LIS 4370 - Module #3 Data Frame

 

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, 19, 1, 21, 19)

Donald Trump is leading by a wide margin, according to polls conducted by CBS and ABC. He is receiving 62% of the vote in the ABC survey and 75% in the CBS poll. These findings imply a substantial preference for Donald Trump that has persisted throughout the two surveys.

With 51% of ABC voters and 43% of CBS voters, Ted Cruz has a sizable lead in both surveys. There is a clear difference between him and Donald Trump, even if he is ranked second in both polls.

With 14% in the ABC poll and 21% in the CBS poll, Hillary Clinton is the front-runner for the Democratic nomination. But compared to the front-runner Republican, Donald Trump, her support is much lower.

In both polls, the percentages of support for other contenders (Jeb, Marco, Carly, and Bernie) are lower. Though the percentages for each candidate vary, there is generally some consistency between the two surveys.

LIS4317 - Module #3 Assignment

 

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 - LIS 4317

 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 like rivers, highways, or topography. To make a visually appealing and educational map, explore and match these decisions with your unique ideas.

Module # 2 Introduction to basic R functions and Data Structures

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(result)

Output:

[1] 19.25

Module # 1 First Assignment

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.