Shiny Project contributed by Jiaxu Luo – Data Science Student in the NYC Data Science Academy Bootcamp

I. Introduction

Do you have any idea how the structure of world economy change over the years? Do you know how the giant companies in the industry you work for evolved and degraded over the years? Which companies in your industry is attracting increasing attention of investors?  My app based on Fortune Global 500 data over the years might give you an opportunity take a fresh look forward.

The Fortune Global 500, is an annual ranking of the top 500 corporations worldwide based on revenue. Every year,the list of companies is compiled and published by Fortune magazine.Since the current form we see today first published in 1995, the Fortune Global 500 has become the definitive study that both examines and defines the world business landscape. For the past decade, the world has witness rapid changes in industries. Benefiting from the technological progress , the process of marketing, integration, industrialization and globalization has been in an in an unprecedented rapid speed.  New economies has been coming up. Industry structure has been redefined. Giant companies have come and gone. The dataset of Fortune Global 500, which spans from 2006 to 2015 (10 years in total), provides us a glimpse of how turbulent fluctuations were like over the years in the level of countries, industries and companies. The assumption here are those Fortune Global 500 companies joined into global competition and therefore the comparative market value fluctuation each year reflects the change of global business landscape.

Thanks to Financial Times–an English-language international daily newspaper specializing in business and economic news ,the dataset of Fortune Global 500 each year is freely accessible ( http://topforeignstocks.com/downloads/ )

II. App Design

Geographical distribution

Composition of an industry The second tab of the application allows users to explore the composition of market value of an industry year by year. The assumption here is the fluctuations of order of composition by market value reflect the general trend of industry evolution and degradation. The darker the rectangle, or larger the rectangle, the higher the market value of that industry as compared with others’. The following  example demonstrates how to draw insight from this tab.Here users are allowed to specify a year of interest and get a glimpse of what the business landscape looked like in the year. From 2007 to 2014, we noticed the software industry, which was once no match for hardware industry, had been gaining momentum and finally surpassed the hardware industry in the composition of market value, indicating the higher acceleration of growth of software industry than hardware industry over the years. If you are interested in deeper exploration of the countries that contribute to the industry most, click the rectangle to see what they are.

Companies comparison The third tab of the application allows users to explore the market value and P/E ratio between companies. On the left hand side of the output is the bubble chart, where the size of the bubble represents the market value of a company in a particular year.The color gradient also facilitates the identification of the rank orders. On the right hand side is the bar chart of P/E ratio in descending order. P/E ratio indicate the value a company for investment. The higher the P/E ratio, the higher growth speed you could expect. The following is an example to demonstrate the insight got from this tab. Back in 2006, when “Apple” was still called “Apple Computer”, Cisco and Intel were leading the industry. It is worth noticing that in 2008, Apple’s P/E ratio jumped to the highest, probably due to the launch of iphone – a revolutionized product at the time. Apple has been keeping its competitive advantage  since that time and by 2015, it has become a dominating and unrivaled giant in the industry.

Data

III. Conclusion

## Server Side
server=shinyServer(function(input, output){
        colfunc=colorRampPalette(c("grey","pink"))
        
        
        ## show map using googleVis
        output$map = renderGvis({
                gvisGeoChart(Fuc_Map(Fortune,input$Year,input$Industry), "Country", "Count",
                             options=list(width="auto", height="490px",colors="['#90EE90' ,'#FFA500', '#FF0000']"))
        })
        
        ## show treemap using "treemap","d3treeR" packages
        output$treemap = renderD3tree2({get(paste("Tree",input$Year,sep=")) 
                
        })
        
        ## show bubbles using "bubbles" package
        output$bubbles=renderBubbles(
                bubbles(value=sqrt(Fuc_Bubble(Fortune,input$Year,input$Industry)[,"Market.value..m"]),
                        label=Fuc_Bubble(Fortune,input$Year,input$Industry)[,"Company"],
                        textColor = "#00008B",
                        color=colorRampPalette(c("light green", "yellow", "orange", "red"))(length(Fuc_Bubble(Fortune,input$Year,input$Industry)[,"Company"])))
        )
        
        ## show histogram using googleVis
        output$hist=renderGvis({
                gvisBarChart(Fuc_Hist(Fortune,input$Year,input$Industry),xvar = "Company",yvar="P.e.ratio",options = list(width="400px",height="550px"))
                #(state_stat[,input$selected, drop=FALSE])
                
        })
        
        ## show data using DataTable
        output$table = DT::renderDataTable({
                datatable(Fuc_Bubble(Fortune,input$Year,input$Industry)[,c(2,3,4,5,6,7,9,13,16),]
                          , rownames=FALSE) %>% 
                        # Highlight selected column using formatStyle
                        formatStyle(input$selected, background="skyblue", fontWeight='bold')
                
        })
        
        
        
        
})

This shiny app can be found :https://joshualuo.shinyapps.io/Fortune/

All the code can be found through the link to Github

The original article can be found here.