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.
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:
# Getting the tidyverse packagelibrary("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
# 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=";")
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
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" )}
# 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
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.
