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personality-type-finder

py-dash-global-displacements

py-dash-globaldisplacementswithhoverfunctionality

python-cheatsheet

Comprehensive Python Cheatsheet

python-dataupdate-dataprocessor-kbn

The python module can be used to scrape data and process data from different sources. The python module can output data as either as a dataframe in the country year format or it will output data in excel files This module has primarily been created for processing data for the International Futures (IFs) Project however, it can be used to process data in general. The module can be used to process data from the following sources, 1) World Bank World Development Indicators (WDI) 2) UNESCO Education indicators(UIS) 3) FAO Food Balance Sheets (FAO) 4) IMF Global Finance Statistics (IMF GFS) 5) Health data from the Institute for Health and Metric Evaluation (IHME) 6) Water data from FAO AQUASTAT 7) Energy data from EIA Currently this module can be run as is on Windows. For usage on Macs, the user may have to make changes to the code lines which specify paths.

r--migration-tracker-application

r-code-for-mass-conversion-from-spss-to-csv

This notebook contains code for the mass conversion of SPSS files in a folder to csv files. This notebook also contains a sample of the code so the user can experiment.

r-shiny-animation-applications

This module contains code for the following, 1.)Animation application showing percent of countries achieveing SDGs across regions under SSP2, across times using the classic gganimate package along with the geom tile package. The user can choose any of the 9 SDGs. The code will create an interactive shiny application that allows a user to select any of the 9 SDGs and see the results for the same. 2.) Animation application showing life expectancy relative to GDP per capita for countries of different regions across time. User can select all groups or any one of the groups.

r2d3

R Interface to D3 Visualizations

residual-analysis-application

Residuals are widely used as a part of statistical analysis. However, there are various dimensions that are available to analyze residuals such as the statistical relationships (relationships with different variables), temporal dimensions (the predicted vs actual value over time), cross-sectional dimensions (the value of individual observations) and metrics over time (summary stats over time). This python dashboard helps a user explore these dimensions for residuals (where Logged GDP is used as the IV) that are dynamically generated using a python function

rgis

R-based Geographic Information System (GIS) utilities

scrape-pdf---itu

The following code scrapes the Active Mobile-Broadband Subscriptions per 100 inhabitants table from the ITU for the years 2013-2017 using the Tabulizer package.

supertrendfor50stocks

Project name- Super Trend Visulaization Author- Kanishka Narayan Description- Use this code to re-create a visulization application used to analyze and visualize stock data. The original visualization can be found here-https://supertrend2.herokuapp.com/

tableau--security-index-parameterization

Tableau workbook showing the parameterization of security index in

xml-parserapplication-python