Wine Quality Analysis

DATA 607: Week 1 Assignment Examplar

Author

Published

September 1, 2026

Wine Quality Analysis

Approach

In this assignment, I selected a dataset from the UCI Machine Learning Repository that contains information about the chemical attributes of wine. The dataset contains 11 features and provides a useful opportunity to practice working with numerical data, feature selection, and classification or regression analysis.

My plan for this assignment is to load the data into R and create a label for the different wine types to use in a later classification task.

I will also perform some simple exploratory data analysis (EDA) on the datasets.

Data source:
https://raw.githubusercontent.com/DW8888/Wine-Quality/refs/heads/main/winequality-white.csv https://raw.githubusercontent.com/DW8888/Wine-Quality/refs/heads/main/winequality-red.csv

Cortez, P., Cerdeira, A., Almeida, F., Matos, T., & Reis, J. (2009). Wine Quality [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C56S3T.

Research Question

Can the chemical attributes of a wine be used to classify it as red or white?

Codebase

First, we need to import the library and datasets:

Code

# Getting the tidyverse package
library("tidyverse")

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Code

# Importing the data: the dataset uses semicolons as delimiters, so I specify that in the read function.

red_wines<- read.csv("winequality-red.csv", sep=",")
white_wines<- read.csv("winequality-white.csv" , sep=",")

Let’s inspect the structure of the datasets.

Code

str(red_wines)

'data.frame':   1599 obs. of  12 variables:
 $ fixed.acidity       : num  7.4 7.8 7.8 11.2 7.4 7.4 7.9 7.3 7.8 7.5 ...
 $ volatile.acidity    : num  0.7 0.88 0.76 0.28 0.7 0.66 0.6 0.65 0.58 0.5 ...
 $ citric.acid         : num  0 0 0.04 0.56 0 0 0.06 0 0.02 0.36 ...
 $ residual.sugar      : num  1.9 2.6 2.3 1.9 1.9 1.8 1.6 1.2 2 6.1 ...
 $ chlorides           : num  0.076 0.098 0.092 0.075 0.076 0.075 0.069 0.065 0.073 0.071 ...
 $ free.sulfur.dioxide : num  11 25 15 17 11 13 15 15 9 17 ...
 $ total.sulfur.dioxide: num  34 67 54 60 34 40 59 21 18 102 ...
 $ density             : num  0.998 0.997 0.997 0.998 0.998 ...
 $ pH                  : num  3.51 3.2 3.26 3.16 3.51 3.51 3.3 3.39 3.36 3.35 ...
 $ sulphates           : num  0.56 0.68 0.65 0.58 0.56 0.56 0.46 0.47 0.57 0.8 ...
 $ alcohol             : num  9.4 9.8 9.8 9.8 9.4 9.4 9.4 10 9.5 10.5 ...
 $ quality             : int  5 5 5 6 5 5 5 7 7 5 ...

Code

str(white_wines)

'data.frame':   4898 obs. of  12 variables:
 $ fixed.acidity       : num  7 6.3 8.1 7.2 7.2 8.1 6.2 7 6.3 8.1 ...
 $ volatile.acidity    : num  0.27 0.3 0.28 0.23 0.23 0.28 0.32 0.27 0.3 0.22 ...
 $ citric.acid         : num  0.36 0.34 0.4 0.32 0.32 0.4 0.16 0.36 0.34 0.43 ...
 $ residual.sugar      : num  20.7 1.6 6.9 8.5 8.5 6.9 7 20.7 1.6 1.5 ...
 $ chlorides           : num  0.045 0.049 0.05 0.058 0.058 0.05 0.045 0.045 0.049 0.044 ...
 $ free.sulfur.dioxide : num  45 14 30 47 47 30 30 45 14 28 ...
 $ total.sulfur.dioxide: num  170 132 97 186 186 97 136 170 132 129 ...
 $ density             : num  1.001 0.994 0.995 0.996 0.996 ...
 $ pH                  : num  3 3.3 3.26 3.19 3.19 3.26 3.18 3 3.3 3.22 ...
 $ sulphates           : num  0.45 0.49 0.44 0.4 0.4 0.44 0.47 0.45 0.49 0.45 ...
 $ alcohol             : num  8.8 9.5 10.1 9.9 9.9 10.1 9.6 8.8 9.5 11 ...
 $ quality             : int  6 6 6 6 6 6 6 6 6 6 ...

Manipulation

I want to create a label that I can use to predict wine color.

