AE 09: Explore coffee taste tests

Suggested answers

Application exercise
Answers
R
Python
Modified

September 26, 2025

The Great American Coffee Taste Test

library(tidyverse)
library(tidymodels)
library(colorspace)
library(skimr)
library(GGally)

set.seed(167)

# preferred theme
theme_set(theme_minimal(base_size = 12, base_family = "Atkinson Hyperlegible"))
import pandas as pd
import numpy as np
from plotnine import *
from skimpy import skim
from sklearn.model_selection import train_test_split

# Set random seed
np.random.seed(167)

# Set preferred theme
theme_set(theme_minimal(base_size=12, base_family="Atkinson Hyperlegible"))

In October 2023, James Hoffmann and coffee company Cometeer held the “Great American Coffee Taste Test” on YouTube, during which viewers were asked to fill out a survey about 4 coffees they ordered from Cometeer for the tasting. Tidy Tuesday published the data set we are using.

coffee_survey <- read_csv(file = "data/coffee_survey.csv")

# partition into training and test sets
coffee_split <- initial_split(coffee_survey, prop = 0.8)
coffee_train <- training(coffee_split)
coffee_test <- testing(coffee_split)
coffee_survey = pd.read_csv("data/coffee_survey.csv")

# partition into training and test sets
coffee_train, coffee_test = train_test_split(coffee_survey, test_size=0.2, random_state=167)

It includes the following features:

variable class description
submission_id character Submission ID
age character What is your age?
cups character How many cups of coffee do you typically drink per day?
where_drink character Where do you typically drink coffee?
brew character How do you brew coffee at home?
brew_other character How else do you brew coffee at home?
purchase character On the go, where do you typically purchase coffee?
purchase_other character Where else do you purchase coffee?
favorite character What is your favorite coffee drink?
favorite_specify character Please specify what your favorite coffee drink is
additions character Do you usually add anything to your coffee?
additions_other character What else do you add to your coffee?
dairy character What kind of dairy do you add?
sweetener character What kind of sugar or sweetener do you add?
style character Before today’s tasting, which of the following best described what kind of coffee you like?
strength character How strong do you like your coffee?
roast_level character What roast level of coffee do you prefer?
caffeine character How much caffeine do you like in your coffee?
expertise numeric Lastly, how would you rate your own coffee expertise?
coffee_a_bitterness numeric Coffee A - Bitterness
coffee_a_acidity numeric Coffee A - Acidity
coffee_a_personal_preference numeric Coffee A - Personal Preference
coffee_a_notes character Coffee A - Notes
coffee_b_bitterness numeric Coffee B - Bitterness
coffee_b_acidity numeric Coffee B - Acidity
coffee_b_personal_preference numeric Coffee B - Personal Preference
coffee_b_notes character Coffee B - Notes
coffee_c_bitterness numeric Coffee C - Bitterness
coffee_c_acidity numeric Coffee C - Acidity
coffee_c_personal_preference numeric Coffee C - Personal Preference
coffee_c_notes character Coffee C - Notes
coffee_d_bitterness numeric Coffee D - Bitterness
coffee_d_acidity numeric Coffee D - Acidity
coffee_d_personal_preference numeric Coffee D - Personal Preference
coffee_d_notes character Coffee D - Notes
prefer_abc character Between Coffee A, Coffee B, and Coffee C which did you prefer?
prefer_ad character Between Coffee A and Coffee D, which did you prefer?
prefer_overall character Lastly, what was your favorite overall coffee?
wfh character Do you work from home or in person?
total_spend character In total, much money do you typically spend on coffee in a month?
why_drink character Why do you drink coffee?
why_drink_other character Other reason for drinking coffee
taste character Do you like the taste of coffee?
know_source character Do you know where your coffee comes from?
most_paid character What is the most you’ve ever paid for a cup of coffee?
most_willing character What is the most you’d ever be willing to pay for a cup of coffee?
value_cafe character Do you feel like you’re getting good value for your money when you buy coffee at a cafe?
spent_equipment character Approximately how much have you spent on coffee equipment in the past 5 years?
value_equipment character Do you feel like you’re getting good value for your money when you buy coffee at a cafe?
gender character Gender
gender_specify character Gender (please specify)
education_level character Education Level
ethnicity_race character Ethnicity/Race
ethnicity_race_specify character Ethnicity/Race (please specify)
employment_status character Employment Status
number_children character Number of Children
political_affiliation character Political Affiliation

Our ultimate goal on a future assignment is to predict whether or not individuals like coffee D based on their survey responses and taste tests for coffees A-C.1 We will use a binary form of coffee_d_personal_preference variable as our target.

A quick skim of the data:

