Red Wine Quality Analysis
Shubham Agrawal
13 April 2019
Introduction
Wine is an alcoholic drink made from fermented grapes. Yeast consumes the sugar in the grapes and converts it to ethanol, carbon dioxide, and heat. Different varieties of grapes and strains of yeasts produce different styles of wine. These variations result from the complex interactions between the biochemical development of the grape, the reactions involved in fermentation, the terroir, and the production process. Many countries enact legal appellations intended to define styles and qualities of wine. These typically restrict the geographical origin and permitted varieties of grapes, as well as other aspects of wine production. Wines not made from grapes include rice wine and fruit wines such as plum, cherry, pomegranate, currant and elderberry. Source: Wikipedia
The dataset for the following analysis is collected from the UCI Machine Learning Repository. The two datasets are related to red and white variants of the Portuguese “Vinho Verde” wine. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.).
Exploratory Data Analysis
First, load the dataset.
library(dplyr)
library(gridExtra)
library(knitr)
library(kableExtra)
library(ggplot2)
library(corrplot)
library(GGally)
library(pander)
wine <- read.csv("winequality-red.csv", sep = ';')
summary(wine)## fixed.acidity volatile.acidity citric.acid residual.sugar
## Min. : 4.60 Min. :0.1200 Min. :0.000 Min. : 0.900
## 1st Qu.: 7.10 1st Qu.:0.3900 1st Qu.:0.090 1st Qu.: 1.900
## Median : 7.90 Median :0.5200 Median :0.260 Median : 2.200
## Mean : 8.32 Mean :0.5278 Mean :0.271 Mean : 2.539
## 3rd Qu.: 9.20 3rd Qu.:0.6400 3rd Qu.:0.420 3rd Qu.: 2.600
## Max. :15.90 Max. :1.5800 Max. :1.000 Max. :15.500
## chlorides free.sulfur.dioxide total.sulfur.dioxide
## Min. :0.01200 Min. : 1.00 Min. : 6.00
## 1st Qu.:0.07000 1st Qu.: 7.00 1st Qu.: 22.00
## Median :0.07900 Median :14.00 Median : 38.00
## Mean :0.08747 Mean :15.87 Mean : 46.47
## 3rd Qu.:0.09000 3rd Qu.:21.00 3rd Qu.: 62.00
## Max. :0.61100 Max. :72.00 Max. :289.00
## density pH sulphates alcohol
## Min. :0.9901 Min. :2.740 Min. :0.3300 Min. : 8.40
## 1st Qu.:0.9956 1st Qu.:3.210 1st Qu.:0.5500 1st Qu.: 9.50
## Median :0.9968 Median :3.310 Median :0.6200 Median :10.20
## Mean :0.9967 Mean :3.311 Mean :0.6581 Mean :10.42
## 3rd Qu.:0.9978 3rd Qu.:3.400 3rd Qu.:0.7300 3rd Qu.:11.10
## Max. :1.0037 Max. :4.010 Max. :2.0000 Max. :14.90
## quality
## Min. :3.000
## 1st Qu.:5.000
## Median :6.000
## Mean :5.636
## 3rd Qu.:6.000
## Max. :8.000kable(head(wine), "html") %>%
kable_styling("striped") %>%
scroll_box(width = "100%")| fixed.acidity | volatile.acidity | citric.acid | residual.sugar | chlorides | free.sulfur.dioxide | total.sulfur.dioxide | density | pH | sulphates | alcohol | quality |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11 | 34 | 0.9978 | 3.51 | 0.56 | 9.4 | 5 |
| 7.8 | 0.88 | 0.00 | 2.6 | 0.098 | 25 | 67 | 0.9968 | 3.20 | 0.68 | 9.8 | 5 |
| 7.8 | 0.76 | 0.04 | 2.3 | 0.092 | 15 | 54 | 0.9970 | 3.26 | 0.65 | 9.8 | 5 |
| 11.2 | 0.28 | 0.56 | 1.9 | 0.075 | 17 | 60 | 0.9980 | 3.16 | 0.58 | 9.8 | 6 |
| 7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11 | 34 | 0.9978 | 3.51 | 0.56 | 9.4 | 5 |
| 7.4 | 0.66 | 0.00 | 1.8 | 0.075 | 13 | 40 | 0.9978 | 3.51 | 0.56 | 9.4 | 5 |
Univariate Analysis
p1 <- ggplot(wine) + geom_histogram(aes(alcohol), color="black", fill="#ce2d4f")
p2 <- ggplot(wine) + geom_histogram(aes(chlorides), color="black", fill="#ce6d8b")
p3 <- ggplot(wine) + geom_histogram(aes(citric.acid), color="black", fill="#cebbc9")
p4 <- ggplot(wine) + geom_histogram(aes(density), color="black", fill="#4056f4")
p5 <- ggplot(wine) + geom_histogram(aes(fixed.acidity), color="black", fill="#470ff4")
p6 <- ggplot(wine) + geom_histogram(aes(free.sulfur.dioxide), color="black", fill="#e54b4b")
p7 <- ggplot(wine) + geom_histogram(aes(pH), color="black", fill="#ffa987")
p8 <- ggplot(wine) + geom_histogram(aes(quality), color="black", fill="#c8d5b9")
p9 <- ggplot(wine) + geom_histogram(aes(residual.sugar), color="black", fill="#4a7c59")
p10 <- ggplot(wine) + geom_histogram(aes(sulphates), color="black", fill="#c4b7cb")
p11 <- ggplot(wine) + geom_histogram(aes(total.sulfur.dioxide), color="black", fill="#98e2c6")
p12 <- ggplot(wine) + geom_histogram(aes(volatile.acidity), color="black", fill="#06bee1")
grid.arrange(p1, p2, p3, p4, ncol= 2)
grid.arrange(p5, p6, p7, p8, ncol= 2)
grid.arrange(p9, p10, p11, p12, ncol= 2)
Observations:
- some ofthe variables have normal distributions (density, fixed acidity, pH, volatile acidity).
