To avoid infinite loops when using a while loop in R, you can ensure that the condition in the while loop eventually becomes false based on the desired logic. This can be achieved by carefully designing the loop condition. Additionally, you can incorporate a counter to limit the number of iterations or use a break statement to exit the loop when a specific condition is met. These techniques help ensure that the loop execution is controlled and does not run indefinitely.

To improve the readability of nested if statements in R, it is advisable to use proper indentation. Indentation helps visually represent the nested structure of the code, making it easier to understand the flow of conditions and code blocks.

To calculate the mean of each numeric variable in a data frame in R, you can use the sapply() or lapply() function to apply the mean() function to the subset of numeric variables. This approach allows you to calculate the mean for each numeric variable individually.

The time complexity of nested loops can significantly impact program performance. If the loops involve large datasets or a high number of iterations, the execution time can increase exponentially, leading to slower program performance. It's important to optimize the code and consider alternative approaches to nested loops for more efficient execution.

R functions can indeed be recursive, meaning a function can call itself within its own definition. This is a common technique used in many programming languages, including R, particularly when working with data structures that have a hierarchical or nested nature.

No, all elements of an array in R must be of the same data type. Arrays are homogeneous structures, meaning they can only contain elements of a single data type, such as numeric, character, or logical. If elements of different data types are passed, R will coerce them to a common type, resulting in an array of that type.

To calculate the median of each row in a matrix in R, you would use the rowMedian() function. However, note that the rowMedian() function is not available in base R. You can use the apply() function with the margin argument set to 1 to calculate the median of each row.

R would be chosen over other languages for a data analysis project because of its rich library support for statistical analysis and data visualization. R's extensive set of packages makes it a better fit for data-centric tasks as compared to tasks like website or mobile app development.

Big data technologies, with enhanced scalability and processing speed, enable organizations to process and analyze vast amounts of data quickly. This facilitates more informed and timely data-driven decision making.

Google BigQuery would be most appropriate. It is a serverless, highly scalable, and cost-effective data warehouse that allows complex data analyses with minimal upfront investment.