10 Advanced Analytical Techniques You Can Perform in R Assignments
R is the most popular and commonly used statistical software performing statistical calculations and graphical visualizations in the sphere of data analysis and research. For students, learning R and its powerful techniques can immensely help to conduct data research in their coursework and assignments. This guide explains the 10 most complex analysis that one can perform in R with examples and coding illustrations.
Linear regression is one of the most basic techniques of statistical modeling. It quantifies the relation between a dependent variable and one or more independent variables.
# Perform linear regression
model <- lm(mpg ~ wt + hp, data = mtcars)
In this example, we use the mtcars dataset to perform a linear regression where mpg (miles per gallon) is the dependent variable, and wt (weight) and hp (horsepower) are the independent variables. The summary function provides detailed statistics about the model.
Logistic regression is used for problems involving binary classification. It estimates the probability of an event belonging to one of two possible classes based on one or more predictor variables.
# Perform logistic regression
logit_model <- glm(type ~ npreg + glu + bp, data = Pima.tr, family =
Using the Pima.tr dataset from the MASS package, we perform logistic regression to predict diabetes (type) based on predictors like the number of pregnancies (npreg), glucose
concentration (glu), and blood pressure (bp).
The process of time series analysis focuses on observation of data that is chronological in nature to understand the patterns and forecast values.
# Generate sample time series data
ts_data <- ts(rnorm(100), frequency = 12)
# Perform time series analysis
fit <- auto.arima(ts_data)
We generate random time series data and use the auto.arima function from the forecast package to fit an ARIMA model, which is then used to forecast future values.
Cluster Analysis groups data points together on the basis of similarities between the points. K-means clustering is one of the most used clustering techniques.
# Perform K-means clustering
kmeans_result <- kmeans(iris[, -5], centers = 3)
clusplot(iris[, -5], kmeans_result$cluster, color = TRUE, shade = TRUE)
We use the iris dataset and perform K-means clustering to group the data into three clusters. The clusplot function visualizes the clusters.
5. Principal Component Analysis (PCA)
PCA serves to minimize the dimensions of data and at the same time retain as much variation of the data as possible. It is helpful to visualize data with high dimensionality.
pca_result <- prcomp(iris[, -5], center = TRUE, scale. = TRUE)
biplot(pca_result, scale = 0)
Using the iris dataset, we perform PCA and visualize the principal components using a biplot. This helps in understanding the variance explained by each principal component.
Survival analysis is concerned with the time to an event or until the event occurs. It is widely applied in medical studies.
# Perform survival analysis
surv_fit <- survfit(Surv(time, status) ~ sex, data = lung)
# Plot the survival curve
plot(surv_fit, col = c("red", "blue"), lty = 1:2, xlab = "Time", ylab =
Using the lung dataset, we perform survival analysis and plot the survival curves for different sexes using the survfit function.
One of the most used techniques in AI is Bayesian analysis which involves using prior knowledge along with new data to update probabilities.
data <- list(y = rnorm(100, mean = 5, sd = 2), N = 100)
model <- jags.model(textConnection(model_string), data = data, n.chains =
samples <- coda.samples(model, variable.names = c("mu", "sigma"), n.iter =
We define a Bayesian model using JAGS and perform MCMC sampling to estimate the parameters. This approach is powerful for incorporating prior beliefs and handling complex models.
Decision tree is a non-parametric model applied in classification and regression analysis. They divided the data into subsets according to feature values.
tree_model <- rpart(Species ~ ., data = iris)
text(tree_model, pretty = 0)
Using the iris dataset, we train a decision tree to classify species. The tree is visualized to show the splits and decision rules.
Random forest can be defined as an advanced machine learning technique that uses multiple decision trees and combines them to enhance accuracy and reduce overfitting..
rf_model <- randomForest(Species ~ ., data = iris, ntree = 100)
We use the iris dataset to train a random forest model with 100 trees. The randomForest function builds and combines multiple decision trees for robust predictions.
Neural networks are a set of algorithms that have been designed in the manner of functioning like the human brain to solve problems.
nn_model <- nnet(Species ~ ., data = iris, size = 5, maxit = 100)
Using the iris dataset, we train a neural network with five hidden units. The nnet function from the nnet package is used to create the model.
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1. Wickham, H., & Grolemund, G. (2017). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O'Reilly Media.
2. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning: With Applications in R. Springer.
