---
title: "Dashboard by Ryan Pierce"
date: "As of `r format(Sys.time(), '%B %d, %Y')`"
output:
  flexdashboard::flex_dashboard:
    navbar:
        - { icon: "fa-envelope", href: "mailto:r.pierce521@gmail.com", align: right }
        - { icon: "fa-linkedin", href: "https://www.linkedin.com/in/ryan-a-pierce", align: right }
        - { icon: "fa-github", href: "https://github.com/ryanapierce", align: right }
        - { icon: "fa-window-restore", href: "https://ryanapierce.github.io", align: right }
    logo: logo.png
    source_code: embed
  orientation: rows
  vertical_layout: fill
---
  

  
```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(plotly)
library(leaflet)
library(readxl)
library(tidyverse)
library(dplyr)
library(lemon)
knit_print.data.frame <- lemon_print
```

```{r}
rawdata <- read_excel("R_Assets/Sample_Ford_Data.xlsx")
data <- select(rawdata, -1)
Values <- rowSums(data)
YRdata <- data.frame("Year" = c(2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020))
TotalData <- cbind(Values, YRdata)

newdata <- rawdata[-c(1:14), ] 

```
Sales Summary
=====================================

-------------------------------------

This dashboard is created using HTML, CSS, and the data is pulled, prepared, and visualized using R.  

This is for demonstration purposes only. The data used is pulled from a public source, specifically the GoodCarBadCar website - https://www.goodcarbadcar.net/ford-motor-company-us-sales-figures/.

-------------------------------------
  
### Ford Sales by Year
  
```{r}
lineg <- plot_ly(x = TotalData$Year, y = TotalData$Values, mode = 'lines')

fig <- lineg %>% layout(title = 'US Sales per Year',
         xaxis = list(title = 'Year'),
         yaxis = list (title = 'Sales ($)'))
fig
```

### Sales V. Prior Year

```{r}
prior <- newdata[1,2:13]
current <- newdata[2,2:13]

Mdata <- data.frame("Month" = c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"))

prior_transpose <- as.data.frame(t(as.matrix(prior)))
colnames(prior_transpose) <- "values"
PriorData <- cbind(prior_transpose, Mdata)

current_transpose <- as.data.frame(t(as.matrix(current)))

colnames(current_transpose) <- "values"
CurrentData <- cbind(current_transpose, Mdata)

linem <- plot_ly(x = PriorData$Month, y = PriorData$values, type = 'scatter', name = '2019', mode = 'lines')
linem <- linem %>% add_trace(y = CurrentData$values, name = '2020', mode = 'lines')

fig <- linem %>% layout(title = 'US Sales V Prior Year',
         xaxis = list(title = 'Month',
                      categoryorder = "array",
                      categoryarray = PriorData$Month),
         yaxis = list (title = 'Sales ($)'))

fig
```

Raw Data
======================================
  
```{r}
kable(rawdata)
```