S-STEM Scholar Abinezer Abebe's Blog

 Abinezer Abebe STEAM  Data Preparation CH 5/6 In Chapter 5 I worked on Multivariate Graphs. They display graphs between multiple variable. The two most known ways to accommodate variables is grouping and faceting. The first example we looked at is grouping and we mapped them to the x and y axes. Grouping allows us to plot multiple groups of data  in a single graph. We looked at the relationship between yrs.since.phd and salary. Next we added rank and gender to the graph. Another method of graphing the years since Ph.D and Salary using the size of the point is referred to a bubble plot. In the bubble plot we have two legends yrs.service and rank. Inferring from the graph we can see that more years in service the more likely to become a Prof and not a AssocProf or AssProf. In the final example we take a look at the life expectancy of counties in North and South America. The life expectancy is increasing in each country expect Haiti they lag behind in the graph. In Chapter ...

 Abinezer Abebe STEAM  Data Preparation CH 4 In Chapter 4 we learned about Bivariate graphs. Bi stands for two so Bivariable graphs displays information between two variables. In the first example we plotted the relationship between automobile class and drive type based on front wheel drive rear wheel drive and all wheel drive. From observing the graph we notice that all 2 seaters sport coupes are all rear wheel drive while most common car the SUV is more commonly 4 wheel drive. We adjusted the graph to a group bar look. Then applied a segmented bar chart using the position = "filled" option. Improving the graph coloring option using the factor scale_y_ labs ,scale_fill_brewer and theme_minimal functions. Next we used the geom_text function to ass labels to each bar. Scatterplots are one of the most easiest ways to display data we graphed one for the years experience vs salary for collage professors. Using the geom_point function to edit our graph. Next we need to add the lin...

 Abinezer Abebe STEAM  Data Preparation CH 3 In Chapter 3 we started to learn about univariable graphs. These types of graphs are plotted based on a singular variable. The first Chart we looked at was a data set that contain the records of 98 individuals in Mobile County Alabama. The data was about marriage records based on race of the citizens in Alabama. In the Rstudio IDE we plotted the data set to see the presented graphs. After graphing the data I noticed some Key points. Coming in first Whites had the highest marriage records in second were Blacks they the second highest the last groups were Hispanics and American Indians. The next step in modifying our graph was using the "aes" shortcut to represent it in a percent. The data also needs to be in ascending order and labeled per bar. This would make the data more presentable , we added percent labels it was more easier to read percent's per bar. We then applied a graph that based marriages on their officiates. Another...