skim(coffee_train)
Data summary
Name coffee_train
Number of rows 3233
Number of columns 57
_______________________
Column type frequency:
character 44
numeric 13
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
submission_id 0 1.00 6 6 0 3233 0
age 26 0.99 13 15 0 7 0
cups 79 0.98 1 11 0 6 0
where_drink 60 0.98 7 44 0 64 0
brew 320 0.90 5 165 0 401 0
brew_other 2687 0.17 2 105 0 130 0
purchase 2659 0.18 5 107 0 77 0
purchase_other 3214 0.01 4 83 0 16 0
favorite 55 0.98 5 32 0 12 0
favorite_specify 3145 0.03 3 64 0 62 0
additions 71 0.98 5 100 0 47 0
additions_other 3194 0.01 3 140 0 34 0
dairy 1876 0.42 8 110 0 148 0
sweetener 2821 0.13 5 99 0 77 0
style 71 0.98 4 11 0 12 0
strength 102 0.97 4 15 0 5 0
roast_level 84 0.97 4 7 0 7 0
caffeine 103 0.97 5 13 0 3 0
coffee_a_notes 1166 0.64 3 377 0 1863 0
coffee_b_notes 1265 0.61 1 980 0 1781 0
coffee_c_notes 1319 0.59 2 438 0 1749 0
coffee_d_notes 1159 0.64 2 528 0 1903 0
prefer_abc 215 0.93 8 8 0 3 0
prefer_ad 223 0.93 8 8 0 2 0
prefer_overall 216 0.93 8 8 0 4 0
wfh 417 0.87 18 26 0 3 0
total_spend 427 0.87 4 8 0 6 0
why_drink 377 0.88 5 93 0 83 0
why_drink_other 3091 0.04 2 194 0 139 0
taste 382 0.88 2 3 0 2 0
know_source 385 0.88 2 3 0 2 0
most_paid 409 0.87 5 13 0 8 0
most_willing 422 0.87 5 13 0 8 0
value_cafe 431 0.87 2 3 0 2 0
spent_equipment 430 0.87 7 16 0 7 0
value_equipment 436 0.87 2 3 0 2 0
gender 417 0.87 4 22 0 5 0
gender_specify 3224 0.00 2 28 0 8 0
education_level 483 0.85 15 34 0 6 0
ethnicity_race 498 0.85 15 29 0 6 0
ethnicity_race_specify 3147 0.03 2 53 0 70 0
employment_status 497 0.85 7 18 0 6 0
number_children 503 0.84 1 11 0 5 0
political_affiliation 596 0.82 8 14 0 4 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
expertise 86 0.97 5.68 1.97 1 5 6 7 10 ▂▃▇▇▁
coffee_a_bitterness 191 0.94 2.14 0.95 1 1 2 3 5 ▅▇▃▂▁
coffee_a_acidity 203 0.94 3.62 1.00 1 3 4 4 5 ▁▂▅▇▃
coffee_a_personal_preference 203 0.94 3.29 1.20 1 2 3 4 5 ▂▅▆▇▅
coffee_b_bitterness 207 0.94 3.02 0.99 1 2 3 4 5 ▂▅▇▆▁
coffee_b_acidity 216 0.93 2.24 0.87 1 2 2 3 5 ▃▇▅▁▁
coffee_b_personal_preference 212 0.93 3.05 1.12 1 2 3 4 5 ▂▆▇▆▂
coffee_c_bitterness 221 0.93 3.07 0.98 1 2 3 4 5 ▁▅▇▆▁
coffee_c_acidity 230 0.93 2.36 0.93 1 2 2 3 5 ▃▇▆▂▁
coffee_c_personal_preference 221 0.93 3.07 1.13 1 2 3 4 5 ▂▆▇▆▃
coffee_d_bitterness 218 0.93 2.17 1.08 1 1 2 3 5 ▇▇▅▂▁
coffee_d_acidity 220 0.93 3.85 1.02 1 3 4 5 5 ▁▂▃▇▆
coffee_d_personal_preference 221 0.93 3.35 1.45 1 2 4 5 5 ▅▃▅▆▇ 
print(coffee_train.info())
<class 'pandas.core.frame.DataFrame'>
Index: 3233 entries, 633 to 2881
Data columns (total 57 columns):
 #   Column                        Non-Null Count  Dtype  
---  ------                        --------------  -----  
 0   submission_id                 3233 non-null   object 
 1   age                           3208 non-null   object 
 2   cups                          3165 non-null   object 
 3   where_drink                   3176 non-null   object 
 4   brew                          2921 non-null   object 
 5   brew_other                    535 non-null    object 
 6   purchase                      550 non-null    object 
 7   purchase_other                24 non-null     object 
 8   favorite                      3189 non-null   object 
 9   favorite_specify              87 non-null     object 
 10  additions                     3171 non-null   object 
 11  additions_other               43 non-null     object 
 12  dairy                         1360 non-null   object 
 13  sweetener                     420 non-null    object 
 14  style                         3168 non-null   object 
 15  strength                      3131 non-null   object 
 16  roast_level                   3156 non-null   object 
 17  caffeine                      3140 non-null   object 
 18  expertise                     3154 non-null   float64
 19  coffee_a_bitterness           3048 non-null   float64
 20  coffee_a_acidity              3032 non-null   float64
 21  coffee_a_personal_preference  3041 non-null   float64
 22  coffee_a_notes                2068 non-null   object 
 23  coffee_b_bitterness           3033 non-null   float64
 24  coffee_b_acidity              3021 non-null   float64
 25  coffee_b_personal_preference  3028 non-null   float64
 26  coffee_b_notes                1967 non-null   object 
 27  coffee_c_bitterness           3022 non-null   float64
 28  coffee_c_acidity              3013 non-null   float64
 29  coffee_c_personal_preference  3022 non-null   float64
 30  coffee_c_notes                1902 non-null   object 
 31  coffee_d_bitterness           3022 non-null   float64
 32  coffee_d_acidity              3019 non-null   float64
 33  coffee_d_personal_preference  3019 non-null   float64
 34  coffee_d_notes                2073 non-null   object 
 35  prefer_abc                    3025 non-null   object 
 36  prefer_ad                     3018 non-null   object 
 37  prefer_overall                3024 non-null   object 
 38  wfh                           2841 non-null   object 
 39  total_spend                   2823 non-null   object 
 40  why_drink                     2867 non-null   object 
 41  why_drink_other               132 non-null    object 
 42  taste                         2862 non-null   object 
 43  know_source                   2858 non-null   object 
 44  most_paid                     2833 non-null   object 
 45  most_willing                  2820 non-null   object 
 46  value_cafe                    2810 non-null   object 
 47  spent_equipment               2812 non-null   object 
 48  value_equipment               2802 non-null   object 
 49  gender                        2825 non-null   object 
 50  gender_specify                10 non-null     object 
 51  education_level               2764 non-null   object 
 52  ethnicity_race                2748 non-null   object 
 53  ethnicity_race_specify        80 non-null     object 
 54  employment_status             2747 non-null   object 
 55  number_children               682 non-null    object 
 56  political_affiliation         2645 non-null   object 
dtypes: float64(13), object(44)
memory usage: 1.4+ MB
None
print(coffee_train.describe())
         expertise  ...  coffee_d_personal_preference
count  3154.000000  ...                   3019.000000
mean      5.693088  ...                      3.395495
std       1.955330  ...                      1.447305
min       1.000000  ...                      1.000000
25%       5.000000  ...                      2.000000
50%       6.000000  ...                      4.000000
75%       7.000000  ...                      5.000000
max      10.000000  ...                      5.000000

[8 rows x 13 columns]
print(coffee_train.isnull().sum())
submission_id                      0
age                               25
cups                              68
where_drink                       57
brew                             312
brew_other                      2698
purchase                        2683
purchase_other                  3209
favorite                          44
favorite_specify                3146
additions                         62
additions_other                 3190
dairy                           1873
sweetener                       2813
style                             65
strength                         102
roast_level                       77
caffeine                          93
expertise                         79
coffee_a_bitterness              185
coffee_a_acidity                 201
coffee_a_personal_preference     192
coffee_a_notes                  1165
coffee_b_bitterness              200
coffee_b_acidity                 212
coffee_b_personal_preference     205
coffee_b_notes                  1266
coffee_c_bitterness              211
coffee_c_acidity                 220
coffee_c_personal_preference     211
coffee_c_notes                  1331
coffee_d_bitterness              211
coffee_d_acidity                 214
coffee_d_personal_preference     214
coffee_d_notes                  1160
prefer_abc                       208
prefer_ad                        215
prefer_overall                   209
wfh                              392
total_spend                      410
why_drink                        366
why_drink_other                 3101
taste                            371
know_source                      375
most_paid                        400
most_willing                     413
value_cafe                       423
spent_equipment                  421
value_equipment                  431
gender                           408
gender_specify                  3223
education_level                  469
ethnicity_race                   485
ethnicity_race_specify          3153
employment_status                486
number_children                 2551
political_affiliation            588
dtype: int64

Examining continuous variables

Your turn: Examine expertise using a histogram and appropriate binwidth. Describe the features of this variable.

ggplot(data = coffee_train, mapping = aes(x = expertise)) +
  geom_histogram(bins = 10, color = "white")

(ggplot(coffee_train) +
 geom_histogram(aes(x='expertise'), bins=10, color='white')).show()

Add response here.

  • Unimodal
  • Slight skew (average expertise around 6)
  • Few consider themselves experts
  • Unsurprising, given the non-representative sample (who else would participate in a taste test except coffee enthusiasts?)

Your turn: Each coffee has three numeric ratings by the respondents: bitterness, acidity, and personal preference. Create a histogram for each of these characteristics, faceted by coffee type. What do you notice?