- some variables are skewed little bit towards the lower end of the values (chlorides, citric acid, residual sugar, total sulfur dioxide).
- variable quality has only 6 discrete values.
wine$rating[5 >= wine$quality] <- 'Average'
wine$rating[5 < wine$quality & wine$quality <8] <- 'Good'
wine$rating[8 <= wine$quality] <- 'Excellent'
wine$rating <- as.factor(wine$rating)
wine$rating <- relevel(wine$rating, 'Average')
qplot(x=rating,data = wine)
Bivariate Analysis
Now, we will explore each variable with respect to quality
p1 <- ggplot(wine, aes(group = cut_width(quality, 1))) +
geom_boxplot(aes(quality, alcohol), colour = "#9cb380")
p2 <- ggplot(wine, aes(group = cut_width(quality, 1))) +
geom_boxplot(aes(quality, chlorides), colour = "#522a27")
p3 <- ggplot(wine, aes(group = cut_width(quality, 1))) +
geom_boxplot(aes(quality, citric.acid), colour = "#c73e1d")
p4 <- ggplot(wine, aes(group = cut_width(quality, 1))) +
geom_boxplot(aes(quality, density), colour = "#c59849")
p5 <- ggplot(wine, aes(group = cut_width(quality, 1))) +
geom_boxplot(aes(quality, fixed.acidity), colour = "#1f2041")
p6 <- ggplot(wine, aes(group = cut_width(quality, 1))) +
geom_boxplot(aes(quality, free.sulfur.dioxide), colour = "#4b3f72")
p7 <- ggplot(wine, aes(group = cut_width(quality, 1))) +
geom_boxplot(aes(quality, pH), colour = "#417b5a")
p8 <- ggplot(wine, aes(group = cut_width(quality, 1))) +
geom_boxplot(aes(quality, residual.sugar), colour = "#417b5a")
p9 <- ggplot(wine, aes(group = cut_width(quality, 1))) +
geom_boxplot(aes(quality, sulphates), colour = "#17183b")
p10 <- ggplot(wine, aes(group = cut_width(quality, 1))) +
geom_boxplot(aes(quality, total.sulfur.dioxide), colour = "#a11692")
p11 <- ggplot(wine, aes(group = cut_width(quality, 1))) +
geom_boxplot(aes(quality, volatile.acidity), colour = "#a11692")
grid.arrange(p1, p2, ncol= 2)
grid.arrange(p3, p4, ncol= 2)
grid.arrange(p5, p6, ncol= 2)
grid.arrange(p7, p8, ncol= 2)
grid.arrange(p9, p10, ncol= 2)
grid.arrange(p11, ncol= 2)
Observations:
- Chlorides have a minimal effect on the Quality of wine.
- Citric acid seems to have a positive correlation with Wine Quality. Better wines have higher Citric Acid.
- Better wines seems to have lower densities. But may be it will be wise not to draw any conclusions here. Because there might be a possibility that the low density is due to higher alcohol content which actually is the driving factor for better wines.
- Fixed Acidity has almost no effect on the Quality. The median values of fixed acidity remains almost unchanged with increase in quality.
- Too low concentration of Free Sulphur Dioxide produces poor wine and too high concentration results in average wine.
- Residual Sugar almost has no effect on the Quality of the Wine.
- Volatile acid seems to have a negative impact on the quality of the wine. As volatile acid level goes up, the quality of the wine degrades.
