Careem-case-study.Rmd

---
title: "Careem Case Study"
author: "Anish Joni"
date: "December 12, 2018"
output: 
  flexdashboard::flex_dashboard:
    vertical_layout: scroll
    orientation: rows
runtime: shiny
editor_options: 
  chunk_output_type: console
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```


```{r Requred packages, include=FALSE}
library(tidyverse)
library(lubridate)
library(ggthemes)
library(viridis)
library(plotly)
library(digest)
library(htmlwidgets)
library(flexdashboard)
library(shiny)
library(DT)
library(devtools)
library(leaflet)
library(leaflet)
library(rgeos)
library(htmltools)
require(rgdal)
```

### Careem Food KPIs Overview
```{r Generate dataset, include=FALSE}
################### 
# Data Generation #
###################

# Delivery Table
set.seed(2);
order_daterange <- seq(as.POSIXlt("2018-08-01"),as.POSIXlt("2018-12-01"), by = "days")

order_range_high <- seq(as.POSIXlt("2018-08-01"),as.POSIXlt("2018-08-17"), by = "days")
order_range_normal <- seq(as.POSIXlt("2018-08-17"),as.POSIXlt("2018-11-03"), by = "days")
order_range_low <- seq(as.POSIXlt("2018-11-04"),as.POSIXlt("2018-12-01"), by = "days")

order_datetime_high <- sample(order_range_high, size = 250, replace = TRUE)
order_datetime_normal <- sample(order_range_normal, size = 650, replace = TRUE)
order_datetime_low <- sample(order_range_low, size = 100, replace = TRUE)
order_datetime <- c(order_datetime_high, order_datetime_normal, order_datetime_low)


#order_datetime <- sample(order_daterange, size = 1000, replace = TRUE)
order_id <- sample(230:1500, size = length(order_datetime), replace = TRUE)
store_name = sample(c("McDonald's", "KFC", "Didi Burger", "Broccoli Pizza", "Dipndip", "Doce"), size = length(order_datetime), replace = TRUE)
store_id = sample(23:150, size = length(order_datetime), replace = TRUE)
destination = paste0("Neighbourhood ",sample( LETTERS[seq( from = 1, to = 12 )],length(order_datetime),rep=T))
delivery_time <- order_datetime + sample(1200:3600, size = length(order_datetime), rep = T)
travel_time = round(difftime(delivery_time, order_datetime), 2)

delivery_df <- tibble(store_id, store_name, order_datetime, order_id, destination, delivery_time, travel_time) %>% 
  mutate(order_day = wday(order_datetime, label = T))


# Orders Table

```

```{r EDA & Plots, echo=FALSE}
orders_by_day <- delivery_df %>% 
  group_by(order_day) %>% 
  count() %>%  
  ggplot(aes(order_day, n)) +
  geom_bar(stat = "identity") +
  scale_fill_viridis(option = "viridis") +
  theme_fivethirtyeight()  +
    labs(title = "Orders by day of the week ", subtitle = 'Between Aug 1st and Dec 1st, 2018', caption = "Careem case study", y = 'Orders') +
    theme(legend.position = "none", axis.title = element_text(), axis.title.x = element_blank())

#ggplotly(orders_by_day)

order_trends<- delivery_df %>% 
  group_by(order_datetime) %>% 
  select(order_datetime) %>% 
  count() %>%  
  ggplot(aes(order_datetime, n)) +
  geom_line() +
  geom_smooth() +
  scale_fill_viridis(option = "viridis") +
  theme_fivethirtyeight()  +
    labs(title = "Trends in orders over time ", subtitle = 'Between Aug 1st and Dec 1st, 2018', caption = "Careem case study", y = '# Orders') +
    theme(legend.position = "none", axis.title = element_text(), axis.title.x = element_blank())
  
#ggplotly(order_trends)


```
Dashboard
=======================================================================
<!-- Column {.sidebar} -->
<!-- ----------------------------------------------------------------------- -->
<!-- ```{r} -->

<!-- ``` -->


Row
-------------------------------------
    
### Total Orders served {.value-box}
```{r}
orderCount <- 24 #Dummy
renderValueBox({
  valueBox(orderCount)
})
```

### Growth rate from last month {.value-box}
```{r}
rate <- "4%" #Dummy
renderValueBox({
  valueBox(rate)
})
```

### Average delivery time {.value-box}
```{r}
deliverTime <- 32 #Dummy
renderValueBox({
  valueBox(deliverTime)
})
```

### Change of Avg.Delivery time from last month {.value-box}
```{r}
Deliveryrate <- "+12 Mins" #Dummy
renderValueBox({
  valueBox(Deliveryrate,color="red")
})
```

Row
-------------------------------------
### Order trend over time

```{r Trends plot, echo=FALSE}
ggplotly(order_trends)
```

Row
-------------------------------------
### Top performing locations
```{r Top performing locations, echo=FALSE}

delivery_df %>% 
  select(destination) %>% 
  group_by(destination) %>% 
  count() %>% 
  arrange(desc(n)) %>% 
  datatable(options = list(lengthMenu = c(5, 10, 15, 20)))
```

### Least performing locations
```{r Least performing, echo=FALSE}
delivery_df %>% 
  select(destination) %>% 
  group_by(destination) %>% 
  count() %>% 
  arrange(n) %>% 
  datatable(options = list(lengthMenu = c(5, 10, 15, 20)))
```

Row
-------------------------------------
```{r Geo plots, eval=FALSE, include=FALSE}
m <- leaflet() %>% addTiles()

m <- m %>%
  setView(46.62, 24.59, 5) %>% # map location
  addMarkers(46.52, 24.6) %>% # add a marker
  #addPopups(46.72, 24.58, popup = "Hello Careem!") %>% # popup
  # add som circles:
  addCircles(color = "black", runif(90, -2, -1), runif(90, 53, 54), runif(90, 10, 500))

setwd("~/GitHub/Careem-case-study/SAU_adm")

SAU_adm <- read_csv("SAU_adm1.csv")

shape <- readOGR(dsn = ".", layer = "SAU_adm1")

pizza <- read_csv("~/GitHub/Careem-case-study/pizza.csv")

leaflet() %>%
  # base map
  addProviderTiles("Hydda.Base") %>%
  addPolygons(data=shape,
              stroke=TRUE, color="white", weight=1, opacity=1,
              fill=TRUE, fillColor="#cccccc", smoothFactor=0.5) 

m %>% 
# add pizza layer
 addCircles(data=pizza,
             lng=~Longitude, lat=~Latitude,
             radius=10, # size is in m for addCircles O_o
             color="white", weight=1, opacity=1,
             fillColor="steelblue", fillOpacity=1)


pizza <- pizza %>% 
  mutate(size = sample(25:45, nrow(pizza)))

```