Wine Data Analysis
Madhurima Sarkar
2025-09-15
library(dplyr) ##
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(ggplot2) wine <- read.csv("/Users/madhurimasarkar/Documents/MS Uni/1. Applied Stats Method - BANA7095/3. Assignment - Wine Data/winequality-red.csv",header = TRUE,sep = ";")head(wine,10)## 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
## 7 7.9 0.60 0.06 1.6 0.069
## 8 7.3 0.65 0.00 1.2 0.065
## 9 7.8 0.58 0.02 2.0 0.073
## 10 7.5 0.50 0.36 6.1 0.071
## 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
## 7 15 59 0.9964 3.30 0.46 9.4
## 8 15 21 0.9946 3.39 0.47 10.0
## 9 9 18 0.9968 3.36 0.57 9.5
## 10 17 102 0.9978 3.35 0.80 10.5
## quality
## 1 5
## 2 5
## 3 5
## 4 6
## 5 5
## 6 5
## 7 5
## 8 7
## 9 7
## 10 5a. What is the sample size?
str(wine)## '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 ...Sample Size: - 1599
b. Any outliers? Do you have any concerns about the data quality?
wine_box <- wine %>%
select_if(is.numeric) %>%
pivot_longer(cols = everything(),
names_to = "variable",
values_to = "value")%>%{ ggplot(., aes(y = value)) +
geom_boxplot(fill = "skyblue", color = "black") +
facet_wrap(~ variable, scales = "free_y") +
theme_minimal() +
labs(title = "Boxplots of Numeric Variables",
x = NULL, y = "Value")
}wine_box
Observations:
- All the variables have outliers.
- Data Quality Concerns exist due to all variables having outliers and higher number of outliers for sulphates,chlorides and free.sulfur.dioxide.
c. How can you summarize the data of each variable in a concise way? What statistics are you going to present?
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 density
## Min. :0.01200 Min. : 1.00 Min. : 6.00 Min. :0.9901
## 1st Qu.:0.07000 1st Qu.: 7.00 1st Qu.: 22.00 1st Qu.:0.9956
## Median :0.07900 Median :14.00 Median : 38.00 Median :0.9968
## Mean :0.08747 Mean :15.87 Mean : 46.47 Mean :0.9967
## 3rd Qu.:0.09000 3rd Qu.:21.00 3rd Qu.: 62.00 3rd Qu.:0.9978
## Max. :0.61100 Max. :72.00 Max. :289.00 Max. :1.0037
## pH sulphates alcohol quality
## Min. :2.740 Min. :0.3300 Min. : 8.40 Min. :3.000
## 1st Qu.:3.210 1st Qu.:0.5500 1st Qu.: 9.50 1st Qu.:5.000
## Median :3.310 Median :0.6200 Median :10.20 Median :6.000
## Mean :3.311 Mean :0.6581 Mean :10.42 Mean :5.636
## 3rd Qu.:3.400 3rd Qu.:0.7300 3rd Qu.:11.10 3rd Qu.:6.000
## Max. :4.010 Max. :2.0000 Max. :14.90 Max. :8.000Observations:
- Mean is greater than Median for most of the variables making the data rightly skewed.
- Range = Max - Min, Range is high for residual_sugar,free.sulfur.dioxide, total.sulfur.dioxide.Hence,spread of data for these variable is very high
d. How can you visualize the distribution of each variable?
Visualising Histogram for each variables for checking skewness:
wine_hist <- wine %>%
select_if(is.numeric) %>%
pivot_longer(cols = everything(),
names_to = "variable",
values_to = "value") %>%
{ ggplot(., aes(x = value)) +
geom_histogram(bins = 30, fill = "skyblue", color = "black") +
facet_wrap(~variable, scales = "free_x") +
theme_minimal()
}wine_hist
Observations :-
- Data is positively skewed for 10 out of 12 variables, meaning most of the values for these variables are concentrated at lower values but very few values are large which are pulling the mean to the right.
e. Do you see any skewed distributions?
Data for all the variables in the red wine dataset is right skewed (mean > median) except for the following variables:
- density : normally distributed (mean(0.9967) and median(0.9968) being almost same)
- pH :normally distributed (mean(3.311) and median(3.3110) )
- Quality has mean < median, making it left skewed.