Let’s view the correlation plot to get more insights about the relationships between different variables.
corMatrix <- cor(wine[, -13])
corrplot(corMatrix, order = "FPC", method = "color", type = "lower",
tl.cex = 0.6, tl.col = 'black')
emphasize.strong.cells(which(abs(corMatrix) > .3 & corMatrix != 1, arr.ind = TRUE))
pandoc.table(corMatrix)##
## ---------------------------------------------------------------------------
## fixed.acidity volatile.acidity citric.acid
## -------------------------- --------------- ------------------ -------------
## **fixed.acidity** 1 -0.2561 **0.6717**
##
## **volatile.acidity** -0.2561 1 **-0.5525**
##
## **citric.acid** **0.6717** **-0.5525** 1
##
## **residual.sugar** 0.1148 0.001918 0.1436
##
## **chlorides** 0.09371 0.0613 0.2038
##
## **free.sulfur.dioxide** -0.1538 -0.0105 -0.06098
##
## **total.sulfur.dioxide** -0.1132 0.07647 0.03553
##
## **density** **0.668** 0.02203 **0.3649**
##
## **pH** **-0.683** 0.2349 **-0.5419**
##
## **sulphates** 0.183 -0.261 **0.3128**
##
## **alcohol** -0.06167 -0.2023 0.1099
##
## **quality** 0.1241 **-0.3906** 0.2264
## ---------------------------------------------------------------------------
##
## Table: Table continues below
##
##
## ------------------------------------------------------------------------------
## residual.sugar chlorides free.sulfur.dioxide
## -------------------------- ---------------- ------------ ---------------------
## **fixed.acidity** 0.1148 0.09371 -0.1538
##
## **volatile.acidity** 0.001918 0.0613 -0.0105
##
## **citric.acid** 0.1436 0.2038 -0.06098
##
## **residual.sugar** 1 0.05561 0.187
##
## **chlorides** 0.05561 1 0.005562
##
## **free.sulfur.dioxide** 0.187 0.005562 1
##
## **total.sulfur.dioxide** 0.203 0.0474 **0.6677**
##
## **density** **0.3553** 0.2006 -0.02195
##
## **pH** -0.08565 -0.265 0.07038
##
## **sulphates** 0.005527 **0.3713** 0.05166
##
## **alcohol** 0.04208 -0.2211 -0.06941
##
## **quality** 0.01373 -0.1289 -0.05066
## ------------------------------------------------------------------------------
##
## Table: Table continues below
##
##
## -----------------------------------------------------------------------------
## total.sulfur.dioxide density pH
## -------------------------- ---------------------- ------------- -------------
## **fixed.acidity** -0.1132 **0.668** **-0.683**
##
## **volatile.acidity** 0.07647 0.02203 0.2349
##
## **citric.acid** 0.03553 **0.3649** **-0.5419**
##
## **residual.sugar** 0.203 **0.3553** -0.08565
##
## **chlorides** 0.0474 0.2006 -0.265
##
## **free.sulfur.dioxide** **0.6677** -0.02195 0.07038
##
## **total.sulfur.dioxide** 1 0.07127 -0.06649
##
## **density** 0.07127 1 **-0.3417**
##
## **pH** -0.06649 **-0.3417** 1
##
## **sulphates** 0.04295 0.1485 -0.1966
##
## **alcohol** -0.2057 **-0.4962** 0.2056
##
## **quality** -0.1851 -0.1749 -0.05773
## -----------------------------------------------------------------------------
##
## Table: Table continues below
##
##
## -------------------------------------------------------------------
## sulphates alcohol quality
## -------------------------- ------------ ------------- -------------
## **fixed.acidity** 0.183 -0.06167 0.1241
##
## **volatile.acidity** -0.261 -0.2023 **-0.3906**
##
## **citric.acid** **0.3128** 0.1099 0.2264
##
## **residual.sugar** 0.005527 0.04208 0.01373
##
## **chlorides** **0.3713** -0.2211 -0.1289
##
## **free.sulfur.dioxide** 0.05166 -0.06941 -0.05066
##
## **total.sulfur.dioxide** 0.04295 -0.2057 -0.1851
##
## **density** 0.1485 **-0.4962** -0.1749
##
## **pH** -0.1966 0.2056 -0.05773
##
## **sulphates** 1 0.09359 0.2514
##
## **alcohol** 0.09359 1 **0.4762**
##
## **quality** 0.2514 **0.4762** 1
## -------------------------------------------------------------------Observations:
- As expected, we see a strong correlation among variables representing acidity like citric acid, pH, volatile acidity and fixed acidity.
- Volatile acidity has a positive correlation with pH. But, we know that as acidity increases, pH value decreases. This paradoxical relationship needs to be further investigated.
- Density has a very strong correlation with fixed acidity.
- The variables most strongly correlated to quality are volatile acidity and alcohol.
- Alcohol has negative correlation with density. This is evident from the fact that the density of water is greater than the density of alcohol.