Code

# Create a factor column, assign 1 to red wine, and then combine the datasets.
red_wines$is_red <- factor(1, levels = c(0, 1), labels = c("white", "red"))

white_wines$is_red <- factor(0, levels = c(0, 1), labels = c("white", "red"))

wines <- rbind(red_wines, white_wines)
print(head(wines))

  fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
1           7.4             0.70        0.00            1.9     0.076
2           7.8             0.88        0.00            2.6     0.098
3           7.8             0.76        0.04            2.3     0.092
4          11.2             0.28        0.56            1.9     0.075
5           7.4             0.70        0.00            1.9     0.076
6           7.4             0.66        0.00            1.8     0.075
  free.sulfur.dioxide total.sulfur.dioxide density   pH sulphates alcohol
1                  11                   34  0.9978 3.51      0.56     9.4
2                  25                   67  0.9968 3.20      0.68     9.8
3                  15                   54  0.9970 3.26      0.65     9.8
4                  17                   60  0.9980 3.16      0.58     9.8
5                  11                   34  0.9978 3.51      0.56     9.4
6                  13                   40  0.9978 3.51      0.56     9.4
  quality is_red
1       5    red
2       5    red
3       5    red
4       6    red
5       5    red
6       5    red

Here is a summary of the data:

Code

summary(wines)

 fixed.acidity    volatile.acidity  citric.acid     residual.sugar  
 Min.   : 3.800   Min.   :0.0800   Min.   :0.0000   Min.   : 0.600  
 1st Qu.: 6.400   1st Qu.:0.2300   1st Qu.:0.2500   1st Qu.: 1.800  
 Median : 7.000   Median :0.2900   Median :0.3100   Median : 3.000  
 Mean   : 7.215   Mean   :0.3397   Mean   :0.3186   Mean   : 5.443  
 3rd Qu.: 7.700   3rd Qu.:0.4000   3rd Qu.:0.3900   3rd Qu.: 8.100  
 Max.   :15.900   Max.   :1.5800   Max.   :1.6600   Max.   :65.800  
   chlorides       free.sulfur.dioxide total.sulfur.dioxide    density      
 Min.   :0.00900   Min.   :  1.00      Min.   :  6.0        Min.   :0.9871  
 1st Qu.:0.03800   1st Qu.: 17.00      1st Qu.: 77.0        1st Qu.:0.9923  
 Median :0.04700   Median : 29.00      Median :118.0        Median :0.9949  
 Mean   :0.05603   Mean   : 30.53      Mean   :115.7        Mean   :0.9947  
 3rd Qu.:0.06500   3rd Qu.: 41.00      3rd Qu.:156.0        3rd Qu.:0.9970  
 Max.   :0.61100   Max.   :289.00      Max.   :440.0        Max.   :1.0390  
       pH          sulphates         alcohol         quality        is_red    
 Min.   :2.720   Min.   :0.2200   Min.   : 8.00   Min.   :3.000   white:4898  
 1st Qu.:3.110   1st Qu.:0.4300   1st Qu.: 9.50   1st Qu.:5.000   red  :1599  
 Median :3.210   Median :0.5100   Median :10.30   Median :6.000               
 Mean   :3.219   Mean   :0.5313   Mean   :10.49   Mean   :5.818               
 3rd Qu.:3.320   3rd Qu.:0.6000   3rd Qu.:11.30   3rd Qu.:6.000               
 Max.   :4.010   Max.   :2.0000   Max.   :14.90   Max.   :9.000

Code

numeric_vars <- names(wines)[sapply(wines, is.numeric)]

par(mfrow = c(5, 3), mar = c(3, 3, 2,1))
# Iterate through the list and plot the variables.

for (variable in numeric_vars) {
  hist(
    wines[[variable]],
    main = variable,
    xlab = "",
    col = "skyblue",
    border = "white"
  )
}

Code

barplot(
  table(wines$is_red),
  main = "Wine type",
  xlab = "is_red",
  ylab = "Count",
  col = c("lightyellow", "maroon"))

Tidyverse Comparison

The following summary compares the number of wines, average alcohol content, and average quality for each wine type.

Code

wines |>
  group_by(is_red) |>
  summarise(
    number_of_wines = n(),
    average_alcohol = round(mean(alcohol), 2),
    average_quality = round(mean(quality), 2)
  )

# A tibble: 2 × 4
  is_red number_of_wines average_alcohol average_quality
  <fct>            <int>           <dbl>           <dbl>
1 white             4898            10.5            5.88
2 red               1599            10.4            5.64

Code

ggplot(wines, aes(x = is_red, y = alcohol, fill = is_red)) +
  geom_boxplot() +
  scale_fill_manual(values = c('white' = 'lightyellow', 'red' = 'maroon')) +
  labs(
    title = "Alcohol Content by Wine Type",
    x = "Wine type",
    y = "Alcohol content"
  ) +
  theme_minimal() +
  theme(legend.position = "none")

Thoughts

The dataset has no missing values and contains 11 numerical variables for each wine. I added a new variable to indicate whether the wine is red or white. Later, I plan to build a classification model that predicts whether a wine is red or white based on its features. It is important to note that the dataset has a class imbalance between red and white wines, which I will need to account for. Many of the variables do not follow a normal distribution and would have to be normalized if we would like to do some sort of linear regression modeling.

AI Use

OpenAI Codex was used as an AI-assisted development tool for code development, debugging, and refinement. Its suggestions and generated code were reviewed, tested, and revised by the author as needed. The author remains responsible for the submitted analysis, code, and conclusions.

Tool/model: OpenAI Codex / GPT-5.6 Terra Developer: OpenAI
Date accessed: September 1, 2026