Furniture company sales analysis.Rmd

---
title: "Furniture company sales analysis"
author: "Afsar Ali"
output:
  prettydoc::html_pretty:
    theme: Cayman
    highlight: github
    df_print: paged
    toc: yes
    toc_depth: '4'  
---

### Code header 

```{r echo=TRUE, message=FALSE, warning=FALSE, paged.print=TRUE}
# Course: BUAN 5210
# Purpose: Find ways to increase profitability for the company
# Author: Afsar Ali
# Data: D7_Office.csv
# Date: Oct 20, 2016
```


```{r echo=TRUE, message=FALSE, warning=FALSE, paged.print=TRUE, results = "hide"}
#	Clear workspace at begining
rm(list = ls(all = TRUE))

library(tidyverse)
library(gridExtra)
library(waterfall)
library(lubridate)
library(plotly)
library(zoo)
library(kableExtra)
library(Hmisc)

# Load data
dat1 <- read.csv("D7_Office.csv")

# Data Manipulation
dat1 <- dat1 %>% 
  mutate(Cost = Sales - Profit) %>% # Added Cost
  mutate(Price = Sales / Quantity) %>%  #Added Price
  mutate(PPQ = Profit / Quantity) %>% #Added Profit per quantity
  mutate(Order.Date = mdy(Order.Date)) %>% #format Date
  mutate(Ship.Date = mdy(Ship.Date)) %>% #format Date
  mutate(qtr=as.character(as.yearqtr(Order.Date))) %>% #Change qtr as Character 
  mutate(Cost = Sales - Profit) %>% # Added Cost
  mutate(Pwodis = (Sales/(1-Discount)) - Cost)  %>% # Profit with out Discount
  mutate(DicountGiven = (Sales * Discount)) #Discount Given

# Look at data
glimpse(dat1)
summary(dat1)
describe(dat1)
```
###Histogram of the relevent Variables

- units sold may have a skewed distribution
- there are Outliers 

```{r echo=TRUE, message=FALSE, warning=FALSE, paged.print=TRUE}
# Units sold may have a skewed distribution
par(mfrow=c(4,2))
  hist(dat1$Sales) # Outliers 
  hist(dat1$Quantity)
  hist(dat1$Discount) # Outliers 
  hist(dat1$Profit) # Outliers 
  hist(dat1$Cost) # Outliers 
  hist(dat1$Price) # Outliers 
  hist(dat1$PPQ) # Outliers 
  hist(dat1$Pwodis)
#looks like there are Outliers 
  
```


```{r echo=TRUE, message=FALSE, warning=FALSE, paged.print=TRUE}
# Units sold may have a skewed distribution
par(mfrow=c(4,2))
  barplot(table(dat1$Category), main = "Category") #lots of Office Supply
  barplot(table(dat1$Sub.Category), main = "Sub Category") 
  barplot(table(dat1$Segment), main = "Segment") #Consumer to Corporate to Home Office
  barplot(table(dat1$Region), main = "Region") #South is the lowest 
  barplot(table(dat1$Ship.Mode ), main = "Ship Mode") #Standard Class is High
  barplot(table(dat1$Order.Date), main = "Order Date")
  barplot(table(dat1$Customer.ID), main = "Customer ID")  
  barplot(table(dat1$qtr), main = "Order date M_Y") #Q4 Has the higest Observation, Q1 is low 
  
```

###Sales Category Analysis
- Review at Mean Profit per by Category
- Why does technology have a big Variance

```{r echo=TRUE, message=FALSE, warning=FALSE, paged.print=TRUE}
# 90% CI, get z-value for upper tail, use .95 since is one sided
z <- qnorm(.95)

dat1 %>%
  group_by(Category) %>%
  summarise(m_p = mean(Profit), sd_p = sd(Profit), 
            n = n(), ci = z * sd_p/sqrt(n)) %>%
  ggplot(aes(x = Category, y = m_p)) +
    geom_bar(stat = "identity") +
    geom_errorbar(aes(ymin = m_p - ci, ymax = m_p + ci), width = 0.5)

#Technology has a big Variance??