Wrangling the data for easier visualization

The original structure of the data is one column for each coffee for each characteristic. You could create separate graphs for each of the 12 columns, but that seems like a lot of work. Instead, consider using the pivot_longer() function to restructure the data to one row per coffee per characteristic. This will make it easier to create the faceted histograms.

coffee_train |>
  select(starts_with("coffee"), -ends_with("notes")) |>
  pivot_longer(
    cols = everything(),
    names_to = c("coffee", "measure"),
    names_prefix = "coffee_",
    names_sep = "_",
    values_to = "value"
  )
# A tibble: 38,796 × 3
   coffee measure    value
   <chr>  <chr>      <dbl>
 1 a      bitterness     2
 2 a      acidity        5
 3 a      personal       1
 4 b      bitterness     4
 5 b      acidity        1
 6 b      personal       4
 7 c      bitterness     2
 8 c      acidity        2
 9 c      personal       5
10 d      bitterness     3
# ℹ 38,786 more rows
# Select columns starting with "coffee" but not ending with "notes"
coffee_cols = [col for col in coffee_train.columns 
               if col.startswith('coffee_') and not col.endswith('_notes')]

# Reshape the data using melt (pandas equivalent of pivot_longer)
coffee_long = coffee_train[coffee_cols].melt(
    var_name='coffee_measure',
    value_name='value'
)

# Split the column names to separate coffee type and measure
coffee_long[['coffee', 'measure']] = coffee_long['coffee_measure'].str.replace('coffee_', '').str.split('_', n=1, expand=True)

# Drop the original combined column and reorder
coffee_long = coffee_long[['coffee', 'measure', 'value']]

coffee_long
      coffee              measure  value
0          a           bitterness    1.0
1          a           bitterness    3.0
2          a           bitterness    1.0
3          a           bitterness    1.0
4          a           bitterness    1.0
...      ...                  ...    ...
38791      d  personal_preference    5.0
38792      d  personal_preference    1.0
38793      d  personal_preference    2.0
38794      d  personal_preference    5.0
38795      d  personal_preference    4.0

[38796 rows x 3 columns]
coffee_train |>
  select(starts_with("coffee"), -ends_with("notes")) |>
  pivot_longer(
    cols = everything(),
    names_to = c("coffee", "measure"),
    names_prefix = "coffee_",
    names_sep = "_",
    values_to = "value"
  ) |>
  ggplot(mapping = aes(x = value)) +
  geom_bar() +
  facet_grid(rows = vars(coffee), cols = vars(measure))

(ggplot(coffee_long) +
   geom_bar(aes(x='value')) +
   facet_grid('coffee ~ measure')).show()

Add response here.

  • Coffees A and D skew more acidic compared to B and C
  • Coffees A and D are less bitter than B and C
  • D tends to have higher personal preference, skews higher distribution overall
  • A also skews a bit higher personal preference, whereas B and C are more normally distributed

Examining categorical variables

Your turn: Examine prefer_overall graphically. Record your observations.

coffee_train |>
  mutate(prefer_overall = fct_infreq(prefer_overall) |> fct_rev()) |>
  ggplot(mapping = aes(y = prefer_overall)) +
  geom_bar()

(ggplot(coffee_train) +
   geom_bar(aes(x='prefer_overall'))).show()

Add response here.

  • Four possible choices - different kind of classification problem than in the past
  • Coffee D is the most popular overall, similar levels of support for A/B/C
  • Some respondents did not select any of the four - will need to drop in the modeling stage
  • Is this preference gap large enough to necessitate undersampling or another class imbalance procedure?

Your turn: Examine cups, brew, and roast_level. Record your observations, in particular how you might need to handle these variables in the modeling stage.

# cups
ggplot(data = coffee_train, mapping = aes(y = cups)) +
  geom_bar()

# correct order
coffee_train |>
  mutate(
    cups = fct(cups) |>
      fct_relevel("Less than 1", "1", "2", "3", "4", "More than 4")
  ) |>
  ggplot(mapping = aes(y = cups)) +
  geom_bar()

# cups
(ggplot(coffee_train) +
   geom_bar(aes(x='cups'))).show()

# correct order
coffee_train['cups'] = pd.Categorical(
    coffee_train['cups'],
    categories=["Less than 1", "1", "2", "3", "4", "More than 4"],
    ordered=True
)

(ggplot(coffee_train) +
   geom_bar(aes(x='cups'))).show()

Add response here.

  • Typical drinker is 1-2 cups per day
  • A decent number of occasional coffee drinkers
  • Long tail - some respondents drink a lot of coffee each day (I can’t imagine the number of bathroom trips…)
ggplot(data = coffee_train, mapping = aes(y = brew)) +
  geom_bar()

(ggplot(coffee_train) +
   geom_bar(aes(x='brew'))).show()

Add response here.

Oh crap. Each value contains potentially multiple types of brews. This is not useful in its current form. Need to split it up into one row per respondent per brew type.

coffee_train |>
  separate_longer_delim(
    cols = brew,
    delim = ", "
  ) |>
  mutate(brew = fct_infreq(brew) |> fct_rev()) |>
  ggplot(mapping = aes(y = brew)) +
  geom_bar()

# Split brew column on delimiter and expand to multiple rows
coffee_brew_expanded = coffee_train.assign(
    brew=coffee_train['brew'].str.split(', ')
).explode('brew')

# Reorder by frequency (reversed for ascending frequency order)
brew_counts = coffee_brew_expanded['brew'].value_counts()
coffee_brew_expanded['brew'] = pd.Categorical(
    coffee_brew_expanded['brew'],
    categories=brew_counts.index.tolist()[::-1],
    ordered=True
)

# Create the plot
(ggplot(coffee_brew_expanded) +
  geom_bar(aes(x='brew'))).show()

Bonus fun! How do the brew types correlate with one another?

This could be an issue later with modeling if we do dummy encoding.

library(ggcorrplot) # for the heatmap

coffee_train |>
  # get into a long format
  separate_longer_delim(
    cols = brew,
    delim = ", "
  ) |>
  # frequency count for each respondent and brew type
  count(submission_id, brew) |>
  # remove NAs
  drop_na() |>
  # restructure to one row per respondent, one column per brew type
  # fill in NAs with 0
  pivot_wider(names_from = brew, values_from = n, values_fill = 0) |>
  # drop submission_id - don't need anymore
  select(-submission_id) |>
  # calculate correlation coefficients
  cor() |>
  # draw the correlation matrix as a heatmap
  ggcorrplot(
    # order the variables by correlation values
    hc.order = TRUE,
    # just show the lower triangle
    type = "lower",
    # make each box a white border
    outline.color = "white"
  ) +
  # more optimal color palette
  scale_fill_continuous_diverging()

TODO

ggplot(data = coffee_train, mapping = aes(y = roast_level)) +
  geom_bar()

coffee_train |>
  mutate(roast_level = fct_infreq(roast_level) |> fct_rev()) |>
  ggplot(mapping = aes(y = roast_level)) +
  geom_bar()

(ggplot(coffee_train) +
   geom_bar(aes(x='roast_level'))).show()

coffee_train['roast_level'] = pd.Categorical(
    coffee_train['roast_level'],
    categories=coffee_train['roast_level'].value_counts().index.tolist()[::-1],
    ordered=True)

(ggplot(coffee_train) +
   geom_bar(aes(x='roast_level'))).show()

Add response here.

By far the most common roast preferences are light, medium, and dark. Others are so infrequent they might not be useful.

Some additional variables for fun

coffee_train |>
  mutate(favorite = fct_infreq(favorite) |> fct_rev()) |>
  ggplot(mapping = aes(y = favorite)) +
  geom_bar()

coffee_train['favorite'] = pd.Categorical(
    coffee_train['favorite'],
    categories=coffee_train['favorite'].value_counts().index.tolist()[::-1],
    ordered=True)

(ggplot(coffee_train) +
   geom_bar(aes(x='favorite'))).show()

  • Pourover is most popular (coffee snobs)
  • Uneven distribution - some are distinctly less popular than others. Might be worth testing to collapse into “Other” category or hashing/effect encoding
  • Not a ton of unique categories though
ggplot(data = coffee_train, mapping = aes(y = political_affiliation)) +
  geom_bar()

(ggplot(coffee_train) +
   geom_bar(aes(x='political_affiliation'))).show()

  • Skews heavily Democratic
  • Is this something that would even be useful?

