Statistics & R Progress Blog

 After some time, I'm thrilled to finally release the source code for my first ever R package. Alongside the first three functions I described in the original description file, I've also added an additional interactive_heatmap function that can create heatmaps of correlating numerical data in a given dataset. It's my hope that this package will make exploratory analysis easier for those dealing with unfamiliar sets of data, leaving more time to figure out more advanced uses for said data. GitHub repository for DescribeR: https://github.com/Retrolovania/DescribeR

 Using Yihui Xie's animation package, I opted to try to make a simple animation of a bouncing ball. While I'm more than pleased with the results, I'm still at a loss as to why the ball isn't bouncing as it should (it's meant to flatten somewhat at the bottom). Regardless, it's still fascinating to see an actual animation having been created with only R code. Code: library (animation) # Function to create the animation frames bounce_animation <- function (height = 10 , gravity = 0.1 , nframes = 25 , x = 0.5 , filename = "bounce_animation.gif" ) {   # Initialize variables   y <- height   velocity <- 0   squished <- FALSE      # Create and save the animation   saveGIF({     for (i in 1 :nframes) {       # Update position and velocity       y <- y + velocity       velocity <- velocity - gravity              # Squish effect when ball hits the ground       if (y < 0 ) {         y <- 0         velocity <- -velocity * 0.9   # Loss

 I opted to attempt to display Piwek's ggplot2 implementation. After some fuss over getting the libraries loaded, I was met with very promising results after running the code provided:

 For this assignment, I once more opted to use RStudio, as it's what I more used to when it comes to creating visual representations of data. To get started, I opted to try out the example code left on the assignment page to get a sense of how RStudio handles projects like this. Once it was made more clear how to make such visual plots, I aimed to experiment with a template dataset and see what I could make. While it was certainly a fun visual exercise, there's still a lot I want to try to research regarding how analysis plots like this can be made, including how to add more details that can be shown through varying visual devices, like colors and different shapes for each node, as well as better labeling for the legend.

 Upon loading in the given code example for this assignment, the first thing I attempted to do was to simply run the code and see what would happen in order to better isolate a potential issue. That gave me the error shown here: Clearly something was amiss with the return argument, so that was the line I checked. It became obvious looking at it further that said argument shouldn't have been part of the same line as the line in brackets, so I isolated it into its own line. Making this fix allowed the code to be properly executed, and for the function to be created. Next was testing the function with an example matrix. Initially, it failed with this error: This exposed the other issue with the functions code; it seemed to depend on another function dubbed tukey.outlier that was completely missing, and therefore was interfering with the function's ability to be properly used. Commenting the line out and redefining the function allowed it to properly work once more. Resulting code

 Experimenting with data over a span of time is something I've done with ggplot2 for quite some time; one such example was during the previous year when I wrote a series of functions to create visualizations of the price of certain videogames over the course of a decade. For this assignment, I opted to go for something more simple: a line graph showing the rate of employment from the late 1960's up to the mid-2010's using data from the economics dataset. ggplot ( economics , aes ( x = date , y = unemploy ) ) + geom_line ( ) + geom_smooth ( ) + labs ( title = "Time Series Plot of Unemployment with Smooth Trend Line" , x = "Date" , y = "Unemployment" ) + theme_minimal ( ) The addition of a trend line makes the data presented here more easily understandable to onlookers, with the main message of the data being made clear in the presentation, that being that unemployment in recent times has been steadily incre