Cesar Vazquez | Data Visualizations

Visual: The number of people infected vid covid-19 as well as the number of deaths in Canada.

In this visualization we get some data from All of Us to plot the number of people in Canada infected with covid-19 and see how it relates to the deaths related to covid-19.

Tasks:

  1. Load in the data.
  2. Clean the data.
  3. Select only Canada.
  4. Create the line graph of the total number of infected and total number of deaths in Canada.

Visual: Graph and compare covid-19 related deaths between Spain, Italy, France, and Germany.

In this visualization we will graph 4 European countries to compare how many new deaths they have gotten every day throughout the pandemic.

Tasks:

  1. Import the necessary libraries as well as reading the file in.
  2. We now filter the data to include only the rows for Spain, Italy, France, and Germany.
  3. Calculate the median age for each country. For each country we will assign a darker colored blue for higher median age and a lighter blue for the lower median age.
  4. A line graph is made of the daily new deaths, set the y-axis limit so we see the values more clearly, and set the titles for the axes.

Visual: Compare the number of new cases in five different European Countries.

In this visualization we graph the number of new cases 5 different European countries report during the pandemic.

Tasks:

  1. Import the necessary libraries as well as reading the file in.
  2. Filter the data to include only the European countries, namely Spain, Italy, France, Germany, Poland.
  3. Use a scatterplot to graph the data obtained from All of Us.
  4. Apply some smoothing so the data is easier interpreted.

Visual: Analyze relations between variables from the University of Texas at El Paso.

We investigate any relationships between variables such as Funding, Classification, Ethnicity, and Gender using visualizations.

Tasks:

  1. Install pywaffle to be able to make waffle graphs.
  2. Import the necessary libraries as well as reading the file in.
  3. Graph food security based on the the different types of government funding using a waffle charts where each square represents 1%.
  4. Provide another view of the charts, so the proportions are seen better.
  5. Output the number of entries in each of Classification, College Choice, and Food Security.
  6. Compare College Choice with Food Security using a Stacked Bar Plot.
  7. Compare Classification with Food Security using a Stacked Bar Plot.
  8. Next, we output the number of entries in each of Classification, College Choice, Ethnicity and Gender.
  9. Compare differences of concentration in gender when it comes to degree progress (classification).
  10. Graph histograms of different ethnicities and their completion of degrees.