The Increasing Global Temperature
Coursera data visualization project 1, created by jean pan.
This is a simple data visualization exercise from Coursera Data Visualization course.
The data used in this assignment is GISTEMP data from NASA.
The visualization tool I'm using is D3.js .
Explanation
This graph visualizes the GISTEMP data for the Globe and the North and South Hemispheres through all the given years ( 1880 - 2014 ). The Blue line is for the Globe, the Orange line describes the data for the Northern Hemisphere and the Green for the South Hemisphere.
From the resulting graph, although there is a little decreasing during 19th century, we can see that the overall trend of global temperature is increasing. Both north and south follow the same trend as the global, but we can find the south increases smoother than the north.
Coursera Data Visualization Programming Assignment 2
Richard seiter, sunday, august 09, 2015, load the data, more about the data, vertex and edge attributes, node degrees, centrality measures, tutorial example, color blindness, tufte’s design rules.
This file implements Programming Assignment 2: Visualize Network Data for the Coursera Data Visualization Class .
Data obtained from https://networkdata.ics.uci.edu/data.php?id=5
American College football : network of American football games between Division IA colleges during regular season Fall 2000. Please cite M. Girvan and M. E. J. Newman, Proc. Natl. Acad. Sci. USA 99, 7821-7826 (2002) Community structure in social and biological networks (Girvan and Newman, 2002)
The values are as follows:
0 = Atlantic Coast 1 = Big East 2 = Big Ten 3 = Big Twelve 4 = Conference USA 5 = Independents 6 = Mid-American 7 = Mountain West 8 = Pacific Ten 9 = Southeastern 10 = Sun Belt 11 = Western Athletic
Normally I would do further exploratory data analysis here, but that would be confusing since we are only submitting a single visualization. See More About the Data below.
Submission Visualization
The assignment should be graded on the contents of this section.
Visualization of the 2000 American College Football Season. Nodes are colleges with the colors being their conference. Edges represent games. The actual visualization in Gephi has a nice interactive feature where hovering over a college dims the display except for the games it played.
I abandonded this approach because I thought it gave an inferior result.
I use the R package networkD3 citep(citation(“networkD3”)) to provide interactive D3 JavaScript Network Graphs from R. This blog post was also helpful: Creating network graphs using javascript directly from R Also see networkD3 example below. This tutorial was also helpful: http://www.kateto.net/wordpress/wp-content/uploads/2015/06/Polnet%202015%20Network%20Viz%20Tutorial%20-%20Ognyanova.pdf
Convert the igraph data into something more suitable for networkD3 (see example below).
networkD3 requires edge references to nodes start from 0.
Create the interactive D3 plot.
Note you can zoom with the scroll wheel.
Additional Data Analysis
Disabled because the output is extermely verbose.
This is non-intuitive to me. I expected the conferences to be cliques.
networkD3 Examples
From http://christophergandrud.github.io/networkD3/
Another example: https://github.com/benjamin-chan/FacebookNetworkAnalysis/blob/master/FacebookNetwork.Rmd
From http://www.kateto.net/wordpress/wp-content/uploads/2015/06/Polnet%202015%20Network%20Viz%20Tutorial%20-%20Ognyanova.pdf
Other Tools
I much prefer the Gephi interactive display of this network. Being able to highlight nodes and see connections is very useful.
See comments in Social and Economic Networks notes
Fixed problem with my Gephi installation. https://medium.com/coder-snorts/gephi-is-broken-on-mac-os-97fbaef4305e Fixed.
The interactive Gephi display has a nice feature where when you hover over the nodes it dims the nodes not connected to it. Here is a screenshot of the static output. Notice how the conferences cluster more than in the networkD3 version above.
For web display see http://sigmajs.org/ http://blogs.oii.ox.ac.uk/vis/ Installed this plugin but was unable to get it to work properly.
A red/blue convention is used in accordance with American presentations of political visualizations. This also works well with colorblindness. Vischeck is helpful for checking graphics.
Some useful sites: http://gis.stackexchange.com/questions/2887/how-to-account-for-colour-blindness-when-designing-maps http://www.vischeck.com/ - simulate colorblindness on image
http://www.sealthreinhold.com/school/tuftes-rules/ Also see Lecture 2.3.1
- Show Your Data
- Use Graphics
- Avoid Chartjunk
- Utilize Data-ink
- Utilize Micro/Macro
- Separate Layers
- Use Multiples
- Utilize Color
- Understand Narrative
File originally created: Sunday, August 9, 2015 File knitted: Sun Aug 09 12:17:01 2015
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Coursera-Project These course materials belong entirely to Coursera. The answers are the only things that show my trials. Key Concepts: Use Pandas to produce a table to visualize the data. Produce a data plot using MatPlotLib Pyplot. Use Seaborn to create a scatterplot graph. Create a Jointplot to show distribution. Create a heatmap to show corr…
prabal5ghosh/Data_Visualization_with_Python_Coursera_Project
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Hi,I am Prabal Ghosh.
Data_Visualization_with_Python_Coursera_Project
Coursera-Project These course materials belong entirely to Coursera. Key Points: Use Pandas to produce a table to visualize the data. Produce a data plot using MatPlotLib Pyplot. Use Seaborn to create a scatterplot graph. Create a Jointplot to show distribution. Create a heatmap to show correlations and distributions. Great thanks to the Coursera course instructor and my University faculty Remya Rajesh from Amrita Vishwa Vidyapeetham,Amritaputi to teach me these concepts.
Course link:
https://www.coursera.org/projects/data-visualization-with-python
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Data visualization is a way of presenting complex data in a form that is graphical and easy to understand. When analyzing large volumes of data and making data-driven decisions, data visualization is crucial. ... Final Assignment: Part 1 - Create Visualizations using Matplotlib, ... Coursera is one of the best places to go." Chaitanya A.
This is a simple data visualization exercise from Coursera Data Visualization course. The data used in this assignment is GISTEMP data from NASA. The visualization tool I'm using is D3.js .
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This is a simple data visualization exercise from Coursera Data Visualization course. The data used in this assignment is GISTEMP data from NASA. The visualization tool I'm using is D3.js. Explanation. This graph visualizes the GISTEMP data for the Globe and the North and South Hemispheres through all the given years ( 1880 - 2014 ). The Blue ...
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0 = Atlantic Coast 1 = Big East 2 = Big Ten 3 = Big Twelve 4 = Conference USA 5 = Independents 6 = Mid-American 7 = Mountain West 8 = Pacific Ten 9 = Southeastern 10 = Sun Belt 11 = Western Athletic. Normally I would do further exploratory data analysis here, but that would be confusing since we are only submitting a single visualization.
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