S-Stem Scholar Kathleen Corfman's Blog

 Yay! The district's computer system is back up, so I can access the blog from my computer again. This week I read about and practiced Univariate Graphs. This variable can be categorical or quantitative. A categorical variable is something such as race or sex, is usually plotted using a bar graph, pie graph, or tree map. A quantitative variable is something like age or height, and is usually plotted using a histogram, kernel density plot or dot plot. Both categorical and quantitative variables use the ggplot2 function, which I used last week. I followed along with the book, and started out by following the examples to demonstrate a categorical variable. The dataset being used is The Marriage dataset that has the records of 98 couples in Mobile County, Alabama.  Categorical Variable Graphs Bar Graph The first graph demonstrated is a bar graph. Just like with the graph from my last blog, you can change and edit everything about the graph including color, using percent symbols fo...

Hello!  My school district is suffering from a cyber attack, and the entire computer system is shut down. I can’t access my blog from my computer since it’s connected to my school email, but I seem to still have access from my phone. My blog won’t be pretty this time, but I’ll make do! The next chapter in “Data Visualization with R” by Rob Kabacoff is about ggplot2, which is one of the graphing packages in R.  I didn’t really experiment with my own data for this one because the book provides examples to use.  ggplot2 is a package used to build a graph in layers. You can start out with a simple graph, and make it gradually more complex by adding new elements. The book uses data from the 1985 Current Population Survey that shows the relationship between work experience and wages. I’m going to keep this blog short this week since I’m on my phone,  but here’s some pictures and descriptions of what I did. This is the code used to load the data, and then create and map the...

Hello! I got a little behind on my blog for this semester, but here I am! This semester we are learning to use a statistical graphic coding language called R. This language seems to be used to visualize data with graphs and tables. I don't know a whole lot about R other than that, but I am excited to learn a new language since my major is actually in Computer Science. This will be a fun opportunity to enjoy both computer science and biology together. So, I started reading chapter 1 in Data Visualization with R  by Rob Kabacoff. The first chapter is about preparing your data for visualization. This can be done by importing your data from Excel, text files, and even databases using different packages.  Text files are imported using the "readr" package, Excel spreadsheets using the readxl package, and statistical packages using the haven package. Importing data from databases is apparently much more complicated, and is not included in the book I am referencing. Using RStudio...