Coffee Flavours

Coffee with Hansel and Gretel

Setting up R Packages

Introduction

This dataset pertains to scores various types of coffees on parameters such as aroma, flavour, after-taste etc.

Breadcrumbs

Since there are some interesting pre-processing actions required of data, and some choices to be made as well, I will leave some breadcrumbs, and some intermediate results, for you to look at and figure out the analysis/EDA path that you might take! You can then vary these at will after getting a measure of confidence!

Read the Data

Rows: 1,339
Columns: 43
$ total_cup_points      <dbl> 90.58, 89.92, 89.75, 89.00, 88.83, 88.83, 88.75,…
$ species               <chr> "Arabica", "Arabica", "Arabica", "Arabica", "Ara…
$ owner                 <chr> "metad plc", "metad plc", "grounds for health ad…
$ country_of_origin     <chr> "Ethiopia", "Ethiopia", "Guatemala", "Ethiopia",…
$ farm_name             <chr> "metad plc", "metad plc", "san marcos barrancas …
$ lot_number            <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ mill                  <chr> "metad plc", "metad plc", NA, "wolensu", "metad …
$ ico_number            <chr> "2014/2015", "2014/2015", NA, NA, "2014/2015", N…
$ company               <chr> "metad agricultural developmet plc", "metad agri…
$ altitude              <chr> "1950-2200", "1950-2200", "1600 - 1800 m", "1800…
$ region                <chr> "guji-hambela", "guji-hambela", NA, "oromia", "g…
$ producer              <chr> "METAD PLC", "METAD PLC", NA, "Yidnekachew Dabes…
$ number_of_bags        <dbl> 300, 300, 5, 320, 300, 100, 100, 300, 300, 50, 3…
$ bag_weight            <chr> "60 kg", "60 kg", "1", "60 kg", "60 kg", "30 kg"…
$ in_country_partner    <chr> "METAD Agricultural Development plc", "METAD Agr…
$ harvest_year          <chr> "2014", "2014", NA, "2014", "2014", "2013", "201…
$ grading_date          <chr> "April 4th, 2015", "April 4th, 2015", "May 31st,…
$ owner_1               <chr> "metad plc", "metad plc", "Grounds for Health Ad…
$ variety               <chr> NA, "Other", "Bourbon", NA, "Other", NA, "Other"…
$ processing_method     <chr> "Washed / Wet", "Washed / Wet", NA, "Natural / D…
$ aroma                 <dbl> 8.67, 8.75, 8.42, 8.17, 8.25, 8.58, 8.42, 8.25, …
$ flavor                <dbl> 8.83, 8.67, 8.50, 8.58, 8.50, 8.42, 8.50, 8.33, …
$ aftertaste            <dbl> 8.67, 8.50, 8.42, 8.42, 8.25, 8.42, 8.33, 8.50, …
$ acidity               <dbl> 8.75, 8.58, 8.42, 8.42, 8.50, 8.50, 8.50, 8.42, …
$ body                  <dbl> 8.50, 8.42, 8.33, 8.50, 8.42, 8.25, 8.25, 8.33, …
$ balance               <dbl> 8.42, 8.42, 8.42, 8.25, 8.33, 8.33, 8.25, 8.50, …
$ uniformity            <dbl> 10.00, 10.00, 10.00, 10.00, 10.00, 10.00, 10.00,…
$ clean_cup             <dbl> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, …
$ sweetness             <dbl> 10.00, 10.00, 10.00, 10.00, 10.00, 10.00, 10.00,…
$ cupper_points         <dbl> 8.75, 8.58, 9.25, 8.67, 8.58, 8.33, 8.50, 9.00, …
$ moisture              <dbl> 0.12, 0.12, 0.00, 0.11, 0.12, 0.11, 0.11, 0.03, …
$ category_one_defects  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ quakers               <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ color                 <chr> "Green", "Green", NA, "Green", "Green", "Bluish-…
$ category_two_defects  <dbl> 0, 1, 0, 2, 2, 1, 0, 0, 0, 4, 1, 0, 0, 2, 2, 0, …
$ expiration            <chr> "April 3rd, 2016", "April 3rd, 2016", "May 31st,…
$ certification_body    <chr> "METAD Agricultural Development plc", "METAD Agr…
$ certification_address <chr> "309fcf77415a3661ae83e027f7e5f05dad786e44", "309…
$ certification_contact <chr> "19fef5a731de2db57d16da10287413f5f99bc2dd", "19f…
$ unit_of_measurement   <chr> "m", "m", "m", "m", "m", "m", "m", "m", "m", "m"…
$ altitude_low_meters   <dbl> 1950.0, 1950.0, 1600.0, 1800.0, 1950.0, NA, NA, …
$ altitude_high_meters  <dbl> 2200.0, 2200.0, 1800.0, 2200.0, 2200.0, NA, NA, …
$ altitude_mean_meters  <dbl> 2075.0, 2075.0, 1700.0, 2000.0, 2075.0, NA, NA, …

Inspect, Clean the Data

What are the non-numeric, or Qualitative variables here?

Look at the number of levels in those Qual variables!!Some are too many and some are so few… Suppose we count the data on the basis of a few?

Breadcrumb 1

Why did I choose these Qual factors to count with?

Data Dictionary

Quantitative Variables

Write in.

Qualitative Variables

Write in.

Observations

Write in.

Research Question

Breadcrumb 1

Among the country_of_origin with the 5 highest average total_cup_points, how do the average ratings vary in ranks on the other coffee parameters?

Why this somewhat long-winded question? Why all this averagestuff??

Why did I choose country_of_origin?Are there any other options?

Analyse/Transform the Data

```{r}
#| label: data-preprocessing
#
# Write in your code here
# to prepare this data as shown below
# to generate the plot that follows
```
Breadcrumb 3

We have too much coffee here! We need to compress this data!

What??? Why? How? Where???

Breadcrumb 4

Where did all that coffee go??? Why are there only 5 rows in the data? Why the names of the columns take on a surname, ’_mean`??

Breadcrumb 5

What just happened? How did we convert those mean numbers to ranks?

Plot the Data

Discussion

Complete the Data Dictionary. Select and Transform the variables as shown. Create the graphs shown below and discuss the following questions:

  • Identify the type of charts
  • Identify the variables used for various geometrical aspects (x, y, fill…). Name the variables appropriately.
  • What research activity might have been carried out to obtain the data graphed here? Provide some details.
  • What might have been the Hypothesis/Research Question to which the response was Chart?
  • Write a 2-line story based on the chart, describing your inference/surprise.
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