Scatter plots are a powerful tool in exploratory data analysis (EDA) in R. They allow you to visualize the relationship between two variables, identify outliers or unusual observations, and uncover patterns or trends in the data. By examining the scatter plot, you can gain insights into the data distribution and potential relationships between variables.

In R, the operator / is used for division. For example, 6 / 2 would result in 3.

The environment() function in R can be used to inspect the environment of a function. It returns the environment in which the function is defined, allowing you to access and analyze the variables and objects present in that environment. This can be useful for debugging or understanding the scope and context of a function.

R is a powerful language for statistical analysis and graphics, and it's also open source. However, it is not recognized for high-speed performance when dealing with larger datasets, which is a characteristic more attributed to languages like Java or C++.

In R, a vector is an ordered collection of elements of the same data type. It is a fundamental data structure in R that allows you to store and manipulate data efficiently. Vectors can contain elements of different types such as numeric, character, logical, etc. and are a key component in many R operations.

The primary input parameters to the scatter plot function in R are the x and y coordinates. These parameters specify the data points' positions on the plot and define the relationship between the two variables being plotted.

All of the mentioned strategies can be used to handle overplotting in scatter plots in R. Using transparency or alpha blending can reveal the density of overlapping points. Jittering can slightly shift points horizontally or vertically to reduce overlap. Using a smaller marker size can also help mitigate overplotting. The choice of strategy depends on the specific dataset and the level of overplotting.

For handling date and time data types in R, we can use built-in functions like as.Date() or as.POSIXct() to convert character data to date/time data. For more sophisticated manipulation, packages like lubridate can be used.

In R, we often use functions like rnorm(), runif(), rbinom(), rpois(), etc. to simulate data for statistical tests. These functions generate random numbers from specific statistical distributions. For example, to simulate 1000 observations from a standard normal distribution, we can use rnorm(1000).

'Inf' or 'NaN' values can occur in R when performing operations that are mathematically undefined. One way to handle these situations is by using the is.finite() function, which checks whether the value is finite and returns FALSE if it's Inf or NaN and TRUE otherwise.