2021/W5: Renewables Overtake Fossil Fuels in Europe summary source: https://ember-climate.org/project/eu-power-sector-2020/ data: https://data.world/makeovermonday/2021w5/workspace/project-summary?agentid=makeovermonday&datasetid=2021w5

This week the topic is global warming. Specifically, the trends of the northern and southern hemispheres are compared against each other.

The focus of this week is on new HIV infections in Africa. The information suggests the young women in Africa need help and support.

Comparing the number of trail counts, bikes, and pedestrians across 2019 and 2020.

This week's makeover is on Bob Ross! Source: https://data.world/makeovermonday/2020w50

This week's Monday Makeover is about the digital gender gap. There are some interesting things going on this week!

Recreating a visual in the latest DataIQ report. Reporting the percent of respondents to industry sectors.

A quick display of findings in a work from home (or remote) survey.

Dashboard for week 31 of Makeover Monday.

Exploring the Palmer Penguins. Exploration performed with the GGally library and the ggpairs function.

Visual analysis to review Amazon Income

This is my Makeover Monday submission for 2020w26. The UN Viz5 data is reviewed.

Makeover Monday 2020w22 submission. I think this represents my 3rd dashboard in R.

This is the dashboard for my #makeovermonday 2020w1 exploration. I am not the best dashboard developer and find this as my opportunity to improve. I will start to practice more and more. I'm very excited about this.

This is the first exploration document for the #makeovermonday social experiment. I take the time to explore the data using ggplot in the hopes of finding a great solution.

The purpose of this rmarkdown is to be my submission for the Makeover Monday w21 submission. Usually, I would do a dashboard in Tableau Public, but this week I wanted to be a little more robust in my exploration. So, instead of doing something like a dashboard, I will do more of an analysis in R. The code and some of my thoughts are presented in full.

Makeover Monday 2020w9 submission. Data: https://data.world/makeovermonday/2020w9 Article: https://savvysleeper.org/costing-kids-sleep/

Makeover Monday 2020w7 submission.

My submission for Makeover Monday. I present a visual trying to answer this question: How much of your life the U.S. has been at war?

This is my submission for Makeover Monday for 2020w5. After my visualization, I wanted to take it to the next step. I used the Chi-Squared test to determine if the two groups were statistically different in their responses.

My submission of Makeover Monday. This week the Deloitte Christmas Survey is covered. There wasn't enough data in the set to do an analysis. I will select a data set from another source to publish a method.

Makeover Monday for the week of Dec 16, 2019. The goal is to review defensive rating against winning percentage in the NBA.

Monday Makeover from Dec 9, 2019. I took it a bit further than just a visual. I used this exercise as a chance to practice some fun statistical methods.

First milestone project for Course Data Science Specialization with R

Week 3 project showing ggplot and plotly working together.

The purpose of this document is to see how ggplot and plotly interact.

Leaflet map showing the locations of all 30 Major League Baseball teams.

Week 2 peer reviewed assignment in the Developing Data Products course in the Coursera Data Science specialization.

Final Project for Reproducible Research Course in Data Science Specialization (www.coursera.org). Purpose: Using NOAA data, determine what weather events had the most population health and economic impacts.

Using the rclinicaltrials library to determine the number of workplace trials over time.