#' CONCEPT INTEGRATION TASK #' In tutorials 1-3, you reviewed fundamental R syntax and data structures, #' as well as using base R functions, particularly the probability distributions, #' and defining your own functions. #' #' For your integration task, you'll need to do the following: #' - create a data.frame #' - with columns t (a real number), cdf (also a real number), and distribution (a factor) #' - those columns represent the cumulative distribution of drawing a value, t, from a distribution (identified by the factor) #' - those distributions should include uniform, normal, and gamma #' - each of the distributions should have the same mean and standard deviation #' - in the data.frame, each distribution should be associated with the same t values #' - include at least 10 t values #' #' Beyond that, how you construct the data.frame is up to you. Some HINTS off-screen below. #' #' BONUS tasks: #' - save the data.frame to a file #' - plot the distributions together #' - create the data.frame based on input arguments #' - ??? #' #' #' #' #' #' #' #' #' #' #' #' #' #' #' #' #' #' #' #' #' #' #' #' #' #' #' #' #' #' HINTS: #' - if you `?punif`, `?pnorm`, and `?pgamma` you can see how they're defined distributions #' - also, the `pXXX` versions (instead of the `qXXX`, `rXXX`, and `dXXX`) functions are the ones associated #' with cdf #' - the wikipedia entries of these distributions have precise relations between parameters and mean & variance #' for all of these (and other!) distributions #' - to simplify the BONUS tasks, consider using packages like `ggplot2` and `data.table` #' - for the arguments BONUS task, checkout `?commandArgs`