Here I am going to use 538’s database of President Trump approval ratings, and data on his tweets from the Trump Tweet Archive. The data consists of the president’s tweets from 2017 and 2018: 1) exploratory analysis, 2) running a model, 3) generalizing the model

Here I use the database of DW-Nominate scores. At its core, the data seeks to use data about legislators’ choices (in this case, roll-call voting) to “map” legislators’ ideological positions. I am using the dataset to practice conducting exploratory data analysis, practice basic concepts in regression, correlation and causation, and running regressions.

I created an interactive map for Cordoba based on the risk assessment scores analyzed by PAHO team. Map delivered to Cordoba Ministry of Health.

I created interactive choropleth maps for Mexico City based on the risk assessment scores analyzed by PAHO team.

Using Google Mobility dataset, I stratified the data and created visualizations of mobility in Mexico City. Visualizations were used for reporting with the Pan-American Health Organization and Ministry of Health at CDMX.

Using Google Mobility dataset, I stratified the data and created visualizations of mobility in Haiti. Visualizations were used for reporting with the Pan-American Health Organization and Ministry of Health in Haiti.

These are some of the ICT initiatives and measures have been introduced around the world in response to COVID-19. Data comes from ITU REG4COVID (https://reg4covid.itu.int/).

Here I sample a study population and created a plot that shows what happens to the variation in the distribution when we increase our sample size.

I made a map of median income using the American Community Survey Data.