flexdashboard.Rmd

---
title: "Dashboard Example"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: scroll
---

```{r setup, include=FALSE}
library(dplyr)
library(ggplot2)
library(choroplethr)
library(choroplethrZip)
library(choroplethrMaps)

setwd("C:/Users/tyler/Desktop/DATA FILES")
#read in data
sales <- read.csv("train.csv", stringsAsFactors=FALSE)%>%
  mutate(Postal.Code = sprintf("%05d", Postal.Code))

#summarize sales and transactions by levels
#total
Totalsales <- sales %>%
  mutate(DATE = as.Date(Order.Date, "%d/%m/%Y"),
         MONTH = month.abb[as.numeric(format(DATE, "%m"))],
         YEAR = as.numeric(format(DATE, "%Y"))
         )%>%
  group_by(YEAR, MONTH)%>%
  summarise(Tot.Sales = sum(Sales),
            Tot.Transactions = n())
#region
Regionsales <- sales %>%
         mutate(DATE = as.Date(Order.Date, "%d/%m/%Y"),
                MONTH = month.abb[as.numeric(format(DATE, "%m"))],
                YEAR = as.numeric(format(DATE, "%Y")))%>%
         group_by(YEAR, MONTH, Region)%>%
         summarise(Sales = sum(Sales),
                   Transactions = n())%>%
  left_join(., Totalsales, by = c("YEAR", "MONTH"))%>%
  mutate(`Sales Distribution` = Sales/Tot.Sales,
         `Transaction Distribution` = Transactions/Tot.Transactions)
#state
Statesales <- sales %>%
  mutate(DATE = as.Date(Order.Date, "%d/%m/%Y"),
         MONTH = month.abb[as.numeric(format(DATE, "%m"))],
         YEAR = as.numeric(format(DATE, "%Y")))%>%
  group_by(YEAR, MONTH, State)%>%
  summarise(Sales = sum(Sales),
            Transactions = n())%>%
  left_join(., Totalsales, by = c("YEAR", "MONTH"))%>%
  mutate(`Sales Distribution` = Sales/Tot.Sales,
         `Transaction Distribution` = Transactions/Tot.Transactions)
#segment
Segmentsales <- sales %>%
  mutate(DATE = as.Date(Order.Date, "%d/%m/%Y"),
         MONTH = month.abb[as.numeric(format(DATE, "%m"))],
         YEAR = as.numeric(format(DATE, "%Y")))%>%
  group_by(YEAR, MONTH, Segment)%>%
  summarise(Sales = sum(Sales),
            Transactions = n())%>%
  left_join(., Totalsales, by = c("YEAR", "MONTH"))%>%
  mutate(`Sales Distribution` = Sales/Tot.Sales,
         `Transaction Distribution` = Transactions/Tot.Transactions)
#category
Categorysales <- sales %>%
  mutate(DATE = as.Date(Order.Date, "%d/%m/%Y"),
         MONTH = month.abb[as.numeric(format(DATE, "%m"))],
         YEAR = as.numeric(format(DATE, "%Y")))%>%
  group_by(YEAR, MONTH, Category)%>%
  summarise(Sales = sum(Sales),
            Transactions = n())%>%
  left_join(., Totalsales, by = c("YEAR", "MONTH"))%>%
  mutate(`Sales Distribution` = Sales/Tot.Sales,
         `Transaction Distribution` = Transactions/Tot.Transactions)
#sub Category
Sub.Categorysales <- sales %>%
  mutate(DATE = as.Date(Order.Date, "%d/%m/%Y"),
         MONTH = month.abb[as.numeric(format(DATE, "%m"))],
         YEAR = as.numeric(format(DATE, "%Y")))%>%
  group_by(YEAR, MONTH, Sub.Category)%>%
  summarise(Sales = sum(Sales),
            Transactions = n())%>%
  left_join(., Totalsales, by = c("YEAR", "MONTH"))%>%
  mutate(`Sales Distribution` = Sales/Tot.Sales,
         `Transaction Distribution` = Transactions/Tot.Transactions)

#Plots
region.sales.plot <- Regionsales %>%
  filter(YEAR >=2017) %>%
  mutate(YEAR = as.factor(YEAR))%>%
  ggplot() +
  geom_line(aes(x = MONTH, y = `Sales Distribution`, group = YEAR, color = YEAR), size = 1)+
  facet_wrap(.~Region)+
  scale_x_discrete(limits = month.abb)+
  labs(title = "Monthly Sales Distribution by Region")+
  theme_bw()+
  theme(axis.text.x = element_text(angle = 90))


region.tx.plot <- Regionsales %>%
  filter(YEAR >=2017) %>%
  mutate(YEAR = as.factor(YEAR))%>%
  ggplot() +
  geom_line(aes(x = MONTH, y = `Transaction Distribution`, group = YEAR, color = YEAR), size = 1)+
  facet_wrap(.~Region)+
  scale_x_discrete(limits = month.abb)+
  labs(title = "Monthly Transaction Distribution by Region")+
  theme_bw()+
  theme(axis.text.x = element_text(angle = 90))


state.sales.plot <- Statesales %>%
  filter(YEAR >=2017) %>%
  mutate(YEAR = as.factor(YEAR))%>%
  ggplot() +
  geom_line(aes(x = MONTH, y = `Sales Distribution`, group = YEAR, color = YEAR), size = 1)+
  facet_wrap(.~State)+
  scale_x_discrete(limits = month.abb)+
