Morning Joe Project


More than 2 billion cups of coffee (cafe arábica) are consumed worldwide each day. The livelihood of 120 million people depends on the coffee supply chain.

Coffee futures are the second most popular commodity traded worth over $100 billion annually.

Coffee rust leads to losses of more than $500 million worldwide. Coffee rust is the main disease that causes plant leaves to turn yellow.

This project will explore the following questions:

  • Does coffee rust affect production and futures prices?
  • Can visualizations be used to help draw conclusions about potential relationships between these variables?

There is no known research on this topic to date.


The project focuses on rust, production and futures data from Brazil, Colombia and Papua New Guinea from 1989-2013.

Data was collected from 10 different sources and the acquisition process is described here.

These three countries produce 48% of the world’s coffee. The coffee maps shows the regions in each country cafe arábica is grown.

  • Is there a link between coffee rust and the amount of coffee produced?
  • Is there a link between coffee rust and futures prices?

Variables include 337 observations with the following features:

  • Rainfall
  • Temperature
  • Rust percent
  • Production amount
  • Futures prices

Data visualizations help to accept or not accept the following hypotheses:

  • More rain = more coffee rust
  • Higher temperatures = more coffee rust
  • More coffee rust = less production
  • More coffee rust = lower futures prices
  • More coffee production = lower futures prices


During the exploratory data analysis part of the project, summary statistics were calculated for each variable.


Rain average was 183.55 cm per month with a range of 0.2-407.7 inches. Temperature average was 25.12 C with a range of 23.36-27.16 C. Rust average was 16.41% with a range of 0.33-50%. Production average was 1731.40 (1000-60 kg bags of beans) per month with a range of 80.33-3832.67. Futures average was $93.19 USD with a range from $21.98-$175.18 USD.

One of the first visualizations created was a futures histogramA bin size of 30 was chosen since it best represented the variation in the data. There is not a high frequency of any data, with the highest value being about 0.014 ($140). The gap in the histogram follows the gap in the data, with the futures ranging from $20-40 in 1989-1991 and 1995 and futures ranging from $78-$175.18 from 2005-2013. This gap is probably caused by natural increases in futures prices between 1995 and 2005.


The next visualization was a simple line plot that represents the relationship between coffee rust and production and coffee rust and futures. From the line plot, it’s difficult to tell if rust is correlated to production amount.


From the line plot of rust versus futures, it appears there may be a positive correlation between the variables when the rust is less than 50%. The ranges in data values for futures is much larger (80-175) than the ranges for rust (0-50). Logarithmic scales were not used for either visualization to prevent misleading the reader.


The next step in the analysis was making a correlation matrix. A correlation matrix visualization shows the amount each variable is correlated to another variable.

Positive correlation (red) values means variables change in the same direction. Negative correlation values (blue) means variables change in the opposite direction. The intensity scale on the right shows how much the variables are correlated. A higher, darker red value of 0.8 is the highest positive correlation while the darkest blue value of -0.8 is the highest negative correlation.


The following conclusions can be drawn from the correlation matrix.

  • Rain and temperature are not correlated to each other.
  • Rain is negatively correlated (about -0.6) with production, meaning that if the rain increases, production decreases.
  • Temperature is negatively correlated (about -0.3) with futures, meaning that if temperature increases, futures decrease.
  • The production is positively correlated (about 0.4) with futures. If production increases, futures increase.

Finally, a linear regression plot of rust versus futures shows the amount that futures decrease as rust increases.

      Linear Regression of Rust versus Futures


  • Futures and Rust are positively correlated to one another using a polynomial regression. If rust increases so do futures prices.


  • More rain does not affect coffee rust
  • Higher temperatures = less coffee rust
  • More coffee rust = more production
  • More coffee rust = lower futures prices
  • More coffee production = higher future prices


  • More rust = lower futures prices (Accept original hypothesis)
  • Do not accept other original hypotheses




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