Coffee with Hansel and Gretel
1 Setting up R Packages
Plot Fonts and Theme
Show the Code
library(systemfonts)
library(showtext)
## Clean the slate
systemfonts::clear_local_fonts()
systemfonts::clear_registry()
##
showtext_opts(dpi = 96) # set DPI for showtext
sysfonts::font_add(
family = "Alegreya",
regular = "../../../../../../fonts/Alegreya-Regular.ttf",
bold = "../../../../../../fonts/Alegreya-Bold.ttf",
italic = "../../../../../../fonts/Alegreya-Italic.ttf",
bolditalic = "../../../../../../fonts/Alegreya-BoldItalic.ttf"
)
sysfonts::font_add(
family = "Roboto Condensed",
regular = "../../../../../../fonts/RobotoCondensed-Regular.ttf",
bold = "../../../../../../fonts/RobotoCondensed-Bold.ttf",
italic = "../../../../../../fonts/RobotoCondensed-Italic.ttf",
bolditalic = "../../../../../../fonts/RobotoCondensed-BoldItalic.ttf"
)
showtext_auto(enable = TRUE) # enable showtext
##
theme_custom <- function() {
font <- "Alegreya" # assign font family up front
"%+replace%" <- ggplot2::"%+replace%" # nolint
theme_classic(base_size = 14, base_family = font) %+replace% # replace elements we want to change
theme(
text = element_text(family = font), # set base font family
# text elements
plot.title = element_text( # title
family = font, # set font family
size = 24, # set font size
face = "bold", # bold typeface
hjust = 0, # left align
margin = margin(t = 5, r = 0, b = 5, l = 0)
), # margin
plot.title.position = "plot",
plot.subtitle = element_text( # subtitle
family = font, # font family
size = 14, # font size
hjust = 0, # left align
margin = margin(t = 5, r = 0, b = 10, l = 0)
), # margin
plot.caption = element_text( # caption
family = font, # font family
size = 9, # font size
hjust = 1
), # right align
plot.caption.position = "plot", # right align
axis.title = element_text( # axis titles
family = "Roboto Condensed", # font family
size = 12
), # font size
axis.text = element_text( # axis text
family = "Roboto Condensed", # font family
size = 9
), # font size
axis.text.x = element_text( # margin for axis text
margin = margin(5, b = 10)
)
# since the legend often requires manual tweaking
# based on plot content, don't define it here
)
}
## Use available fonts in ggplot text geoms too!
ggplot2::update_geom_defaults(geom = "text", new = list(
family = "Roboto Condensed",
face = "plain",
size = 3.5,
color = "#2b2b2b"
))
ggplot2::update_geom_defaults(geom = "label", new = list(
family = "Roboto Condensed",
face = "plain",
size = 3.5,
color = "#2b2b2b"
))
## Set the theme
ggplot2::theme_set(new = theme_custom())2 Introduction
This dataset pertains to scores various types of coffees on parameters such as aroma, flavour, after-taste etc.
NoteBreadcrumbs
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!
3 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, …4 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?
NoteBreadcrumb 1
Why did I choose these Qual factors to count with?
5 Data Dictionary
NoteQuantitative Variables
Write in.
NoteQualitative Variables
Write in.
NoteObservations
Write in.
6 Research Question
NoteBreadcrumb 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?
7 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
```
NoteBreadcrumb 3
We have too much coffee here! We need to compress this data!
What??? Why? How? Where???
NoteBreadcrumb 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`??
NoteBreadcrumb 5
What just happened? How did we convert those mean numbers to ranks?
8 Plot the Data
9 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.