Making comparisons

Your turn: Compare the relationship between coffee_d_personal_preference and the respondents’ preferred roast levels. Use a proportional bar chart to visualize the relationship.

Tip

Use position = "fill" with geom_bar() to automatically calculate percentages for the chart.

coffee_train |>
  filter(roast_level %in% c("Dark", "Medium", "Light")) |>
  mutate(
    roast_level = factor(roast_level, levels = c("Light", "Medium", "Dark"))
  ) |>
  ggplot(
    mapping = aes(y = coffee_d_personal_preference, fill = fct_rev(roast_level))
  ) +
  geom_bar(position = "fill") +
  scale_fill_discrete_sequential(rev = FALSE, guide = guide_legend(rev = TRUE))

# Filter and prepare data
coffee_filtered = coffee_train[
    coffee_train['roast_level'].isin(['Dark', 'Medium', 'Light'])
].copy()

# Set roast_level as ordered categorical
coffee_filtered['roast_level'] = pd.Categorical(
    coffee_filtered['roast_level'], 
    categories=['Light', 'Medium', 'Dark'], 
    ordered=True
)

# Reverse roast_level for fill aesthetic
coffee_filtered['roast_level_rev'] = pd.Categorical(
    coffee_filtered['roast_level'],
    categories=['Dark', 'Medium', 'Light'],  # Reversed order
    ordered=True
)

# Create the proportional bar chart
(ggplot(coffee_filtered) +
  geom_bar(aes(x='coffee_d_personal_preference', fill='roast_level_rev'), position='fill')).show()

Add response here.

  • People who prefer light roast tend to like coffee D more
  • Those who like coffee D tend to prefer light or medium roasts

Your turn: Examine the relationship between the respondents’ numeric ratings for acidity, bitterness, and personal preference for each of the four coffees and compare to their overall preference. Record your observations.

pref_by_traits <- coffee_train |>
  select(starts_with("coffee"), -ends_with("notes"), prefer_overall) |>
  pivot_longer(
    cols = -prefer_overall,
    names_to = c("coffee", "measure"),
    names_prefix = "coffee_",
    names_sep = "_",
    values_to = "value"
  ) |>
  drop_na() |>
  mutate(coffee = fct_rev(coffee))

ggplot(data = pref_by_traits, mapping = aes(x = factor(value), y = coffee)) +
  geom_count() +
  facet_grid(rows = vars(prefer_overall), cols = vars(measure))

ggplot(data = pref_by_traits, mapping = aes(x = factor(value), y = coffee)) +
  geom_bin2d() +
  scale_fill_continuous_sequential() +
  facet_grid(rows = vars(prefer_overall), cols = vars(measure))

ggplot(
  data = pref_by_traits,
  mapping = aes(x = value, color = fct_rev(coffee))
) +
  geom_freqpoly(binwidth = 1) +
  scale_fill_discrete_qualitative() +
  facet_grid(rows = vars(prefer_overall), cols = vars(measure))

ggplot(data = pref_by_traits, mapping = aes(x = value, y = coffee)) +
  geom_boxplot() +
  facet_grid(rows = vars(prefer_overall), cols = vars(measure))

# Data preparation
coffee_cols = [col for col in coffee_train.columns 
               if col.startswith('coffee_') and not col.endswith('_notes')]

pref_by_traits = coffee_train[coffee_cols + ['prefer_overall']].melt(
    id_vars=['prefer_overall'],
    var_name='coffee_measure',
    value_name='value'
).dropna()

# Split the column names to separate coffee type and measure
pref_by_traits[['coffee', 'measure']] = pref_by_traits['coffee_measure'].str.replace('coffee_', '').str.split('_', n=1, expand=True)

# Reverse coffee order for consistent ordering
coffee_order = ['d', 'c', 'b', 'a']
pref_by_traits['coffee'] = pd.Categorical(
    pref_by_traits['coffee'],
    categories=coffee_order,
    ordered=True
)

# Create the visualizations
# Plot 1: geom_count equivalent
(ggplot(pref_by_traits, aes(x='factor(value)', y='coffee')) +
  geom_count() +
  facet_grid('prefer_overall ~ measure')).show()

# Plot 2: geom_bin2d 
(ggplot(pref_by_traits, aes(x='factor(value)', y='coffee')) +
  geom_bin2d() +
  facet_grid('prefer_overall ~ measure')).show()

# Plot 3: geom_freqpoly
# Reverse coffee order for color mapping
pref_by_traits['coffee_rev'] = pd.Categorical(
    pref_by_traits['coffee'],
    categories=['d', 'c', 'b', 'a'],
    ordered=True
)

(ggplot(pref_by_traits, aes(x='value', color='coffee_rev')) +
  geom_freqpoly(binwidth=1) +
  facet_grid('prefer_overall ~ measure')).show()

# Plot 4: geom_boxplot
(ggplot(pref_by_traits, aes(x='coffee', y='value')) +
  geom_boxplot() +
  facet_grid('prefer_overall ~ measure')).show()

Add response here.

People who prefer coffee A

  • Rate it higher for personal preference and acidity
  • Rate it lower for bitterness

People who prefer coffee B

  • Rate it higher for personal preference, mid for bitterness, and lower for acidity

People who prefer coffee C

  • Rate it higher for personal preference, mid for bitterness, and lower for acidity

People who prefer coffee D

  • Rate it higher for personal preference and acidity, lower for bitterness

Overall, seems like coffees A/D and B/C have similar evaluations

Data quality

Missingness

Demonstration: Use {visdat} or missingno to visualize missingness patterns in the data set.

library(visdat)

# quick glance of missingness by row/column order
vis_dat(coffee_train)

# reorder columns based on % missing
vis_miss(coffee_train, sort_miss = TRUE)

# cluster rows based on similarity in missingness patterns
vis_miss(coffee_train, cluster = TRUE)

import missingno as msno

# Quick glance of missingness by row/column order
msno.matrix(coffee_train)

# Reorder columns based on % missing
msno.matrix(coffee_train.loc[:, coffee_train.isnull().sum().sort_values(ascending=False).index])

# Cluster rows based on similarity in missingness patterns
msno.dendrogram(coffee_train)

Your turn: Record your observations on the missingness patterns in the data set. What variables have high missingness? Is this surprising? What might you do to variables or observations with high degrees of missingness?

Add response here.

  • Some missingness throughout the dataset
  • Some variables are almost entirely missing (gender specify, lots of “other” columns). A lot of this is unsurprising since they are columns only to catch specific conditions or exceptions to other columns
  • “Sweetener” is unusually high missingness - is this because people don’t use sweeteners or because they didn’t answer the question?
  • Some observations have missing values for almost all the columns - are these valid observations? Is there enough info there to include in resulting models?

