MATH2270/MATH2237 Assignment 1

Turning Tables

Student Details

• Caitlin Lonski (s3679850)
  

RPubs Publication

Data Tables

This study aims to visualise the relationship between Internet Speeds and Gross Domestic Product, (where GDP is used as a general measure of economy) between countries.
Data detailing Internet Speeds was adapted from Akamai’s State of the Internet Report from Q1 2017 which explores several aspects of both mobile and broadband internet globally. The table used for this visualization is located in the Appendix on pages 54 - 55.
- Akamai State of the Internet Connectivity Report Q1 2017
A table on Statisticstimes.com contains the projected Gross Domestic Product (GDP) for each country. GDP per capita Nominal for 2017 was used where the Nominal method uses market exchange rates for conversion.
- List of Countries by Projected GDP per capita
Both tables were combined in Microsoft Excel using VLOOKUP() functions prior to being imported in to R.

Code

Internet_GDP <- read.csv("Internet_Speeds_and_GDP_Per_Capita.csv")
# divide all GDP values by 1,000 to make the plot easier to read
Internet_GDP$GDP <- Internet_GDP$GDP/1000
# Create an object for the plot, start the ggplot by specifiying the dataset and variables
plot1 <- ggplot(Internet_GDP, aes(x = Connection_Speed, y = GDP))
# add a line of best fit before plotting the points to ensure it appears below the points on the final plot
plot1 <- plot1 + geom_smooth(alpha=0.15, method="lm", color="light grey")
# add a layer for the plot type (point/scatter), colour the points by region
plot1 <- plot1 + geom_point(aes(colour = Region), size = 2) + theme_minimal(base_size = 16)
# add title, make main and legend titles bold for better clarity
plot1 <- plot1 + ggtitle("Internet Speeds vs Gross Domestic Product", subtitle = "by country, 2017") + 
  theme(plot.title = element_text(lineheight=.8, face="bold"), legend.title = element_text(lineheight=.8, face="bold"))
# label x and y axis
plot1 <- plot1 + xlab("Average Internet Connection Speed (Mbps)") + ylab("GDP per Capita ($1,000 USD)")
# Add annotations; Australia = country of target audience, Luxembourg & South Korea = outliers
plot1 <- plot1 + annotate("text", x = 11, y = 53, label = "Australia", size = 5) + 
  annotate("text", x = 12, y = 100, label = "Luxembourg", size = 5) + annotate("text", x = 27, y = 27, label = "South Korea", size = 5)

Visualisation

Caption

A scatterplot was used to explore the continuous variables of Internet Speed and GDP for 74 countries and illustrate the relationship between both variables. Generally, as GDP increases so does a country’s Interned speeds.

Two outliers are very prominent in the plot; Luxembourg has an incredibly high GDP (the highest of any country explored) compared to countries with similar Internet speeds. Conversely, South Korea has relatively low GDP given its very high Internet Speeds (the fastest of any country).

Australia is a less obvious outlier but still sits above the line of best fit due to the country’s relatively low Internet speeds compared to other nations with similar GDP.

The colour-coding of the points illuminates some broad trends between regions, specifically that countries in the Americas and Middle East & Africa tend to have lower GDP and Internet speeds overall. The upper-right side of the plot is populated almost entirely by Asia Pacific and European countries.

Whilst the plot doesn’t determine if this relationship is significant, it does provide a quick visual summary and exploration of GDP and Internet Speeds.