DanielTLis4317

Daniel Tafmizi Dr. Friedman November 24, 2024 Lis 4317 Module 13 Github:  daniel.R/Work.R/LIS4370Rprog/AnimationR.R at main · DanielDataGit/daniel.R     After some researching, I decided to use ggplot's gganimate package to create my visualization. I used this because I am familiar with ggplot's ecosystem. It offers a useful function for animating line plots. The " transition_reveal(year)" method, initiates a program that renders the data to appear by year. I applied this to my precious Stock Exchange Data and am pleased with the result. I initially colored the lines by the exchange name, but it was kind of difficult to link the legend and lines in real time. I decided to group by region to simplify the links between the data and the legend.      I like this design. A static graph is great for displaying complex data visualizations. However, the animation allows the data tell its own story.  

Daniel Tafmizi Dr. Friedman November 17, 2024 Lis 4317 Module 12 Github:  daniel.R/Work.R/LIS4370Rprog/networkAnalysis.R at main · DanielDataGit/daniel.R I enjoyed working on this network analysis. I was hoping to incorporate a similar element into my text mining final project, so this lesson acts a precursor to that. I used some reddit data that I retrieved from their API. I started by preprocessing the data, then I tokenized it, finally I found co-occurrences. The ggnet2 streamlines well with igraph. This allowed me to create a network analysis of co-occurrences from the reddit text. Further research will be done to add more elements to the graph. I would like to color in the nodes based on their connection to the keywords, "Kamala":Blue and "Trump":Red. Words related to both will be colored purple. 

Daniel Tafmizi Dr. Friedman November 4, 2024 Lis 4317 Module 11 Github:  daniel.R/Work.R/LIS4370Rprog/tufteviz.R at main · DanielDataGit/daniel.R Piwek's paper on Tufte visualized data in a refreshing and invigorating way. The art of the visualization really stood out. I honestly would rather go to a museum of Tufte's work then one with modern art. I used the ggplot minimal boxplot design to map my YOY stock exchange change. I think my graph needs some metadata to explain it further. I feel that most people will not understand what the graph is displaying. I think a label showing where each exchange's 2014 and 2024 values lie on the graph would help. I thought Tufte's graphs were not colorful enough, so I added color to mine.

 Daniel Tafmizi Dr. Friedman November 4, 2024 Lis 4317 Module 10 Github:  daniel.R/Work.R/LIS4370Rprog/YOYexchanges.R at main · DanielDataGit/daniel.R My first graph is a simple time series analysis with multiple variables. This is an extension of my last assignments plots, which showed market cap over time.  This graph had the issue of not showing the changes in the smaller exchanges, as the nasdaq and nyse were about 5 times bigger than them. Market cap also does not offer much value. To fix this issue, I accumulated how the year over year percentages changed. This was fairly difficult to do. I had trouble finding resources on an efficient way to sum for each year. Eventually, I used dplyr mutate and cumsum to calculate the changes cumulatively. I used ggplot to graph it                                           My second graph is a time series fo...

Daniel Tafmizi Dr. Friedman October 29, 2024 Lis 4317 Module 9 Github:  daniel.R/Work.R/LIS4370Rprog/stockexchange.R at main · DanielDataGit/daniel.R This week I worked on preparing for the final project. I want to create something similar to the Gapminder life-expectancy vs gdp per capita graph. I would like to create one that shows the market cap of major stock exchanges vs their yty % change, over time. In beginning this endeavor, I searched and have found a good resource for the data at world-exchanges.org. I used their "statistics portal" to get some data for a multivariable visualization.  The graph shows the market cap and YTY change in 2023 for a few of the largest stock exchanges. I used colors to group by region. I used size to show how many companies make up the stock exchange.  Alignment: This can be added by maintaining a certain style amongst elements of a similar connection. Repetition: This can be added by using a consistent style to tie together separate ...

Daniel Tafmizi Dr. Friedman October 17, 2024 Lis 4317 Module 8 Github:  daniel.R/Work.R/LIS4370Rprog/mod8.R at main · DanielDataGit/daniel.R (github.com) I attempted to recreate a visualization seen in Few's book on pg. 277. The goal of this graph was to break down many elements of the large dataset into a visually approachable manner, while also showing correlation data. I initially ran into some trouble because I tried using facet_wrap, which is only for one discrete variable. After viewing some documentation (links in the github), I realized I need to use facet grid since I had two discrete variables (auto and manual). Using stat_cor implemented the correlation coefficient and the p value. I used theme elements to make the graph prettier. I agree with Few's recommendations. Correlation analysis on a large dataset can get very confusing very quickly. It is important to break down the data set to make it more approachable. I think I accomplished this in the above graph that di...