Module #5 Assignment: Create Your Own Visualizations assignment Plotly vs. Datawrapper

 







1.)  I decided on a bar chart to show its ranking. As you can see it going from least to greatest. This makes it easier to look at first glance. For example, looking at 20, I see it has the largest time. Ranking    makes it easy to compare in this case average position and time.


2.) I chose a pie chart to show a part-to-whole relationship. The slices represent the numbers and how much the make up making it easier for viewers to see which part had the most. This design shows similarities and differences in size. 

3.) Part to whole shows each piece and the differences while creating a whole which can make it easier to see. But if there were too many pieces, it can be difficult to compare. Ranking with bar char is great because you can see the least to greatest or vice versa, but it doesn't show how each part adds up to a whole part to a whole does.

Comments

Post a Comment

Popular posts from this blog

Final Project: Visual Analytics

Image
For this project, I was not sure which dataset to use, so I went through the R built-in datasets and chose the ToothGrowth dataset. It was easy to work with, and it has enough observations for me to make different visuals and compare patterns. The main question I wanted to go over in this project was how different supplement types and dose amounts affect tooth length. I also wanted to see if there was a big difference when increasing the doses. My goal was to understand the patterns in the ToothGrowth dataset. Related works I looked back on was the mtcars dataset we worked on for some of the assignments. We used boxplots, scatterplots, and other visuals to compare variables, which helped me decide which graphs to use for this project and gave me ideas for the visuals I made for the ToothGrowth dataset. For my solution, I used RStudio to make all the visuals and used a dataset that was already built into R. I created three graphs that helped me explore the dataset further. The first gra...
Read more

Module #9: Visualizing Multivariate Data with ggplot2 & Corrgram

Image
  I used the Iris, which has many flowers and includes details on sepal length and width, as well as petal length and width. I used this dataset because it measures different flower types, such as setosa, versicolor, and virginica.  My visualization measures sepal length and compares it across the three species of iris flowers. The boxplot shows that setosa has the smallest sepal length, virginica has the largest, and versicolor is in between. This visualization helps tell the differences and similarities.  Yes, the boxplot is a multivariate visualization because it compares sepal length across three species. Making it easy to compare them side by side.  In my boxplot, I used alignment by keeping everything easy to read and compare. I also used contrast by using a gray background with red boxes so the boxplots would stand out, and I used balance by spacing the boxplots evenly so the chart looks neat and easier to read and understand. 
Read more

Assignment # 12 using Python with netwokxx and plotnine

Image
  The code ran successfully. It was difficult at first because I don’t usually use Python, so I had to install it and figure out how it works. At first, I was typing the commands into the Python shell and the code didn’t run. After doing some research, I realized I needed to use Command Prompt instead to install everything correctly. Once I ran the code there, it worked and generated the ggplot output confirming the graph was created. I personally would not use this method because it was confusing to set up and the graph did not show up in the command prompt.
Read more