Assignment 2

Original

Objective

This graph is describing the characteristics of the current market and to what extent the digital health market has become attractive lately inside the health industry. From this chart, the Research2Guidance, which is a market research company working on strategic advisory, was trying to grab attention to how the market is changing and how mHealth app companies (publishers) nowadays are successfully controlling the global digital market, especially, the healthcare industry. In doing so, this graph might helps stakeholders, as directors or owners (shareholders) of their business, to comprehend current and future market developments.

The visualisation chosen had the following three main issues:

• This graph used a combination of green and red in different sections in the same display where should be avoided because that has an effect on people who are suffering from colour blindness which might face difficulty to distinguish between groups.

• The author relied on doughnut charts to present too many information, whereas the doughnut chart can present effectively when the number of categories are limited. Therefore, it might create some difficulties to the audience, e.g. it is forced the audience to read the numeric values and compare the area of each doughnut slice.

• The size of the similar segments in value of the doughnuts, e.g. the three categories that contribute by 1% in the the market of traditional healthcare, are not constant which might deceive the audience if not read the numeric values.

Reference

• Research2Guidance team (2017, November). mHealth App Developer Economics study 2017. Research2Guidance. Retrieved from Research2Guidance website: Website_link.

Code

The following code was used to fix the issues identified in the original.

# Create date frame using the summary statistics in the original plot.

DM_1 <- data.frame(Var_1 = c("Organization", "Organization", "Organization", "Organization"),
                             Var_2 = c("Healthcare", "Non Healthcare", "Institutions", "Other"),
                             Percent= c(60,23,10,7))

DM_2 <- data.frame(Var_1 = c("Healthcare","Healthcare",
                                        "Non Healthcare", "Non Healthcare", "Non Healthcare", "Non Healthcare",
                                        "Institutions","Institutions","Institutions","Institutions",
                                        "Other","Other"),
                               Var_2 = c("Traditional healthcare","Digital healthcare",
                                        "IT/Tech Company", "Consultancy/ Market research company", "App developer/ Agency","Telecomunications",
                                        "University", "Non-profit organization (NGO)", "Education/Training Company", "Government",
                                        "Other", "Investor"),
                               Percent= c(32,28,
                                          13,5,4,1,
                                          4,3,2,1,
                                          6,1))
DM_3 <- data.frame(Var_1 = c("Traditional healthcare","Traditional healthcare","Traditional healthcare", "Traditional healthcare",
                                        "Traditional healthcare","Traditional healthcare","Traditional healthcare", "Traditional healthcare",
                                        "Digital healthcare", "Digital healthcare"),
                               Var_2 = c("Medical device","Pharma","Health insurance","Hospital",
                                        "Telehealth service", "Independent practitioner", "Medical Publishers", "Sport/ Fitness company",
                                        "mHealth app company", "Accelerator/Incubator"),
                               Percent= c(9,6,6,5,
                                          3,1,1,1,
                                          26,2))


DM_1$Var_1<-paste("1.",DM_1$Var_1) 
DM_1$Var_2<-paste("2.",DM_1$Var_2)

DM_2$Var_1<-paste("2.",DM_2$Var_1) 
DM_2$Var_2<-paste("3.",DM_2$Var_2)

DM_3$Var_1<-paste("3.",DM_3$Var_1) 
DM_3$Var_2<-paste("4.",DM_3$Var_2)

# combine the data frames by rows using rbind() function.
Digital_Market<-rbind(DM_1,DM_3,DM_2)


library(googleVis) # to produce a Sankey diagram

# We will use the next function to highlight the objectives
objective <- paste0("{
               iterations: 0,
               link: { colorMode: 'source',
               colors: ['lightgray','papayawhip','lightgray','lightgray',
               'lightgray','lightgray','sandybrown'] }
               }")

#Generate the Sankey diagram HTML
Digital_Market1=gvisSankey(Digital_Market[,c('Var_1','Var_2','Percent')],
                  options=list(height=600, width=600,
                               title='The current state of healthcare market and the impact of digital intruders on the healthcare industry',
                               sankey = objective),
                  options(gvis.plot.tag='chart') # This option is used to pull out the Sankey diagram into R
                  )

#For use in Rmd/knitr, set the block parameter: results='asis', fig.align="center"

Data Reference

• Research2Guidance team (2017, November). mHealth App Developer Economics study 2017. Research2Guidance. Retrieved from Research2Guidance website: Website_link.

Reconstruction