Outliers

Demonstration: Generate a scatterplot matrix for all the numeric variables in the data set.2

coffee_train |>
  select(where(is.numeric)) |>
  ggpairs()

import matplotlib.pyplot as plt
from pandas.plotting import scatter_matrix

# Get all numeric columns
numeric_cols = coffee_train.select_dtypes(include=[np.number]).columns

# Create scatterplot matrix using pandas
fig, axes = plt.subplots(figsize=(15, 15))
scatter_matrix(coffee_train[numeric_cols], alpha=0.6, figsize=(15, 15), diagonal='hist')
array([[<Axes: xlabel='expertise', ylabel='expertise'>,
        <Axes: xlabel='coffee_a_bitterness', ylabel='expertise'>,
        <Axes: xlabel='coffee_a_acidity', ylabel='expertise'>,
        <Axes: xlabel='coffee_a_personal_preference', ylabel='expertise'>,
        <Axes: xlabel='coffee_b_bitterness', ylabel='expertise'>,
        <Axes: xlabel='coffee_b_acidity', ylabel='expertise'>,
        <Axes: xlabel='coffee_b_personal_preference', ylabel='expertise'>,
        <Axes: xlabel='coffee_c_bitterness', ylabel='expertise'>,
        <Axes: xlabel='coffee_c_acidity', ylabel='expertise'>,
        <Axes: xlabel='coffee_c_personal_preference', ylabel='expertise'>,
        <Axes: xlabel='coffee_d_bitterness', ylabel='expertise'>,
        <Axes: xlabel='coffee_d_acidity', ylabel='expertise'>,
        <Axes: xlabel='coffee_d_personal_preference', ylabel='expertise'>],
       [<Axes: xlabel='expertise', ylabel='coffee_a_bitterness'>,
        <Axes: xlabel='coffee_a_bitterness', ylabel='coffee_a_bitterness'>,
        <Axes: xlabel='coffee_a_acidity', ylabel='coffee_a_bitterness'>,
        <Axes: xlabel='coffee_a_personal_preference', ylabel='coffee_a_bitterness'>,
        <Axes: xlabel='coffee_b_bitterness', ylabel='coffee_a_bitterness'>,
        <Axes: xlabel='coffee_b_acidity', ylabel='coffee_a_bitterness'>,
        <Axes: xlabel='coffee_b_personal_preference', ylabel='coffee_a_bitterness'>,
        <Axes: xlabel='coffee_c_bitterness', ylabel='coffee_a_bitterness'>,
        <Axes: xlabel='coffee_c_acidity', ylabel='coffee_a_bitterness'>,
        <Axes: xlabel='coffee_c_personal_preference', ylabel='coffee_a_bitterness'>,
        <Axes: xlabel='coffee_d_bitterness', ylabel='coffee_a_bitterness'>,
        <Axes: xlabel='coffee_d_acidity', ylabel='coffee_a_bitterness'>,
        <Axes: xlabel='coffee_d_personal_preference', ylabel='coffee_a_bitterness'>],
       [<Axes: xlabel='expertise', ylabel='coffee_a_acidity'>,
        <Axes: xlabel='coffee_a_bitterness', ylabel='coffee_a_acidity'>,
        <Axes: xlabel='coffee_a_acidity', ylabel='coffee_a_acidity'>,
        <Axes: xlabel='coffee_a_personal_preference', ylabel='coffee_a_acidity'>,
        <Axes: xlabel='coffee_b_bitterness', ylabel='coffee_a_acidity'>,
        <Axes: xlabel='coffee_b_acidity', ylabel='coffee_a_acidity'>,
        <Axes: xlabel='coffee_b_personal_preference', ylabel='coffee_a_acidity'>,
        <Axes: xlabel='coffee_c_bitterness', ylabel='coffee_a_acidity'>,
        <Axes: xlabel='coffee_c_acidity', ylabel='coffee_a_acidity'>,
        <Axes: xlabel='coffee_c_personal_preference', ylabel='coffee_a_acidity'>,
        <Axes: xlabel='coffee_d_bitterness', ylabel='coffee_a_acidity'>,
        <Axes: xlabel='coffee_d_acidity', ylabel='coffee_a_acidity'>,
        <Axes: xlabel='coffee_d_personal_preference', ylabel='coffee_a_acidity'>],
       [<Axes: xlabel='expertise', ylabel='coffee_a_personal_preference'>,
        <Axes: xlabel='coffee_a_bitterness', ylabel='coffee_a_personal_preference'>,
        <Axes: xlabel='coffee_a_acidity', ylabel='coffee_a_personal_preference'>,
        <Axes: xlabel='coffee_a_personal_preference', ylabel='coffee_a_personal_preference'>,
        <Axes: xlabel='coffee_b_bitterness', ylabel='coffee_a_personal_preference'>,
        <Axes: xlabel='coffee_b_acidity', ylabel='coffee_a_personal_preference'>,
        <Axes: xlabel='coffee_b_personal_preference', ylabel='coffee_a_personal_preference'>,
        <Axes: xlabel='coffee_c_bitterness', ylabel='coffee_a_personal_preference'>,
        <Axes: xlabel='coffee_c_acidity', ylabel='coffee_a_personal_preference'>,
        <Axes: xlabel='coffee_c_personal_preference', ylabel='coffee_a_personal_preference'>,
        <Axes: xlabel='coffee_d_bitterness', ylabel='coffee_a_personal_preference'>,
        <Axes: xlabel='coffee_d_acidity', ylabel='coffee_a_personal_preference'>,
        <Axes: xlabel='coffee_d_personal_preference', ylabel='coffee_a_personal_preference'>],
       [<Axes: xlabel='expertise', ylabel='coffee_b_bitterness'>,
        <Axes: xlabel='coffee_a_bitterness', ylabel='coffee_b_bitterness'>,
        <Axes: xlabel='coffee_a_acidity', ylabel='coffee_b_bitterness'>,
        <Axes: xlabel='coffee_a_personal_preference', ylabel='coffee_b_bitterness'>,
        <Axes: xlabel='coffee_b_bitterness', ylabel='coffee_b_bitterness'>,
        <Axes: xlabel='coffee_b_acidity', ylabel='coffee_b_bitterness'>,
        <Axes: xlabel='coffee_b_personal_preference', ylabel='coffee_b_bitterness'>,
        <Axes: xlabel='coffee_c_bitterness', ylabel='coffee_b_bitterness'>,
        <Axes: xlabel='coffee_c_acidity', ylabel='coffee_b_bitterness'>,
        <Axes: xlabel='coffee_c_personal_preference', ylabel='coffee_b_bitterness'>,
        <Axes: xlabel='coffee_d_bitterness', ylabel='coffee_b_bitterness'>,
        <Axes: xlabel='coffee_d_acidity', ylabel='coffee_b_bitterness'>,
        <Axes: xlabel='coffee_d_personal_preference', ylabel='coffee_b_bitterness'>],
       [<Axes: xlabel='expertise', ylabel='coffee_b_acidity'>,
        <Axes: xlabel='coffee_a_bitterness', ylabel='coffee_b_acidity'>,
        <Axes: xlabel='coffee_a_acidity', ylabel='coffee_b_acidity'>,
        <Axes: xlabel='coffee_a_personal_preference', ylabel='coffee_b_acidity'>,
        <Axes: xlabel='coffee_b_bitterness', ylabel='coffee_b_acidity'>,
        <Axes: xlabel='coffee_b_acidity', ylabel='coffee_b_acidity'>,
        <Axes: xlabel='coffee_b_personal_preference', ylabel='coffee_b_acidity'>,
        <Axes: xlabel='coffee_c_bitterness', ylabel='coffee_b_acidity'>,
        <Axes: xlabel='coffee_c_acidity', ylabel='coffee_b_acidity'>,
        <Axes: xlabel='coffee_c_personal_preference', ylabel='coffee_b_acidity'>,
        <Axes: xlabel='coffee_d_bitterness', ylabel='coffee_b_acidity'>,
        <Axes: xlabel='coffee_d_acidity', ylabel='coffee_b_acidity'>,
        <Axes: xlabel='coffee_d_personal_preference', ylabel='coffee_b_acidity'>],
       [<Axes: xlabel='expertise', ylabel='coffee_b_personal_preference'>,
        <Axes: xlabel='coffee_a_bitterness', ylabel='coffee_b_personal_preference'>,
        <Axes: xlabel='coffee_a_acidity', ylabel='coffee_b_personal_preference'>,
        <Axes: xlabel='coffee_a_personal_preference', ylabel='coffee_b_personal_preference'>,
        <Axes: xlabel='coffee_b_bitterness', ylabel='coffee_b_personal_preference'>,
