Alec Gremer's Profile

  Module #3 Data Frame The dataset showed hypothetical results of the 2016 Presidential campaign. The dataset provided had a couple of mistakes that resulted in errors. Original Code: > Name <- c("Jeb", “Donald”, "Ted”, “Marco” “Carly”, “Hillary”, “Berine”) > ABC political poll results <- c(4, 62 51, 21, 2, 14, 15) > CBS political poll results <- c(12, 75, 43, 19, 1, 21, 19)  There  are missing commas in the "Name" vector, as well as in the "ABC political poll results" vector. Additionally, the names of the results vectors were not underscored, therefore they could not be interpreted in R. For the values to be interpreted, the code should look as follows: Modified Code: > Name <- c("Jeb", "Donald", "Ted", "Marco", "Carly", "Hillary", "Bernie") > ABC_political_poll_results <- c(4, 62, 51, 21, 2, 14, 15) > CBS_political_poll_results <- c(12, 75, 43,...

  The following image is a sample illustration for a Florida Parks and Recreation advert. The map in the background is a composition of data from the location of parks and recreational areas throughout Florida. Each dot represented on the map contains variables including Latitudinal and Longitudinal Coordinates, Park/Area Name, Address, City, County, Zip codes and more.

 Module # 2 Google Table, Tableau and Geographic map In order to improve the way a Tableau map communicates, I would consider adding different visual elements and features. A couple of examples could involve the use of a color gradient for measurements like population density or temperature fluctuations, and use of color coding to indicate different categories or values. To highlight greater values or relevance, you could resize data points, such as circles, according to the relevant variable. To provide readers with more context, add labels to important data points and include legends explaining color coding and size variance.  You can effectively arrange and structure visual elements by utilizing Gestalt Laws, such as closeness, resemblance, and continuity. In order to communicate spatial patterns and interdependence, highlight the connections between neighboring data points. For geographical context, it's important to think about including a background map with elements lik...

Module # 2 Introduction to basic R functions and Data Structures The function myMean determines the mean (average) of a given numeric vector. Assignment2 is the vector that was utilized in this instance. The only parameter required by the myMean function is assignment2. Using sum(assignment2), the function determines the sum of the components in assignment 2. Using / length(assignment2), it then divides the total by the number of components in assignment 2. The function's output is the outcome of this division, which is returned. The mean of the numeric vector assignment2 is calculated by the myMean function. The mean of the values in the assignment2 vector can be obtained by using this function with the argument myMean(assignment2). The function ran through RStudio is as follows: assignment2 <- c(16, 18, 14, 22, 27, 17, 19, 17, 17, 22, 20, 22) myMean <- function(assignment2) {   return(sum(assignment2) / length(assignment2)) } result <- myMean(assignment2) print(resu...

Module #1 First Assignment  Along with the Earthquake map presented by Cindy Wang, I researched further through her visualizations and found an interesting Geocordinate map of cities around the world. What I really noticed were the differences in colors and plot size from the Earthquake map. It served as a really clear distinction between the overall purposes between the two graphs. From maps/graphs such as these, users can find patterns, trends, and hidden insights by using interactive visual representations to better understand complex and sizable datasets. Visual analytics enables users to uncover important information and make well-informed decisions across a range of industries by combining data analysis, domain expertise, visualization, and analytical models in a cooperative manner.