenue In 2018
```{r}
valueBox("48,770,707", icon = "fa fa-globe", color="rgb(200,100,100)")
```

##Row 2

### Distribution of Transaction over years
    
```{r}
t1<-ggplot(data = dd2, aes(x = as.numeric(Month), y = dd2$trans,group=Medium,color=Medium)) +
    geom_line() + geom_point(size = 1.5) +
    facet_wrap(~ Year, ncol = 1,scales = "free_x") +
    scale_x_discrete( name="",limits=c(1,2,3,4,5,6,7,8,9,10,11,12))+theme(panel.spacing = unit(2, "lines"))+ labs(title="Distribution of Transaction over years", x = "Month",y="Sales")+theme(panel.spacing = unit(1, "lines"))+ theme(axis.title.y = element_text(margin = margin(t = 0, r = 20, b = 0, l = 0)))
ggplotly(t1)
```

##Row 3

### Popularity of the website over the three years

```{r}
t1<-ggplot(data = dd7, aes(x = Year, y = sess)) +
    geom_line(stat='identity') + geom_point(size = 3.0,color="red")+ scale_x_discrete( name="",limits=c(2016,2017,2018))+theme_set(theme_bw())+ geom_smooth(method="lm", se=F)+labs(title = "Popularity of website " ,y = "Session")
ggplotly(t1)

```

### Revenue earned for each month over the three years
```{r}
t2<-ggplot(data = dd10, aes(x = Month, y = rev))+
    geom_line(stat='identity') + geom_point(size = 3.0,color="red")+ scale_x_discrete( name="",limits=c(1,2,3,4,5,6,7,8,9,10,11,12))+theme_set(theme_bw())+labs(title = "Revenue procured over the three months " ,y = "Revenue",x="Month")
ggplotly(t2)
```