        <Axes: xlabel='coffee_b_acidity', ylabel='coffee_b_personal_preference'>,
        <Axes: xlabel='coffee_b_personal_preference', ylabel='coffee_b_personal_preference'>,
        <Axes: xlabel='coffee_c_bitterness', ylabel='coffee_b_personal_preference'>,
        <Axes: xlabel='coffee_c_acidity', ylabel='coffee_b_personal_preference'>,
        <Axes: xlabel='coffee_c_personal_preference', ylabel='coffee_b_personal_preference'>,
        <Axes: xlabel='coffee_d_bitterness', ylabel='coffee_b_personal_preference'>,
        <Axes: xlabel='coffee_d_acidity', ylabel='coffee_b_personal_preference'>,
        <Axes: xlabel='coffee_d_personal_preference', ylabel='coffee_b_personal_preference'>],
       [<Axes: xlabel='expertise', ylabel='coffee_c_bitterness'>,
        <Axes: xlabel='coffee_a_bitterness', ylabel='coffee_c_bitterness'>,
        <Axes: xlabel='coffee_a_acidity', ylabel='coffee_c_bitterness'>,
        <Axes: xlabel='coffee_a_personal_preference', ylabel='coffee_c_bitterness'>,
        <Axes: xlabel='coffee_b_bitterness', ylabel='coffee_c_bitterness'>,
        <Axes: xlabel='coffee_b_acidity', ylabel='coffee_c_bitterness'>,
        <Axes: xlabel='coffee_b_personal_preference', ylabel='coffee_c_bitterness'>,
        <Axes: xlabel='coffee_c_bitterness', ylabel='coffee_c_bitterness'>,
        <Axes: xlabel='coffee_c_acidity', ylabel='coffee_c_bitterness'>,
        <Axes: xlabel='coffee_c_personal_preference', ylabel='coffee_c_bitterness'>,
        <Axes: xlabel='coffee_d_bitterness', ylabel='coffee_c_bitterness'>,
        <Axes: xlabel='coffee_d_acidity', ylabel='coffee_c_bitterness'>,
        <Axes: xlabel='coffee_d_personal_preference', ylabel='coffee_c_bitterness'>],
       [<Axes: xlabel='expertise', ylabel='coffee_c_acidity'>,
        <Axes: xlabel='coffee_a_bitterness', ylabel='coffee_c_acidity'>,
        <Axes: xlabel='coffee_a_acidity', ylabel='coffee_c_acidity'>,
        <Axes: xlabel='coffee_a_personal_preference', ylabel='coffee_c_acidity'>,
        <Axes: xlabel='coffee_b_bitterness', ylabel='coffee_c_acidity'>,
        <Axes: xlabel='coffee_b_acidity', ylabel='coffee_c_acidity'>,
        <Axes: xlabel='coffee_b_personal_preference', ylabel='coffee_c_acidity'>,
        <Axes: xlabel='coffee_c_bitterness', ylabel='coffee_c_acidity'>,
        <Axes: xlabel='coffee_c_acidity', ylabel='coffee_c_acidity'>,
        <Axes: xlabel='coffee_c_personal_preference', ylabel='coffee_c_acidity'>,
        <Axes: xlabel='coffee_d_bitterness', ylabel='coffee_c_acidity'>,
        <Axes: xlabel='coffee_d_acidity', ylabel='coffee_c_acidity'>,
        <Axes: xlabel='coffee_d_personal_preference', ylabel='coffee_c_acidity'>],
       [<Axes: xlabel='expertise', ylabel='coffee_c_personal_preference'>,
        <Axes: xlabel='coffee_a_bitterness', ylabel='coffee_c_personal_preference'>,
        <Axes: xlabel='coffee_a_acidity', ylabel='coffee_c_personal_preference'>,
        <Axes: xlabel='coffee_a_personal_preference', ylabel='coffee_c_personal_preference'>,
        <Axes: xlabel='coffee_b_bitterness', ylabel='coffee_c_personal_preference'>,
        <Axes: xlabel='coffee_b_acidity', ylabel='coffee_c_personal_preference'>,
        <Axes: xlabel='coffee_b_personal_preference', ylabel='coffee_c_personal_preference'>,
        <Axes: xlabel='coffee_c_bitterness', ylabel='coffee_c_personal_preference'>,
        <Axes: xlabel='coffee_c_acidity', ylabel='coffee_c_personal_preference'>,
        <Axes: xlabel='coffee_c_personal_preference', ylabel='coffee_c_personal_preference'>,
        <Axes: xlabel='coffee_d_bitterness', ylabel='coffee_c_personal_preference'>,
        <Axes: xlabel='coffee_d_acidity', ylabel='coffee_c_personal_preference'>,
        <Axes: xlabel='coffee_d_personal_preference', ylabel='coffee_c_personal_preference'>],
       [<Axes: xlabel='expertise', ylabel='coffee_d_bitterness'>,
        <Axes: xlabel='coffee_a_bitterness', ylabel='coffee_d_bitterness'>,
        <Axes: xlabel='coffee_a_acidity', ylabel='coffee_d_bitterness'>,
        <Axes: xlabel='coffee_a_personal_preference', ylabel='coffee_d_bitterness'>,
        <Axes: xlabel='coffee_b_bitterness', ylabel='coffee_d_bitterness'>,
        <Axes: xlabel='coffee_b_acidity', ylabel='coffee_d_bitterness'>,
        <Axes: xlabel='coffee_b_personal_preference', ylabel='coffee_d_bitterness'>,
        <Axes: xlabel='coffee_c_bitterness', ylabel='coffee_d_bitterness'>,
        <Axes: xlabel='coffee_c_acidity', ylabel='coffee_d_bitterness'>,
        <Axes: xlabel='coffee_c_personal_preference', ylabel='coffee_d_bitterness'>,
        <Axes: xlabel='coffee_d_bitterness', ylabel='coffee_d_bitterness'>,
        <Axes: xlabel='coffee_d_acidity', ylabel='coffee_d_bitterness'>,
        <Axes: xlabel='coffee_d_personal_preference', ylabel='coffee_d_bitterness'>],
       [<Axes: xlabel='expertise', ylabel='coffee_d_acidity'>,
        <Axes: xlabel='coffee_a_bitterness', ylabel='coffee_d_acidity'>,
        <Axes: xlabel='coffee_a_acidity', ylabel='coffee_d_acidity'>,
        <Axes: xlabel='coffee_a_personal_preference', ylabel='coffee_d_acidity'>,
        <Axes: xlabel='coffee_b_bitterness', ylabel='coffee_d_acidity'>,
        <Axes: xlabel='coffee_b_acidity', ylabel='coffee_d_acidity'>,
        <Axes: xlabel='coffee_b_personal_preference', ylabel='coffee_d_acidity'>,
        <Axes: xlabel='coffee_c_bitterness', ylabel='coffee_d_acidity'>,
        <Axes: xlabel='coffee_c_acidity', ylabel='coffee_d_acidity'>,
        <Axes: xlabel='coffee_c_personal_preference', ylabel='coffee_d_acidity'>,
        <Axes: xlabel='coffee_d_bitterness', ylabel='coffee_d_acidity'>,
        <Axes: xlabel='coffee_d_acidity', ylabel='coffee_d_acidity'>,
        <Axes: xlabel='coffee_d_personal_preference', ylabel='coffee_d_acidity'>],
       [<Axes: xlabel='expertise', ylabel='coffee_d_personal_preference'>,
        <Axes: xlabel='coffee_a_bitterness', ylabel='coffee_d_personal_preference'>,
        <Axes: xlabel='coffee_a_acidity', ylabel='coffee_d_personal_preference'>,
        <Axes: xlabel='coffee_a_personal_preference', ylabel='coffee_d_personal_preference'>,
        <Axes: xlabel='coffee_b_bitterness', ylabel='coffee_d_personal_preference'>,
        <Axes: xlabel='coffee_b_acidity', ylabel='coffee_d_personal_preference'>,
        <Axes: xlabel='coffee_b_personal_preference', ylabel='coffee_d_personal_preference'>,
        <Axes: xlabel='coffee_c_bitterness', ylabel='coffee_d_personal_preference'>,
        <Axes: xlabel='coffee_c_acidity', ylabel='coffee_d_personal_preference'>,
        <Axes: xlabel='coffee_c_personal_preference', ylabel='coffee_d_personal_preference'>,
        <Axes: xlabel='coffee_d_bitterness', ylabel='coffee_d_personal_preference'>,
        <Axes: xlabel='coffee_d_acidity', ylabel='coffee_d_personal_preference'>,
        <Axes: xlabel='coffee_d_personal_preference', ylabel='coffee_d_personal_preference'>]],
      dtype=object)
plt.show()