```

###Quantity and sales analysis by Rigion

```{r echo=TRUE, message=FALSE, warning=FALSE, paged.print=TRUE}

# CI on mean Quantity by Region and Segment type
dat1 %>%
  group_by(Region, Segment) %>%
  summarise(m_p = mean(Quantity), sd_p = sd(Quantity), 
            n = n(), ci = z * sd_p/sqrt(n)) %>%
  ggplot(aes(x = Region, y = m_p, fill = as.factor(Segment))) +
    geom_bar(stat = "identity", position = "dodge") +
    geom_errorbar(aes(ymin = m_p - ci, ymax = m_p + ci), 
                width = 0.5, position = position_dodge(0.9))

#Qantity sold doesnt have any insight 

```

```{r echo=TRUE, message=FALSE, warning=FALSE, paged.print=TRUE}
# CI on mean profit by Region and Segment type
dat1 %>%
  group_by(Region, Segment) %>%
  summarise(m_p = mean(Profit), sd_p = sd(Profit), 
            n = n(), ci = z * sd_p/sqrt(n)) %>%
  ggplot(aes(x = Region, y = m_p, fill = as.factor(Segment))) +
    geom_bar(stat = "identity", position = "dodge") +
    geom_errorbar(aes(ymin = m_p - ci, ymax = m_p + ci), 
                width = 0.5, position = position_dodge(0.9))

#Central has issues with Consumer Segment and Corporate sector has big Variance, Why??
#South has huge variance in Home Office, Centeal is more alarming 

## Side note --Sales has potential in south Home Office ***
```

```{r echo=TRUE, message=FALSE, warning=FALSE, paged.print=TRUE}
# CI on mean Discount by Region and Segment type
dat1 %>%
  group_by(Region, Segment) %>%
  summarise(m_p = mean(Discount), sd_p = sd(Discount), 
            n = n(), ci = z * sd_p/sqrt(n)) %>%
  ggplot(aes(x = Region, y = m_p, fill = as.factor(Segment))) +
    geom_bar(stat = "identity", position = "dodge") +
    geom_errorbar(aes(ymin = m_p - ci, ymax = m_p + ci), 
                width = 0.5, position = position_dodge(0.9))

#Nothing significant 

```

```{r echo=TRUE, message=FALSE, warning=FALSE, paged.print=TRUE}
# CI on mean profit by Region and Category
dat1 %>%
  group_by(Region, Category) %>%
  summarise(m_p = mean(Profit), sd_p = sd(Profit), 
            n = n(), ci = z * sd_p/sqrt(n)) %>%
  ggplot(aes(x = Region, y = m_p, fill = as.factor(Category))) +
    geom_bar(stat = "identity", position = "dodge") +
    geom_errorbar(aes(ymin = m_p - ci, ymax = m_p + ci), 
                width = 0.5, position = position_dodge(0.9))

#Central Office supply Makes the least profit
#Furniture and Office Supplies in general makes less profit. 
#Recomend selling only Techonology in central 
#Recomend Closing Furniture in east 

## Side note --Sales has potential in south Technology ***
```




```{r echo=TRUE, message=FALSE, warning=FALSE, paged.print=TRUE}
# Mean cost
# Filter to find significance in Central 

(g <- dat1 %>%
  filter(Region %in% c("Central")) %>%
  group_by(Sub.Category, Segment) %>%
  mutate(Cost = Sales - Profit) %>% 
  summarise(t_r = sum(Cost), m_r = mean(Cost), sd_r = sd(Cost),
            n = n(), ci = z * sd_r/sqrt(n)) %>%
  ggplot(aes(x = reorder(Sub.Category, m_r), y = m_r, fill = as.factor(Segment))) +
    geom_bar(stat = "identity", position = "dodge") +
    geom_errorbar(aes(ymin = m_r - ci, ymax = m_r + ci), 
                  width = 0.3, position = position_dodge(0.9)) +
  coord_flip()
)

#Cant really tell much from here
```




```{r echo=TRUE, message=FALSE, warning=FALSE, paged.print=TRUE}
# Mean cost

(g <- dat1 %>%
  group_by(Sub.Category, Region) %>%
  mutate(Cost = Sales - Profit) %>% 
  summarise(t_r = sum(Cost), m_r = mean(Cost), sd_r = sd(Cost),
            n = n(), ci = z * sd_r/sqrt(n)) %>%
  ggplot(aes(x = reorder(Sub.Category, m_r), y = m_r, fill = as.factor(Region))) +
    geom_bar(stat = "identity", position = "dodge") +
    geom_errorbar(aes(ymin = m_r - ci, ymax = m_r + ci), 
                  width = 0.3, position = position_dodge(0.9)) +
  coord_flip()
)

#Cant really tell much from here
```

