Base R provides fundamental functions for data manipulation and analysis, while the tidyverse is a collection of packages like dplyr and ggplot2 designed for streamlined data wrangling and visualization. I use base R for simple tasks or when working in environments with limited resources. The tidyverse is my go-to for complex data manipulation, as its syntax is more intuitive and consistent. For example, I use dplyr for data transformation and ggplot2 for creating advanced visualizations. Both have their place, and I choose based on the task's complexity and the need for readability and efficiency.

Handling missing data in R depends on the context. I typically start by identifying missing values using functions like is.na() or complete.cases(). For small datasets, I might remove rows with missing values using na.omit(). For larger datasets, I often use imputation methods like mean, median, or predictive modeling with packages like mice or Amelia. In exploratory analysis, I might flag missing values for further investigation. The approach varies based on the dataset size, the proportion of missing data, and the analysis goals, ensuring the integrity of the results.

Optimizing R code involves several strategies. I start by vectorizing operations to avoid loops, as vectorized functions are faster. I also use efficient data structures like data.table for large datasets. Profiling tools like Rprof help identify bottlenecks. For computationally intensive tasks, I leverage parallel processing with packages like parallel or foreach. Additionally, I avoid unnecessary object copying and preallocate memory for large objects. Writing efficient code not only improves performance but also reduces resource consumption, which is crucial when working with big data or in production environments.

To create a correlation matrix, I use the cor() function, passing the dataset as an argument. For visualization, I use corrplot or ggplot2 to plot the matrix. The values range from -1 to 1, indicating negative to positive correlations. I interpret the matrix by identifying strong correlations (close to 1 or -1) and weak ones (close to 0). For example, a high positive correlation between two variables suggests a strong relationship. I also check for multicollinearity in regression models. Understanding these relationships helps in feature selection and identifying patterns in the data.

I have extensive experience building Shiny apps for interactive data visualization and analysis. To create a basic Shiny app, I start by defining the UI using fluidPage() and server logic using server(). For example, I might create a dropdown menu for user input and a plot output that updates dynamically. I use reactive programming to ensure the app responds to user inputs efficiently. Additionally, I deploy apps using shinyapps.io or RStudio Connect. Shiny is a powerful tool for creating user-friendly interfaces, and I enjoy leveraging its capabilities to make data insights accessible to non-technical stakeholders.