Exploratory Data Analysis (EDA) is the process of analyzing datasets to summarize their main characteristics, uncover patterns, detect anomalies, and test hypotheses before formal modeling. R provides many tools for both numerical and graphical exploration of data.<\/p>\n\n\n\n
Before starting EDA, understand the structure and contents of your data:<\/p>\n\n\n\n
# Load data
data <- read.csv(\"data.csv\")# View first rows
head(data)# Structure and types
str(data)# Summary statistics
summary(data)<\/pre>\n\n\n\n2. Univariate Analysis<\/strong><\/h1>\n\n\n\n
Univariate analysis examines one variable at a time.<\/p>\n\n\n\n
a) Numerical Variables<\/strong><\/h3>\n\n\n\n
# Histogram
hist(data$Age, main=\"Age Distribution\", xlab=\"Age\", col=\"skyblue\", border=\"black\")# Boxplot
boxplot(data$PurchaseAmount, main=\"Purchase Amount Boxplot\", ylab=\"Amount\", col=\"lightgreen\")# Summary statistics
mean(data$PurchaseAmount)
median(data$PurchaseAmount)
sd(data$PurchaseAmount)<\/pre>\n\n\n\nb) Categorical Variables<\/strong><\/h3>\n\n\n\n
# Frequency table
table(data$Gender)# Proportions
prop.table(table(data$Gender))# Bar plot
barplot(table(data$Gender), main=\"Gender Distribution\", col=\"orange\")<\/pre>\n\n\n\n3. Bivariate Analysis<\/strong><\/h1>\n\n\n\n
Bivariate analysis examines relationships between two variables.<\/p>\n\n\n\n
a) Numerical vs Numerical<\/strong><\/h3>\n\n\n\n
# Scatter plot
plot(data$Age, data$PurchaseAmount, main=\"Age vs Purchase Amount\", xlab=\"Age\", ylab=\"Purchase Amount\", pch=19, col=\"blue\")# Correlation
cor(data$Age, data$PurchaseAmount)<\/pre>\n\n\n\nb) Numerical vs Categorical<\/strong><\/h3>\n\n\n\n
# Boxplot by category
boxplot(PurchaseAmount ~ Gender, data=data, main=\"Purchase by Gender\", ylab=\"Purchase Amount\", col=c(\"pink\",\"lightblue\"))<\/pre>\n\n\n\nc) Categorical vs Categorical<\/strong><\/h3>\n\n\n\n
# Contingency table
table(data$Gender, data$ProductCategory)# Mosaic plot
mosaicplot(table(data$Gender, data$ProductCategory), color=TRUE, main=\"Gender vs Product Category\")<\/pre>\n\n\n\n4. Using dplyr and ggplot2 for EDA<\/strong><\/h1>\n\n\n\n
dplyr<\/code> andggplot2<\/code> allow more powerful and flexible data exploration.<\/p>\n\n\n\nlibrary(dplyr)
library(ggplot2)# Summary by group
data %>%
group_by(Gender) %>%
summarise(
AvgPurchase = mean(PurchaseAmount),
MaxPurchase = max(PurchaseAmount),
MinPurchase = min(PurchaseAmount)
)# Scatter plot with ggplot2
ggplot(data, aes(x=Age, y=PurchaseAmount, color=Gender)) +
geom_point(size=3) +
ggtitle(\"Age vs Purchase Amount by Gender\")<\/pre>\n\n\n\n5. Detecting Outliers and Missing Values<\/strong><\/h1>\n\n\n\n
# Missing values
colSums(is.na(data))# Boxplot for outliers
boxplot(data$PurchaseAmount)<\/pre>\n\n\n\n6. Advantages of EDA<\/strong><\/h1>\n\n\n\n
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- Understand data distributions and relationships<\/li>\n\n\n\n
- Detect anomalies, outliers, and missing values<\/li>\n\n\n\n
- Generate hypotheses for modeling<\/li>\n\n\n\n
- Guide feature selection and transformation<\/li>\n<\/ul>\n\n\n\n
Conclusion<\/strong><\/h1>\n\n\n\n
Exploratory Data Analysis (EDA) is a crucial first step in any data analysis project. By summarizing data, visualizing patterns, and detecting issues, EDA provides insights that inform modeling and decision-making. Tools in R, including base functions,
dplyr<\/code>, andggplot2<\/code>, make EDA efficient, flexible, and visually informative.<\/p>\n\n\n