Your turn: Examine the distribution of roast/gender and roast/cups. Describe the patterns you see and anything that is of particular interest given the model we will estimate.

coffee_train |>
  filter(roast_level %in% c("Dark", "Medium", "Light")) |>
  mutate(
    roast_level = factor(roast_level, levels = c("Light", "Medium", "Dark"))
  ) |>
  ggplot(mapping = aes(x = roast_level, y = gender)) +
  geom_count()

coffee_train |>
  filter(roast_level %in% c("Dark", "Medium", "Light")) |>
  mutate(
    roast_level = factor(roast_level, levels = c("Light", "Medium", "Dark")),
    cups = fct(cups) |>
      fct_relevel("Less than 1", "1", "2", "3", "4", "More than 4")
  ) |>
  ggplot(mapping = aes(x = roast_level, y = cups)) +
  geom_count()

coffee_train |>
  filter(roast_level %in% c("Dark", "Medium", "Light")) |>
  mutate(
    roast_level = factor(roast_level, levels = c("Light", "Medium", "Dark")),
    cups = fct(cups) |>
      fct_relevel("Less than 1", "1", "2", "3", "4", "More than 4")
  ) |>
  ggplot(mapping = aes(x = roast_level, y = cups)) +
  geom_bin2d() +
  scale_fill_continuous_sequential(limits = c(0, NA))

# Filter and prepare data
coffee_filtered = coffee_train[
    coffee_train['roast_level'].isin(['Dark', 'Medium', 'Light'])
].copy()

# Set roast_level as ordered categorical
coffee_filtered['roast_level'] = pd.Categorical(
    coffee_filtered['roast_level'], 
    categories=['Light', 'Medium', 'Dark'], 
    ordered=True
)

# Create the plot
(ggplot(coffee_filtered) +
  geom_count(aes(x='roast_level', y='gender'))).show()

# Filter and prepare data
coffee_filtered = coffee_train[
    coffee_train['roast_level'].isin(['Dark', 'Medium', 'Light'])
].copy()

# Set both categorical variables with proper ordering
coffee_filtered['roast_level'] = pd.Categorical(
    coffee_filtered['roast_level'], 
    categories=['Light', 'Medium', 'Dark'], 
    ordered=True
)

coffee_filtered['cups'] = pd.Categorical(
    coffee_filtered['cups'],
    categories=["Less than 1", "1", "2", "3", "4", "More than 4"],
    ordered=True
)

# Plot 1: geom_count
(ggplot(coffee_filtered) +
  geom_count(aes(x='roast_level', y='cups'))).show()

# Plot 2: geom_bin2d
(ggplot(coffee_filtered) +
  geom_bin2d(aes(x='roast_level', y='cups'))).show()

Add response here.

1-2 cups of light and medium roast are the most popular combinations. Everything else is significantly underrepresented. This could be a problem for the model if we don’t have enough data to make accurate predictions for these categories.

Acknowledgments

  • Python examples are adapted from the R code and translated with support from Anthropic Claude 4 Sonnet.
sessioninfo::session_info()
─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.5.1 (2025-06-13)
 os       macOS Tahoe 26.0
 system   aarch64, darwin20
 ui       X11
 language (EN)
 collate  C.UTF-8
 ctype    C.UTF-8
 tz       America/New_York
 date     2025-09-26
 pandoc   3.6.3 @ /Applications/Positron.app/Contents/Resources/app/quarto/bin/tools/aarch64/ (via rmarkdown)
 quarto   1.8.24 @ /Applications/quarto/bin/quarto

