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590V assignment 6

  1. My dataset consists of health tweets by news agencies. It consists of approximately 463410 words. I am using it to understand the top topics tweeted by the news agency on the health domain. All the files were concatenated to a single one. You can get more information of the dataset here: https://archive.ics.uci.edu/ml/datasets/Health+News+in+Twitter
  2. A word cloud was created based on the top words after removing some common prepositions.
  3. A pie chart is created.
  4. Hovering and selection are enabled. A dark red/brown color can be seen in the pie chart that highlights the data when you hover over data in the word cloud. You can click multiple words for selecting multiple sections on the pie chart. Similarly when you hover over pie chart sections of the word cloud are highlighted.
  5. Done on both visualization
  6. Drop down provided for both visualizations.

Some code for selector, tooltip were reused from my previous assignment submissions.

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590V assignment 5

  1. The dataset is the EU core dataset obtained from https://snap.stanford.edu/data/. The data consists of emails sent between different persons in a large EU organization. It consists of 986 nodes and 332334 edges. I sampled 300 nodes and 1000+ edges for the assignment, as the entire dataset was causing the browser to crash. The scatterplot shows aggregated data for the entire dataset and the graph represents a sample of 300 nodes.
  2. Force directed graph is created.
  3. Scatter plot is created
  4. Lasso selector and probbing are linked.
  5. Drop down is given.

I have used the open source d3.lasso library for lasso selector. I have reused my code from previous assignments for scatterplot and selector. I have used the force graph lab in 590v.

I didn't get time to put the visualizations side by side so zoom out your browser if you want to see both the graphs together.

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590V assignment 4

  1. My dataset contains historic S&P BSE sensex data. It is a weighted average of the stocks of 30 companies in Bombay stock exchange. My dataset contains data for 3 years so roughly 365 x 3 data points. Please note that stock exchanges are close on public holidays and hence it won't contain data for every day.
  2. Spiral visualizaion was created.
  3. Line graph was created.
  4. Tooltip added for both
  5. Drop down for both graphs added.

Code reference: https://bl.ocks.org/arpitnarechania/027e163073864ef2ac4ceb5c2c0bf616. I based my work on the open source code. The data, tooltip and interactions are extra additions that I made. I also changed some parameter values and added some additional behaviours to make the graph look different. I have also reused some code for the scatterplot part of my linegraph from my previous assignments.

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590V assignment 3

  1. The data set is the number of car crash dataset by state. The different features ofthe dataset are: State,Population,Vehicle miles traveled (millions),fatal_crashes,Deaths,"Deaths per 100,000 population",Deaths per 100 million vehicle miles traveled. The dataset consists of data for each of the 52 states in USA. It can be used for analyzing the number of crashes in USA per state. The dataset was obtained from http://www.iihs.org/iihs/topics/t/general-statistics/fatalityfacts/state-by-state-overview
  2. I am using Albers equal-area conic projection. It uses two standard parallels and distortion is kept to its minimum.
  3. Since the data is bound to states I am using a choropleth type visualization (https://piazza.com/class/jcqxf871plh65w?cid=111)
  4. Tooltip is displayed while hovering.
  5. Dropdown shows the state in red for the range of fatal crashes.
  6. Pan and zoom in svg are enabled.
View README.md

590V assignment 2

  1. A scatterplot is a plot that uses cartesian coordinates to display values of a dataset. It is a graph of points which helps us to visualize the relationship between different dimensions(features).
    • The basic algorithm for creating a scatter plot is follows:
    #Reference: Interactive Data Visualization: Foundations, Techniques, and Applications, Second Edition by Daniel A. Keim, Georges G. Grinstein, and Matthew O. Ward
    
    def scatterplot(xDim,yDim,cDim,rDim,rMin,rMax):
        for each record i:
            x = NORMALIZE(i,xDim)
    
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