  # labs(title = "Monthly Sales Distribution by State")+
  geom_rect(data = subset(Statesales, State %in% c("Indiana" ,"Washington")), 
            fill = "yellow", alpha = 0.005, xmin = -Inf,xmax = Inf,
            ymin = -Inf,ymax = Inf)+
  theme_bw()+
  theme(axis.text.x = element_text(angle = 90))


category.plot <- Sub.Categorysales %>%
  filter(YEAR >=2017) %>%
  mutate(YEAR = as.factor(YEAR))%>%
  ggplot() +
  geom_line(aes(x = MONTH, y = `Sales Distribution`, group = YEAR, color = YEAR), size = 1)+
  facet_wrap(.~Sub.Category)+
  scale_x_discrete(limits = month.abb)+
  # labs(title = "Monthly Category Sales Distribution")+
  geom_rect(data = subset(Sub.Categorysales, Sub.Category == "Copiers"), 
            fill = "yellow", alpha = 0.005, xmin = -Inf,xmax = Inf,
            ymin = -Inf,ymax = Inf)+
  theme_bw()+
  theme(axis.text.x = element_text(angle = 90))


category.plot2 <- Sub.Categorysales %>%
  filter(YEAR >=2017) %>%
  mutate(YEAR = as.factor(YEAR))%>%
  ggplot() +
  geom_line(aes(x = MONTH, y = `Transaction Distribution`, group = YEAR, color = YEAR), size = 1)+
  facet_wrap(.~Sub.Category)+
  scale_x_discrete(limits = month.abb)+
  labs(title = "Monthly Category Transaction Distribution")+
  geom_rect(data = subset(Sub.Categorysales, Sub.Category == "Copiers"), 
            fill = "yellow", alpha = 0.005, xmin = -Inf,xmax = Inf,
            ymin = -Inf,ymax = Inf)+
  theme_bw()+
  theme(axis.text.x = element_text(angle = 90))


##################################################################################3
#shipping time

shipping <- sales %>%
  mutate(Order.Date = as.Date(Order.Date, "%d/%m/%Y"),
         Ship.Date = as.Date(Ship.Date, "%d/%m/%Y"))%>%
  mutate(Days.to.ship = Ship.Date - Order.Date)%>%
  group_by(State, City, Postal.Code)%>%
  summarise(`Average Days to Ship` = round(mean(Days.to.ship),1))
         
shippingmap2017 <- sales %>%
  mutate(DATE = as.Date(Order.Date, "%d/%m/%Y"),
         YEAR = as.numeric(format(DATE, "%Y")),
         Order.Date = as.Date(Order.Date, "%d/%m/%Y"),
         Ship.Date = as.Date(Ship.Date, "%d/%m/%Y"))%>%
    filter(YEAR ==2017)%>%
  mutate(Days.to.ship = Ship.Date - Order.Date)%>%
  group_by(State)%>%
  summarise(`value` = as.integer(round(mean(Days.to.ship),1)))%>%
  rename(region = State)%>%
  mutate(region = tolower(region))

shippingmap2018 <- sales %>%
  mutate(DATE = as.Date(Order.Date, "%d/%m/%Y"),
         YEAR = as.numeric(format(DATE, "%Y")),
         Order.Date = as.Date(Order.Date, "%d/%m/%Y"),
         Ship.Date = as.Date(Ship.Date, "%d/%m/%Y"))%>%
    filter(YEAR ==2018)%>%
  mutate(Days.to.ship = Ship.Date - Order.Date)%>%
  group_by(State)%>%
  summarise(`value` = as.integer(round(mean(Days.to.ship),1)))%>%
  rename(region = State)%>%
  mutate(region = tolower(region))

#average days to ship state map
statemap2017 <- state_choropleth(shippingmap2017,
                 title      = "2017",
                 num_colors = 1,
                 legend     = "Days to Ship")

#average days to ship state map
statemap2018 <- state_choropleth(shippingmap2018,
                 title      = "2018",
                 num_colors = 1,
                 legend     = "Days to Ship")

```


Region
======================================================================
### Monthly Sales & Transaction Distribution by Region
```{r figures-side, fig.show="hold", out.width="50%"}
region.sales.plot
region.tx.plot
```

State
======================================================================
### Monthly Sales Distribution by State
```{r echo=FALSE, error=TRUE, fig.height=16, fig.width=16, message=FALSE, warning=FALSE}
state.sales.plot
```

Category
======================================================================
### Monthly Sales Distribution by Category
```{r echo=FALSE, error=TRUE, fig.height=8, fig.width=14, message=FALSE, warning=FALSE}
category.plot
```

Shipping
======================================================================
### Average Number of Days to Ship 

```{r fig.show="hold", out.width="50%"}
statemap2017
statemap2018
```

Code
======================================================================
### Average Number of Days to Ship