### Profitability Analysis

Machines and makes negative profit in central maybe consider stop selling Machines on loss!

```{r echo=TRUE, message=FALSE, warning=FALSE, paged.print=TRUE}
# Mean profit
# Filter to find significance in Central 
# Took out Copier and foused in on Consumer and corporate sectore
(g <- dat1 %>%
  filter(Region %in% c("Central")) %>%
  filter(!Sub.Category %in% c("Copiers")) %>%
  filter(Segment %in% c("Consumer", "Corporate")) %>%
  group_by(Sub.Category, Segment) %>%
  summarise(t_r = sum(Profit), m_r = mean(Profit), sd_r = sd(Profit),
            n = n(), ci = z * sd_r/sqrt(n)) %>%
  ggplot(aes(x = reorder(Sub.Category, m_r), y = m_r, fill = as.factor(Segment))) +
    geom_bar(stat = "identity", position = "dodge") +
    geom_errorbar(aes(ymin = m_r - ci, ymax = m_r + ci), 
                  width = 0.3, position = position_dodge(0.9)) +
  coord_flip()
)

# Looks like Machines and makes negetive profit in central stop selling Machines on loss!
```

There seems to be cyclical effect

```{r echo=TRUE, message=FALSE, warning=FALSE, paged.print=TRUE}
#Profit over time!

dat1 %>%
  group_by(qtr) %>%
  summarise(TotalProfit = sum(Profit), TotalPPQ = sum(PPQ), 
            TotalQuantity = sum(Quantity), TotalPwodis = sum(Pwodis))  %>%
  plot_ly(x = ~qtr, y = ~TotalQuantity, name = 'TotalQuantity', type = 'scatter', mode = 'lines') %>%
    add_trace(y = ~TotalPPQ, name = 'TotalPPQ', mode = 'lines') %>%
    add_trace(y = ~TotalProfit, name = 'TotalProfit', mode = 'lines') %>%
  add_trace(y = ~TotalPwodis, name = 'TotalPwodis', mode = 'lines') #This is profit without Discount 

#Why is there a Huge lows in Q2? 
#the differnt lines looks normal.

```

```{r echo=TRUE, message=FALSE, warning=FALSE, paged.print=TRUE}

#profit just by it self

dat1 %>%
  group_by(qtr) %>%
  summarise(TotalProfit = sum(Profit))  %>%
  plot_ly(x = ~qtr, y = ~TotalProfit, type = 'scatter', mode = 'lines')

```

```{r echo=TRUE, message=FALSE, warning=FALSE, paged.print=TRUE}

#review Total profit

dat1 %>%
  group_by(Region, Category)%>%
  summarise(TotalProfit = sum(Profit))%>%
  ggplot(aes(x= Region, y= TotalProfit, fill = Category))+
  geom_bar(stat = "identity", position = "dodge")
 
        
    
```

Tables, Machines, binder and Furniture has loss due to 80% discount

```{r fig.width = 10, fig.height = 10, echo=TRUE, message=FALSE, warning=FALSE, paged.print=TRUE}
# Facets seperating scatter plot by brand
ggplot(dat1, aes(x = qtr, y = Profit)) +
  geom_point(aes(color = as.character(Discount))) +
  facet_wrap(~ Sub.Category)  +
  labs(color = "Discount") +
  labs(x = "Region", y = "Profit") +
  ggtitle("Loss on binders and Maschines") +
  theme(text = element_text(size = 20))

#Tables, Machines, binder and Funitures has loss due to 80% discount

```

Furniture has less quantity and profit density followed by Office Supplies

```{r echo=TRUE, message=FALSE, warning=FALSE, paged.print=TRUE}
# Scatter with volume and units contour but seperate by promo (a factor variable)
ggplot(dat1, aes(x = Profit, y = Quantity)) +
  stat_density2d(aes(color = as.character(Category)), size = 0.9) 

#Furniture has less quntity and profit density followed by Office Subbplies Looks like 

```

### Discount Analysis

```{r echo=TRUE, message=FALSE, warning=FALSE, paged.print=TRUE}
#Does Discount effect Quantity Sold? 

dat1 %>%
  group_by(Category, Sub.Category)%>%
  summarise(QuantitySold = sum(Quantity))%>%
    ggplot(aes(x=Category, y= QuantitySold, fill = Sub.Category)) + 
      geom_bar(stat = "identity", position = "dodge", color = "black") + 
      ggtitle("Quantity Sold per Sub-Category") + 
      labs(y="Quantity Sold")

```

```{r echo=TRUE, message=FALSE, warning=FALSE, paged.print=TRUE}
#whats the Discount taken look like?

dat1 %>%
  group_by(Category, Sub.Category)%>%
  summarise(TotalDiscounts = sum(DicountGiven))%>%
   ggplot(aes(x=Category, y= TotalDiscounts, fill = Sub.Category)) + 
     geom_bar(stat = "identity", position = "dodge", color = "black") + 
     ggtitle("Discounts Given per Sub-Category") + 
     labs(y="Total Discounts ($)")

#Discount doesnt seem to help with quantity on the trouble items like furnitures, espacially tables.

```