─ Packages ───────────────────────────────────────────────────────────────────
 ! package      * version    date (UTC) lib source
 P archive        1.1.12     2025-03-20 [?] CRAN (R 4.5.0)
 P backports      1.5.0      2024-05-23 [?] RSPM (R 4.5.0)
 P base64enc      0.1-3      2015-07-28 [?] RSPM (R 4.5.0)
 P bit            4.6.0      2025-03-06 [?] RSPM (R 4.5.0)
 P bit64          4.6.0-1    2025-01-16 [?] RSPM (R 4.5.0)
 P broom        * 1.0.9      2025-07-28 [?] RSPM (R 4.5.0)
 P class          7.3-23     2025-01-01 [?] CRAN (R 4.5.1)
 P cli            3.6.5      2025-04-23 [?] RSPM (R 4.5.0)
 P codetools      0.2-20     2024-03-31 [?] CRAN (R 4.5.1)
 P colorspace   * 2.1-1      2024-07-26 [?] RSPM (R 4.5.0)
 P crayon         1.5.3      2024-06-20 [?] RSPM (R 4.5.0)
 P data.table     1.17.8     2025-07-10 [?] RSPM (R 4.5.0)
 P dials        * 1.4.1      2025-07-29 [?] RSPM
 P DiceDesign     1.10       2023-12-07 [?] RSPM (R 4.5.0)
 P digest         0.6.37     2024-08-19 [?] RSPM (R 4.5.0)
 P dplyr        * 1.1.4      2023-11-17 [?] RSPM (R 4.5.0)
 P evaluate       1.0.4      2025-06-18 [?] RSPM (R 4.5.1)
 P farver         2.1.2      2024-05-13 [?] RSPM (R 4.5.0)
 P fastmap        1.2.0      2024-05-15 [?] RSPM (R 4.5.0)
 P forcats      * 1.0.0      2023-01-29 [?] RSPM (R 4.5.0)
 P foreach        1.5.2      2022-02-02 [?] RSPM
 P furrr          0.3.1      2022-08-15 [?] RSPM
 P future         1.67.0     2025-07-29 [?] RSPM
 P future.apply   1.20.0     2025-06-06 [?] RSPM
 P generics       0.1.4      2025-05-09 [?] RSPM (R 4.5.0)
 P GGally       * 2.3.0      2025-07-18 [?] RSPM
 P ggcorrplot   * 0.1.4.1    2023-09-05 [?] RSPM
 P ggplot2      * 4.0.0      2025-09-11 [?] RSPM
 P ggstats        0.10.0     2025-07-02 [?] RSPM
 P globals        0.18.0     2025-05-08 [?] RSPM
 P glue           1.8.0      2024-09-30 [?] RSPM (R 4.5.0)
 P gower          1.0.2      2024-12-17 [?] RSPM
 P GPfit          1.0-9      2025-04-12 [?] RSPM (R 4.5.0)
 P gtable         0.3.6      2024-10-25 [?] RSPM (R 4.5.0)
 P hardhat        1.4.1      2025-01-31 [?] RSPM
 P here           1.0.1      2020-12-13 [?] RSPM (R 4.5.0)
 P hms            1.1.3      2023-03-21 [?] RSPM (R 4.5.0)
 P htmltools      0.5.8.1    2024-04-04 [?] RSPM (R 4.5.0)
 P htmlwidgets    1.6.4      2023-12-06 [?] RSPM (R 4.5.0)
 P infer        * 1.0.9      2025-06-26 [?] RSPM
 P ipred          0.9-15     2024-07-18 [?] RSPM
 P iterators      1.0.14     2022-02-05 [?] RSPM
 P jsonlite       2.0.0      2025-03-27 [?] RSPM (R 4.5.0)
 P knitr          1.50       2025-03-16 [?] RSPM (R 4.5.0)
 P labeling       0.4.3      2023-08-29 [?] RSPM (R 4.5.0)
 P lattice        0.22-7     2025-04-02 [?] CRAN (R 4.5.1)
 P lava           1.8.1      2025-01-12 [?] RSPM
 P lhs            1.2.0      2024-06-30 [?] RSPM (R 4.5.0)
 P lifecycle      1.0.4      2023-11-07 [?] RSPM (R 4.5.0)
 P listenv        0.9.1      2024-01-29 [?] RSPM
 P lubridate    * 1.9.4      2024-12-08 [?] RSPM (R 4.5.0)
 P magrittr       2.0.3      2022-03-30 [?] RSPM (R 4.5.1)
 P MASS           7.3-65     2025-02-28 [?] CRAN (R 4.5.1)
 P Matrix         1.7-3      2025-03-11 [?] CRAN (R 4.5.1)
 P modeldata    * 1.5.0      2025-07-31 [?] RSPM
 P nnet           7.3-20     2025-01-01 [?] CRAN (R 4.5.1)
 P parallelly     1.45.1     2025-07-24 [?] RSPM
 P parsnip      * 1.3.2      2025-05-28 [?] RSPM
 P pillar         1.11.0     2025-07-04 [?] RSPM (R 4.5.1)
 P pkgconfig      2.0.3      2019-09-22 [?] RSPM (R 4.5.0)
 P plyr           1.8.9      2023-10-02 [?] RSPM (R 4.5.0)
 P png            0.1-8      2022-11-29 [?] RSPM (R 4.5.0)
 P prodlim        2025.04.28 2025-04-28 [?] RSPM
 P purrr        * 1.1.0      2025-07-10 [?] RSPM (R 4.5.0)
 P R6             2.6.1      2025-02-15 [?] RSPM (R 4.5.0)
 P RColorBrewer   1.1-3      2022-04-03 [?] RSPM (R 4.5.0)
 P Rcpp           1.1.0      2025-07-02 [?] RSPM (R 4.5.0)
 P readr        * 2.1.5      2024-01-10 [?] RSPM (R 4.5.0)
 P recipes      * 1.3.1      2025-05-21 [?] RSPM
   renv           1.1.5      2025-07-24 [1] RSPM (R 4.5.0)
 P repr           1.1.7      2024-03-22 [?] RSPM
 P reshape2       1.4.4      2020-04-09 [?] RSPM (R 4.5.0)
 P reticulate   * 1.43.0     2025-07-21 [?] CRAN (R 4.5.0)
 P rlang          1.1.6      2025-04-11 [?] RSPM (R 4.5.0)
 P rmarkdown      2.29       2024-11-04 [?] RSPM
 P rpart          4.1.24     2025-01-07 [?] CRAN (R 4.5.1)
 P rprojroot      2.1.0      2025-07-12 [?] RSPM (R 4.5.0)
 P rsample      * 1.3.1      2025-07-29 [?] RSPM
 P rstudioapi     0.17.1     2024-10-22 [?] RSPM (R 4.5.0)
 P S7             0.2.0      2024-11-07 [?] RSPM (R 4.5.0)
 P scales       * 1.4.0      2025-04-24 [?] RSPM (R 4.5.0)
 P sessioninfo    1.2.3      2025-02-05 [?] RSPM (R 4.5.0)
 P skimr        * 2.2.1      2025-07-26 [?] RSPM
 P stringi        1.8.7      2025-03-27 [?] RSPM (R 4.5.0)
 P stringr      * 1.5.1      2023-11-14 [?] RSPM (R 4.5.1)
 P survival       3.8-3      2024-12-17 [?] CRAN (R 4.5.1)
 P tibble       * 3.3.0      2025-06-08 [?] RSPM (R 4.5.0)
 P tidymodels   * 1.3.0      2025-02-21 [?] RSPM
 P tidyr        * 1.3.1      2024-01-24 [?] RSPM (R 4.5.0)
 P tidyselect     1.2.1      2024-03-11 [?] RSPM (R 4.5.0)
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 P timechange     0.3.0      2024-01-18 [?] RSPM (R 4.5.0)
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 P tune         * 1.3.0      2025-02-21 [?] RSPM
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 P utf8           1.2.6      2025-06-08 [?] RSPM (R 4.5.0)
 P vctrs          0.6.5      2023-12-01 [?] RSPM (R 4.5.0)
 P visdat       * 0.6.0      2023-02-02 [?] RSPM
 P vroom          1.6.5      2023-12-05 [?] RSPM (R 4.5.1)
 P withr          3.0.2      2024-10-28 [?] RSPM (R 4.5.0)
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 P workflowsets * 1.1.1      2025-05-27 [?] RSPM
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 P yaml           2.3.10     2024-07-26 [?] RSPM (R 4.5.0)
 P yardstick    * 1.3.2      2025-01-22 [?] RSPM

 [1] /Users/bcs88/Projects/info-4940/course-site/renv/library/macos/R-4.5/aarch64-apple-darwin20
 [2] /Users/bcs88/Library/Caches/org.R-project.R/R/renv/sandbox/macos/R-4.5/aarch64-apple-darwin20/4cd76b74

 * ── Packages attached to the search path.
 P ── Loaded and on-disk path mismatch.

─ Python configuration ───────────────────────────────────────────────────────
 python:         /Users/bcs88/Projects/info-4940/course-site/.venv/bin/python
 libpython:      /Users/bcs88/.local/share/uv/python/cpython-3.13.6-macos-aarch64-none/lib/libpython3.13.dylib
 pythonhome:     /Users/bcs88/Projects/info-4940/course-site/.venv:/Users/bcs88/Projects/info-4940/course-site/.venv
 virtualenv:     /Users/bcs88/Projects/info-4940/course-site/.venv/bin/activate_this.py
 version:        3.13.6 (main, Aug 14 2025, 16:07:26) [Clang 20.1.4 ]
 numpy:          /Users/bcs88/Projects/info-4940/course-site/.venv/lib/python3.13/site-packages/numpy
 numpy_version:  2.3.2
 
 NOTE: Python version was forced by VIRTUAL_ENV

──────────────────────────────────────────────────────────────────────────────

Footnotes

  1. Think of it as a recommendation engine for future customers who do a survey and taste test for three varieties of coffee.↩︎

  2. Not particularly helpful for this dataset, but a good practice to get into.↩︎